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文章基本信息

  • 标题:Machine learning techniques for quality control in high conformance manufacturing environment
  • 作者:Carlos A Escobar ; Ruben Morales-Menendez
  • 期刊名称:Advances in Mechanical Engineering
  • 印刷版ISSN:1687-8140
  • 电子版ISSN:1687-8140
  • 出版年度:2018
  • 卷号:10
  • 期号:2
  • DOI:10.1177/1687814018755519
  • 语种:English
  • 出版社:Sage Publications Ltd.
  • 摘要:In today’s highly competitive global market, winning requires near-perfect quality. Although most mature organizations operate their processes at very low defects per million opportunities, customers expect completely defect-free products. Therefore, the prompt detection of rare quality events has become an issue of paramount importance and an opportunity for manufacturing companies to move quality standards forward. This article presents the learning process and pattern recognition strategy for a knowledge-based intelligent supervisory system, in which the main goal is the detection of rare quality events. Defect detection is formulated as a binary classification problem. The l1-regularized logistic regression is used as the learning algorithm for the classification task and to select the features that contain the most relevant information about the quality of the process. The proposed strategy is supported by the novelty of a hybrid feature elimination algorithm and optimal classification threshold search algorithm. According to experimental results, 100% of defects can be detected effectively.
  • 关键词:Manufacturing; l1-regularized logistic regression; classification threshold algorithm; defect detection; feature elimination algorithm; model selection criterion; quality control; unbalanced data
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