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Using GANs to predict milling stability from limited data

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Abstract

Milling is a key manufacturing process that requires the selection of operating parameters that provide efficient performance. However, the presence of chatter, a self-excited vibration causing poor surface finish and potential damage to the machine and cutting tool, makes it challenging to select the appropriate parameters. To predict chatter, stability maps are commonly used, but their generation requires expensive data, making it difficult to employ these maps in industry. Therefore, there is a pressing need for an approach that can accurately predict stability maps using limited experimental data. This study introduces the new Encoder GAN (EGAN) approach based on Generative Adversarial Networks (GANs) that predicts stability maps using limited experimental data. The approach consists of the encoder, generator, and discriminator subnetworks and uses the trained encoder and generator to predict the target stability map. This versatile method can be applied to various tool setups and can accurately predict stability maps with limited experimental data (five to 10 cutting tests) even when there is little information available for unknown parameters. The study evaluates the proposed approach using both numerical data and experiments and demonstrates its superior performance compared to state-of-the-art benchmarks.

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Data availability

The data that support the findings of this study are available at https://github.com/srezaei90/GANs-to-predict-stability-maps-in-milling-machining.git.

Notes

  1. The code is available at https://github.com/srezaei90/GANs-to-predict-stability-maps-in-milling-machining.git.

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Acknowledgements

The research was partially supported by the Department of Energy (DOE), Advanced Manufacturing Office (AMO), Award Number: DE-EE0009400. The authors would also like to acknowledge support from the NSF Engineering Research Center for Hybrid Autonomous Manufacturing Moving from Evolution to Revolution (ERC-HAMMER) under Award Number EEC-2133630.

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Authors and Affiliations

  1. Industrial & System Engineering, University of Tennessee, Knoxville, TN, USA

    Shahrbanoo Rezaei & Anahita Khojandi

  2. Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA

    Aaron Cornelius & Tony Schmitz

  3. Manufacturing Science Division, Oak Ridge National Lab, Oak Ridge, TN, USA

    Jaydeep Karandikar & Tony Schmitz

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  1. Shahrbanoo Rezaei
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Correspondence to Anahita Khojandi.

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Appendix

Appendix

See Figs. 29, 30 and 31.

Fig. 29
figure 29

Sample test points for numerical experiments: Setup 1

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Fig. 30
figure 30

Sample test points for numerical experiments: Setup 2

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Fig. 31
figure 31

Sample test points for numerical experiments: Setup 3

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Rezaei, S., Cornelius, A., Karandikar, J. et al. Using GANs to predict milling stability from limited data. J Intell Manuf (2024). https://doi.org/10.1007/s10845-023-02291-1

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