Modeling and Optimization of Machining Problems

Biermann, D.1, a; Kersting, P.1, b; Wagner, T.1, c; Zabel, A.1, d

Institut für Spanende Fertigung, Technische Universität Dortmund, Baroper Str. 303, 44227 Dortmund

a); b); c); d)


In this chapter, applications of computational intelligence methods in the field of production engineering are presented and discussed. Although a special focus is set to applications in machining, most of the approaches can be easily transferred to respective tasks in other fields of production engineering, e.g., forming and coating. The complete process chain of machining operations is considered: The design of the machine, the tool, and the workpiece, the computation of the tool paths, the model selection and parameter optimization of the empirical or simulation-based surrogate model, the actual optimization of the process parameters, the monitoring of important properties during the process, as well as the posterior multicriteria decision analysis. For all these steps, computational intelligence techniques provide established tools. Evolutionary and genetic algorithms are commonly utilized for the internal optimization tasks. Modeling problems can be solved using artificial neural networks. Fuzzy logic represents an intuitive way to formalize expert knowledge in automated decision systems.


In: Springer Handbook of Computational Intelligence, 2015, ISBN 978-3-662-43504-5, S. 1173-1184, doi: 10.1007/978-3-662-43505-2