A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

doi:10.1007/s40436-022-00427-9

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (2): 280-294.doi: 10.1007/s40436-022-00427-9

• ARTICLES • 上一篇

A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

Li-Ping Zhao¹, Bo-Hao Li¹, Yi-Yong Yao²

1. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China;
2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China

收稿日期:2021-11-03 修回日期:2022-03-22 发布日期:2023-05-20
通讯作者: Li-Ping Zhao,E-mail:lipingzh@mail.xjtu.edu.cn E-mail:lipingzh@mail.xjtu.edu.cn
作者简介:Li?Ping Zhao received the Ph.D. degree in mechanical manufacture and automation from the School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China, in 1996. Currently, she is a professor with the School of Mechanical Engineering, Xi'an Jiaotong University. Her research interests include quality control engineering, manufacturing system modeling, and integrating computer aided design/computer aided process planning/computer aided engineering (CAD/CAPP/CAE).
Bo?Hao Li received the B.S. degree in Mechanical engineering and automation from School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China, in 2016. Currently, he is pursuing the Ph.D. degree at the School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an. His research interests include manufacturing system modeling and machining process quality prediction.
Yi?Yong Yao received the Ph.D. degree in mechatronic engineering from the School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, in 2001. He is currently an associate professor with the School of Mechanical Engineering, Xi'an Jiaotong University. His research interests include intelligent control technology, mechatronics, and product security technology.
基金资助:
The research work presented in this paper is supported by the National Natural Science Foundation of China (Grant No. 51675418).

A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

Li-Ping Zhao¹, Bo-Hao Li¹, Yi-Yong Yao²

1. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China;
2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China

Received:2021-11-03 Revised:2022-03-22 Published:2023-05-20
Contact: Li-Ping Zhao,E-mail:lipingzh@mail.xjtu.edu.cn E-mail:lipingzh@mail.xjtu.edu.cn
Supported by:
The research work presented in this paper is supported by the National Natural Science Foundation of China (Grant No. 51675418).

摘要/Abstract

摘要： Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00427-9

关键词: Zero defection manufacturing (ZDM), Multi-stage manufacturing process (MMP), Moving window, Deep supervised long-short term memory (SLSTM) network, Assembly quality optimization

Abstract: Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00427-9

Key words: Zero defection manufacturing (ZDM), Multi-stage manufacturing process (MMP), Moving window, Deep supervised long-short term memory (SLSTM) network, Assembly quality optimization

Li-Ping Zhao, Bo-Hao Li, Yi-Yong Yao. A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing[J]. Advances in Manufacturing, 2023, 11(2): 280-294.

参考文献

1. Oztemel E, Gursev S (2020) Literature review of Industry 4.0 and related technologies. J Intell Manuf 31:127–182
2. Tao F, Qi Q, Liu A et al (2018) Data-driven smart manufacturing. J Manuf Syst 48:157–169
3. Zhong RY, Xu X, Klotz E et al (2017) Intelligent manufacturing in the context of Industry 4.0: a review. Engineering 3:616–630
4. Farid AM (2017) Measures of reconfigure ability and its key characteristics in intelligent manufacturing systems. J Intell Manuf 28:353–369
5. Lu B, Zhou X (2019) Quality and reliability oriented maintenance for multi-stage manufacturing systems subject to condition monitoring. J Manuf Syst 52:76–85
6. Djurdjanovi? D, Ul Haq A, Magnanini MC et al (2019) Robust model-based control of multi-stage manufacturing processes. CIRP Ann 68:479–482
7. Psarommatis F, May G, Dreyfus PA et al (2020) Zero defect manufacturing: state-of-the-art review, shortcomings and future directions in research. Int J Prod Res 58:1–17
8. Eger F, Reiff C, Brantl B et al (2018) Correlation analysis methods in multi-stage production systems for reaching zero-defect manufacturing. Procedia CIRP 72:635–640
9. Psarommatis F, Prouvost S, May G et al (2020) Product quality improvement policies in Industry 4.0: characteristics, enabling factors, barriers, and evolution toward zero defect manufacturing. Front Comput Sci 2:6. https://doi.org/10.3389/fcomp.2020.00026
10. Bai B, Zhang J (2018) Quality cost model improvement based on 6σ management. Int J Manuf Technol Manag 32:396–411
11. Eleftheriadis RJ, Myklebust O (2016) A guideline of quality steps towards zero defect manufacturing in industry. In: Proceedings of the international conference on industrial engineering and operations management, pp 332–340, 23–25 September, Detroit, Michigan, USA
12. Psarommatis F, Kiritsis D (2018) A scheduling tool for achieving zero defect manufacturing (ZDM): a conceptual framework. In: Moon I, Lee G, Park J et al (eds) Advances in production management systems: smart manufacturing for Industry 4.0. APMS 2018. IFIP Advances in information and communication technology, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-99707-0_34
13. Lindstr?m J, Ky?sti P, Birk W et al (2020) An initial model for zero defect manufacturing. Appl Sci 10(13):4570. https://doi.org/10.3390/app10134570
14. Shojaie AA, Kahedi E (2019) Auto parts manufacturing quality assessment using design for six sigma (DFSS), case study in ISACO company. Int J Syst Assur Eng Manag 10:35–43
15. Wang Y, Perry M, Whitlock D et al (2020) Detecting anomalies in time series data from a manufacturing system using recurrent neural networks. J Manuf Syst 62:823–834
16. Xu LH, Huang CZ, Niu JH et al (2021) Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process. Adv Manuf 9:388–402
17. Mourtzis D, Doukas M, Psarommatis F (2015) A toolbox for the design, planning and operation of manufacturing networks in a mass customisation environment. J Manuf Syst 36:274–286
18. Psarommatis F, Zheng X, Kiritsis D (2020) A two-layer criteria evaluation approach for re-scheduling efficiently semi-automated assembly lines with high number of rush orders. Procedia CIRP 97:172–177
19. Lindstr?m J, Ky?sti P, Lejon E et al (2020) Zero defect manufacturing in an industry 4.0 context: a case study of requirements for change and desired effects. SSRN Electron J. https://doi.org/10.2139/ssrn.3717709
20. Peres RS, Barata J, Leitao P et al (2019) Multistage quality control using machine learning in the automotive industry. IEEE Access 7:79908–79916
21. Psarommatis F (2021) A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM. J Manuf Syst 59:507–521
22. Zhao L, Li B, Chen H et al (2018) An assembly sequence optimization oriented small world networks genetic algorithm and case study. Assem Autom 38:387–397
23. Guo MX, Liu J, Pan LM et al (2022) An integrated machine-process-controller model to predict milling surface topography considering vibration suppression. Adv Manuf 10:443–458
24. Liu JH, Li XY, Xia HX et al (2021) Effects of assembly errors and bonding defects on the centroid drift of a precision sleeve structure. Adv Manuf 9:509–519
25. Chang F, Zhou G, Zhang C et al (2019) A service-oriented dynamic multi-level maintenance grouping strategy based on prediction information of multi-component systems. J Manuf Syst 53:49–61
26. Li B, Zhao L, Yao Y (2021) Failure time prognosis in manufacturing process using multi-dislocated time series convolutional neural network. Proc Inst Mech Eng Part E J Process Mech Eng 235:832–840
27. Zhao L, Li B, Yao Y (2018) Research on evaluation method of product processing state based on multidimensional entropy space. In: Proceedings of the 30th Chinese control and decision conference, pp 5999–6003, 9–11 June, Shenyang, China
28. Li Z, Wang Y, Wang K (2020) A data-driven method based on deep belief networks for backlash error prediction in machining centers. J Intell Manuf 31:1693–1705
29. Li BH, Zhao LP, Yao YY (2022) Multiconditional machining process quality prediction using deep transfer learning network. Adv Manuf. https://doi.org/10.1007/s40436-022-00415-z
30. Mao J, Chen D, Zhang L (2016) Mechanical assembly quality prediction method based on state space model. Int J Adv Manuf Technol 86:107–116
31. Hassan M, Sadek A, Damir A et al (2018) A novel approach for real-time prediction and prevention of tool chipping in intermittent turning machining. CIRP Ann 67:41–44
32. Ren L, Meng Z, Wang X et al (2021) A data-driven approach of product quality prediction for complex production systems. IEEE Trans Ind Inform 17:6457–6465
33. Huang CG, Huang HZ, Li YF (2019) A bidirectional LSTM prognostics method under multiple operational conditions. IEEE Trans Ind Electron 66:8792–8802
34. China National Standardization Administration Committee (2017) GB/T 3853–2017 displacement compressors-acceptance tests. China Standards Press, Beijing, China

A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

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