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Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

Author

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  • Christian Kubik

    (Technische Universität Darmstadt)

  • Sebastian Michael Knauer

    (Technische Universität Darmstadt)

  • Peter Groche

    (Technische Universität Darmstadt)

Abstract

In consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose, it is necessary to be able to make a real-time prediction about the current and future process conditions even at high production rates. Therefore, this study investigates the influence of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine to classify abrasive wear states during blanking based on force signals. The performance of the model was quantitatively evaluated based on the model accuracy and the separability of the classes. As a result, it was shown, that the deviation of time series represents the key parameter for the resulting performance of the classification model and strongly depends on the sensor type and position, the preprocessing procedure as well as the feature extraction and selection. Furthermore, it is shown that the consideration of domain knowledge in the phases of data acquisition, preprocessing and transformation improves the performance of the classification model and is essential to successfully implement AI projects. Summarizing the findings of this study, trustworthy data sets play a crucial role for implementing an automated process monitoring as a basis for resilient manufacturing systems.

Suggested Citation

  • Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:1:d:10.1007_s10845-021-01789-w
    DOI: 10.1007/s10845-021-01789-w
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    References listed on IDEAS

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    1. Andres Bustillo & Danil Yu. Pimenov & Mozammel Mia & Wojciech Kapłonek, 2021. "Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 895-912, March.
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    5. Deepam Goyal & Anurag Choudhary & B. S. Pabla & S. S. Dhami, 2020. "Support vector machines based non-contact fault diagnosis system for bearings," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1275-1289, June.
    6. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
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    8. Shujie Liu & Yawei Hu & Chao Li & Huitian Lu & Hongchao Zhang, 2017. "Machinery condition prediction based on wavelet and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 1045-1055, April.
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    Cited by:

    1. Philipp Niemietz & Mia J. K. Kornely & Daniel Trauth & Thomas Bergs, 2022. "Relating wear stages in sheet metal forming based on short- and long-term force signal variations," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2143-2155, October.
    2. Zaher Salah & Esraa Abu Elsoud, 2023. "Enhancing Network Security: A Machine Learning-Based Approach for Detecting and Mitigating Krack and Kr00k Attacks in IEEE 802.11," Future Internet, MDPI, vol. 15(8), pages 1-21, August.

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