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Machine learning-based instantaneous cutting force model for end milling operation

Author

Listed:
  • Shubham Vaishnav

    (Indian Institute of Technology Jodhpur)

  • Ankit Agarwal

    (Indian Institute of Technology Jodhpur)

  • K. A. Desai

    (Indian Institute of Technology Jodhpur)

Abstract

Cutting force is the fundamental parameter determining the productivity and quality of the milling operation. The development of a generic cutting force model for end milling operation necessitates a large number of experiments. The experimental data contains multiple outliers due to noise and process disturbances lowering prediction accuracy of the model. This paper presents a novel approach combining the mechanistic model and the supervised neural network (NN) model to predict instantaneous cutting force variation during the end milling operation. The approach proposes training of an NN model using datasets generated from the mechanistic force model instead of using experimental data. The methodology generates a large number of datasets for the training of an NN model without conducting rigorous experimentation. A set of NN architectures were developed, and an appropriate network was derived by comparing performance parameters. A series of end milling experiments were conducted to examine the efficacy of the proposed approach in predicting cutting forces over a wide range of cutting conditions.

Suggested Citation

  • Shubham Vaishnav & Ankit Agarwal & K. A. Desai, 2020. "Machine learning-based instantaneous cutting force model for end milling operation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1353-1366, August.
  • Handle: RePEc:spr:joinma:v:31:y:2020:i:6:d:10.1007_s10845-019-01514-8
    DOI: 10.1007/s10845-019-01514-8
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    References listed on IDEAS

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    1. Unknown, 2004. "End Materials," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 19(4), pages 1-1.
    2. PoTsang B. Huang, 2016. "An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 689-700, June.
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    Cited by:

    1. Aniket Nagargoje & Pavan Kumar Kankar & Prashant Kumar Jain & Puneet Tandon, 2023. "Application of artificial intelligence techniques in incremental forming: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 985-1002, March.

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