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Open AccessArticle

Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules

1
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
2
Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32003, Taiwan
*
Author to whom correspondence should be addressed.
Processes 2020, 8(7), 751; https://doi.org/10.3390/pr8070751
Received: 28 May 2020 / Revised: 19 June 2020 / Accepted: 26 June 2020 / Published: 28 June 2020
Companies accumulate a large amount of production process data during product manufacturing. Sequence data from the mining production process can enable a company to evaluate the manufacturing process, to find the key factors affecting product quality, and to improve product quality. However, the production process mainly exists in the form of text. To solve this problem, we propose a novel frequent pattern mining algorithm (EABMC) based on the text context semantics and rules of the manufacturing process to remove redundant sequences and to obtain good mining results. In this algorithm, first, we use embeddings from language models (ELMo ) to improve the process of text similarity matching and to classify similar semantic processes into one class. Then, the manufacturing process unit (MPU) is proposed by extracting the characteristics of manufacturing process data according to the constraints of the manufacturing process and other conditions. The above two steps cause the complex manufacturing process sequence to merge and simplify. Once again, a frequent pattern mining algorithm (CloFAST) is used to explore the important manufacturing process relationships behind a large amount of manufacturing data. In addition, taking the data from a production enterprise in Guizhou Province as an example, the validity of the method is verified. Compared with other methods, this method is shown to have greater mining efficiency and better results and can find out the key factors that affect product quality, especially for text data. View Full-Text
Keywords: product quality detection; manufacturing process diagnostics; deep semantic learning; manufacturing process rule; frequent pattern mining product quality detection; manufacturing process diagnostics; deep semantic learning; manufacturing process rule; frequent pattern mining
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Yao, L.; Huang, H.; Chen, S.-H. Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules. Processes 2020, 8, 751.

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