- author = "Michael Kommenda and Bogdan Burlacu and Reinhard Holecek and Andreas Gebeshuber and Michael Affenzeller",
- title = "Heat Treatment Process Parameter Estimation using Heuristic Optimization Algorithms",
- booktitle = "Proceedings of the 27th European Modeling and Simulation Symposium EMSS 2015",
- year = "2015",
- pages = "222--228",
- address = "Bergeggi, Italy",
- month = sep,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.msc-les.org/proceedings/emss/2015/EMSS2015_222.pdf",
- size = "6 pages",
-
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.",
