FIELD OF THE DISCLOSURE

[0001 ] The present disclosure relates to methods and systems for optimization of injection cycles, and specifically to determining a range of operational parameters based upon compression rates for injection molding of a material.

BACKGROUND

[0002] Injection molding is a technology commonly used for high- volume manufacturing of parts constructed of thermoplastic materials. During repetitive injection molding processes, a thermoplastic resin, typically in the form of small pellets or beads, is introduced into an injection molding machine which melts the pellets under heat and pressure. In an injection cycle, the molten material is forcefully injected into a mold cavity having a particular desired cavity shape. The injected plastic is held under pressure in the mold cavity and is subsequently cooled and removed as a solidified part having a shape closely resembling the cavity shape of the mold. A single mold may have any number of individual cavities which can be connected to a flow channel by a gate that directs the flow of the molten resin into the cavity. A typical injection molding process generally includes four basic operations: (1) heating the plastic in the injection molding machine to allow the plastic to flow under pressure; (2) injecting the melted plastic into a mold cavity or cavities defined between two mold halves that have been closed; (3) allowing the plastic to cool and harden in the cavity or cavities while under pressure; and (4) opening the mold halves and ejecting the part from the mold.

[0003] In these systems, a control system controls the injection molding process according to an injection pattern that defines a series of setpoint values for control parameters of the various components of the injection molding machine. For example, the injection cycle can be driven by a fixed and/or a variable melt pressure profile whereby the controller uses sensed pressures at a nozzle as the input for determining a driving force applied to the material.

[0004] Each injection molding cycle typically has an initial phase wherein an injection molding system rapidly increases the pressure of an injection mold to a setpoint pressure value, a fill phase where the pressure of the injection mold is held at a steady-state as molten material is injected into the injection mold cavity, a hold phase where the pressure is held at the steady-state pressure value without injecting further molten material into the injection mold cavity, and finally an ejection where the injection mold cavity is open and the part is ejected from the injection molding system. Each of these phases may have varied pressures and temporal lengths to ensure the proper formation of a defect free injection molded part. For example, short shot defects may occur if too little pressure is applied during one or more of the described phases, or if the phases are not performed for an adequate amount of time. Additionally, flashing defects may occur if pressures are too high or if the phases are performed for too long of a time. Any change in injection molding material, mold geometries, environmental factors, or changes in other elements and factors may require different pressures and times for the different phases of injection cycles, also referred to as injection cycles, to fabricate defect free injection molded parts.

[0005] Performing design of experiments (DOE) for injection molding allows for a user to determine ranges of operational parameters for performing injection molding cycles. For example, DOE may be utilized to determine the setpoint values and/or durations that control the above-described mold cycle. To this end, DOE analysis can provide operational parameter ranges that provide robust injection cycle performance associated with the fabrication of parts with reduced numbers of defects amid potentially varying environmental conditions and operational and/or mold cycle parameters (e.g., a fill pressure, a fill time, cooling time, etc.). DOE can also be useful for analyzing relationships between mold cycle parameters and the size, mechanical properties, or density of the injection molded parts produced when executing a mold cycle in accordance with the mold cycle parameters.

[0006] However, performing DOE analyses can be extremely time consuming and expensive as the number of DOE experiments increases exponentially with the number of parameters being tested. For example, four experiments are generally performed when considering two parameters, and eight experiments are generally performed when considering three parameters.

It is therefore beneficial only to consider parameters that may be the most impactful of an injection cycle performance. Similarly, it may be useful to reduce the number of values tested for each parameter. For example, for DOE involving four parameters (e.g., fill pressures, fill times, etc.) and three test values at each parameter, the DOE process requires 3 ⁴ (81) experiments,. For injection molding, it is common to work with multiple molds and collect statistical data for several part dimensions which can increase the number of experiments required. Executing a large number of experiments for DOE is often expensive and very time consuming, and must be repeated for any change in the mold, or injection molding material.

SUMMARY

[0007] A method and system for optimization of design of experiments (DOE) for an injection molding system of the present disclosure includes performing multiple series of injection molding cycles to obtain pluralities of injection process parameters, compressibility metrics, and secondary injection pressures. The optimization process analyzes compressibility metrics to identify an optimal range of operational parameters that may be applied to a DOE process related to fabricating injection molded parts of a desired weight and/or density, and with minimal to no defects.

[0008] In injection molding systems, a control system controls the injection molding process according to an injection cycle or pattern that defines a series of setpoint values for control parameters of the various components of the injection molding machine. For example, the injection cycle can be driven by a fixed and/or a variable melt pressure profile whereby the controller uses sensed pressures at a nozzle as the input for determining a driving force applied to the material. Injection molding cycles typically have an initial phase wherein the injection molding system rapidly increases the pressure of an injection mold to a setpoint pressure value, a fill phase where the injection mold is held at a steady-state pressure as molten material is injected into a cavity of the injection mold, a hold phase where the pressure is held at the steady-state pressure value, and an ejection phase where the injection mold cavity is opened and the part is ejected from the injection molding system. Each of the phases may have variable pressures and temporal lengths to ensure the proper formation of an injection molded part that is free of defects, such as short shot defects and flashing defects that may occur due to improperly set injection molding cycle pressures and phase times. Further, changes in injection molding material, mold geometries, environmental factors, or changes in other elements and factors may require adjustments of the pressures and times for the different phases of injection cycles to fabricate defect-free injection molded parts.

[0009] Typically, during DOE, it is important for a user to induce as much variation, by altering different variables (e.g., fill time, fill pressure, holding time, etc.), as possible into a part without causing obvious aesthetic defects. That said, limits on the variation, such as maximum and minimum fill pressures and fill times, can make the interaction between variables difficult to predict. As a result, multiple DOE runs are often required to determine a range of values for each independent variable that is tested. The described optimization method predefines ranges of operational values so that generally a single DOE is required, saving significant time in both the running of the DOE and the time spent performing inspection of the injection molded parts, potentially saving days or weeks of development time. Further, the disclosed methods allow for tuning of the density of an injection molded part while operating within a known set of ranges that reduces or eliminates fabrication errors due to the injection molding parameters being improperly set.

[0010] The systems and methods disclosed provide an efficient optimization technique for determining a range of operational values for performing DOE. The optimization reduces the number of DOE experiments required by determining a defect-free, or minimal defect, operating range for various operational parameters based on obtained compressibility metrics. The determined ranges of operating values reduces the number of values that may be tested by DOE experiments, and therefore, reduces the time and money spent performing DOE for an injection molding system.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The embodiments set forth in the drawings are illustrative and exemplary in nature and not intended to limit the subject matter defined by the claims. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals which:

[0012] FIG. 1 illustrates an injection molding apparatus that generally includes an injection system and a clamping system for performing injection cycles.

[0013] FIG. 2 is a plot of melt pressure values against time for a mold cycle executed by an injection molding machine.

[0014] FIG. 3A is a plot of fill pressure vs. time for use in an optimization method that combines LECR optimization with compressibility metric based optimization.

[0015] FIG. 3B is a plot of compression rate versus process factor A (PFA) pressure. [0016] FIG. 4 is a flowchart illustrating an embodiment of a process for determining optimized operational process parameters for performing a design of experiment (DOE) using a compressibility metric and according to the optimization techniques described.

[0017] FIG. 5 is a flowchart illustrating an embodiment of a process for determining optimized operational process parameters for performing a design of experiment (DOE) that combines the compressibility metric optimization techniques described with a largest empty comer rectangle (LECR) optimization technique.

[0018] FIG. 6 is a flowchart illustrating an embodiment of a process for determining DOE parameters from a plurality of LECR’s and an optimal compressibility rate geometric shape.

DETAILED DESCRIPTION

[0019] An optimization process for determining optimal operating parameters for a design of experiments (DOE) of an injection molding cycle is herein described. A DOE in the injection molding context is a planned study or set of experiments for determining how different operating parameters (e.g., fill pressure, fill time, holding time, cooling time, etc.) affect the size, density, and defects of fabricated parts.

[0020] Embodiments of the present invention generally relate to systems, machines, products, and methods of producing products by injection molding and more specifically to the optimization of systems, products, and methods of producing products by low substantially constant pressure injection molding. However, the described systems and methods are not limited to low substantially constant pressure injection molding machines and process, and can be applied to any injection molding system or process, or for any DOE for an injection molding system or process. For example, the disclosed systems and methods may be applied to machines or processes including, but not limited to, high pressure processes, low pressure processes, variable pressure processes, and constant or substantially constant pressure processes.

[0021] The term “low pressure” as used herein with respect to melt pressure of a thermoplastic material, means melt pressures in a vicinity of a nozzle of an injection molding machine of 10000 psi and lower.

[0022] The term “substantially constant pressure” as used herein with respect to a melt pressure of a thermoplastic material, means that deviations from a baseline melt pressure do not produce meaningful changes in physical properties of the thermoplastic material. For example, “substantially constant pressure” includes, but is not limited to, pressure variations for which viscosity of the melted thermoplastic material do not meaningfully change. The term “substantially constant” in this respect includes deviations of approximately 30% from a baseline melt pressure. For example, the term “a substantially constant pressure of approximately 4600 psi” includes pressure fluctuations within the range of about 6000 psi (30% above 4600 psi) to about 3200 psi (30% below 4600 psi). A melt pressure is considered substantially constant as long as the melt pressure fluctuates no more than 30% from the recited pressure.

[0023] The term “cycle,” “cycle time,” or “injection molding cycle” is defined as a single iteration of an injection molding process that is required to fully form an injection molded part. Cycle time or injection molding cycle includes the steps of advancing molten thermoplastic material into a mold cavity, substantially filling the mold cavity with thermoplastic material, cooling the thermoplastic material, separating first and second mold sides to expose the cooled thermoplastic material, removing the thermoplastic material, and closing the first and second mold sides.

[0024] The term “injection process parameter” is defined to include any measurable or controllable parameter of an injection molding system, or injection cycle. For example, an injection process parameter includes, without limitation, an injection velocity, a fill pressure, a pack and hold pressure, a fill duration, a pack and hold duration, a process factor A, a process factor A pressure, and a compressibility metric among other parameters.

[0025] Referring to the figures in detail, FIG. 1 illustrates an example injection molding apparatus 10 that generally includes an injection system 12 and a clamping system 14. A thermoplastic material may be introduced to the injection system 12 in the form of thermoplastic pellets 16. The thermoplastic pellets 16 may be placed into a hopper 18, which feeds the thermoplastic pellets 16 into a heated barrel 20 of the injection system 12. The thermoplastic pellets 16, after being fed into the heated barrel 20, may be driven to the end of the heated barrel 20 by a reciprocating screw 22. The heating of the heated barrel 20 and the compression of the thermoplastic pellets 16 by the reciprocating screw 22 causes the thermoplastic pellets 16 to melt, forming a molten thermoplastic material 24. The molten thermoplastic material is typically processed at a temperature of about 130° C to about 410° C. [0026] The reciprocating screw 22 forces the molten thermoplastic material 24, toward a nozzle 26 to form a shot of thermoplastic material, which will be injected into a mold cavity 32 of a mold 28 via one or more gates 30 that direct the flow of the molten thermoplastic material 24 into the mold cavity 32. In other embodiments, the nozzle 26 may be separated from one or more gates 30 by a feed system (not shown). The mold cavity 32 is formed between first and second mold sides 25, 27 of the mold 28 and the first and second mold sides 25, 27 are held together under pressure by a press or clamping unit 34. The press or clamping unit 34 applies a clamping force during the molding process that is greater than the force exerted by the injection pressure acting to separate the two mold halves 25, 27, thereby holding the first and second mold sides 25, 27 together while the molten thermoplastic material 24 is injected into the mold cavity 32. To support these clamping forces, the clamping system 14 may include a mold frame and a mold base.

[0027] Once the shot of molten thermoplastic material 24 is injected into the mold cavity 32, the reciprocating screw 22 stops traveling forward. The molten thermoplastic material 24 takes the form of the mold cavity 32 and the molten thermoplastic material 24 cools inside the mold 28 until the thermoplastic material 24 solidifies. Once the thermoplastic material 24 has solidified, the press 34 releases the first and second mold sides 25, 27, the first and second mold sides 25,

27 are separated from one another, and the finished part may be ejected from the mold 28. The mold 28 may include a plurality of mold cavities 32 to increase overall production rates. The shapes of the cavities of the plurality of mold cavities may be identical, similar or different from each other. The latter may be considered a family of mold cavities.

[0028] A controller 50 is communicatively connected with a nozzle sensor 52, located in the vicinity of the nozzle 26, a linear transducer 57 located proximate the reciprocating screw 22, a screw control 36, and a clamp control 38 via one or more communication links. The one or more communication links may include a wired connection, a wireless connection, a mechanical connection, a hydraulic connection, a pneumatic connection, or any other type of wired or wireless communication connection known to those having ordinary skill in the art that will allow the controller 50 to with the sensors 52 or 57 and/or to send a control signal to the screw control 36, clamp control 38, or any other component of the injection molding apparatus 10. A workstation computer 40 is communicatively connected to the controller to provide the controller with operational parameters for performing the optimization methods described herein. The workstation computer 40 may include a memory that stores processor executable instructions that, when executed by a microprocessor of the workstation computer 40, implement the disclosed DOE optimization techniques. In embodiments, the workstation computer 40 may be communicatively coupled to the controller 50 via a wired connection or a wireless connection.

[0029] The linear transducer 57 may measure an amount of linear movement of the reciprocating screw 22 mechanically, optically, pneumatically, magnetically, electrically, ultrasonically, or the linear transducer 57 may use any other method of measuring linear movement. Similarly, the nozzle sensor 52 may sense the presence of thermoplastic material optically, pneumatically, electrically, ultrasonically, mechanically or otherwise by sensing changes due to the arrival of the thermoplastic material. When pressure of the thermoplastic material is measured by the nozzle sensor 52, the nozzle sensor 52 may send a signal indicative of the pressure to the controller 50 via one of the communication links. The signal may be analyzed by the controller 50 and/or the workstation computer 40 for performing the compression metric based parameter optimization for performing a DOE as described herein.

[0030] In the embodiment of FIG. 1, the nozzle sensor 52 may be a pressure sensor that measures (directly or indirectly) melt pressure of the molten thermoplastic material 24 in vicinity of the nozzle 26. The nozzle sensor 52 generates an electrical signal that is transmitted to the controller 50. The controller 50 then commands the screw control 36 to advance the screw 22 at a rate that maintains a desired melt pressure of the molten thermoplastic material 24 in the nozzle 26. The screw control 36 may utilize a cavity pressure as an input to determine how for to advance the screw 22. This is known as a pressure controlled process. While the nozzle sensor 52 may directly measure the melt pressure, the nozzle sensor 52 may also indirectly measure the melt pressure by measuring other characteristics of the molten thermoplastic material 24, such as temperature, viscosity, flow rate, etc., which are indicative of melt pressure. Likewise, the nozzle sensor 52 need not be located directly in the nozzle 26, but rather the nozzle sensor 52 may be located at any location within the injection system 12 that is fluidly connected with the nozzle 26. If the nozzle sensor 52 is not located within the nozzle 26, appropriate correction factors may be applied to the measured characteristic to calculate an estimate of the melt pressure in the nozzle 26. The pressure sensor 52 need not be in direct contact with the injected fluid and may alternatively be in dynamic communication with the fluid and able to sense the pressure of the fluid and/or other fluid characteristics. In some embodiments, the nozzle sensor 22 may additionally or alternative include a velocity sensor that senses the velocity of the molten thermoplastic material 24 as it exits the nozzle 26.

[0031 ] During an injection molding cycle, sensors (such as the nozzle sensor 52, the linear transducer 57, and/or other sensors) may be employed for measuring various parameters, such as cavity pressure, cavity temperature, etc. The sensors may generate electrical signals indicative of the parameters. In some embodiments, the sensors provide the electrical signals directly to the workstation computer 40. In other embodiments, the sensors provide the electrical signals to the controller 50, which then communicates the electrical signals to the workstation computer 40 for further analysis. The workstation computer 40 may determine a range of parameter values for performing the optimization for a DOE from the signals. Additionally, the workstation computer 40 may configure the controller 50 with new settings and parameter values for performing a next mold cycle, based on the received signals from the sensors. The workstation computer 40, in communication with the controller 50, may perform the optimization as described herein to reduce the number of experiments performed in a DOE for an injection mold system. This reduces the time and cost required for performing a DOE.

[0032] As described further herein, a processor, such as the workstation computer 40, may analyze metrics of the injection molded parts to determine a compressibility metric of the part. The compressibility metric is indicative of a mass or density of the part under a set of operational parameters (e.g., fill pressure, hold time, etc.) controllable by the controller 50 and/or the workstation computer 40. The compressibility metric may be obtained or determined by one or more sensors (such as the nozzle sensor 52, linear transducer 57, a pressure sensor, a temperature sensor, an ultrasound transducer, and/or other sensors), measuring one or more of the weight of an injection molded part, cavity pressure, screw movement during PFA, force/stress/strain of the mold or clamping unit, material temperature in the cavity, or by another metric indicative of a compressibility of a material. The compressibility metric will be described further herein in reference to FIGS. 3-4.

[0033] FIG. 2 is a plot 100 of melt pressure values 102 against time for a mold cycle executed by the injection molding machine 10. The melt pressure values 102 may be generated by the nozzle sensor 52 and communicated to the controller 50 during the execution of the mold cycle. During an initial phase 104 of the mold cycle, pressure rapidly increases to a set point value (set point PFill). In the fill phase 106, the pressure is held at the steady-state pressure value as the mold cavity 32 is filled. The duration time of the fill phase 106 determines how quickly, and at what pressure, molten plastic material 24 is injected into the mold. A shorter duration fill phase 106 duration requires a greater injection pressure while a longer duration fill phase 106 allows for a slower molten material injection velocity. When molten plastic material 24 nears the end of the mold cavity 32, pressure is reduced to second, lower, set point value (set point PHold). In the pack and hold phase 108, the pressure is held at the steady-state pressure value as the material 24 in the mold cavity 32 cools, this pressure is also referred to herein as the pack and hold pressure. The duration time of the pack and hold phase 108 determines how long the part is hold in the mold as the part cools and solidifies. After the material 24 is cooled, the mold 28 is opened in the molded part is ejected from the mold cavity 32.

[0034] The duration times and pressures for each of the initial phase 104, fill phase 106, hold phase 108, set point PFill, and set point PHold influence whether a part is fabricated with defects (e.g., flashing and short-shot defects) which is material and mold dependent.

[0035] FIG. 3A is a plot of PFill injection pressure vs. time for use in a optimization method. While the illustrated plot may be used in an embodiment that combines largest empty corner rectangle (LECR) optimization with compressibility metric based optimization, other embodiments of the optimization method may operate independently of LECR optimization techniques. LECR optimization techniques are described in more detail in U.S. National Stage Application No. 17,290,584, filed April 30, 2021, the entire disclosure of which is incorporated herein by reference.

[0036] The plot 200 in FIG. 3A has a vertical pressure axis indicative of the PFill set point during the fill phase 106, and a horizontal time axis indicative of the duration of the fill phase 106. The data plotted in FIG. 3 A has a short- shot limit line 203, a flashing limit line 205, a maximum pressure limit 208, and a minimum pressure limit 210 that together define and form the bounds of a defect- free fabrication space 212. While referred to as the defect-free fabrication space 212, a part may be fabricated with parameter values within the defect- free fabrication space 212 that contains a defect. When the injection molding machine 10 is configured with operating parameter values (e.g., PFill setpoint values and fill phase duration values) inside of the defect- free fabrication space 212, to the injection molding machine 10 typically generates parts with no visible defects when executing an injection cycle. It should be appreciated that in alternate embodiments which associate different operating parameters (e.g., screw velocity and fill phase duration) with short- shot limits and flashing limits, the defect- free fabrication space 212 may define the bounds for values of those operating parameters that cause the injection molding machine 10 to produce defect-free parts. As it is used herein, the term “defect-free” may refer to threshold expected proportion of molded parts that do not exhibit defects (e.g., 95%, 99%, 99.9%, etc.)

[0037] The shot- shot limit line 203 is a line that defines a boundary in the operating parameter space below which the injection molding machine 10 will typically produce injection molded parts that exhibit short-shot defects where insufficient molten material was injected into the mold due to underpressurization. Conversely, operating parameter values in the region above the short-shot limit line 203 will typically produce injection molded parts that do not exhibit short- shot defects. Accordingly, the short- shot limit line 203 may also be referred to herein as the “minimum pressure curve.” To determine the short-shot limit line 203 and the flashing limit line 205, the injection molding machine 10 performs a set of injection cycles with various PFill pressures and fill phase durations. To generate the data points along the short-shot limit line 203, the operator may begin with an initial PFill pressure and execute a set of injection cycles, each with a shorter fill phase duration than the prior. The operating parameter values of the last injection cycle that produced an injection molded part that does not exhibit a short-shot defect is used as a data point on the short-shot limit line 203. The operator repeats this process for varying PFill pressures within a range PFill values associated with normal operation of the injection molding machine 10.

[0038] On the other hand, the flashing limit line 205 defines a boundary in the operating parameter space above which the injection molding machine 10 will typically produce injection molded parts that exhibit flashing defects resulting from overpressurization. As such, operating parameter values in the region below the flashing limit line 205 will typically produce injection molded parts that do not exhibit flashing defects. Accordingly, the flashing limit line 205 may also be referred to herein as the “high pressure curve.” The region of operating parameters between the short- shot limit line 203 and the flashing limit line 205 typically do no exhibit either short- shot defects or flashing defects. To generate the data points along the flashing limit line 205, the operator may begin with the initial PFill pressure used to generate the short-shot limit line 203 and execute a set of injection cycles, each with a longer fill phase duration than the prior. The operating parameter values of the last injection cycle that produced an injection molded part that does not exhibit a flashing defect is used as a data point on the flashing limit line 205. The operator repeats this process for varying PFill pressures within the range PFill values used to generate the short-shot limit line 203.

[0039] A processor, such as a processor of the workstation computer 40, then obtains pressure versus time data points that form the short-shot limit line 203 and the flashing limit line 205. Based on these data points, the processor applies an interpolation technique to define the shape of the short- shot limit line 203 and the flashing limit line 205 between the obtained data points. While the foregoing describes the process for generating the short-shot limit line 203 and the flashing limit line 205 in an embodiment that utilizes PFill pressure and fill phase duration as the operating parameters, in embodiments that utilize alternate operating parameters, the operator may instead vary the alternate operating parameter values to generate the short-shot limit line 203 and the flashing limit line 205.

[0040] FIG. 3B is a plot 250 of compression rate vs. process factor A (PFA) pressure. To determine the relationship between compression rate and PFA pressure illustrated in FIG. 3B, the workstation computer 40 obtains data sets associated with one or more sets of injection molding cycles. While Fig. 3B illustrates a relationship associated with compression rate, in alternate embodiments the workstation computer 40 may instead generate the relationship for other compressibility metrics, such as part weight, part mass, part density, a cavity pressure, injection material temperature, injection material cooling rate, cushion, mold stress, or mold strain. Similarly, while Fig. 3B illustrates a relationship associated with PFA pressure, in alternate embodiments the workstation computer 40 may instead generate the relationship for other secondary pressures, such as a hold pressure, a pack and hold pressure, or another secondary pressure known to those skilled in the art. In examples, the secondary pressure may be greater than a fill pressure for a given injection cycle, such as when a PHold is greater than a PFill in the example profile of the cycle of FIG. 2.

[0041] As it is used herein, PFA is a multiplier applied to the amount of cavity pressure measured in the mold. Accordingly, the PFA pressure is the resultant pressure setpoint value determined by multiplying the sensed cavity pressure by the PFA multiplier. That is, the controller 50 applies the PFA multiplier to the cavity pressure to set a plastic melt pressure setpoint during the pack and hold phase (i.e., the PFA pressure). In other words, if the cavity pressure starts to increase, the PFA pressure setpoint will decrease or increase by an amount calculated by the algorithm using the PFA multiplier determined during initial process development. A range of PFA multipliers may be determined for a DOE process either during a LECR optimization, a compressibility metric optimization as described herein, manually by another process performed by a processor, or determined by a user. The PFA multiplier can be adjusted as necessary to make a quality part.

[0042] In some embodiments, the workstation computer 40 may obtain compressibility metric and PFA pressure data associated with the injection molding cycles performed as part of defining the low- and high-pressure versus time curves. Alternatively, in embodiments that do not define the defect- free boundary 212 of Fig. 3 A, the curve 252 of the plot 250 of FIG. 3 can be generated by executing a set of injection cycles where the melt pressure set point during the fill phase (PFill) is kept constant and the PFA multiplier is adjusted or a set of injection cycles where the PFill set point is adjusted and the PFA multiplier is kept constant. Additionally, the workstation computer 40 may generate the curve 252 by interfacing with the controller 50 and the controller 50 varying other operating parameter values, such as one or more of an injection pressure, hold pressure, hold cycle time, injection time, cooling time, injection velocity, injection material density, screw rotation speed, screw recovery pressure (back pressure), barrel temperatures, mold temperatures, etc.

[0043] The workstation computer 40 may determine the compression rate, or another compressibility metric, based upon the Pfill pressure, velocity of molten material, or another measure of the injection cycles or injection molded parts that were fabricated during the sets of injection molding cycles. The compression rate is one of a plurality of potential compressibility metrics that may be used to determine a range of values of operational parameters for fabricating parts with little or no defects.

[0044] As illustrated, the resultant compression rate vs. PFA pressure curve 252 has a substantially linear region 255 and a nonlinear region 258. As the name implies, the linear region 255 is a region of the curve 252 that exhibits a substantially linear relationship between the compression rate of the molded part and the PFA pressure during the corresponding mold cycle. As used herein, “substantially linear” may be determined by the maximum range of data points which produce a higher R-squared (R ² ) value for a linear trend line (y = mx + b) than any other type of trend line (e.g., exponential, logarithmic, polynomial, etc.) for the same range of data points along the compression rate vs. PFA pressure curve 252. When the injection molding machine 10 is configured with operational parameter values that result in an injection molded part that falls within the linear region 255, the injection molding machine 10 is configured with operational parameter values within a range of values that are optimal for fabricating parts that have little to no defects. As such, starting the DOE process using operational parameter values that result in an injection molded part that falls within the linear region 255 also allow for shorter cooling times than performing fabrication with parameters within the nonlinear region 258.

[0045] On the other hand, the nonlinear region 258 is a region of the curve 252 that exhibits a non-linear relationship between the compression rate of the molded part and the PFA pressure during the corresponding mold cycle. For example, the nonlinear region 258 may exhibit quadratic, parabolic, exponential, or another nonlinear relationship. When the injection molding machine 10 is configured to produce parts that fall within the nonlinear region 258 of the curve 252, the operating parameter values of the injection molding machine 10 are more likely to result in overfill, flashing, or other defects associated with applying too high a pressure, for too long of a period of time.

[0046] FIG. 4 is a flowchart illustrating an embodiment of a process 400, performed by an injection molding apparatus, such as the injection molding machine 10 of FIG. 1, for determining optimized operational parameters for performing a DOE for a particular mold according to the optimization techniques described herein. It should be appreciated that the process 400 is one example process for implementing the disclosed optimization techniques, and other embodiments may implement alternate processes, including those described elsewhere herein.

[0047] At block 402, the injection molding machine 10 performs a first series of injection cycles to produce a first set of injection molded parts. To this end, the injection molding machine 10 may be configured to perform an injection cycle that conforms with the pattern illustrated in FIG. 2. In some embodiments, the injection cycle pattern exhibits a low substantially constant melt pressure. To execute the first series of injection cycles, the injection molding machine 10 may be configured with sets of operational parameter values that define a boundary below which the resultant injection molded part is likely to exhibit short-shot defects. For example, the first series of injection cycles may include the injection cycles that form the short-shot defect limit line 203 of Fig. 3B. As another example, the operator may configure the injection molding machine 10 to execute a series of injection cycles by varying operational parameters that define an alternate short-shot boundary. As yet another example, the operator may configure the injection molding machine 10 with a PFill value that is known to result in a short shot for a given fill phase duration and execute a series of injection cycles via which the duration of the fill phase increased while keeping the PFill value constant.

[0048] At block 404, a processor, such as the workstation computer 40, analyzes data associated with the first series of injection cycles to obtain a first plurality of injection process parameter versus time data sets associated with the first series of injection cycles. For example, the injection process parameters may include the PFill pressure, PFill duration, or other operating process parameters. The workstation computer 40 may interface with the controller 50, sensors 52 and 57, or other elements of the injection molding machine 10 to obtain the data associated with the first series of injection cycles, or other data required for the described optimization process. After obtaining the data, the workstation computer 40 may extract a plurality of data sets that associate one or more injection process parameters with corresponding time data to define corresponding injection process parameter vs. time functions.

[0049] Additionally, the workstation computer 40 further analyzes the data associated with the first series of injection cycles to obtain a first plurality of compressibility metrics indicative of compressibility of the injection material in forming the molded part.

[0050] At block 406, the injection molding machine 10 performs a second series of injection cycles to produce a second set of injection molded parts. The injection molding machine may implement a similar injection pattern used to perform the first series of injection cycles. That said, to execute the second series of injection cycles, the injection molding machine 10 may instead be configured with sets of operational parameter values that define a boundary above which the resultant injection molded part is likely to exhibit flashing defects. For example, the second series of injection cycles may include the injection cycles that form the flashing defect limit line 205 of Fig. 3B. As another example, the operator may configure the injection molding machine 10 to execute a series of injection cycles by varying operational parameters that define an alternate flashing boundary. As yet another example, the operator may configure the injection molding machine 10 with a PFill value known to be a sufficiently high pressure that is known to result in a flashing defect for a given fill phase duration. Accordingly, the duration of the fill phase stage may be varied across the cycles of the second series of injection cycles while keeping the PFill value constant.

[0051] At block 408, a processor, such as the workstation computer 40, obtains and analyzes data associated with the second series of injection cycles to obtain a second plurality of injection process parameter versus time data sets associated with the second series of injection cycles.

The second plurality of injection process parameter vs. time data sets define a maximum injection process parameter vs. injection time function. It should be appreciated that the workstation computer 40 may perform a similar analysis to the data from the second series of injection cycles as performed on the data from the first series of injection cycles at block 404 to generate additional data points for defining the one or more process parameter vs. time functions. Additionally, at the block 408, the workstation computer 40 further analyzes the data associated with the second series of injection cycles to obtain a second plurality of compressibility metrics indicative of compressibility of the injection material that the molded parts are fabricated of.

The workstation computer 40 further analyzes the data associated with the second series of injection cycles to obtain a plurality of secondary injection pressures for the second series of injection cycles. The secondary pressures may include one or more of a PFA pressure, a cavity pressure, a cavity temperature, cavity force/stress/strain, or another detectable metric indicative of the filling of injection material or densification of the injection material in the mold. The secondary pressure may be measured using the nozzle sensor 52, linear transducer 57, another pressure sensor, a temperature sensor, and/or another sensor.

[0052] At block 410, the workstation computer 40 compares the obtained first and second pluralities of compressibility metrics to the obtained secondary injection pressures to identify and define a range of optimal compression rates for the given injection process. As previously discussed, the range of optimal compression rates may correspond to a range of compressibility metric values that exhibit a linear relationship with the secondary injection pressure (e.g., the linear region 255 of Fig. 3B). For example, the range of optimal compression rates may correspond to a range of compressibility metric values that exhibit a normal distribution in the compressibility metric vs. secondary injection pressure space. [0053] At block 412 the workstation computer 40 determines a range of values for operational parameters that achieve a compression rate within the identified optimal compression rates. For example, the workstation computer 40 may identify a maximum and minimum secondary injection pressures associated with the range of compression rates in the linear region 255. Because the secondary injection pressure is a dependent variable that results from control of the operational parameters, the workstation computer 40 is able to calculate values for the operational parameters that result in the identified maximum and minimum secondary pressure values. Accordingly, the workstation computer 40 may define the range of values for the operational parameter based upon the operational parameter values that results in the minimum secondary pressure value and the operational parameter values that results in the maximum secondary pressure value.

[0054] The workstation computer 40 may determine multiple ranges of values, each range of values corresponding to a different operational parameter. For example, the operational parameters associated with a range of value that result in a compression rate within the range of optimal compression rates may include one or more of an injection pressure, an injection velocity, an injection material density, a pack and hold pressure, an injection pressure duration, a pack and hold duration, screw rotation speed, screw recovery pressure (i.e., back pressure), one or more barrel temperatures, one or more mold temperatures, a cavity pressure, cavity temperature, cavity force/stress/strain, or another detectable metric indicative of the filling of injection material or densification of the injection material in the mold.

[0055] At block 414, the workstation computer 40 determines DOE parameters from the range of operational parameter values that achieve a compression rate within the identified optimal compression rates. For example, the determined DOE parameters may be chosen to be initial DOE parameters that may be further tuned throughout the DOE process to achieve a desired or target injection molded part density, weight, mass, or another material parameter. As a result, DOE process can be executed on a smaller range of operational parameter values reducing the number of experiments required to complete the DOE process.

[0056] FIG. 5 is a flowchart illustrating an embodiment of a process 500, performed by a processor of the workstation computer 40 that controls the injection molding apparatus 10 of FIG. 1, for determining optimized operational parameters for performing a DOE that combines the optimization techniques described herein with a largest empty corner rectangle (LECR) optimization technique, such as described in US Patent Appl. No. 17/290,584, which is hereby incorporated by reference in its entirety. While the following generally describes the processor as being a component of the workstation computer 40, in other embodiments, the processor is a component of the controller 50 or another computational device. It should be appreciated that the process 500 is one example process for implementing the disclosed optimization techniques, and other embodiments may implement alternate processes, including those described elsewhere herein. In combination with the optimization technique described herein, the LECR optimization method can further reduce the amount of time for a DOE for an injection molding system which allows for faster commission of an injection molding machine, and prevents material waste and extra costs associated with DOE.

[0057] With simultaneous reference to FIGS. 1, 3 A, and 5, at block 502, the workstation computer 40 controls the injection molding machine 10 to perform a plurality of injection cycles. The injection cycles may be performed at various fill pressures (PFill), fill pressure times, pack and hold pressures (PHold), hold pressure times, and other ranges of values for other injection process parameters. For example, the workstation computer 40 may control the controller 50 to perform the series of injection cycles described with respect to Figs. 3 A and 3B.

[0058] At block 504, the workstation computer 40, obtains a plurality of pressure vs. time data sets associated with the plurality of injection cycles. During the execution of the plurality of mold cycles, sensors of the injection molding machine 10 may provide the pressure versus time data directly to the workstation controller 40 or indirectly via the controller 50. These pressure vs. time data sets may include a fill pressure vs. time data set, a pack and hold pressure vs. time data set, PFA pressure vs. time data set, or another pressure vs. time data set.

[0059] At block 506, the workstation computer 40 defines the short-shot limit line 203 and the flashing limit line 205 in the pressure vs. time coordinate space, for example, following the techniques described with respect to Fig. 3A. A maximum pressure limit 208 and a minimum pressure limit 210 may then be defined as the line connecting the maximum pressures of the short-shot limit line 203 and the flashing limit line 205. Short-short limit line 203, flashing limit line 205, maximum pressure limit 208, and minimum pressure limit 210 together define and form the bounds of the defect- free fabrication space 212 that pressures values and corresponding fill pressure durations that reduces, or eliminates, manufacturing errors in the fabricated injection molded parts.

[0060] At block 508, the workstation computer 40 defines a first geometric shape that provides bounds for the defect free fabrication space 212. Boundaries of the first geometric shape may be determined by the short- shot limit line 203, a flashing limit line 205, the maximum pressure limit 208, and the minimum pressure limit 210 that together define and form the bounds of the defect- free fabrication space 212.

[0061 ] At block 510, the workstation computer 40 obtains a first plurality of injection process parameter vs. time data sets. The first plurality of injection process parameter vs. time data sets define a minimum injection process parameter vs. time function. At block 512 the workstation computer 40 obtains a second plurality of injection process parameter vs. time data sets. The second plurality of injection process parameter vs. time data sets define a maximum injection process parameter vs. time function.

[0062] At block 514, the workstation computer 40 determines an optimal compression rate geometric shape. The optimal compression rate geometric shape is determined from the minimum injection time vs. process parameter function and the maximum injection time vs. process parameter function. To generate the optimal compression rate geometric shape, the workstation computer 40 may first define the curve 252 of FIG. 3B to identify the maximum and minimum PFA pressure values corresponding to the optimal compression rate range (e.g., the linear region 255). The workstation computer 40 may convert the minimum PFA pressure value of the linear region 255 to an injection pressure value along the short-shot limit line 203 and the maximum PFA pressure value of the linear region 255 to an injection pressure value along the flashing limit line 205. The workstation computer 40 may then use these points as opposing vertices of the optimal compression rate geometric shape. The workstation computer 40 may then define the an optimal compression rectangle 220 based upon the two determined vertices While illustrated as the optimal compression rectangle 220, the optimal compression geometric shape may be a polygon, circle, ellipsoid, or another geometric shape determined from the minimum and maximum injection time vs. process parameter functions.

[0063] At block 516, the workstation computer 40 may select DOE parameters from the determined one or more geometric shapes and the optimal compression geometric shape. For example, the DOE parameter values may be determined to be any pressure and time point in the overlapping area 222 of the rectangle 215 of the one or more geometric shapes, and the optimal compression rectangle 220. Alternatively, the DOE parameters may be determined to be a pressure and time point in the overlapping area 225 of the rectangle 218 and the optimal compression rectangle 220.

[0064] FIG. 6 is a flowchart illustrating an embodiment of a process 600, performed by a processor, such as the workstation computer 40, for determining DOE parameters using a compressibility metric optimization techniques described herein. The process 600 pertains to ranking a plurality of geometric shapes for determining DOE and the process 600 may be integrated with LECR techniques to further define DOE parameters for an injection molding system.

[0065] At block 602, the workstation computer 40 determines a plurality of first geometric shapes within the defect-free boundary 212, such as the rectangles 215 and 218. For example, the workstation computer 40 may determine 3 geometric shapes, 4 geometric shapes, 5 geometric shapes, 10 geometric shapes, 50 geometric shapes, 100 geometric shapes, 1000 geometric shapes, or any number of first geometric shapes capable of being determined and stored in a memory, with each of the first geometric shapes being determined in part by the short-shot limit line 203 and the flashing limit line 205. For example, as illustrated in FIG. 3A, the workstation computer 40 may determine various empty corner rectangles 215 and 218 in the defect free fabrication space 212. The workstation computer 40 may also determine a largest empty corner rectangle for determining parameters for performing a DOE. The rectangles 215 and 218 may have one vertex that is a point on the short-shot limit line 203, and an opposite vertex that is a point of the flashing limit line 205. Each of the one or more first geometric shapes may have vertices or contacts that abut any of the boundaries of the defect-free fabrication space 212. The vertices or contacts may include one or more of a fixed contact, an independent sliding contact, a dependent sliding contact, a reflex contact, or a fixed edge contact. Some of the first geometric shapes may have vertices within the defect- free fabrication space 212 that do not abut any of the boundaries of the defect-free fabrication space 212, as illustrated by the rectangles 215 and 218. Each of the first geometric shapes is restricted to a subset of points defined by the boundaries of the defect-free fabrication space 212. While illustrated as rectangles 215 and 218, the first geometric shapes may be any polygon, circle, ellipsoid, or other geometric shape that may be contained within the defect free fabrication space 212.

[0066] At block 604, the workstation computer 40 determines the plurality of overlap areas 222 and 225 of the rectangles 215 and 218, respectively, with the optimal compression rectangle 220. Although each of the plurality of first geometric shapes and the optimal compression rate geometric shape are illustrated as rectangles 215, 218, and 220, each of the plurality of first geometric shapes, and the optimal compression rate geometric shape may independently be a polygon, circle, ellipsoid, or another geometric shape. In determining the plurality of overlap areas 222, the workstation computer 40 may determine that one or more of the plurality of first geometric shapes have no overlap area with the optimal compression rate geometric shape, and may assign those overlap areas with a value of zero, or add them to a null set.

[0067] At block 606, the workstation computer 40 determines a largest overlap area of the plurality of overlap areas. For example, the rectangle 215 has a smaller overlap area 222 with the optimal compression rate geometric shape 220 than the overlap area 225 of the rectangle 218 with the optimal compression rate geometric shape 220. Therefore, the workstation computer 40 determines that the rectangle 218 has a larger overlap area with the optimal compression geometric shape.

[0068] At block 608, the workstation computer 40 determines DEO parameters from the geometric shape of the plurality of first geometric shapes having the largest overlap area with the optimal compression rate geometric shape 220. In the current example, the workstation computer 40 determines the DOE parameters from the rectangle 218 having the larger overlap area 625 with the optimal compression rate geometric shape 220. The workstation computer 40 may determine the DOE parameters from points in the pressure vs. time space that are the overlap area 225, from other points in the rectangle 218, from a center point of the overlap area 225, from a center point of the rectangle 218, or from any points on the border of the rectangle 218.

[0069] In examples, multiple geometric shapes in the defect free fabrication space 212 may have a same or similar overlap area with the optimal compression rate geometric shape 220. For example, two or more different geometric shapes may have equal overlap areas with the optimal compression rate geometric shape 220, or similar overlap areas that are within 1%, 5%, 10%, 20%, or 30% of each other. To determine which geometric shape to determine DOE parameters from, the workstation computer may assign rankings to each of the geometric shapes according to a ranking metric. For example, the workstation computer 40 may rank each geometric shape based on an overall area or size of the geometric shape, a shape of the geometric shape, a location of a center point of the geometric shape in the defect free fabrication space 612, an overall length of a time range (i.e., range along the x-axis of the plot in FIG. 3A) of the shape, a pressure range (i.e., range along the y-axis of the plot in FIG. 3A) of the shape, or the relative location of any of the boundaries (i.e., maximum pressure, minimum pressure, maximum time, minimum time) of the shape within the defect free fabrication space 212.

[0070] In determining the DOE parameters, the workstation computer 40 may identify a sub group or subset of geometric shapes of the plurality of first geometric shapes, with the sub-group having a rank value above a rank threshold value. For example, the workstation computer 40 may identify the sub-group as the geometric shapes having an overall area above, or below, a threshold. In other examples, the workstation computer 40 may identify the sub-group as all of the geometric shapes that are squares, or the sub-group may be all geometric shapes of another shape. The ranking threshold may be based on the total number of geometric shapes in the first plurality of geometric shapes. For example, the rank threshold may be the top 10% of the largest area geometric shapes. Therefore, a set of 100 first geometric shapes results in a sub-group of 10 highest ranking geometric shapes, a set of 1000 first geometric shapes results in a sub-group of 100 highest ranking geometric shapes, and so on.

[0071] In some examples the workstation computer 40 may identify the sub-group as geometric shapes having a ranking below a threshold ranking, for example the sub-group may include all geometric shapes having an overall area below a certain value.

[0072] As described above, the process for determining the highest ranking geometric shape may be a tiered approach that uses multiple metrics to determine the DOE parameters. For example, the workstation computer 40 may first identify multiple geometric shapes that are substantially similar for determining the DOE parameters, and then determine which geometric shape to determine DOE parameters from, based on a ranking. For example, two geometric shapes of the first plurality of geometric shapes may have a same, or substantially similar, overlap area with the optimal compression rate geometric shape. The workstation computer 40 may then determine which of the two geometric shapes has a higher ranking (e.g., largest overall area, location within the defect free fabrication space 612, specific type of shape, etc.) and the workstation computer 40 may determine the DOE parameters from the highest ranking geometric shape.

[0073] The process for determining the highest ranking geometric shape may be a multi-tiered approach that uses more than two metrics to determine the geometric shape from which the DOE parameters are obtained. For example, the workstation computer 40 may first identify multiple geometric shapes that are substantially similar for determining the DOE parameters that have a same ranking. The workstation computer 40 may then determine a secondary ranking, based on one or more additional metrics and select a particular geometric shaped based upon the secondary rankings. Further, if the workstation computer 40 determines there is a ranking tie at the secondary level, the workstation computer 40 may determine a tertiary ranking based on one or more additional metrics . The workstation computer 40 may then select the particular geometric shape based on the tertiary rank.

[0074] The following list of aspects reflects a variety of the embodiments explicitly contemplated by the present disclosure. Those of ordinary skill in the art will readily appreciate that the aspects below are neither limiting of the embodiments disclosed herein, nor exhaustive of all of the embodiments conceivable from the disclosure above, but are instead meant to be exemplary in nature.

[0075] To the extent that the term “includes” or “including” is used in the specification or the claims, it is intended to be inclusive in a manner similar to the term “comprising” as that term is interpreted when employed as a transitional word in a claim. Furthermore, to the extent that the term “or” is employed (e.g., A or B) it is intended to mean “A or B or both.” When the applicants intend to indicate “only A or B but not both” then the term “only A or B but not both” will be employed. Thus, use of the term “or” herein is the inclusive, and not the exclusive use. See, Bryan A. Gamer, A Dictionary of Modem Legal Usage 624 (2d. Ed. 1995). Also, to the extent that the terms “in” or “into” are used in the specification or the claims, it is intended to additionally mean “on” or “onto.” Furthermore, to the extent the term “connect” is used in the specification or claims, it is intended to mean not only “directly connected to,” but also “indirectly connected to” such as connected through another component or components. [0076] Those skilled in the art will recognize that a wide variety of modifications, alterations, and combinations can be made with respect to the above described embodiments without departing from the scope of the invention, and that such modifications, alterations, and combinations are to be viewed as being within the ambit of the inventive concept.

[0077] The patent claims at the end of this patent application are not intended to be construed under 35 U.S.C. § 112(f) unless traditional means-plus-function language is expressly recited, such as “means for” or “step for” language being explicitly recited in the claim(s). The systems and methods described herein are directed to an improvement to computer functionality, and improve the functioning of conventional computers.