Machine learning algorithms analyze live and historical production data to identify patterns in behavior that have previously led to quality failures. Relationships between key process variables, such as line speed, pressures and temperatures and the quality measurements such as dimensional data of the product, are determined to build a predictive quality model.
When models are deployed in a production environment, they look for patterns or sets of conditions that provide early indications of inefficiencies, scrap rates or quality failures.