Title:Autonomous Supervision and Optimization of Product Quality in a Multi-Stage Manufacturing Process based on Self-Adaptive Prediction ModelsAuthor(s):Edwin Lughofer, Ciprian Zavoianu, Robert Pollak, Mahardhika Pratama, Pauline Meyer-Heye, Helmut Zörrer, Christian Eitzinger, Thomas RadauerAbstract:In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potentially arising problems in product quality, the second phase concerns proper reactions upon the recognition of such problems. In this paper, we address a holistic approach for handling both issues consecutively within a predictive maintenance framework at an on-line production system. Thereby, we address multi-stage functionality based on i) data-driven forecast models for (measure-able) product quality criteria (QCs) at a latter stage, which are established and executed through process values (and their time series trends) recorded at an early stage of production (describing its progress), and ii) process optimization cycles whose outputs are suggestions for proper reactions at an earlier stage in the case of forecasted downtrends or exceeds of allowed boundaries in product quality.Journal:Journal of Process ControlISSN:1873-2771Page Reference:page 27-45, 19 page(s)Publishing:4/2019Volume:76