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2022년 12권 2호 p.1 ~ p.22
Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
Tola Pheng
Tserenpurev Chuluunsaikhan , Ga-Ae Ryu , Sung-Hoon Kim , Aziz Nasridinov, Kwan-Hee Yoo
- Abstract -
Published Date: 12 Jan 2022
Impact Factor : 2.838(2021)
ISSN 2076-3417

Abstract: In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.
- Key Words -
long short-term memory, smart manufacturing, statistical process analysis, process capability index, process quality performance
long short-term memory, smart manufacturing, statistical process analysis, process capability index, process quality performance
- 논문 구분 -
국외전문학술지, SCI(E)
- 사사 정보 -
This work was supported by the Technology Innovation Program (2004367, development of a cloud big data platform for innovative manufacturing in the ceramic industry) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), and by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2021-2020- 0-01462) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).
- Downloads -
applsci-12-0073.pdf