Using GANs to predict milling stability from limited data

Shahrbanoo Rezaei, Aaron Cornelius, Jaydeep Karandikar, Tony Schmitz and Anahita Khojandi ()
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Shahrbanoo Rezaei: University of Tennessee
Aaron Cornelius: University of Tennessee
Jaydeep Karandikar: Oak Ridge National Lab
Tony Schmitz: University of Tennessee
Anahita Khojandi: University of Tennessee

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 2, No 22, 1235 pages

Abstract: 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.

Keywords: Milling; Chatter; Generative adversarial network; Deep learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02291-1

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