Product Quality Improvement Using Multivariate Data Analysis
| Authors: | Kano Manabu, Kyoto University, Japan
Fujiwara Koichi, Kyoto University, Japan
Hasebe Shinji, Kyoto University, Japan
Ohno Hiromu, Kobe University, Japan |
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| Topic: | 6.2 Mining, Mineral & Metal Processing |
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| Session: | Technology in Mining and Metal Processing Industry |
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| Keywords: | Quality Control, Statistical Process Control, Optimization, PrincipalComponent Analysis, Linear Discriminant Analysis |
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Abstract
A data-based methodology for improving product quality is proposed. Referred to as Data-Driven Quality Improvement (DDQI), the proposed method can cope with qualitative as well as quantitative variables, determine the operating conditions that can achieve the desired product quality, optimize operating condition under various constraints, and thus can provide useful information to improve product quality. This paper aims to formulate DDQI and demonstrate its usefulness with an case study of an industrial steel process. In addition, possible extensions and remaining problems are discussed based on the authors' experience of succeeding in improving product quality by applying DDQI to several industrial processes.
