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ML Pro: digital assistance system for interactive machine learning in production

Author

Listed:
  • Christian Neunzig

    (Ruhr-Universität Bochum
    Bosch Rexroth AG)

  • Dennis Möllensiep

    (Ruhr-Universität Bochum)

  • Bernd Kuhlenkötter

    (Ruhr-Universität Bochum)

  • Matthias Möller

    (Bosch Rexroth AG)

Abstract

The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by subject matter experts with knowledge of statistical mathematics, coding, and engineering process knowledge. By having humans and computers work together in a digital assistance system, the special characteristics of human and artificial intelligence can be used synergistically. This paper presents the development of a digital human-centered assistance system for employees in the production and development departments of industrial manufacturing companies. This assistance system enables users to apply production-specific data mining and machine learning techniques without programming to typical tabular production data, which is often inherently high-dimensional, nonstationary, and highly imbalanced data streams. Through tight interactive process guidance that considers the dependencies between machine learning process modules, users are empowered to build and optimize predictive models. Compared to existing commercial and academic tools with similar objectives, the digital assistance system offers the added value that both classical shallow and deep learning as well as generative and oversampling methods can be interactively applied to all feature table use cases for different user modes without programming.

Suggested Citation

  • Christian Neunzig & Dennis Möllensiep & Bernd Kuhlenkötter & Matthias Möller, 2024. "ML Pro: digital assistance system for interactive machine learning in production," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3479-3499, October.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02214-0
    DOI: 10.1007/s10845-023-02214-0
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    References listed on IDEAS

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    1. Ke Zhao & Hongkai Jiang & Zhenghong Wu & Tengfei Lu, 2022. "A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 151-165, January.
    2. Md Doulotuzzaman Xames & Fariha Kabir Torsha & Ferdous Sarwar, 2023. "A systematic literature review on recent trends of machine learning applications in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2529-2555, August.
    3. Lee, Keon Myung & Yoo, Jaesoo & Kim, Sang-Wook & Lee, Jee-Hyong & Hong, Jiman, 2019. "Autonomic machine learning platform," International Journal of Information Management, Elsevier, vol. 49(C), pages 491-501.
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