TY - GEN
T1 - Manufacturing Quality Risk Analysis Approach Considering Production Process Scheme Robustness
AU - Feng, Tianyu
AU - He, Yihai
AU - Shi, Rui
AU - Cai, Yuqi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 5M1E(Man, Machine, Material, Method, Measurement, Environment) are the risk sources of the manufacturing quality, and the production process scheme is the kernel and carrier of the method. However, previous studies of manufacturing quality risk mainly focused on the qualified rate of material and degradation state of machine but ignored the influence of the robustness of the process scheme. Therefore, an extended manufacturing system Reliability-process Quality-product Reliability chain (E-RQR) model was proposed to consider the effect of process robustness on manufacturing quality risk. Firstly, the product process scheme and the connotation of manufacturing quality risk are expounded, and the formation mechanism of manufacturing quality risk based on E-RQR chain is proposed. Secondly, through the collection of manufacturing system operation data and product quality data, the manufacturing quality risk of the manufacturing system and the robustness of process scheme were quantified. On this basis, the technical framework of manufacturing quality risk analysis based on E-RQR chain was proposed. Thirdly, to verify the influence of the robustness of product process scheme on manufacturing quality risk, a robust optimization method of process parameters based on Back Propagation(BP) neural network and Taguchi design was proposed, which proved that robust process scheme can significantly improve manufacturing quality risk. Finally, the feasibility of the proposed method was proved by taking a hydraulic pump as the research object.
AB - 5M1E(Man, Machine, Material, Method, Measurement, Environment) are the risk sources of the manufacturing quality, and the production process scheme is the kernel and carrier of the method. However, previous studies of manufacturing quality risk mainly focused on the qualified rate of material and degradation state of machine but ignored the influence of the robustness of the process scheme. Therefore, an extended manufacturing system Reliability-process Quality-product Reliability chain (E-RQR) model was proposed to consider the effect of process robustness on manufacturing quality risk. Firstly, the product process scheme and the connotation of manufacturing quality risk are expounded, and the formation mechanism of manufacturing quality risk based on E-RQR chain is proposed. Secondly, through the collection of manufacturing system operation data and product quality data, the manufacturing quality risk of the manufacturing system and the robustness of process scheme were quantified. On this basis, the technical framework of manufacturing quality risk analysis based on E-RQR chain was proposed. Thirdly, to verify the influence of the robustness of product process scheme on manufacturing quality risk, a robust optimization method of process parameters based on Back Propagation(BP) neural network and Taguchi design was proposed, which proved that robust process scheme can significantly improve manufacturing quality risk. Finally, the feasibility of the proposed method was proved by taking a hydraulic pump as the research object.
KW - E-RQR chain
KW - Manufacturing quality risk
KW - Production process scheme
KW - Risk analysis
KW - Robustness of process parameters
UR - https://www.scopus.com/pages/publications/85191748287
U2 - 10.1109/PHM-HANGZHOU58797.2023.10482596
DO - 10.1109/PHM-HANGZHOU58797.2023.10482596
M3 - 会议稿件
AN - SCOPUS:85191748287
T3 - 2023 Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023
BT - 2023 Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023
A2 - Guo, Wei
A2 - Li, Steven
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023
Y2 - 12 October 2023 through 15 October 2023
ER -