A Data-Driven Approach for Identifying Possible Manufacturing Processes and Production Parameters That Cause Product Defects: A Thin-Film Filter Company Case Study

• Published in: IEEE access Vol. 8; pp. 49395 - 49411
• Main Authors: Lyu, Jrjung, Liang, Chia Wen, Chen, Ping-Shun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; association rule mining; Big Data; Business competition; Chi-square test; Data mining; Data processing; Data-driven; decision tree; Decision trees; Defects; Internet of Things; Manufacturers; Manufacturing; Manufacturing processes; Optical filters; Parameter identification; Photoelectricity; process improvement; Process parameters; Production lines; Statistical tests; Thin films; thin-film filter (TFF)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2974535

A semiconductor or photoelectric manufacturer faces a more competitive market with small quantities of many products. These products require hundreds of processes for production, thereby generating huge manufacturing data. With the help of the Internet of Things (IoT) technology, the manufacturer can collect manufacturing process data in a timely manner. Due to the massive quantities of manufacturing process data, it has become difficult for manufacturers to determine the causes of product defects, by which machine, and by what manufacturing process (or recipe) parameters. This research proposes a six-step data-driven solution to this problem. The chi-square test of independence, the Apriori algorithm, and the decision tree method identify the process that is generating the defective products and extract rules to identify the lot identification of product defects and their associated manufacturing process parameters. An empirical study was conducted at an optical thin-film filter (TFF) company in Taiwan. Based on the data of the optical TFF production lines, the coating process was identified as the source of the defective products, and the extracted rules were validated and implemented. The product defect rate decreased from 20% to 5%. Hence, the proposed data-driven solution was found to be capable of helping manufacturers enhance their product yield.