Fuzzy decision-making model for process quality improvement of machine tool industry chain

Industry 4.0 has fostered innovation in industries around the world. Manufacturing industries in particular are advancing towards smart manufacturing by integrating and applying relevant technologies. The output value of machine tools in Taiwan is among the top of the world and the central region is...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2022-01, Vol.42 (3), p.1547-1558
Main Authors: Chen, Kuen-Suan, Yu, Chun-Min
Format: Article
Language:English
Subjects:
Chains
Confidence limits
Customers
Decision making
Innovations
Knowledge bases (artificial intelligence)
Machine tool industry
Machine tools
Manufacturers
Manufacturing
Process parameters
Sampling error
Statistical analysis
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Summary:Industry 4.0 has fostered innovation in industries around the world. Manufacturing industries in particular are advancing towards smart manufacturing by integrating and applying relevant technologies. The output value of machine tools in Taiwan is among the top of the world and the central region is a key area for this industry chain, which supplies manufacturers in Taiwan and their international downstream customers. To support innovation in this industry, the current study used the Six Sigma quality indices for smaller-the-better, larger-the-better, and nominal-the-best quality characteristics to construct a fuzzy decision-making model. Based on this model, we propose a process quality fuzzy analysis chart (PQFAC) for process quality improvement. Our use of fuzzy decision values to replace lower confidence limits decreases the probability of misjudgment made by sampling errors. The proposed fuzzy model also offers a more accurate assessment of process improvement requirements. We provide a real-world example to demonstrate the applicability of the proposed approach. Machine tool manufacturers can apply the platform and proposed model to evaluate their process capabilities for the vital parts suppliers and downstream customers, determine optimal machine parameter settings for processes with inadequate accuracy or precision, establish more suitable machine repair and maintenance systems, and combine the improvement experiences of customers to create an improvement knowledge base. This will enhance product value and industry competitiveness for the entire machine tool industry chain.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-210868