Heat treatment process parameter estimation using heuristic optimization algorithms

Michael Kommenda, Bogdan Burlacu, Reinhard Holecek, Andreas Gebeshuber, Michael Affenzeller

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

4 Citations (Scopus)

Abstract

We present an approach for estimating control parameters of a plasma nitriding process, so that materials with desired product qualities are created. We achieve this by solving the inverse optimization problem of finding the best combination of parameters using a real-vector optimization algorithm, such that multiple regression models evaluated with a concrete parameter combination predict the desired product qualities simultaneously. The results obtained on real-world data of the nitriding process demonstrate the effectiveness of the presented methodology. Out of various regression and optimization algorithms, the combination of symbolic regression for creating prediction models and covariant matrix adaptation evolution strategies for estimating the process parameters works particularly well. We discuss the influence of the concrete regression algorithm used to create the prediction models on the parameter estimations and the advantages, as well as the limitations and pitfalls of the methodology.

Original languageEnglish
Title of host publication27th European Modeling and Simulation Symposium, EMSS 2015
EditorsMichael Affenzeller, Francesco Longo, Lin Zhang, Agostino G. Bruzzone, Yuri Merkuryev, Emilio Jimenez
PublisherDIME UNIVERSITY OF GENOA
Pages222-227
Number of pages6
ISBN (Electronic)9788897999485
Publication statusPublished - 2015
Event27th European Modeling and Simulation Symposium, EMSS 2015 - Bergeggi, Italy
Duration: 21 Sep 201523 Sep 2015

Publication series

Name27th European Modeling and Simulation Symposium, EMSS 2015

Conference

Conference27th European Modeling and Simulation Symposium, EMSS 2015
CountryItaly
CityBergeggi
Period21.09.201523.09.2015

Keywords

  • Genetic programming
  • Heuristic optimization
  • Parameter estimation
  • Symbolic regression

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