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

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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.

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Acknowledgements

The research work presented in this paper is supported by the National Natural Science Foundation of China (Grant No. 51675418).

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Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Li-Ping Zhao & Bo-Hao Li

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Yi-Yong Yao

Authors
  1. Li-Ping Zhao
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  2. Bo-Hao Li
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Corresponding author

Correspondence to Li-Ping Zhao.

Appendix A: Roulette wheel selection method

Appendix A: Roulette wheel selection method

The implementation steps of roulette wheel selection method are described as follows.

Step 1 Calculate the fitness function \(f\left( {x_{i} } \right)\) of each individual \(x_{i}\), and n is group size.

Step 2 Calculate the possibility of each individual being passed on to the next generation:

$$P(x_{i} ) = {{f(x_{i} )} \mathord{\left/ {\vphantom {{f(x_{i} )} {\sum\nolimits_{j = 1}^{n} {f(x_{j} )} }}} \right. \kern-\nulldelimiterspace} {\sum\nolimits_{j = 1}^{n} {f(x_{j} )} }}.$$

Step 3 Calculate the cumulative probability of each individual:

$$q_{i} = \sum\nolimits_{j = 1}^{n} {P(x_{i} )}.$$

Step 4 Generate a pseudo-random array \({\varvec{R}} = [r_{1} ,r_{2} , \cdots ,r_{i} , \cdots ,r_{n} ]\) with uniform distribution in the interval [0,1].

Step 5 If \(q_{i} < r_{i}\), select \(x_{i}\). Otherwise select \(x_{k}\) that \(x_{k - 1} < r_{i} < x_{k}\), \(i = i + 1.\)

Step 6 Repeat Steps 4 and 5 for n times. \(n\) individuals are selected.

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Zhao, LP., Li, BH. & Yao, YY. A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing. Adv. Manuf. 11, 280–294 (2023). https://doi.org/10.1007/s40436-022-00427-9

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  • DOI: https://doi.org/10.1007/s40436-022-00427-9

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