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

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.

Automated summary

This study proposes a novel predict-prevention quality control method for zero defection manufacturing (ZDM) in multi-stage manufacturing processes (MMP). The method includes quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. By analyzing the distribution and considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction, and a long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. Empirical evaluations on compressors' MMP validate the applicability and practicability of the proposed method.