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Linear vs. Symbolic Regression for Adaptive Parameter Setting in Manufacturing Processes

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Book cover Data Management Technologies and Applications (DATA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 862))

Abstract

Product and process quality is playing an increasingly important role in the competitive success of manufacturing companies. To ensure a high quality level of the produced parts, the appropriate selection of parameters in manufacturing processes plays in important role. Traditional approaches for parameter setting rely on rule-based schemes, expertise and domain knowledge of highly skilled workers or trial and error. Automated and real-time adjustment of critical process parameters, based on the individual properties of a part and its previous production conditions, have the potential to reduce scrap and increase the quality. Different machine learning methods can be applied for generating parameter estimation models based on experimental data. In this paper, we present a comparison of linear and symbolic regression methods for an adaptive parameter setting approach. Based on comprehensive real-world data, collected in a long-term study, multiple models are generated, evaluated and compared with regard to their applicability in the studied approach for parameter setting in manufacturing processes.

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Acknowledgements

The authors gratefully acknowledge financial support with the projects ADAPT and BAPDEC, which are funded by the country of Upper Austria in their program “Innovative Upper Austria 2020” and the project “Smart Factory Lab”, which is funded by the European Fund for regional development (EFRE) and the country of Upper Austria as part of the program “Investing in Growth and Jobs 2014–2020”.

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Authors and Affiliations

  1. University of Applied Sciences Upper Austria, 4400, Steyr, Austria

    Sonja Strasser, Shailesh Tripathi, Lukas Schimpelsberger & Herbert Jodlbauer

  2. University of Applied Sciences Upper Austria, 4232, Hagenberg, Austria

    Jan Zenisek

Authors
  1. Sonja Strasser
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  2. Jan Zenisek
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  3. Shailesh Tripathi
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  4. Lukas Schimpelsberger
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  5. Herbert Jodlbauer
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Corresponding author

Correspondence to Sonja Strasser .

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Editors and Affiliations

  1. RWTH Aachen University, Aachen, Nordrhein-Westfalen, Germany

    Christoph Quix

  2. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

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Strasser, S., Zenisek, J., Tripathi, S., Schimpelsberger, L., Jodlbauer, H. (2019). Linear vs. Symbolic Regression for Adaptive Parameter Setting in Manufacturing Processes. In: Quix, C., Bernardino, J. (eds) Data Management Technologies and Applications. DATA 2018. Communications in Computer and Information Science, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-26636-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-26636-3_3

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-26636-3

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