Machine Learning and Pattern Recognition Techniques for Information Extraction to Improve Production Control and Design Decisions

Abstract

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 paper 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 through binary classification. The proposed strategy is validated using data derived from an automotive manufacturing systems. The \(l_1\)-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. According to experimental results, 100% of defects can be detected effectively.