abstract
- © 2020 SAE International; General Motors.More than four decades ago, the concept of zero defects was coined by Phillip Crosby. It was only a vision at the time, but the introduction of Artificial Intelligence (AI) in manufacturing has since enabled it to become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few Defects Per Million of Opportunities (DPMO). Detecting these rare quality events is one of the modern intellectual challenges posed to this industry. Process Monitoring for Quality (PMQ) is an AI and big data-driven quality philosophy aimed at defect detection and empirical knowledge discovery. Detection is formulated as a binary classification problem, where the right Machine Learning (ML), optimization, and statistics techniques are applied to develop an effective predictive system. Manufacturing-derived data sets for binary classification of quality tend to be highly/ultra-unbalanced, making it very difficult for the learning algorithms to learn the minority (defective) class. The vision of how traditional quality philosophies (based on statistics) and PMQ can collaborate, interact, and complement each other to enable the development of zero-defect processes is presented. This vision is validated by a real case study in which 100% of the defects are detected.