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Modelling and Optimization of Machining with the Use of Statistical Methods and Soft Computing

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Design of Experiments in Production Engineering

Part of the book series: Management and Industrial Engineering ((MINEN))

Abstract

This book chapter pertains to the use of statistical methods and soft computing techniques that can be used in the modelling and optimization of machining processes. More specifically, the factorial design method , Taguchi method , response surface methodology (RSM), analysis of variance , grey relational analysis (GRA) , statistical regression methods , artificial neural networks (ANN) , fuzzy logic and genetic algorithms are thoroughly examined. As part of the design of experiments (DOE) the aforementioned methods and techniques have proven to be very powerful and reliable tools. Especially in machining, a plethora of works have already been published indicating the importance of these methods.

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Authors and Affiliations

  1. Section of Manufacturing Technology, School of Mechanical Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780, Athens, Greece

    Angelos P. Markopoulos, Nikolaos I. Galanis & Nikolaos E. Karkalos

  2. Rzeszów University of Technology, 12 Powstańców Warszawy Str, 35-959, Rzeszów, Poland

    Witold Habrat

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    J. Paulo Davim

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Markopoulos, A.P., Habrat, W., Galanis, N.I., Karkalos, N.E. (2016). Modelling and Optimization of Machining with the Use of Statistical Methods and Soft Computing. In: Davim, J. (eds) Design of Experiments in Production Engineering. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-23838-8_2

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