Planning and Multi-Objective Optimization of Manufacturing Processes by Means of Empirical Surrogate Models

This dissertation presents a methodology for planning of production processes. This methodology is based on kriging models and two approaches to the optimization of process chains. Extensions of the models for an application on noisy data are presented and assessed the improvements achieved by means of simulation studies. In addition, new criteria for a sequential design of experiments to the approach of the Paretofront, i.e. the set of optimal solutions of compromise with regard to the component properties and process parameters, are presented and compared with theoretical requirements and experimental results. These criteria allow it to benefit from the possibility of a local model refinement.