Title:
PARTS SURFACE ROUGHNESS AND CUTTING TOOL WEAR PREDICTION METHOD BASED ON MULTI-TASK LEARNING
Document Type and Number:
WIPO Patent Application WO/2021/174525
Kind Code:
A1
Abstract:
A parts surface roughness and cutting tool wear prediction method based on multi-task learning, relating to the technical field of machining. Firstly, vibration signals in the machining process are collected; next, the parts surface roughness and a wear condition of a cutting tool are measured, and the measured results respectively correspond to vibration signals; secondly, sample expansion is performed, and features are extracted and normalized; then, a multi-task prediction model based on a deep belief network is constructed, the parts surface roughness and the cutting tool wear condition serve as model output, features are extracted as input, and a multi-task DBN network prediction model is established; and finally, test verification is performed, the vibration signals are inputted into the multi-task prediction model, and the surface roughness and the cutting tool wear condition are predicted. The method is mainly advantaged in that: online prediction of the parts surface roughness and the cutting tool wear is achieved by means of one-time modeling, hidden information contained in monitoring data is fully utilized, and the workload and model building costs are reduced.
Inventors:
WANG YONGQING (CN)
QIN BO (CN)
LIU KUO (CN)
SHEN MINGRUI (CN)
NIU MENGMENG (CN)
WANG HONGHUI (CN)
HAN LINGSHENG (CN)
QIN BO (CN)
LIU KUO (CN)
SHEN MINGRUI (CN)
NIU MENGMENG (CN)
WANG HONGHUI (CN)
HAN LINGSHENG (CN)
Application Number:
PCT/CN2020/078180
Publication Date:
September 10, 2021
Filing Date:
March 06, 2020
Export Citation:
Assignee:
UNIV DALIAN TECH (CN)
International Classes:
B23Q17/09; G06F17/00; G01B21/30
Foreign References:
| CN110000610A | 2019-07-12 | |||
| CN109396957A | 2019-03-01 | |||
| CN109514349A | 2019-03-26 | |||
| CN110059442A | 2019-07-26 | |||
| CN110598299A | 2019-12-20 | |||
| CN109746765A | 2019-05-14 | |||
| CN107584334A | 2018-01-16 | |||
| JP2018086712A | 2018-06-07 |
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