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Abstract <p>The article considers prediction of cutting force components based on analysis of acoustic emission (AE) signals using deep learning algorithms. Based on pre-processing and synchronization of experimental data obtained during grinding of a heat-resistant nickel alloy, a training sample based on spectrograms of AE signals is compiled. Using a trained and specially modified ResNet-34 network, a highly accurate (coefficient of determination <i>R</i><sup>2</sup> = 0.903) predictive model is created.</p>

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Application of Deep Learning Algorithms to the Study of the Relationship between Acoustic Emission Signals and Grinding Force Parameters

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • A. P. Mitrofanov,
  • I. A. Rastegaev,
  • A. V. Novikov

摘要

Abstract

The article considers prediction of cutting force components based on analysis of acoustic emission (AE) signals using deep learning algorithms. Based on pre-processing and synchronization of experimental data obtained during grinding of a heat-resistant nickel alloy, a training sample based on spectrograms of AE signals is compiled. Using a trained and specially modified ResNet-34 network, a highly accurate (coefficient of determination R2 = 0.903) predictive model is created.