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A comparison of machine learning methods for cutting parameters prediction in high speed turning process

Author

Listed:
  • Zoran Jurkovic

    (University of Rijeka)

  • Goran Cukor

    (University of Rijeka)

  • Miran Brezocnik

    (University of Maribor)

  • Tomislav Brajkovic

    (University of Rijeka)

Abstract

Support vector machines are arguably one of the most successful methods for data classification, but when using them in regression problems, literature suggests that their performance is no longer state-of-the-art. This paper compares performances of three machine learning methods for the prediction of independent output cutting parameters in a high speed turning process. Observed parameters were the surface roughness (Ra), cutting force $$(F_{c})$$ ( F c ) , and tool lifetime (T). For the modelling, support vector regression (SVR), polynomial (quadratic) regression, and artificial neural network (ANN) were used. In this research, polynomial regression has outperformed SVR and ANN in the case of $$F_{c}$$ F c and Ra prediction, while ANN had the best performance in the case of T, but also the worst performance in the case of $$F_{c}$$ F c and Ra. The study has also shown that in SVR, the polynomial kernel has outperformed linear kernel and RBF kernel. In addition, there was no significant difference in performance between SVR and polynomial regression for prediction of all three output machining parameters.

Suggested Citation

  • Zoran Jurkovic & Goran Cukor & Miran Brezocnik & Tomislav Brajkovic, 2018. "A comparison of machine learning methods for cutting parameters prediction in high speed turning process," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1683-1693, December.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:8:d:10.1007_s10845-016-1206-1
    DOI: 10.1007/s10845-016-1206-1
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    Citations

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    Cited by:

    1. Juncheng Wang & Bin Zou & Mingfang Liu & Yishang Li & Hongjian Ding & Kai Xue, 2021. "Milling force prediction model based on transfer learning and neural network," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 947-956, April.
    2. 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.
    3. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    4. Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
    5. Xianli Liu & Bowen Zhang & Xuebing Li & Shaoyang Liu & Caixu Yue & Steven Y. Liang, 2023. "An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 885-902, February.
    6. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    7. Kristina Zgodavova & Peter Bober & Vidosav Majstorovic & Katarina Monkova & Gilberto Santos & Darina Juhaszova, 2020. "Innovative Methods for Small Mixed Batches Production System Improvement: The Case of a Bakery Machine Manufacturer," Sustainability, MDPI, vol. 12(15), pages 1-20, August.
    8. Muhammad Asif & Hang Shen & Chunlin Zhou & Yuandong Guo & Yibo Yuan & Pu Shao & Lan Xie & Muhammad Shoaib Bhutta, 2023. "Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions," Sustainability, MDPI, vol. 15(10), pages 1-28, May.
    9. Sheng Yang & Thomas Page & Ying Zhang & Yaoyao Fiona Zhao, 2020. "Towards an automated decision support system for the identification of additive manufacturing part candidates," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1917-1933, December.
    10. Lucas Costa Brito & Márcio Bacci Silva & Marcus Antonio Viana Duarte, 2021. "Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 127-140, January.

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