Management of Unplanned Changes in Production Processes: AI Control Systems

Abstract

Quality risk management in industrial plants involves big calculations, the scale of which is often not only incomprehensible but also difficult to manage due to many parameters that affect the quality of production. Unsurprisingly, artificial intelligence-based quality management models are being introduced in manufacturing, only in niche, narrow areas, mostly for tracking product defects or identifying local quality defects. However, detecting the defect stage already is a late stage of the problem, which is almost always associated with a loss. Here comes the importance of prediction of problems or identifying of problematic patterns at an early stage before having production losses. Such attempts are rare and require a special approach. This type of module is needed for wide range problem forecasting in manufacturing. It should be configurable and clear not only by narrow area professionals, but also by medium-sized factory technologists who can configure such a system themselves to control their production quality risks. So here we are developing an approach whose strengths would be its simplicity, comprehensibility, fastness, and accessibility in its training, allowing us to understand why in one case or another the system predicts one decision or another.