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Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric

Author

Listed:
  • Alexander Gerling

    (Furtwangen University of Applied Science
    Université de Haute-Alsace
    Université de Strasbourg)

  • Holger Ziekow

    (Furtwangen University of Applied Science)

  • Andreas Hess

    (Furtwangen University of Applied Science)

  • Ulf Schreier

    (Furtwangen University of Applied Science)

  • Christian Seiffer

    (Furtwangen University of Applied Science)

  • Djaffar Ould Abdeslam

    (Université de Haute-Alsace
    Université de Strasbourg)

Abstract

In order to manufacture products at low cost, machine learning (ML) is increasingly used in production, especially in high wage countries. Therefore, we introduce our PREFERML AutoML system, which is adapted to the production environment. The system is designed to predict production errors and to help identifying the root cause. It is particularly important to produce results for further investigations that can also be used by quality engineers. Quality engineers are not data science experts and are usually overwhelmed with the settings of an algorithm. Because of this, our system takes over this task and delivers a fully optimized ML model as a result. In this paper, we give a brief overview of what results can be achieved with a state-of-the-art classifier. Moreover, we present the results with optimized tree-based algorithms based on RandomSearchCV and HyperOpt hyperparameter tuning. The algorithms are optimized based on multiple metrics, which we will introduce in the following sections. Based on a cost-oriented metric we can show an improvement for companies to predict the outcome of later product tests. Further, we compare the results from the mentioned optimization approaches and evaluate the needed time for them.

Suggested Citation

  • Alexander Gerling & Holger Ziekow & Andreas Hess & Ulf Schreier & Christian Seiffer & Djaffar Ould Abdeslam, 2022. "Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 555-573, February.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:2:d:10.1007_s10845-021-01890-0
    DOI: 10.1007/s10845-021-01890-0
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    References listed on IDEAS

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