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Semi-supervised roughness prediction with partly unlabeled vibration data streams

Author

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  • Maciej Grzenda

    (Warsaw University of Technology)

  • Andres Bustillo

    (University of Burgos)

Abstract

Experimental data sets that include tool settings, tool and machine-tool behavior, and surface roughness data for milling processes are usually of limited size, due mainly to the high costs of machining tests. This fact restricts the application of machine-learning techniques for surface roughness prediction in industrial settings. The primary objective of this work is to investigate the way data streams that are missing product features (i.e. unlabeled data streams) can contribute to the development of prediction models. The investigation is followed by a proposal for a semi-supervised approach to the development of roughness prediction models that can use partly unlabeled data to improve the accuracy of roughness prediction. Following this strategy, records collected during the milling process, which miss roughness measurements, but contain vibration data are used to increase the accuracy of the prediction models. The method proposed in this work is based on the selective use of such unlabelled instances, collected at tool settings that are not represented in the labeled data. This strategy, when applied properly, yields both extended training data sets and higher accuracy in the roughness prediction models that are derived from them. The scale of accuracy improvement and its statistical significance are shown in the study case of high-torque face milling of F114 steel. The semi-supervised approach proposed in this work has been used in combination with supervised k Nearest Neighbours and random forest techniques. Furthermore, the study of both continuous and discretized roughness prediction, showed higher gains in accuracy in the second.

Suggested Citation

  • Maciej Grzenda & Andres Bustillo, 2019. "Semi-supervised roughness prediction with partly unlabeled vibration data streams," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 933-945, February.
  • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-018-1413-z
    DOI: 10.1007/s10845-018-1413-z
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    References listed on IDEAS

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    1. Nurezayana Zainal & Azlan Mohd Zain & Nor Haizan Mohamed Radzi & Muhamad Razib Othman, 2016. "Glowworm swarm optimization (GSO) for optimization of machining parameters," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 797-804, August.
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    Cited by:

    1. Jiyoung Song & Young Chul Lee & Jeongsu Lee, 2023. "Deep generative model with time series-image encoding for manufacturing fault detection in die casting process," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3001-3014, October.
    2. 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.
    3. Yu Wang & Wei Cui & Nhu Khue Vuong & Zhenghua Chen & Yu Zhou & Min Wu, 2023. "Feature selection and domain adaptation for cross-machine product quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1573-1584, April.
    4. Kai Liao & Wenjun Wang & Xuesong Mei & Wenwen Tian & Hai Yuan & Mingqiong Wang & Bozhe Wang, 2023. "Shape regulation of tapered microchannels in silica glass ablated by femtosecond laser with theoretical modeling and machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2907-2924, October.
    5. Victor Flores & Brian Keith, 2019. "Gradient Boosted Trees Predictive Models for Surface Roughness in High-Speed Milling in the Steel and Aluminum Metalworking Industry," Complexity, Hindawi, vol. 2019, pages 1-15, July.
    6. Andres Bustillo & Roberto Reis & Alisson R. Machado & Danil Yu. Pimenov, 2022. "Improving the accuracy of machine-learning models with data from machine test repetitions," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 203-221, January.
    7. Zhenyu Liu & Donghao Zhang & Weiqiang Jia & Xianke Lin & Hui Liu, 2020. "An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1511-1529, August.
    8. Saksham Jain & Gautam Seth & Arpit Paruthi & Umang Soni & Girish Kumar, 2022. "Synthetic data augmentation for surface defect detection and classification using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1007-1020, April.

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