Dataset augmentation method for milling tool breakage monitoring based on auxiliary classifier generative adversarial networks

Huan Liu; Chao Long; Ziteng Li; Shuhao Kang; Zhichao You; Xi Wang; Duo Li

doi:10.1117/12.3032479

10 October 2024 Dataset augmentation method for milling tool breakage monitoring based on auxiliary classifier generative adversarial networks

Huan Liu, Chao Long, Ziteng Li, Shuhao Kang, Zhichao You, Xi Wang, Duo Li

Author Affiliations +

Proceedings Volume 13278, Seventh Global Intelligent Industry Conference (GIIC 2024); 132780V (2024) https://doi.org/10.1117/12.3032479
Event: Seventh Global Intelligent Industry Conference (GIIC 2024), 2024, Shenzhen, China

Abstract

The objective of this study is to address the issue of data imbalance by augmenting the milling tool breakage dataset using Auxiliary Classifier Generative Adversarial Networks (ACGAN). The research team developed an ACGAN architecture capable of producing samples labeled with various states of tool breakage. To assess the fidelity of the ACGAN-generated data, this study employed evaluation metrics such as the Kullback-Leibler divergence, Euclidean distance, and the Pearson correlation coefficient, comparing the generated samples against actual samples. The findings indicate a high degree of similarity in data distribution between the synthetic and real samples, suggesting the effectiveness of the generated data for training purposes. This research introduces a cost-effective and efficient approach for data augmentation, significantly enhancing the capabilities of milling tool condition monitoring systems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huan Liu, Chao Long, Ziteng Li, Shuhao Kang, Zhichao You, Xi Wang, and Duo Li "Dataset augmentation method for milling tool breakage monitoring based on auxiliary classifier generative adversarial networks", Proc. SPIE 13278, Seventh Global Intelligent Industry Conference (GIIC 2024), 132780V (10 October 2024); https://doi.org/10.1117/12.3032479

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