MANUFACTURE

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority from U.S. Provisional Patent Application No. 62/557,590 filed September 12, 2017, and entitled "COMPUTER-DRIVEN SYSTEM AND COMPUTER-IMPLEMENTED METHODS CONFIGURED FOR PERFORMING CALIBRATION OF NEW BUILD MATERIAL IN ADDITIVE MANUFACTURE," and U.S. Provisional Patent Application No. 62/567,112 filed October 2, 2017, and entitled "COMPUTER-DRIVEN SYSTEMS AND COMPUTER- IMPLEMENTED METHODS CONFIGURED FOR RECALIBRATION BASED ON DIGITAL TWIN IN ADDITIVE MANUFACTURE,"which are incorporated herein by reference in their entirety for all purposes.

FIELD OF TECHNOLOGY

[0002] The subject matter herein generally relates to additive manufacture ("AM"), and specifically relates to systems and methods of creating three-dimensional volume quality models of additively manufactured parts (e.g. AM parts) implementing calibrating of materials and updating digital twin during the AM build.

BACKGROUND OF TECHNOLOGY

[0003] Additive manufacturing may be used to build, via computer control, successive layers of an AM part. Defects in the AM part may occur due to errors in parameters of the AM process.

SUMMARY OF THE INVENTION

[0004] The present disclosure provides systems and methods for creating three- dimensional volume quality models of AM parts implementing calibrating of materials and updating digital twin during the AM build. An embodiment of the present disclosure provides a method including (A) laying down, by an Additive Manufacturing (AM) machine, a first AM testing layer of an additive material; (B) subjecting, by the AM machine, the first AM testing layer to the AM build process based at least in part on an initial digital twin of an actual AM part to be build; wherein the initial digital twin comprises at least one of: i) one or more initial operational parameters of the AM machine for building the actual AM part, ii) one or more properties of a desired design for the actual AM part, or iii) one or more initial AM build process parameters; (C) determining, by at least one processor, a compliance of the first AM testing layer to one or more predefined criteria by comparing testing data to the one or more predefined criteria, defined based on the initial digital twin, wherein the testing data has been obtained from at least one in-situ monitoring sensor that has utilized one or more in-situ monitoring techniques to collect the testing data; (D) simulating, by the at least one processor, based on at least one AM simulation model, at least one testing adjustment to at least one of i) the one or more initial operational parameters of the AM machine or ii) the one or more initial AM build process parameters, when the first AM testing layer fails the compliance to the one or more predefined criteria; wherein the at least one AM simulation model is generated by estimating, from historical experimental data of the AM build process of one or more other AM parts, most likely value for at least one of i) each respective of the one or more initial operational parameters of the AM machine or ii) each respective of the one or more initial AM build process parameters; wherein the at least one testing adjustment is configured to compensate at least one difference in the actual AM part from the desired design due to the first AM testing layer; (E) causing, by the at least one processor, the AM machine to implement the at least one testing adjustment in the AM build process for at least one subsequent AM testing layer that is laid down after the first AM testing layer; (F) repeating (A) through (E) to determine one of: i) a lack of a particular testing adjustment that compensates the at least one difference in the actual AM part from the desired design, or ii) at least one actual build adjustment that compensates the at least one difference in the actual AM part from the desired design; (G) generating, by the at least one processor, an updated digital twin from the initial digital twin based on the at least one testing adjustment; and (H) utilizing, by the AM machine, the material to build the actual AM part based on the updated digital twin; wherein the updated digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, the compliance of the actual AM part to the desired design.

[0005] In some embodiments, the method further includes laying down, by the AM machine, at least one second particular testing layer of the material on a calibration coupon; determining, by the at least one processor, based on the at least one AM simulation model, at least one actual calibration adjustment to the one or more initial operational parameters of the AM machine to compensate for at least one machine-to-machine variation; and causing, by the at least one processor, to recalibrate the AM machine based on the at least one actual calibration adjustment.

[0006] In some embodiments, the method further includes updating, by the at least one processor, the initial digital twin or the updated digital twin based on the at least one actual calibration adjustment.

[0007] In some embodiments, characteristics of the recalibration dynamically change with time such that repetitions of the recalibration take place routinely at a predetermined rate or a dynamically adjusted rate.

[0008] In some embodiments, a periodicity of the calibration is determined by identifying a likely time interval after which a departure of actual machine behaviour of the AM machine from an intended machine behavior of the AM machine: i) is no longer acceptable or results in a variation that exceeds a pre-determined threshold. [0009] In some embodiments, determining, based on the at least one AM simulation model, the at least one actual calibration adjustment to the one or more initial operational parameters of the AM machine to compensate for at least one machine-to-machine variation comprises: i) translating uncertainty about the machine-to-machine variation into uncertainty parameters of the at least one AM simulation model; ii) assigning posterior probability distributions to the uncertainty parameters reflecting expected distributions of actual values of the parameters; iii) obtaining updated measurement data about the calibration coupon; and iv) creating posterior distributions of the uncertainty parameters of the at least one AM simulation model.

[00010] In some embodiments, at least one uncertainty parameter is at least one of: at least one machine power source parameter, at least one raster partem, at least one overlap rate, or at least one speed build parameter.

[00011] In some embodiments, the prior probability distributions are uniform probability distributions of predetermined widths around predetermined nominal values.

[00012] In some embodiments, the posterior distributions of the uncertain parameters are generated by applying Bayesian inference.

[00013] In some embodiments, the one or more in-situ monitoring techniques are at least one of: i) taking temperature measurements of build plates); ii) measuring beam power; iii) taking visual images, thermal images, or both, by a camera; iv) obtaining acoustic data from the build plate from at least one acoustic emission sensor; v) performing layer-by-layer optical topography; vi) obtaining gas flow data from one of more gal flow sensor; vii) monitoring a molten pool size by the camera, an optical sensor, or both; viii) monitoring a powder distribution by the camera, the acoustic emission sensor; or ix) monitoring a powder contamination by the camera, the optical sensor, or both. [00014] In some embodiments, the one or more predefined criteria identify at least one of: melting temperature, hardness, traces of one or more chemical elements, emissivity, or one or more electric properties.

[00015] In some embodiments, the compliance of the first AM testing layer to one or more predefined criteria is determined based one or more in-situ monitoring measurements related to at least one or more particular points in a build portion of the actual AM part that are determined to deviate from one or more threshold conditions by more than a predetermined tolerance value.

[00016] An embodiment of the present disclosure provides a method including (A) laying down, by an Additive Manufacturing (AM) machine, a first AM testing layer of an additive material; (B) subjecting, by the AM machine, the first AM testing layer to the AM build process based at least in part on an initial digital twin of an actual AM part to be build; (C) determining, by at least one processor, a compliance of the first AM testing layer to one or more predefined criteria by comparing testing data to the one or more predefined criteria, defined based on the initial digital twin, wherein the testing data has been obtained from at least one in-situ monitoring sensor that has utilized one or more in-situ monitoring techniques to collect the testing data; (D) simulating, by the at least one processor, based on at least one AM simulation model, at least one testing adjustment; (E) generating, by the at least one processor, an updated digital twin from the initial digital twin based on the at least one testing adjustment; and (F) utilizing, by the AM machine, the material to build the actual AM part based on the updated digital twin; wherein the updated digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, the compliance of the actual AM part to the desired design.

[00017] An embodiment of the present disclosure provides a method including (A) laying down, by an Additive Manufacturing (AM) machine, a first AM testing layer of an additive material; (B) subjecting, by the AM machine, the first AM testing layer to the AM build process based at least in part on an initial digital twin of an actual AM part to be build; (C) determining, by at least one processor, a compliance of the first AM testing layer to one or more predefined criteria by comparing testing data to the one or more predefined criteria, defined based on the initial digital twin, wherein the testing data has been obtained from at least one in-situ monitoring sensor that has utilized one or more in-situ monitoring techniques to collect the testing data; (D) simulating, by the at least one processor, based on at least one AM simulation model, at least one testing adjustment to at least one of i) the one or more initial operational parameters of the AM machine or ii) the one or more initial AM build process parameters, when the first AM testing layer fails the compliance to the one or more predefined criteria; wherein the at least one testing adjustment is configured to compensate at least one difference in the actual AM part from a desired design due to the first AM testing layer; (E) causing, by the at least one processor, the AM machine to implement the at least one testing adjustment in the AM build process for a subsequent AM testing layer that is laid down after first AM testing layer; (F) generating, by the at least one processor, an updated digital twin from the initial digital twin based on the at least one testing adjustment; and (G) utilizing, by the AM machine, the material to build the actual AM part based on the updated digital twin; wherein the updated digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, the compliance of the actual AM part to the desired design.

[00018] In an embodiment of a system of the present disclosure, the system includes an Additive Manufacturing (AM) machine, configured to: (A) lay down a first AM testing layer of an additive material; (B) subject the first AM testing layer to an AM build process based at least in part on an initial digital twin of an actual AM part to be build; wherein the initial digital twin comprises at least one of: i) one or more initial operational parameters of the AM machine for building the actual AM part, ii) one or more properties of a desired design for the actual AM part, or iii) one or more initial AM build process parameters; at least one in-situ monitoring sensor, wherein the at least one in-situ monitoring sensor is configured to collect and transmit, during the AM build process, testing data related to first AM testing layer; a processor; and a non-transitory computer readable storage medium storing thereon program logic, wherein, when executing the program logic, the processor is configured to: (A) receive the testing data; (B) determine a compliance of the first AM testing layer to one or more predefined criteria; and (C) simulate, based on at least one AM simulation model, at least one testing adjustment to at least one of i) the one or more initial operational parameters of the AM machine or ii) the one or more initial AM build process parameters, when the first AM testing layer fails the compliance to the one or more predefined criteria; wherein the at least one AM simulation model is generated by estimating, from historical experimental data of the AM build process of one or more other AM parts, most likely value for at least one of i) each respective of the one or more initial operational parameters of the AM machine or ii) each respective of the one or more initial AM build process parameters; wherein the at least one testing adjustment is configured to compensate at least one difference in the actual AM part from the desired design due to the first AM testing layer; (D) generate an updated digital twin from the initial digital twin based on the at least one testing adjustment; (E) instruct, based on the updated digital twin, the AM machine to implement the at least one testing adjustment in the AM build process for at least one subsequent AM testing layer; and wherein the updated digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, the compliance of the actual AM part to the desired design.

BRIEF DESCRIPTION OF THE DRAWINGS

[00019] The present disclosure can be further explained with reference to the attached drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.

[00020] FIG. 1 is a schematic illustration of an overall architecture of various activities that may occur within an exemplary inventive computer-based AM systems and related methods according to one or more embodiments of the present disclosure;

[00021] FIG. 2 is a schematic representation of an exemplary inventive computer- based AM system according to an embodiment of the present disclosure;

[00022] FIG. 3 is a schematic workflow of an exemplary inventive calibration feedback process according to embodiments of the present disclosure;

[00023] FIG. 4 is a schematic workflow of a process AM building an exemplary inventive calibration coupon according to an embodiment of the present disclosure;

[00024] FIG. 5 is a schematic workflow of an exemplary inventive adjustment process that determines an exemplary inventive calibrated computer simulation model for a particular individual machine by analyzing the exemplary inventive calibration coupon according to an embodiment of the present disclosure;

[00025] FIG. 6 is a schematic representation of a diagram of time dependent calibration routine for the exemplary inventive calibrated computer simulation model according to an embodiment of the present disclosure;

[00026] FIGs. 7A-7E are schematic representations of an exemplary analysis workflow for calibrating probability distributions to reflect the uncertain nature of parameters including but not limited to melt pool width and yield strength during the AM process according to an embodiment of the present disclosure; [00027] FIGs. 8A-8E are schematic representations of an exemplary analysis workflow for calibrating the probabilistic porosity simulation model during the AM process according to an embodiment of the present disclosure; and

[00028] FIGs. 9A-9E are schematic representations of an exemplary analysis workflow for calibrating the probabilistic tensile yield strength simulation model during the AM process according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

[00029] The present disclosure can be further explained with reference to the included drawings, wherein like structures are referred to by like numerals throughout the several views. The drawings shown are not necessarily to scale, with emphasis instead generally being placed upon illustrating the principles of the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.

[00030] Among those benefits and improvements that have been disclosed, other objects and advantages of this invention can become apparent from the following description taken in conjunction with the accompanying figures. Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative of the invention that may be embodied in various forms. In addition, each of the examples given in connection with the various embodiments of the present disclosure is intended to be illustrative, and not restrictive.

[00031] Throughout the specification, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrases "in one embodiment" and "in some embodiments" as used herein do not necessarily refer to the same embodiment(s), though they may. Furthermore, the phrases "in another embodiment" and "in some other embodiments" as used herein do not necessarily refer to a different embodiment, although they may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention. Further, when a particular feature, structure, or characteristic is described in connection with an implementation, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other implementations whether or not explicitly described herein.

[00032] The term "based on" is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of "a," "an," and "the" include plural references. The meaning of "in" includes "in" and "on. "

[00033] It is understood that at least one aspect/functionality of various embodiments described herein can be performed in real-time, faster-than-real-time, and/or dynamically. As used herein, the term "real-time" is directed to an event/action that can occur instantaneously or almost instantaneously in time when another event/action has occurred. For example, the "real-time processing," "real-time computation," and "real-time execution" all pertain to the performance of a computation prior to an actual time that the related physical process or physical transformation occurs (e.g., adding a build layer to an Additive Manufacture (AM) part), so that results of the real-time computation (e.g., a simulated dynamics model of the AM part being built) can be used in guiding the physical process (e.g., AM process). As used herein, the term "faster-than-real-time" is directed to simulations in which advancement of simulation time may occur faster than real world time. For example, some of the "faster- than-real-time" simulations of the present disclosure may be configured in accordance with one or more principles detailed in D. Anagnostopoulos, 2002, "Experiment scheduling in faster-than-real-time simulation," 148-156. 10.1 109/P ADS.2002.1004212. [00034] As used herein, the term "dynamically" means that events and/or actions can be triggered and/or occur without any human intervention. In some embodiments, events and/or actions in accordance with the present disclosure can be in real-time and/or based on a predetermined periodicity of at least one of: nanosecond, several nanoseconds, millisecond, several milliseconds, second, several seconds, minute, several minutes, hourly, several hours, daily, several days, weekly, monthly, etc.

[00035] As used herein, the term "runtime" corresponds to any behavior that is dynamically determined during an execution of a software application or at least a portion of software application.

Additive Manufacturing

[00036] As used herein, "additive manufacturing" means "a process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies", as defined in ASTM F2792-12a entitled "Standard Terminology for Additively Manufacturing Technologies". The AM parts described herein may be manufactured via any appropriate additive manufacturing technique described in this ASTM standard, such as binder jetting, directed energy deposition, material extrusion, material jetting, powder bed fusion, or sheet lamination, among others. In one embodiment, an additive manufacturing process includes depositing successive layers of one or more materials (e.g., powders of materials) and then selectively melting and/or sintering the materials to create, layer-by-layer, an AM part/product. In one embodiment, an additive manufacturing processes uses one or more of Selective Laser Sintering (SLS), Selective Laser Melting (SLM), and Electron Beam Melting (EBM), among others. In one embodiment, an additive manufacturing process uses an EOSINT M 280 Direct Metal Laser Sintering (DMLS) additive manufacturing system, or comparable system, available from EOS GmbH (Robert-Stirling-Ring 1, 82152 Krailling/Munich, Germany). Additive manufacturing techniques (e.g. when utilizing metallic feedstocks) may facilitate the selective heating of materials above the liquidus temperature of the particular alloy, thereby forming a molten pool followed by rapid solidification of the molten pool. Non-limiting examples of additive manufacturing processes useful in producing AM products include, for instance, DMLS (direct metal laser sintering), SLM (selective laser melting), SLS (selective laser sintering), and EBM (electron beam melting), among others. Any suitable feedstocks may be used, including one or more materials, one or more wires, and combinations thereof. In various embodiments, AM is configurable to utilize various feedstocks - e.g. metallic feedstocks (e.g. with additives to promote various properties, e.g. grain refiners and/or ceramic materials), plastic feedstocks, and polymeric feedstocks (or reagent-based feedstock materials which form polymeric AM builds/ AM parts), to name a few. In some embodiments the additive manufacturing feedstock is comprised of one or more materials. Shavings are types of particles. In some embodiments, the additive manufacturing feedstock is comprised of one or more wires. A ribbon is a type of wire.

[00037] In one approach, the AM parts metal alloys described herein are in the form of an additive manufacturing feedstock.

[00038] As noted above, additive manufacturing may be used to create, layer-by-layer, an AM part/product. In one embodiment, a powder bed is used to create an AM part/product (e.g., a tailored alloy product and/or a unique structure unachievable through traditional manufacturing techniques (e.g. without excessive post-processing machining)).

[00039] In one approach, a method comprises (a) dispersing an AM feedstock (e.g. metal alloy powder in a bed), (b) selectively heating a portion of the material (e.g., via an energy source or laser) to a temperature above the liquidus temperature of the particular AM part/product to be formed, (c) forming a molten pool and (d) cooling the molten pool at a cooling rate of at least 1000°C per second. In one embodiment, the cooling rate is at least 10,000°C per second. In another embodiment, the cooling rate is at least 100,000°C per second. In another embodiment, the cooling rate is at least 1,000,000°C per second. Steps (a)-(d) may be repeated as necessary until the AM part/product is completed.

[00040] In another approach, a method comprises (a) dispersing a feedstock (e.g. AM material powder) in a bed, (b) selectively binder jetting the AM material powder, and (c) repeating steps (a)-(b), thereby producing a final additively manufactured product (e.g. including optionally heating to burn off binder and form a green form, followed by sintering to form the AM part).

[00041] In another approach, electron beam (EB) or plasma arc techniques are utilized to produce at least a portion of the AM part/product. Electron beam techniques may facilitate production of larger parts than readily produced via laser additive manufacturing techniques. An illustrative example provides feeding a to the wire feeder portion of an electron beam gun. The wire may comprise a metal feedstock (e.g. metal alloy including titanium, cobalt, iron, nickel, aluminum, or chromium alloys to name a few). The electron beam heats the wire or tube, as the case may be, above the liquidus point of the alloy to be formed, followed by rapid solidification of the molten pool to form the deposited material.

[00042] The alloy may be, for instance, an aluminum-based alloy, a titanium-based alloy (including titanium aluminides), a nickel-based alloy, an iron-based alloy (including steels), a cobalt-based alloy, or a chromium-based alloy, among others.

[00043] Any suitable alloy composition may be used with the techniques described above to produce AM part/product. Some non-limiting examples of alloys that may be utilized are described below. However, other alloys may also be used, including copper- based, zinc-based, silver-based, magnesium-based, tin-based, gold-based, platinum-based, molybdenum-based, tungsten-based, and zirconium-based alloys, among others. [00044] As used herein, "aluminum alloy" means a metal alloy having aluminum as the predominant alloying element. Similar definitions apply to the other corresponding alloys referenced herein (e.g. titanium alloy means a titanium alloy having titanium as the predominant alloying element, and so on).

[00045] In some embodiments, the inventive specially programmed computing systems with associated devices are configured to operate in the distributed network environment, communicating over a suitable data communication network (e.g., the Internet, etc.) and utilizing at least one suitable data communication protocol (e.g., IPX/SPX, X.25, AX.25, AppleTalk(TM), TCP/IP (e.g., HTTP), etc.). Of note, the embodiments described herein may, of course, be implemented using any appropriate hardware and/or computing software languages. In this regard, those of ordinary skill in the art are well versed in the type of computer hardware that may be used, the type of computer programming techniques that may be used (e.g., object-oriented programming), and the type of computer programming languages that may be used (e.g., C++, Objective-C, Swift, Java, Javascript). The aforementioned examples are, of course, illustrative and not restrictive.

[00046] The material disclosed herein may be implemented in software or firmware or a combination of them or as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. As used herein, the machine-readable medium may include any medium and/or mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). By way of example, and not limitation, the machine-readable medium may comprise computer readable storage media, for tangible or fixed storage of data, or communication media for transient interpretation of code- containing signals. Machine-readable storage media, as used herein, refers to physical or tangible storage (as opposed to signals) and includes without limitation volatile and nonvolatile, removable and non-removable media implemented in any method or technology for the tangible storage of information such as computer-readable instructions, data structures, program modules or other data. Machine-readable storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, flash memory storage, or any other physical or material medium which can be used to tangibly store the desired information or data or instructions, including but not limited to electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and which can be accessed by a computer or processor.

[00047] In another form, a non-transitory article, such as non-volatile and nonremovable computer readable media, may be used with any of the examples mentioned above or other examples except that it does not include a transitory signal per se. It does include those elements other than a signal per se that may hold data temporarily in a "transitory" fashion such as RAM and so forth. In some embodiments, the present disclosure may rely on one or more distributed and/or centralized databases (e.g., data center).

[00048] As used herein, the term "server" should be understood to refer to a service point which provides processing, database, and communication facilities. By way of example, and not limitation, the term "server" can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. Servers may vary widely in configuration or capabilities, but generally a server may include one or more central processing units and memory. A server may also include one or more mass storage devices, one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

[00049] As used herein, a "network" should be understood to refer to a network that may couple devices so that communications may be exchanged, such as between a server and a client device or other types of devices, including between wireless devices coupled via a wireless network, for example. A network may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), or other forms of computer or machine readable media, for example. A network may include the Internet, one or more local area networks (LANs), one or more wide area networks (WANs), wireD line type connections, wireless type connections, cellular or any combination thereof. Likewise, subD networks, which may employ differing architectures or may be compliant or compatible with differing protocols, may interoperate within a larger network. Various types of devices may, for example, be made available to provide an interoperable capability for differing architectures or protocols. As one illustrative example, a router may provide a link between otherwise separate and independent LANs.

[00050] As used herein, the terms "computer engine" and "engine" identify at least one software component and/or a combination of at least one software component and at least one hardware component which are designed/programmed/configured to manage/control other software and/or hardware components (such as the libraries, software development kits (SDKs), objects, etc.).

[00051] Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), and so forth.

[00052] Software may refer to 1) libraries; and/or 2) software that runs over the internet or whose execution occurs within any type of network. Examples of software may include, but are not limited to, software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

[00053] One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as "IP cores" may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that actually make the logic or processor. [001] In some embodiments, the exemplary inventive AM processes of the present disclosure may include one or more steps detailed, without limitation, in U.S. Patent Pub. No. 2016/0224017 which is hereby incorporated herein by reference. For example, the AM process may be a process of joining materials to make objects from 3D model data, usually layer upon layer. In some embodiments, additive manufacturing includes building successive layers of an AM material (e.g., aluminium alloy powder) by depositing a feed stock powder of the AM material (e.g., metal powder) and then selectively melted and/or sintered (e.g. with a laser or other heat source) to create, layer-by-layer, an AM part (e.g., an aluminium alloy product, a titanium alloy product, a nickel alloy product). Additive build processes utilizing a powder feedstock that can employ one or more of the embodiments of the instant disclosure include: direct metal laser sintering (e.g. a powder bed fusion process used to make metal AM parts directly from metal powders without intermediate "green" or "brown" parts); directed energy deposition (e.g., an AM process in which focused thermal energy is used to fuse materials by melting as they are being deposited); powder bed fusion (e.g. an AM process in which thermal energy selectively fuses regions of a powder bed); or laser sintering (e.g., a powder bed fusion process used to produce objects from powdered materials using one or more lasers to selective fuse or melt the particles at the surface, layer by layer, in an enclosed chamber) to name a few. Some non-limiting examples of suitable additive manufacturing systems include the EOSINT M 280 Direct Metal Laser Sintering (DMLS) additive manufacturing system, available from EOS GmbH (Robert-Stirling-Ring 1, 82152 Krailling/Munich, Germany). Other suitable additive manufacturing systems include Selective Laser Sintering (SLS) systems, Selective Laser Melting (SLM) systems, and Electron Beam Melting (EBM) systems, among others.

[00054] Fig. 1 shows an illustrative example of an overall architecture 100 of various activities that may occur within an exemplary inventive computer-based AM system 102 that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. While some activities identified in Fig. 1 are detailed herein as occurring in sequential order, such description is done for purposes of convenience and should not be viewed as being limited since, as a skilled practitioner would readily recognize, at least some activities may occur concurrently, in reverse order, or not occur at al under certain condition(s).

[00055] Referring to item 104 of Fig. 1, in at least some embodiments, the exemplary inventive computer-based AM system may receive/obtain electronical data describing one or more parts to be manufactured ("part data"). In some embodiments, the exemplary inventive computer-based AM system may analyze the part data to determine one or more functions that are desired for each AM part. In some embodiments, when an AM part may be constructed from a plurality of sub-parts and/or when the AM part may be intended to be combined with at least one other part, which may or may not be manufactured utilizing an AM process, to perform its intended function, the exemplary inventive computer-based AM system may further determine one or more characteristics that may influence how the AM part would perform for its intended purpose(s).

[00056] In some embodiments, any individual part manufactured via AM may be subject to one or more additional processes, such as machining for finishing purposes and/or forging for inducing desired microstructural properties. In some embodiments, at least one sub-part may not be manufactured via AM. In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of preparation for generating software instructions and/or software model(s) that may direct how the AM part is created during the additive manufacturing process. In some embodiments, the exemplary inventive computer-based AM system may be configured to perform the above analysis/determination as part of a real-time feedback mechanism that may be configured to utilize the analysis/determination performed during the activity of item 104 to influence, in real time, how an exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1.

[00057] Referring to, for example, item 106 of Fig. 1, in at least some embodiments, based on the part data and additional data generated at preceding stage(s), the exemplary inventive computer-based AM system may analyze/determine how a proposed (initial) design of the AM part in the part data received/obtained by the exemplary inventive computer-based AM system would be suitable/fit to perform its intended function(s). In some embodiments, the exemplary inventive computer-based AM system may be configured to analyze/determine how the design of the AM part would influence the overall performance of the exemplary inventive computer-based AM system. In some embodiments, during a part of the activity of item 106, the exemplary inventive computer-based AM system may be configured to dynamically alter the material composition of the initial design of the AM part to improve performance of the exemplary inventive computer-based AM system during one or more subsequent activities without sacrificing and/or improving how the AM part would perform for its intended function(s). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of a real-time feedback mechanism that may be configured to utilize the analysis/determination during the activity of item 106 to influence, in real time, how the exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1.

[00058] Referring to item 108 of Fig. 1, in at least some embodiments, based on the part data and additional data generated at preceding stage(s), the exemplary inventive computer-based AM system may select at least one of: i) material composition(s) from one or more pre-determined material compositions that would be sufficiently suitable to the intended function(s) of the AM part, and/or ii) AM processing path(s).

[00059] In some embodiments, the exemplary inventive computer-based AM system may be configured to analyze how the material composition of the AM part would influence the overall performance of the exemplary inventive computer-based AM system. For example, the exemplary inventive computer-based AM system may be configured to analyze life expectancy, cost, weight, corrosion resistance, and other parameter(s) of AM build part.

[00060] In some embodiments, a part of the activity of item 108, the exemplary inventive computer-based AM system may be configured to select, from one or more predetermined material compositions, an initial material composition of the AM part in the part data to improve performance of the exemplary inventive computer-based AM system during one or more subsequent activities without sacrificing and/or improving how the AM part would perform for its intended function(s). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of a real-time feedback mechanism that may be configured to utilize the analysis/determination during the activity of item 108 to influence, in real time, how the exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1.

[00061] Referring to item 110 of Fig. 1, in at least some embodiments, based on the part data and additional data generated at preceding stage(s), the exemplary inventive computer-based AM system may run one or more part build simulations to analyze/test how, for example without limitation, one or more characteristics of the AM part would influence and/or be influenced by one or more subsequent activities of the exemplary inventive computer-based AM system. In some embodiments, as a part of the activity 110, the exemplary inventive computer-based AM system may be configured to dynamically alter, in real-time, the one or more part build simulation parameters based, at least in part, on one or more real-time characteristics of the exemplary inventive computer-based AM system and/or one or more real-time internal and/or external conditions associated with the exemplary inventive computer-based AM system (e.g., a temperature inside of an AM machine). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform such analysis/determination as part of a real-time feedback control mechanism that may be configured to utilize the one or more AM part build simulations developed during the activity of item 110 to influence, in real time, how the exemplary AM process performs during one or more preceding and/or subsequent activities of the exemplary inventive computer-based AM system of Fig. 1. In some embodiments, the one or more AM part build simulations may be based, at least in part, on at least in part, any given simulation of any given part, may be influenced by and compared to simulation(s) of other sufficiently similar AM part(s).

[00062] In some embodiments, during the activity of item 110, the exemplary inventive computer-based AM system may be configured to generate a dynamically adjustable digital representation ("digital twin") 138 of the AM part that would be manufactured. In some embodiments, the digital twin 138 includes current and/or historical data related to function(s) of the AM part (e.g., estimations based on historical experimental data from the AM build process of one or more other AM parts); the design of the AM part, and/or the material composition of the AM part (the part-centered data such as design data 128 and material data 130). In some embodiments, in addition to the part-centered data, the digital twin 138 may include AM process parameter(s) associated with the exemplary AM process to be employed to manufacture the AM part and/or code instructions that are configured to direct an exemplary AM machine to build the AM part (the build-centered data such as simulation data 132 and process data 134). In some embodiments, the build-centered data may include historical error data generated during the additive manufacturing of other similar AM part(s) (i.e., digital twin(s) of previously manufactured other similar AM part(s)).

[00063] In some embodiments, in addition to the part-centered data and the build- centered data, the digital twin 138 may include certification requirement data (e.g., defect determination parameter(s)) that may be employed to certify that the AM part would be fit for its intended function(s) in connection with in-situ monitoring (item 1 16) and post-build inspection (item 1 18) (the certification-centered data such as inspection data 136). In some embodiments, the digital twin 138 may be configured to be self-contained, self-adjustable, and/or self-executing computer entity that is agnostic to a type of an AM machine that may be employed to build the AM part.

[00064] Referring to item 112 of Fig. 1, in at least some embodiments, the exemplary inventive computer-based AM system may be configured to utilize the digital twin 138 to determine one or more settings for the exemplary AM machine for building the AM part (AM machine setting data). In some embodiments, the exemplary inventive computer-based AM system may be configured, during the activity of item 112 to incorporate the AM machine setting data into the digital twin 138. In some embodiments, the AM machine setting data may include data that cause the exemplary machine to calibrate itself in a particular way prior to building the AM part (AM machine calibration data). In some embodiments, as a part of the activity of item 1 12, the exemplary inventive computer-based AM system may be configured to utilize the monitoring data collected, in real-time, about the exemplary AM machine, while the exemplary AM machine builds other AM part(s), to dynamically adjust the AM machine setting data in the digital twin 138 of the AM part to account, without limitation, for machine-to-machine parameter variability.

[00065] In some embodiments, the monitoring data may include at least one of: i) operational parameter(s) of the exemplary AM machine, ii) internal (in-situ) conditions of the exemplary AM machine (e.g., temperature within a build chamber, O ₂ concentration, etc.), which may be generated, for example without limitation, during activity of item "6", and/or iii) external conditions associated with the exemplary AM machine (e.g., environmental conditions (e.g., surrounding temperature, atmospheric pressure, humidity, etc.)).

[00066] Referring to item 114 of Fig. 1, the exemplary inventive computer-based AM system may be configured to execute the digital twin 138 so that the AM machine may be instructed to build the AM part in accordance with the corresponding digital twin 138. For example, based on the digital twin 138, the AM machine may be instructed to deposit an initial layer of AM part based on an estimated build position, extract actual coordinates of the build layer, compare the coordinates of the initial build layer with the estimated coordinates sent to the AM machine, and determining a deviation, if any, between an ideal (estimated) build of the digital twin 138 and the actual build layer.

[00067] In some embodiments, as a part of activity of item 114, based on the digital twin 138, if particular point(s) in the build portion of the AM part is/are determined to deviate from a threshold condition to a tolerable degree (e.g., an out-of-compliance-but-reparable condition), the exemplary inventive computer-based AM system may be configured to mitigate such noncompliance by adjusting build instruction(s) for next build layer(s) and/or build portion(s) of the same layer in which the noncompliance has been determined. In some embodiments, the out-of-compliance-but-reparable condition may be a condition in which repair would not be needed. In one embodiment, the out-of-compliance-but-reparable condition may be a condition that would be within tolerances without need to repair. In one embodiment, the out-of-compliance-but-reparable condition may be a condition that would be outside of tolerances but still repairable.

[00068] In some embodiments, as a part of activity of item 114, based on the digital twin 138, if particular point(s) in the build portion of the AM part is/are determined to deviate from a threshold condition to a non-tolerable degree (unrepairable condition), the exemplary inventive computer-based AM system may be configured to cause the exemplary AM machine to stop the build process. In such case, the defective intermediate may be discarded, avoiding deposition of additional layers which would save cost associated with material for those layers and the time to complete them.

[00069] In some embodiments, the exemplary inventive computer-based AM system may be configured to execute an active feedback control mechanism (item 126 of Fig. 1) which may be triggered based, at least in part, on the in-situ monitoring data (item 1 16) whenever there is/are discrepancy(ies)/deviation(s) within at least one of: i) definitions determined during the material selection activity (item 108), with or without executing the iterative adjustment of build material selection (item 122 of Fig. 1); ii) definitions determined during the part build simulation activity (item 1 10), with or without the interposition of the optimization step 124); and/or iii) definitions determined during the AM machine's set points determination (item 1 12).

[00070] In some embodiments, independently of the discrepancies identified during the material selection activity (item 108) (optionally influenced by item 122), during the part build simulation activity (item 110) (optionally influenced by item 124) and during the AM machine's set points determination (item 112) being known or quantifiable, the inventive active feedback control mechanism (item 126) may be configured to either interrupt the build process of the AM part and/or re-run the iterative adjustments (items 122 and/or 124) to affect values of items 108, 1 10, and 112 of Fig. 1 until quality metrics identified in item 1 16 meet the specification. Consequently, in at least some embodiments, the in-situ monitoring (item 1 16) drives the inventive active feedback control mechanism (item 126) to dynamically specify machine set points that result in a successful completion of the build or in sufficiently earlier stop of the build process to minimize the waste of material and/or time. In some embodiments, the inventive active feedback control mechanism (item 126) may be configured as at least one of suitable control strategies such as, without limitation, classical Proportional-Integral-Derivative (PID) control, adaptive control, optimal control, and combinations thereof, etc.

[00071] Referring to item 1 18 of Fig. 1 , in at least some embodiments, the exemplary inventive computer-based AM system may be configured to generate a final state of the digital twin 138 after the physical AM part (item 120: the physical twin) has passed the post-build inspection (item 118) so that the final state of the digital twin 138 is utilized to certify a subsequently built AM part as being fit for its intended function(s) (e.g., compliance with the certification requirements and other desired requirement(s)) without actual/physical evaluation of the subsequent AM part itself. In some embodiments, the post-build inspection (item 1 18) may include non-destructive testing, destructive testing (completed on parts), or both.

[00072] Referring to at least activities of items 1 14 and 116 of Fig. 1, the exemplary inventive computer-based AM system may be configured to dynamically adjust, in real-time, the digital twin 138 and/or the AM build process based, at least in part, on and may include at least one of: i) the part design data (item 128 of Fig. 1), ii) the material composition data (item 130 of Fig. 1), iii) the part-build simulation data (item 132 of Fig. 1), iv) the AM process data (item 134 of Fig. 1), and/or v) the inspection/certification data (item 136 of Fig. 1).

[00073] In some embodiment, the AM process data may be the process data collected during the production and/or certification of similar AM part(s), which may be then used to complete one or more of i) the part design data 128, ii) the material composition data 130, and/or iii) the part-build simulation data 132. In some embodiment, the inspection/certification data 136 is used to adjust one or more of i) the part design data 128, ii) the material composition data 130, and/or iii) the part-build simulation data 132.

[00074] In some embodiment, the digital twin 138 may be stored according to a predetermined data model and/or schema and include all of data items 128-136. In some embodiment, the digital twin 138 may include data that describes the machine setup changes resulting from the inventive active feedback control mechanism (item 126). In some embodiments, the digital twin 138 of AM process parts may be configured to be processed by applying at least one of suitable analytical techniques such as, without limitation, machine learning algorithms, neural networks, and/or predictive modelling techniques.

Production and Processing

[00075] In some embodiments, the AM part/product may be subject to any appropriate dissolving (e.g. includes homogenization), working and/or precipitation hardening steps. If employed, the dissolving and/or the working steps may be conducted on an intermediate form of the additively manufactured body and/or may be conducted on a final form of the additively manufactured body. If employed, the precipitation hardening step is generally conducted relative to the final form of the AM part product.

[00076] After or during production, an AM part/product may be deformed (e.g., by one or more of rolling, extruding, forging, stretching, compressing). The final deformed product may realize, for instance, improved properties due to the tailored regions and thermo- mechanical processing of the final deformed AM part product. Thus, in some embodiments, the final product is a wrought AM part/product, the word "wrought" referring to the working (hot working and/or cold working) of the AM part/product, wherein the working occurs relative to an intermediate and/or final form of the AM part/product. In other approaches, the final product is a non- wrought product, i.e., is not worked during or after the additive manufacturing process. In these non-wrought product embodiments, any appropriate number of dissolving and precipitating steps may still be utilized.

Product Applications

[00077] The resulting AM part/products made in accordance with the systems and methods described herein may be used in a variety of product applications. In one embodiment, the AM parts (e.g. metal alloy parts) are utilized in an elevated temperature application, such as in an aerospace or automotive vehicle. In one embodiment, an AM part or product is utilized as an engine component in an aerospace vehicle (e.g., in the form of a blade, such as a compressor blade incorporated into the engine). In another embodiment, the AM part or product is used as a heat exchanger for the engine of the aerospace vehicle. The aerospace vehicle including the engine component / heat exchanger may subsequently be operated. In one embodiment, the AM part or product is an automotive engine component. The automotive vehicle including an automotive component (e.g. engine component) may subsequently be operated. For instance, the AM part or product may be used as a turbo charger component (e.g., a compressor wheel of a turbo charger, where elevated temperatures may be realized due to recycling engine exhaust back through the turbo charger), and the automotive vehicle including the turbo charger component may be operated. In another embodiment, an AM part or product may be used as a blade in a land based (stationary) turbine for electrical power generation, and the land-based turbine included the AM part or product may be operated to facilitate electrical power generation. In some embodiments, the AM part or products are utilized in defense applications, such as in body armor, and armed vehicles (e.g., armor plating). In other embodiments, the AM part or products are utilized in consumer electronic applications, such as in consumer electronics, such as, laptop computer cases, battery cases, cell phones, cameras, mobile music players, handheld devices, computers, televisions, microwaves, cookware, washers/dryers, refrigerators, and sporting goods, among others.

[00078] In another aspect, the AM part or products are utilized in a structural application. In one embodiment, the AM part or products are utilized in an aerospace structural application. For instance, the AM part or products may be formed into various aerospace structural components, including floor beams, seat rails, fuselage framing, bulkheads, spars, ribs, longerons, and brackets, among others. In another embodiment, the AM part or products are utilized in an automotive structural application. For instance, the AM part or AM part or products may be formed into various automotive structural components including nodes of space frames, shock towers, and subframes, among others. In one embodiment, the AM part or product is a body-in-white (BIW) automotive product.

[00079] In another aspect, the AM part or products are utilized in an industrial engineering application. For instance, the AM part or products may be formed into various industrial engineering products, such as tread-plate, tool boxes, bolting decks, bridge decks, and ramps, among others.

[00080] Fig. 2 shows an illustrative example of an overview of a distributed computer network system 200 including an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. In some embodiments, the exemplary inventive AM system may include several different entities, such as an AM operator's terminal 208 and customers 204 all operatively communicable via a shared communication network 206, such that data, such as the inventive AM digital twin files, may be transferred between any one of the aforementioned connected entities 202 and 204. The customer logical environment 204 may include an authentication server that may be arranged to authenticate if a customer entity is authorized to access a relevant data file, such as a particular AM digital twin. In some embodiments, the shared communication network 206 may relate to the Internet, a LAN, a WAN, or any other suitable computer network. In some embodiments, the AM process logic environment 202 may effectively be a print farm, comprising one or more different operatively connected AM Machines/3D printers 210. Accordingly, the terms "AM machines" and "3D print farm" may be used interchangeably to refer to the same physical entity/entities in the ensuing description, and the term "3D print farm" is analogous to the term "3D printing bureau."

[00081] The customer environment 204 may include a server 218 operatively connected to the communication network 206, enabling direct data connections and communication with the attached terminal 208 and the 3D print farm 202. In addition, the server 218 may host a website through which a user using any one of the different operatively connected terminals 202 and 208, may interact with the customer environment 204 using standard web browsers.

[00082] In some embodiments, the server 218 may be operatively connected to a database 220, which may be stored in a storage device local to the server 218, or in an external storage unit (not shown). In some embodiments, the exemplary inventive AM system may be configured so that the customer environment 204 provides several different functions. For example, it provides a centralized network peer, which is entrusted with managing access rights to proprietary information included in the inventive AM digital twin file. It may also provide a centralized networked means for advertising and accessing content, such as the inventive AM digital twin files, AM parameter settings, and for securely distributing content between different networked terminals. Such content may also relate to CAD software made available by a software developer who can be the AM operator.

[00083] In some embodiments, the exemplary inventive AM system may be configured so that access to information included in an exemplary inventive AM digital twin file may be controlled via the customer environment 204, using a combination of unique identifier(s) and data encryption. By unique identifiers is intended any electronically verifiable identifier. For example, the unique identifier associated with a 3D printer may relate to the printer's serial number. The database 220 maintains a record of all parties registered to use the 3D printers (AM machines). Such parties may include, but are not limited to, registered AM operators 208. This information may be stored as one or more records and/or tables within the database 220.

[00084] In some embodiments, the exemplary inventive AM system may be configured to require a registration capability in order for each operatively connected entity to be uniquely identifiable by the customer environment 204, to thereby enable the customer environment 204 to manage access rights to encrypted content. For example, to manage access rights to the encrypted content of exemplary inventive AM digital twin files. In some embodiments, the exemplary inventive AM system may be configured so that the exemplary 3D print farm 202 may include a server 212, which is operatively connected to the shared communication network 206. The server 212 may itself be operatively connected to one or more different AM machines/3D printers 210. In some embodiments, the function of the server 212 is to execute one or more activities identified in Fig. 1 such as dynamically instructing an appropriate AM machine 210 to AM produce an exemplary AM part based on exemplary inventive AM digital twin.

[00085] The exemplary inventive computer-based AM system may be configured to dynamically detect/adjust/re-calibrate, in real-time, the AM machine to confirm that a new batch of build material (e.g., build powder) would not negatively influence the build of the AM manufactured part (referring to at least activities of items 112, 114 and 116 of Fig. 1) according to some embodiments. [00086] In some embodiment, individual setup parameters of a particular AM machine

(item 124 of Fig. 1) may be determined based on an execution of an exemplary inventive calibration routine utilized at step 112 of Fig. 1. In some embodiments, the setup parameters of each AM machine may be stored in a database, such as item 214 of Fig. 2, associated with the AM machine's corresponding serial number, such as item 216 of Fig. 2. An exemplary general calibration routine performed by the exemplary inventive computer-based AM system is shown at section 4 of J. Palomo, et al, Journal of Statistical Software, "SAVE: An R Package for the Statistical Analysis of Computer Models" (2015).

[00087] Fig. 3 shows an illustrative example of an exemplary inventive calibration feedback process that may occur within an exemplary inventive computer-based AM system that may be configured to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. In one embodiment, the exemplary inventive calibration feedback process of the exemplary inventive computer-based AM system may begin at step 302, in which a layer of material that is not intended to be part of actual part is laid down.

[00088] In some embodiments, the exemplary inventive computer-based AM system may then test the built layer at step 304. In one embodiment, the exemplary inventive computer-based AM system may test the build layer by collecting in-situ AM monitoring data via one or more in-situ monitoring techniques (item 116), utilizing one or more corresponding in-situ monitoring sensors, and compare the collected data to a pre-defined specification. In some embodiment, the data collected via the in-situ monitoring may or may not feed a simulation model used to infer any feature and/or parameter that cannot be measured and, thus has to be simulated before comparing to the pre-defined specification. In some embodiments, the one or more corresponding in-situ built-in monitoring sensors may be built into an AM machine. In some embodiments, the one or more corresponding in-situ physically-independent monitoring sensors may be physically distinct from an AM machine but may be operationally distinct from or operationally connected to the AM machine. In some embodiments, the plurality of corresponding in-situ monitoring sensors may include the one or more corresponding in-situ built-in monitoring sensors and the one or more corresponding in-situ physically-independent monitoring sensors.

[00089] In some embodiments, when one or more of the systems or methods of the present disclosure are directed towards an AM process utilizing a wire feedstock and/or wire- based feedstock (e.g. hybrid type wires like entwined fibers of differing materials and/or wire tube with core (e.g. powder core) of different material), the exemplary inventive computer- based AM system may be configured to utilize one or more of the following in-situ AM build monitoring techniques that may include, but are not limited to one or more of those set out in the above portion, including: wire deposition rate, z height (height between deposition and surface (e.g. build layer or substrate); bead orientation, feedstock thickness, or others.

[00090] In some embodiment, the exemplary inventive computer-based AM system may configure, for economic purposes, that the coupon, having one or more AM testing layers, may be built from the same material which would be used in step 302 during the exemplary inventive AM process. If there is a rejection, the precursor would be easily and economically discarded. In some embodiment, the exemplary inventive computer-based AM system may be configured to build one or more AM testing layers at any point of the AM build process (i.e., any build layer) and may include both the material not intended to be part of actual part and the material intended to be part of the actual AM part.

[00091] In some embodiments, a test field may be analyzed utilizing one or more in- situ monitoring technique (item 116) to determine the compliance of the one or more AM testing layers against predefined criteria according to some embodiment. Some non-limiting examples of predefined criteria, specific for determining if the material is still acceptable, may be melting temperature, hardness, traces/percentages of key chemical elements, emissivity, and/or electric properties such as, but not limited to, conductivity. In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize one or more of the in-situ AM build monitoring techniques, such as, without limitation, techniques described in A. Sharma et al., 2006, "Apparatus and Method for Z- Height Measurement and Control for a Material Deposition Based Additive Manufacturing Process"; Proc. National Seminar on Non-Destructive Evaluation Dec. 7 - 9, 2006, Hyderabad, Indian Society for Non-Destructive Testing Hyderabad Chapter D. N.Trushnikov et al, 2012, "Online Monitoring of Electron Beam Welding of TI6AL4V Alloy Through Acoustic Emission"; and Mat.-wiss. u. Werkstofftech. 2012, 43, No. 10, (DOI 10.1002/mawe.201200933), "Secondary-Emission signal for weld formation monitoring and control in electron beam welding (EBW)".

[002] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize one or more of the following in-situ AM build monitoring techniques that may include, but are not limited to:

i) measuring the build layer temperature (e.g., via contact thermal couples);

ii) measuring the laser/electron beam power (e.g., per the control system and/or calculated wattage from some feedback on the system);

iii) performing the visual / thermal imaging (e.g., from images or video obtained during the build);

iv) acquiring sound recordings (e.g., via acoustic emission sensors located on or around the build plate);

v) performing the layer by layer optical topography;

vi) measuring gas flows (e.g., via gas flow meters / sensors placed on the entry into (e.g., pressure sensor feedback) and/or within the chamber); vii) monitoring the molten pool size (e.g., via a camera and/or optical sensor);

viii) monitoring the powder distribution (e.g., via a camera and/or acoustic emission sensor); and

ix) monitoring the powder contamination (e.g., via a camera and/or optical sensor and/or communicated from the control system with inputs/information from the feedstock source material supply).

[00092] When the built layer is tested, at step 306, the exemplary inventive computer- based AM system recalibrates the AM machine based, at least in part, on digital twin to maximize compliance of the next layer relative to the above predefined criteria. In one embodiment, the exemplary inventive digital twin may be dynamically updated by utilizing the active feedback control mechanism (item 126 of Fig. 1). Then at step 308, the exemplary inventive computer-based AM system repeats steps 302, 304, and 306 until the above predefined criteria, a predetermined "pass" condition, have been reached. In some embodiments, the predetermined "pass" condition has not been reached within X iterations, the exemplary inventive computer-based AM system may be configured to use the material in the AM process. In some embodiments, the predetermined "pass" condition has not been reached within X iterations, the exemplary inventive computer-based AM system may be configured to reject the build material (e.g., metal powder). In some embodiments, X is 1. In some embodiments, X is 5 or less. In some embodiments, X is 10 or less. In some embodiments, X is 50 or less. In some embodiments, X is between 1 and 100.

[00093] In some embodiments, in relation to step 306 of Fig. 3 as describe supra, the exemplary inventive computer-based AM system may be configured to recalibrate the AM machine based, at least in part, on digital twin to maximize compliance of the next layer relative to the above predefined criteria. In some embodiments, the invention may include an exemplary inventive two-way calibration procedure, whereby results from standard calibration procedures would be captured in digital form to form exemplary inventive calibration digital twin(s) (or exemplary inventive calibrated computer simulation model(s)) of the exemplary inventive calibration AM build part(s)/coupons(s). In some embodiments, the exemplary inventive computer-based AM system may be also configured to utilize the inventive calibration digital twin to perform periodic calibration procedure(s) of each AM machine to compensate for, inter alia, usage induced wear. In some embodiments, the exemplary inventive calibration procedure of the instant application may include the use of periodic calibrations to create a time series of calibrated values and/or predict into the forecasting horizon based, at least in part, on one or more suitable predictive algorithms when the next calibration should take place. A more illustration is described herein in connection with Fig. 6.

Probabilistic materials discovery for the exemplary inventive calibration procedures

[00094] In some embodiments, the exemplary inventive computer-based AM system is configured to perform estimation of material characteristics. Typically, physics-based simulation models are inaccurate relative to reality. Typically, sources of inaccuracy may include but not limited to: i) incorrect parameter values in important constitutive relationships; ii) incorrect or oversimplified modeling assumptions; and/or iii) missing physics. Even when uncertainty is accounted for, many methods lead to overfitting, which leads to non-robust results and is also detrimental to performance. Efforts to limit over-fitting typically require a large amount of experimental data, which is usually cost prohibitive.

[00095] The exemplary inventive simulation models of the present disclosure are calibrated or "tuned" to existing experimental data to at least reduce the impact of the above mentioned sources of inaccuracy, by utilizing the exemplary inventive probabilistic framework as further detailed.

Bavesian inference [00096] In some embodiment, the exemplary inventive computer-based AM system is configured/programmed to utilize the exemplary inventive probabilistic framework based on only a small set of experimental measurements to leam the uncertainty for each input. For cost effectiveness purposes, this methodology optimizes the balance between measurements required (minimizing them) and accuracy (maximizing it).

[00097] In some embodiments, the exemplary inventive computer-based AM system is configured/programmed to receive pre-determined constraints, a particular simulation model, and the noisy experimental measurements to update, by utilizing, for example without limitation, an exemplary Bayesian inference approach, the uncertainty associated with each input (Inverse Uncertainty Quantification), and/or backwards uncertainty quantification.

[00098] In some embodiments, the exemplary inventive computer-based AM system is configured/programmed, as part of the inventive Bayesian inference approach, construct/generate statistical surrogate model(s) for the inventive simulation model(s) to accelerate the inventive Bayesian calibration process.

[00099] In some embodiments, the exemplary inventive Bayesian calibration procedure may be based at least in part on, but not limited to, Kennedy and O'Hagan method as detailed, for example, in M. C. Kennedy and A. O'Hagan, DOI: 10.11 11/1467-9868.00294, 2001, "Bayesian calibration of computer models".

[000100] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize the exemplary inventive calibration techniques of the present disclosure to minimize or eliminate variations so that AM manufactured parts can be reliably produced in series, remotely and/or on premise, addressing the need of complying to technical and/or legal specifications in the AM market. In some embodiments, the exemplary inventive computer-based AM system may be configured to address material batch variation and/or machine-to-machine variation by, for example, performing one or more exemplary inventive calibration procedures detailed herein. Fig. 4 shows an illustrative example of flowchart 400 that details an exemplary inventive process of analyzing an exemplary inventive calibration coupon which may have one or more AM testing layers and may be formed by various AM processes and from various incoming feedstocks (e.g., wire, powder, etc.).

[000101] In some embodiments, the AM process begins at step 402 wherein the exemplary inventive computer-based AM system may be configured to take measurements of the exemplary inventive calibration coupon. At step 404, the exemplary inventive computer- based AM system may be configured to compare the measured parameters to the pre-stored simulation results. In some embodiments, based, at least in part, on differences between these 2 sets of results to determine/identify, among other criteria, machine-to-machine variation. At step 406, the exemplary inventive computer-based AM system may be configured to utilize an optimal testing adjustment process (e.g., an inverse mathematical problem) to adjust parameters of the computational model until its results, within an acceptable tolerance level, match those measured at the exemplary inventive calibration coupon. In some embodiments, as detailed herein, one or more testing adjustments implemented during the inventive optimal testing adjustment process may be an increase in a particular variable/parameter or a decrease in the particular variable/parameter.

[000102] Fig. 5 shows an illustrative example of flowchart 500 that details an exemplary inventive adjustment process that determines an exemplary inventive calibrated computer simulation model for a particular individual machine by analyzing the exemplary inventive calibration coupon which may have one or more AM testing layers and may be formed by various AM processes and from various incoming feedstocks (e.g., wire, powder, etc.). In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize the AM machine exemplary inventive calibration process that is based, at least in part, on measuring quantities such as an inherent strain of the exemplary inventive calibration coupon.

[000103] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize a fully Bayesian statistical methodology, such as detailed, without limitation, by Kenendy and O'Haggan, in the exemplary inventive operation of the optimal testing adjustment of the calibration simulation model (the calibration digital twin) for a corresponding individual machine that has been used to produce the corresponding calibration coupon. In one embodiment, the inventive process of optimal testing adjustment may begin at step 502 when the exemplary inventive computer-based AM system may translate uncertainty about the machine-to-machine variation into uncertain parameters "0(t)" of the exemplary inventive calibration simulation model. In some embodiments, the uncertainty parameters include but not limited to the machine power source, raster patterns, overlap rates, speeds, heat transfer coefficients, friction coefficients, and others similarly suitable parameters. In some embodiments, at step 504, given that all of these parameters may be treated as being uncertain, the exemplary inventive computer-based AM system may be configured to assign prior probability distributions (compared to posterior distributions at step 506) to uncertainty parameters, reflecting expected distribution of actual values of the parameters, as illustrated in Fig. 6.

[000104] In one embodiment, a uniform probability distribution of a certain width may be assumed around nominal values as provided by an AM machine manufacturer. For example, when building Aluminum alloy-based parts, EOS GmbH Electro Optical Systems may assume for its AM machines at least the following parameters: a power of X(l) watts, a velocity of Y(l) mm/sec (machine is capable of 50 to 7000 mm/sec), and a hatch spacing of Z(l) micrometers. In another example, when building Titanium alloy-based parts, an SLM- type machine may be programmed by a manufacturer to assume at least the following parameters: a power of X(2) watts, a velocity of Y(2) mm/sec, and a hatch spacing of Z(2) micrometers. In another example, when building Nickel and/or Cobalt alloys-based parts, an Optomec-type machine may be programmed to assume at least the following parameters: a power of X(3) watts, a velocity of Y(2) inches/min, and a hatch spacing of Z(3) inches. In the above examples, X(l), X(2), and X(3) are distinct but may have an overlapping range of values. In the above examples, Y(l), Y(2), and Y(3) are distinct but may have an overlapping range of values. In the above examples, Z(l), Z(2), and Z(3) are distinct but may have an overlapping range of values.

[000105] In some embodiments, the uncertainty or tolerance data supplied by the AM machine manufacturer may be factored into the construction of the prior distributions. In some embodiments, the uncertainty or tolerance data supplied by the AM machine manufacturer is not factored into the construction of the prior distributions. In some embodiments, once the exemplary inventive calibration coupon is executed (i.e., one or more testing layers have been built) and measured, at step 506, the exemplary inventive computer- based AM system may be configured to apply Bayes theorem to create posterior distributions of the uncertain parameters of the exemplary inventive calibration simulation model based on at least in-situ and/or other measurements.

[000106] In some embodiments, a posterior distribution may be a probability distribution. In some embodiments, an exemplary posterior distribution may be represented in a parametric form, such as, but not limiting to, exemplified in http://www.itl.nist.gov/div898/handbook/eda/section3/eda366. htm, "Gallery of

Distributions." In some other embodiments, the exemplary posterior distribution may be based on a dataset of empirical probability distribution.

[000107] Fig. 6 shows an illustrative example of a diagram of exemplary inventive time dependent calibration routine for an exemplary inventive AM calibrated computer simulation model. In one embodiment, at time to, a calibrated posterior distribution reflects that an exemplary AM machine is in pristine condition and, by virtue of the calibration, the exemplary inventive calibration simulation model reflects the AM machine's full characterization by having utilized actual measurements of the exemplary inventive calibration coupon to mathematically determine the most likely values of its uncertain parameters.

[000108] In some embodiments, the exemplary inventive calibration executed as described above does not have an unlimited validity term, and as the exemplary AM machine starts to wear out, its underlying characteristics may dynamically change with time, such that the repetition of the same exemplary inventive calibration procedure in accordance with the present disclosure has to take place routinely at a predetermined rate or a dynamically adjusted rate. In some embodiment, the periodicity of running the exemplary inventive calibration procedure may be determined by identifying a likely time interval after which a departure of actual machine behavior from the intended is no longer acceptable. In some embodiments, a certain margin of safety may be embedded before such departure is reached. In some embodiment, if there is no periodic calibration procedure, the exemplary inventive computer-based AM system may be configured so that a variation in output above a certain threshold may trigger the calibration.

[000109] In some embodiments, the present disclosure may utilize the repeated exemplary inventive calibrations to establish a time series of values of calibration parameters of the exemplary inventive calibration simulation model over time, such as in a future at instance ti after the AM machine is no longer pristine, drift of the calibrated posterior distribution may be determined/identified as shown in Fig. 6. In some embodiments, using the many values of t's obtained by successive exemplary inventive calibrations executed between to and t _1; the exemplary inventive computer-based AM system may be configured to forecast (e.g., generate an indication) when the AM machine has to be recalibrated next. For example, referring to Fig. 6, if values of the calibration parameters are L (at a limit time the deviation in AM machine response would have an unacceptable deviation from what it would have been in pristine condition. In some embodiments, whether or not the deviation is unacceptable depends on factors such as but not limited to geometric and/or functional parameters, type of AM machine, type of recipe, type of application, and other similar suitable factors/conditions.

[000110] In some embodiments, the exemplary inventive computer-based AM system may be configured to perform the predictions by any time-domain method such as but not limited to ARIMA (Auto-Regressive Integrated Moving Average), or GARCH (Generalized Auto-Regressive Conditional Heteroscedasticity). In some embodiments, the exemplary inventive computer-based AM system may be configured to perform the predictions by one or more methods such as but not limited to Kalman filters and Auto-Regressive Neural Networks.

[00011 1] By forecasting the limit time for the machine calibration, the exemplary inventive calibration methodology utilized by the exemplary inventive computer-based AM system may allow periodic calibrations in which only initial time series of 6(t) would be collected, and from a certain point onwards, the calibration may be repeated only when the forecast determines that is necessary, taking into consideration of maximizing the AM machine availability. In some embodiment, the determination of the certain point may be based on compliance criteria identified via the tests during the exemplary inventive calibration procedures described above. In some embodiment, determination of whether or not it is necessary to calibrate may be based on whether or not the deviation is unacceptable as described above. [000112] In some embodiments, the machine may wear out at tw as shown in Fig. 6, so that the calibration may no longer be possible beyond such point. In some embodiments, as detailed above, this point in time may be forecasted into the calibration digital twin (via its updated set of calibration parameters) before it may actually happen to the hardware. In some embodiments, the present disclosure allows to cease production in a "worn-out" machine with sufficient time buffer before the tw point in time is reached to minimize the possibility of defective parts being produced when the AM machine is worn out. In some embodiments, the life expectancy of each AM machine determined based on the exemplary inventive calibration process of the present disclosure may be incorporated into corresponding digital twin(s) of exemplary AM manufactured part(s) to be produced by the respective AM machine.

[000113] In some embodiments, an exemplary inventive time dependent calibration methodology of the present disclosure may include at least the following time-indexes:

1) Digital twin uncertain parameters (i) which may be calibrated via Kennedy and O'Haggan methodology;

2) AM machine response status in time (j); and

3) AM machine hardware knobs that are calibrated via optimization to match a desired response range (k).

[000114] In some embodiment, the exemplary inventive computer-based AM system may be configured to combine the above 3 entities into a 3 -dimensional matrix Mijk.

[000115] For example, at the Mij projection, the direction of calibration would be from the physical to the digital twin, whereby a given response status j would be used to calibrate the simulation model parameters in state i. The calibrated status would be when i = j, that is, the parameters of the simulation model may reflect the timely state of the AM machine (pristine or not) in an accurate fashion. When i≠ j, the calibrated simulation model would be no longer representative of the current machine state (pristine or not) and has to be recalibrated.

[000116] In another example, at the Mjk projection, when the machine response j would deviate from a machine specification, and the exemplary inventive computer-based AM system may be configured to employ an inventive optimal testing adjustment to adjust the AM machine's operating parameters k to eliminate the deviation. For example, regarding the AM machine state, when j = k, that is, the hardware configuration of the machine would generate a response (in terms of printed product attributes) would be equivalent to pristine and, otherwise, the state of j ≠ k may trigger a reconfiguration of the machine operating parameters, such that it can produce pristine-like outputs as predicted by the current model i, connecting the 3 dimensions of the data matrix.

[000117] In the above illustrative examples, the subscripts i and j would be time indexes, therefore they would indicate a state or snapshot. In some embodiment, parameters such as heat transfer coefficients and cooling rates may be characterized by probability distributions, because their actual values might not be observed directly and would be therefore unknown. In some embodiment, each state i may have its own set of probability distributions for such parameters. In some embodiment, the index k may represent any tuning knobs that affect a probability distribution in state i and/or a response in state j.

[000118] In some embodiment, machine responses may be quantities that may be measured during the manufacturing process, at any state j. In some embodiment, the machine responses can be strictly machine related (e.g., currents, voltages, powers, temperatures) and/or product related (e.g., temperatures, voids, geometrical measures). In some embodiments, if the machine responses are product related, the machine response values may be presented in a form of maps rather than single value quantities. In some embodiments, the machine response qualities may be in the form of single value quantities or any other forms. [000119] In some embodiment, the exemplary inventive computer-based AM system may be configured to solve the inverse mathematical problem to determine the optimal testing adjustment requirement(s) at a particular time. For example, each of the j responses may depend on the k parameters, and by adjusting these parameters the optimizer may be continuously and actively search for the best match between intended and observed performance. For example, the tuning of the k parameters such that the j responses may converge to their target values can be achieved by one or more suitable optimization algorithms. Examples of parameter k of AM machine hardware knobs that are calibrated via optimization to match a desired response range includes but not limited to laser power, speed, angle, raster patterns, overlay rates, and other similarly suitable parameters. Examples of the optimization algorithms include but not limited to:

i) Simplex algorithm of George Dantzig, designed for linear programming;

ii) Extensions of the simplex algorithm, designed for quadratic programming and for linear-fractional programming;

iii) Variants of the simplex algorithm that are especially suited for network optimization; and/or

iv) Combinatorial algorithms.

[000120] Methods of the iterative adjustment of build material selection (item 122 of Fig. 1) include but not limited to:

i) Methods that evaluate Hessians (or approximate Hessians, using finite differences) such as but not limited to:

(a) Newton's method;

(b) Sequential quadratic programming: A Newton-based method for small- medium scale constrained problems. Some versions can handle large-dimensional problems; and/or (c) Interior point methods: This is a large class of methods for constrained optimization. Some interior-point methods use only (sub)gradient information, and others of which require the evaluation of Hessians;

ii) Methods that evaluate gradients, or approximate gradients in some way (or even subgradients):

(a) Coordinate descent methods: Algorithms which update a single coordinate in each iteration;

(b) Conjugate gradient methods: Iterative methods for large problems (In theory, these methods terminate in a finite number of steps with quadratic objective functions, but this finite termination is not observed in practice on finite-precision computers.);

(c) Gradient descent (alternatively, "steepest descent" or "steepest ascent"): A (slow) method of historical and theoretical interest, which has had renewed interest for finding approximate solutions of enormous problems;

(d) Subgradient methods - An iterative method for large locally Lipschitz functions using generalized gradients. Following Boris T. Polyak, subgradient- projection methods are similar to conjugate-gradient methods;

(e) Bundle method of descent: An iterative method for small-medium-sized problems with locally Lipschitz functions, particularly for convex minimization problems (similar to conjugate gradient methods);

(f) Ellipsoid method: An iterative method for small problems with quasiconvex objective functions and of great theoretical interest, particularly in establishing the polynomial time complexity of some combinatorial optimization problems and it has similarities with Quasi-Newton methods; (g) Reduced gradient method (Frank-Wolfe) for approximate minimization of specially structured problems with linear constraints, especially with traffic networks. For general unconstrained problems, this method reduces to the gradient method, which is regarded as obsolete (for almost all problems);

(h) Quasi-Newton methods: Iterative methods for medium-large problems (e.g. N<1000);

(i) Simultaneous perturbation stochastic approximation (SPSA) method for stochastic optimization; uses random (efficient) gradient approximation;

(j) Methods that evaluate only function values: If a problem is continuously differentiable, then gradients can be approximated using finite differences, in which case a gradient-based method can be used;

(k) Interpolation methods; and/or

(1) Pattern search methods, which have better convergence properties than the Nelder-Mead heuristic (with simplices), which is listed below; and/or

(iii) Heuristics such as but not limited to: memetic algorithm, differential evolution, evolutionary algorithms, dynamic relaxation, genetic algorithms, hill climbing with random restart; Nelder-Mead simplicial heuristic: A popular heuristic for approximate minimization (without calling gradients), particle swarm optimization, gravitational search algorithm, artificial bee colony optimization, simulated annealing, stochastic tunneling, Tabu search, and reactive Search Optimization (RSO) implemented in LIONsolver.

[000121] In some embodiments, the desired convergence is determined when the expected value of a parameter and the observed value would be close enough to satisfy numerical convergence or to satisfy the constraints of the system.

[000122] In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize data cubes and n-D arrays to store the inventive digital twin data. In some embodiments, the exemplary inventive computer-based AM system may be configured to utilize a data schema to store the inventive digital twin data for AM. Examples of data schema include but not limited to a NIST data schema with a visual representation shown at page 8 of Y. Lu et al, "AMMD- An Open Database for Additive Manufacturing Analytics," Engineering Lab, National Institute of Standards and Technology, RAPID 2017, May 9, 2017; and/or GRANTA schema as shown in GRANTA schema (Granta Design, Materials Park, OH).

Exemplary illustrations

[000123] The following describes exemplary illustrations of the method for metal AM implementing the above describe the exemplary inventive calibration process by considering below relevant quantities:

i) Melt pool width: a high-fidelity physics simulation model that predicts the rapid solidification rate of the metal while the laser scans a particular region. The predicted widths are compared to experimental measurements for the width of melt pools at various operating conditions;

ii) Porosity: a mix of high-fidelity physics simulation models and empirically derived models predict the resulting porosity in metal AM test specimens. The predicted porosity values are compared to experimental measurements at various operating conditions; and

iii) Yield strength: a mix of high-fidelity physics simulation models and empirically derived models predict the tensile yield strength in metal AM test specimens. The predicted tensile yield strengths are compared to experimental measurements at various operating conditions.

[000124] Figs. 7A-7E illustrate an exemplary analysis workflow for calibrating the probabilistic melt pool width simulation model during the AM process that may be used by the exemplary inventive computer-based AM system to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Specifically, while a traditional use of simulation is by obtaining simulation results at a single set of modeling assumptions. On the other hand, Fig. 7 A illustrates a diagram of melt pool width as a function of laser scan speed obtained from the probabilistic melt pool width simulation process as implemented in the exemplary inventive computer-based AM system according to some embodiment. The exemplary inventive computer-based AM system is configured to vary the melt pool width modeling assumptions and generate different simulation results accordingly, referred to as Sensitivity Analysis ("SA").

[000125] The different simulation results from the probabilistic melt pool width simulation as presented in Fig. 7A represent the uncertainty in the response, traditionally referred to as Uncertainty Quantification ("UQ"). On the other hand, Fig. 7B illustrates results obtained from the probabilistic melt pool width simulation process as implemented in the exemplary inventive computer-based AM system that is configured to repeat UQ for different plausible groups of probabilistic melt pool width simulation modeling assumptions. The simulation results as shown in Fig. 7B enables illustration of a distribution of distributions of uncertainties of the probabilistic melt pool width simulations.

[000126] The distribution of distributions of uncertainties of the probabilistic melt pool width simulations as presented in Fig. 7B enables the exemplary inventive computer-based AM system to further compare the distribution of distributions with noisy measurements according to some embodiment. Fig. 7C illustrates comparison results obtained from the probabilistic melt pool width simulations process as implemented in the exemplary inventive computer-based AM system, which is configured to compare the distribution of distributions with noisy measurements, denoted by experimental data points. The experimental data points as shown in Fig. 7C reflect the limited experimental measurements.

[000127] In some embodiments, the results from the comparison of the distribution of distributions with noisy measurements as presented by Fig. 7C may be used to perform an exemplary inventive Bayesian calibration as shown in Fig. 7D to determine a set of assumptions that represent most closely to the actual experiment. Fig. 7D illustrates a sub- process of the probabilistic melt pool width simulations process as implemented in the exemplary inventive computer-based AM system that is configured to perform the exemplary inventive Bayesian calibration to determine the set of assumptions that most closely represent the actual experiment.

[000128] The sub-process begins at step 702 in which the exemplary inventive computer-based AM system is configured to run the probabilistic melt pool width simulations for a large number of candidate modeling assumptions. Then at step 704, the exemplary inventive computer-based AM system is configured to build statistical surrogate model that approximates the input to output relationships, e.g., Output = f(Input), within the simulation. Finally, at step 706, the exemplary inventive computer-based AM system is configured to use the surrogate within a Bayesian inference computational engine to find all plausible assumptions that match the level of noise within the measurements.

[000129] Fig. 7E illustrates results obtained from the process as implemented in the exemplary inventive computer-based AM system that is configured to use the Bayesian calibrated probabilistic melt pool width simulation model to predict responses at arbitrary input configurations, based on the all plausible assumptions that match the level of noise within the measurements as obtained from the sub-process as described supra in relation with Fig. 7D.

[000130] Figs. 8A-8E illustrate an exemplary analysis workflow for calibrating the probabilistic porosity simulation model during the AM process that may be used by the exemplary inventive computer-based AM system to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Specifically, Fig. 8A illustrates a diagram of porosity as a function of laser scan speed obtained from the probabilistic porosity simulation process as implemented in the exemplary inventive computer-based AM system according to some embodiment. The exemplary inventive computer-based AM system is configured to perform SA, i.e., varying the porosity modeling assumptions and generating different simulation results accordingly.

[000131] The different simulation results from the probabilistic porosity simulation as presented in Fig. 8A represent the UQ. On the other hand, Fig. 8B illustrates results obtained from the probabilistic porosity simulation process as implemented in the exemplary inventive computer-based AM system that is configured to repeat UQ for different plausible groups of probabilistic porosity simulation modeling assumptions. The simulation results as shown in Fig. 8B enables illustration of a distribution of distributions of uncertainties of the probabilistic porosity simulations.

[000132] The distribution of distributions of uncertainties of the probabilistic porosity simulations as presented in Fig. 8B enables the exemplary inventive computer-based AM system to further compare the distribution of distributions with noisy measurements according to some embodiment. Fig. 8C illustrates comparison results obtained from the of the probabilistic porosity simulations process as implemented in the exemplary inventive computer-based AM system, which is configured to compare the distribution of distributions with noisy measurements, denoted by experimental data points. The experimental data points as shown in Fig. 8C reflect the limited experimental measurements.

[000133] In some embodiments, the results from the comparison of the distribution of distributions with noisy measurements as presented by Fig. 8C may be used to perform an exemplary inventive Bayesian calibration as shown in Fig. 8D to determine a set of assumptions that represent most closely to the actual experiment. Fig. 8D illustrates a sub- process of the probabilistic porosity simulations process as implemented in the exemplary inventive computer-based AM system that is configured to perform the exemplary inventive Bayesian calibration to determine the set of assumptions that most closely represent the actual experiment.

[000134] The sub-process begins at step 802 in which the exemplary inventive computer-based AM system is configured to run the probabilistic porosity simulations for a large number of candidate modeling assumptions. Then at step 804, the exemplary inventive computer-based AM system is configured to build statistical surrogate model that approximates the input to output relationships within the simulation. Finally, at step 806, the exemplary inventive computer-based AM system is configured to use the surrogate within a Bayesian inference computational engine to find all plausible assumptions that match the level of noise within the measurements.

[000135] Fig. 8E illustrates results obtained from the process as implemented in the exemplary inventive computer-based AM system that is configured to use the Bayesian calibrated probabilistic porosity simulation model to predict responses at arbitrary input configurations, based on the all plausible assumptions that match the level of noise within the measurements as obtained from the sub-process as described supra in relation with Fig. 8D.

[000136] Figs. 9A-9E illustrate an exemplary analysis workflow for calibrating the probabilistic tensile yield strength simulation model during the AM process that may be used by the exemplary inventive computer-based AM system to operate in accordance with at least some embodiments and principles of the present disclosure detailed herein. Specifically, Fig. 9A illustrates a diagram of tensile yield strength as a function of laser scan speed obtained from the probabilistic tensile yield strength simulation process as implemented in the exemplary inventive computer-based AM system according to some embodiment. The exemplary inventive computer-based AM system is configured to perform SA, i.e., varying the tensile yield strength modeling assumptions and generating different simulation results accordingly. [000137] The different simulation results from the probabilistic tensile yield strength simulation as presented in Fig. 9A represent the UQ. On the other hand, Fig. 9B illustrates results obtained from the probabilistic tensile yield strength simulation process as implemented in the exemplary inventive computer-based AM system that is configured to repeat UQ for different plausible groups of probabilistic tensile yield strength simulation modeling assumptions. The simulation results as shown in Fig. 9B enables illustration of a distribution of distributions of uncertainties of the probabilistic tensile yield strength simulations.

[000138] The distribution of distributions of uncertainties of the probabilistic tensile yield strength simulations as presented in Fig. 9B enables the exemplary inventive computer- based AM system to further compare the distribution of distributions with noisy measurements according to some embodiment. Fig. 9C illustrates comparison results obtained from the of the probabilistic tensile yield strength simulations process as implemented in the exemplary inventive computer-based AM system, which is configured to compare the distribution of distributions with noisy measurements, denoted by experimental data points. The experimental data points as shown in Fig. 9C reflect the limited experimental measurements.

[000139] In some embodiments, the results from the comparison of the distribution of distributions with noisy measurements as presented by Fig. 9C may be used to perform an exemplary inventive Bayesian calibration as shown in Fig. 9D to determine a set of assumptions that represent most closely to the actual experiment. Fig. 9D illustrates a sub- process of the probabilistic tensile yield strength simulations process as implemented in the exemplary inventive computer-based AM system that is configured to perform the exemplary inventive Bayesian calibration to determine the set of assumptions that most closely represent the actual experiment. [000140] The sub-process begins at step 902 in which the exemplary inventive computer-based AM system is configured to run the probabilistic tensile yield strength simulations for a large number of candidate modeling assumptions. Then at step 904, the exemplary inventive computer-based AM system is configured to build statistical surrogate model that approximates the input to output relationships within the simulation. Finally, at step 906, the exemplary inventive computer-based AM system is configured to use the surrogate within a Bayesian inference computational engine to find all plausible assumptions that match the level of noise within the measurements.

[000141] Fig. 9E illustrates results obtained from the process as implemented in the exemplary inventive computer-based AM system that is configured to use the Bayesian calibrated probabilistic tensile yield strength simulation model to predict responses at arbitrary input configurations, based on the all plausible assumptions that match the level of noise within the measurements as obtained from the sub-process as described supra in relation with Fig. 9D.

[000142] Aspects of the invention will now be described with reference to the following numbered clauses:

1. A method, comprising:

(A) laying down, by an Additive Manufacturing (AM) machine, a first AM testing layer of an additive material;

(B) subjecting, by the AM machine, the first AM testing layer to the AM build process based at least in part on an initial digital twin of an actual AM part to be build;

wherein the initial digital twin comprises at least one of:

i) one or more initial operational parameters of the AM machine for building the actual AM part,

ii) one or more properties of a desired design for the actual AM part, or iii) one or more initial AM build process parameters;

(C) determining, by at least one processor, a compliance of the first AM testing layer to one or more predefined criteria by comparing testing data to the one or more predefined criteria, defined based on the initial digital twin, wherein the testing data has been obtained from at least one in-situ monitoring sensor that has utilized one or more in-situ monitoring techniques to collect the testing data;

(D) simulating, by the at least one processor, based on at least one AM simulation model, at least one testing adjustment to at least one of i) the one or more initial operational parameters of the AM machine or ii) the one or more initial AM build process parameters, when the first AM testing layer fails the compliance to the one or more predefined criteria; wherein the at least one AM simulation model is generated by estimating, from historical experimental data of the AM build process of one or more other AM parts, most likely value for at least one of i) each respective of the one or more initial operational parameters of the AM machine or ii) each respective of the one or more initial AM build process parameters;

wherein the at least one testing adjustment is configured to compensate at least one difference in the actual AM part from the desired design due to the first AM testing layer;

(E) causing, by the at least one processor, the AM machine to implement the at least one testing adjustment in the AM build process for at least one subsequent AM testing layer that is laid down after the first AM testing layer;

(F) repeating (A) through (E) to determine one of:

i) a lack of a particular testing adjustment that compensates the at least one difference in the actual AM part from the desired design, or

ii) at least one actual build adjustment that compensates the at least one difference in the actual AM part from the desired design; (G) generating, by the at least one processor, an updated digital twin from the initial digital twin based on the at least one testing adjustment; and

(H) utilizing, by the AM machine, the material to build the actual AM part based on the updated digital twin;

wherein the updated digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, the compliance of the actual AM part to the desired design.

2. The method of Clause 1 , further comprising:

laying down, by the AM machine, at least one second particular testing layer of the material on a calibration coupon;

determining, by the at least one processor, based on the at least one AM simulation model, at least one actual calibration adjustment to the one or more initial operational parameters of the AM machine to compensate for at least one machine-to-machine variation; and

causing, by the at least one processor, to recalibrate the AM machine based on the at least one actual calibration adjustment.

3. The method of Clauses 1 or 2, further comprising: updating, by the at least one processor, the initial digital twin or the updated digital twin based on the at least one actual calibration adjustment.

4. The method of Clauses 1 or 2, wherein characteristics of the recalibration dynamically change with time such that repetitions of the recalibration take place routinely at a predetermined rate or a dynamically adjusted rate.

5. The method of Clauses 1-4, wherein a periodicity of the calibration is determined by identifying a likely time interval after which a departure of actual machine behaviour of the AM machine from an intended machine behavior of the AM machine: i) is no longer acceptable or results in a variation that exceeds a pre-determined threshold. 6. The method of Clauses 1 or 2, wherein determining, based on the at least one AM simulation model, the at least one actual calibration adjustment to the one or more initial operational parameters of the AM machine to compensate for at least one machine-to- machine variation comprises:

i) translating uncertainty about the machine-to-machine variation into uncertainty parameters of the at least one AM simulation model;

ii) assigning posterior probability distributions to the uncertainty parameters reflecting expected distributions of actual values of the parameters;

iii) obtaining updated measurement data about the calibration coupon; and iv) creating posterior distributions of the uncertainty parameters of the at least one AM simulation model.

7. The method of Clauses 1 -6, wherein at least one uncertainty parameter is at least one of: at least one machine power source parameter, at least one raster pattem, at least one overlap rate, or at least one speed build parameter.

8. The method of Clauses 1 -6, wherein the prior probability distributions are uniform probability distributions of predetermined widths around predetermined nominal values.

9. The method of Clauses 1 -6, wherein the posterior distributions of the uncertain parameters are generated by applying Bayesian inference.

10. The method of Clause 1 -9, wherein the one or more in-situ monitoring techniques are at least one of:

i) taking temperature measurements of build plates);

ii) measuring beam power;

iii) taking visual images, thermal images, or both, by a camera;

iv) obtaining acoustic data from the build plate from at least one acoustic emission sensor; v) performing layer-by-layer optical topography;

vi) obtaining gas flow data from one of more gal flow sensor;

vii) monitoring a molten pool size by the camera, an optical sensor, or both;

viii) monitoring a powder distribution by the camera, the acoustic emission sensor; or ix) monitoring a powder contamination by the camera, the optical sensor, or both.

11. The method of Clause 1 -10, wherein the one or more predefined criteria identify at least one of: melting temperature, hardness, traces of one or more chemical elements, emissivity, or one or more electric properties.

12. The method of Clause 1 -11 , wherein the compliance of the first AM testing layer to one or more predefined criteria is determined based one or more in-situ monitoring measurements related to at least one or more particular points in a build portion of the actual AM part that are determined to deviate from one or more threshold conditions by more than a predetermined tolerance value.

13. A system comprising:

an Additive Manufacturing (AM) machine, configured to:

(A) lay down a first AM testing layer of an additive material;

(B) subject the first AM testing layer to an AM build process based at least in part on an initial digital twin of an actual AM part to be build;

wherein the initial digital twin comprises at least one of:

i) one or more initial operational parameters of the AM machine for building the actual AM part,

ii) one or more properties of a desired design for the actual AM part, or iii) one or more initial AM build process parameters;

at least one in-situ monitoring sensor, wherein the at least one in-situ monitoring sensor is configured to collect and transmit, during the AM build process, testing data related to first AM testing layer; a processor; and

a non-transitory computer readable storage medium storing thereon program logic, wherein, when executing the program logic, the processor is configured to:

(A) receive the testing data;

(B) determine a compliance of the first AM testing layer to one or more predefined criteria; and

(C) simulate, based on at least one AM simulation model, at least one testing adjustment to at least one of i) the one or more initial operational parameters of the AM machine or ii) the one or more initial AM build process parameters, when the first AM testing layer fails the compliance to the one or more predefined criteria; wherein the at least one AM simulation model is generated by estimating, from historical experimental data of the AM build process of one or more other AM parts, most likely value for at least one of i) each respective of the one or more initial operational parameters of the AM machine or ii) each respective of the one or more initial AM build process parameters;

wherein the at least one testing adjustment is configured to compensate at least one difference in the actual AM part from the desired design due to the first AM testing layer;

(D) generate an updated digital twin from the initial digital twin based on the at least one testing adjustment;

(E) instruct, based on the updated digital twin, the AM machine to implement the at least one testing adjustment in the AM build process for at least one subsequent AM testing layer; and wherein the updated digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, the compliance of the actual AM part to the desired design.

14. The system of Clause 13, wherein the AM machine is further configured to:

lay down at least one second particular testing layer of the material on a calibration coupon;

wherein the processor is further configured to:

determine, based on the at least one AM simulation model, at least one actual calibration adjustment to the one or more initial operational parameters of the AM machine to compensate for at least one machine-to-machine variation; and

cause to recalibrate the AM machine based on the at least one actual calibration adjustment.

15. The system of Clause 13, wherein the processor is further configured to:

update the initial digital twin or the updated digital twin based on the at least one actual calibration adjustment.

16. The system of Clauses 13 or 14, wherein characteristics of the recalibration dynamically change with time such that repetitions of the recalibration take place routinely at a predetermined rate or a dynamically adjusted rate.

17. The system of Clause 13-16, wherein a periodicity of the calibration is determined by identifying a likely time interval after which a departure of actual machine behaviour of the AM machine from an intended machine behavior of the AM machine: i) is no longer acceptable or results in a variation that exceeds a pre-determined threshold.

18. The system of Clauses 13 or 14, wherein the processor is further configured to determine, based on the at least one AM simulation model, the at least one actual calibration adjustment to the one or more initial operational parameters of the AM machine to compensate for at least one machine-to-machine variation, comprising:

i) translate uncertainty about the machine-to-machine variation into uncertainty parameters of the at least one AM simulation model;

ii) assign posterior probability distributions to the uncertainty parameters reflecting expected distributions of actual values of the parameters;

iii) obtain updated measurement data about the calibration coupon; and

iv) create posterior distributions of the uncertainty parameters of the at least one AM simulation model.

19. The system of Clauses 13-18, wherein at least one uncertainty parameter is at least one of: at least one machine power source parameter, at least one raster partem, at least one overlap rate, or at least one speed build parameter.

20. The system of Clauses 13-18, wherein the prior probability distributions are uniform probability distributions of predetermined widths around predetermined nominal values.

21. A method, comprising:

(A) laying down, by an Additive Manufacturing (AM) machine, a first AM testing layer of an additive material;

(B) subjecting, by the AM machine, the first AM testing layer to the AM build process based at least in part on an initial digital twin of an actual AM part to be build;

(C) determining, by at least one processor, a compliance of the first AM testing layer to one or more predefined criteria by comparing testing data to the one or more predefined criteria, defined based on the initial digital twin, wherein the testing data has been obtained from at least one in-situ monitoring sensor that has utilized one or more in-situ monitoring techniques to collect the testing data; (D) simulating, by the at least one processor, based on at least one AM simulation model, at least one testing adjustment;

(E) generating, by the at least one processor, an updated digital twin from the initial digital twin based on the at least one testing adjustment; and

(F) utilizing, by the AM machine, the material to build the actual AM part based on the updated digital twin;

wherein the updated digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, the compliance of the actual AM part to the desired design. 22. A method, comprising:

(A) laying down, by an Additive Manufacturing (AM) machine, a first AM testing layer of an additive material;

(B) subjecting, by the AM machine, the first AM testing layer to the AM build process based at least in part on an initial digital twin of an actual AM part to be build;

(C) determining, by at least one processor, a compliance of the first AM testing layer to one or more predefined criteria by comparing testing data to the one or more predefined criteria, defined based on the initial digital twin, wherein the testing data has been obtained from at least one in-situ monitoring sensor that has utilized one or more in-situ monitoring techniques to collect the testing data;

(D) simulating, by the at least one processor, based on at least one AM simulation model, at least one testing adjustment to at least one of i) the one or more initial operational parameters of the AM machine or ii) the one or more initial AM build process parameters, when the first AM testing layer fails the compliance to the one or more predefined criteria; wherein the at least one testing adjustment is configured to compensate at least one difference in the actual AM part from a desired design due to the first AM testing layer; (E) causing, by the at least one processor, the AM machine to implement the at least one testing adjustment in the AM build process for a subsequent AM testing layer that is laid down after first AM testing layer;

(F) generating, by the at least one processor, an updated digital twin from the initial digital twin based on the at least one testing adjustment; and

(G) utilizing, by the AM machine, the material to build the actual AM part based on the updated digital twin;

wherein the updated digital twin is suitable to be used to certify, without a physically inspection of the actual AM part, the compliance of the actual AM part to the desired design.

[000143] Publications cited throughout this document are hereby incorporated by reference in their entirety. All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. While a number of embodiments of the present disclosure have been described, it is understood that these embodiments are illustrative only, and not restrictive, and that many modifications may become apparent to those of ordinary skill in the art, including that the inventive methodologies, the inventive systems, and the inventive devices described herein can be utilized in any combination with each other. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same. Further still, the various steps may be carried out in any desired order (and any desired steps may be added and/or any desired steps may be eliminated).