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Modeling and Optimization of Machining Problems

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Springer Handbook of Computational Intelligence

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Abstract

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.

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Abbreviations

2-D:

two-dimensional

CAD:

computer-aided design

CAM:

computer-assisted manufacturing

CI:

computational intelligence

CMA:

covariance matrix adaptation

EA:

evolutionary algorithm

EC:

evolutionary computation

ES:

evolution strategy

GA:

genetic algorithm

NC:

numerical control

NN:

neural network

NSGA:

nondominated sorting genetic algorithm

NURBS:

nonuniform rational B-spline

PSO:

particle swarm optimization

SOM:

self-organizing map

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Authors and Affiliations

  1. Dep. Mechanical Engineering, TU Dortmund University, Baroper Str. 303, 44227, Dortmund, Germany

    Dirk Biermann, Petra Kersting, Tobias Wagner & Andreas Zabel

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  1. Dirk Biermann
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  2. Petra Kersting
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  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Biermann, D., Kersting, P., Wagner, T., Zabel, A. (2015). Modeling and Optimization of Machining Problems. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_59

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