MATEC Web Conf.
Volume 123, 20172017 The 2nd International Conference on Precision Machinery and Manufacturing Technology (ICPMMT 2017)
|Number of page(s)||6|
|Published online||21 September 2017|
Quality prediction modeling for multistage manufacturing based on classification and association rule mining
1 Central Industry Research & Service Division (CID), Institute for Information Industry, Nantou, 540, Taiwan
2 NSF I/UCRC for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH 45221, USA
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For manufacturing enterprises, product quality is a key factor to assess production capability and increase their core competence. To reduce external failure cost, many research and methodology have been introduced in order to improve process yield rate, such as TQC/TQM, Shewhart CycleDeming's 14 Points, etc. Nowadays, impressive progress has been made in process monitoring and industrial data analysis because of the Industry 4.0 trend. Industries start to utilize quality control (QC) methodology to lower inspection overhead and internal failure cost. Currently, the focus of QC is mostly in the inspection of single workstation and final product, however, for multistage manufacturing, many factors (like equipment, operators, parameters, etc.) can have cumulative and interactive effects to the final quality. When failure occurs, it is difficult to resume the original settings for cause analysis. To address these problems, this research proposes a combination of principal components analysis (PCA) with classification and association rule mining algorithms to extract features representing relationship of multiple workstations, predict final product quality, and analyze the root-cause of product defect. The method is demonstrated on a semiconductor data set.
© The Authors, published by EDP Sciences, 2017
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