Automated Process Optimization in Manufacturing Systems Based on Static and Dynamic Prediction Models

A key aspect in predictive maintenance is the early recognition of product downtrends and a proper reaction on it, to reduce production waste and to avoid machine failures, components destruction, and risks for operators. We propose an approach for the automated optimization of process parameters in manufacturing systems in order to automatically compensate possible downtrends in product quality at an early stage. This should significantly reduce or even avoid manual (reaction) efforts for operators which are often time-intensive and laborious. Such downtrends are recognized by prediction models for product quality, which are extracted from process data and which come in two different variants: (1) static predictive mappings established based on process (machining) parameter settings through a combination of a new hybrid variant of design of experiment (DoE), cross-correlation analysis, and data …