Machine learning techniques for quality control in high conformance manufacturing environment

• Published in: Advances in mechanical engineering Vol. 10; no. 2
• Main Authors: Escobar, Carlos A, Morales-Menendez, Ruben
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-02-2018; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Artificial intelligence; Authorship; Big Data; Business competition; Classification; Defective products; Defects; Global marketing; Knowledge based systems; Machine learning; Pattern recognition; Product reviews; Quality; Quality control; Quality standards; Search algorithms; defect detection; classification threshold algorithm; feature elimination algorithm; quality control; model selection criterion; l 1-regularized logistic regression; unbalanced data; Manufacturing
• ISSN: 1687-8132; 1687-8140
• DOI: 10.1177/1687814018755519

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.