Autonomous Supervision and Optimization of Product Quality in a Multi-Stage Manufacturing Process based on Self-Adaptive Prediction Models
Sprache des Titels:
Englisch
Original Kurzfassung:
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.