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 datadriven mapping construction; and (2) dynamic forecast models which respect the time-series trends of process values measured during online production, being able to properly recognize undesired changes and dynamics happening (unexpectedly) during the process. These models will have the property to be able to self-adapt and evolve over time based on new recordings; they employ generalized (flexible) evolving fuzzy systems (GEFS) combined with a new incremental update of the latent variable space obtained through partial least squares (PLS). Both types of prediction models can then be used as surrogate mappings within a multiobjective, evolutionary optimization process for important target quality criteria, which relies on a fast co-evolution strategy. Several results from a micro-fluidic chip production process will be included to demonstrate the applicability and performance of the proposed methods and to discuss open challenges.