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NJUST-CCTD: An Image Database for Milling Tool Wear Classification with Deep Learning

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Abstract

Deep learning has gained popularity in the task of tool wear identification recently. As an important application of deep learning, however, there exists few public datasets and benchmarks for the research of visual identification of tool wear. To address this issue, we present a classification-based image dataset for carbide milling tool wear (NJUST-CCTD) and make it publicly available on the Github website. This dataset includes two categories: wear tools and no-wear tools. The two categories contain 5000 and 3000 photos, respectively. Based on this dataset, eight baselines are evaluated as references against this benchmark. To further improve the classification performance, we propose a novel cemented carbide milling tool wear intelligent classification framework (CMCNet). The framework consists of two modules: a deep learning based classification network and a multi-scale feature fusion based denoising network called DSSNet. DSSNet is constructed with deeper network structure, connections across layers, and multi-scale sequence fusion module. It is capable of explicitly modeling the semantic and spatial correlation. Apart from DSSNet, the denoising module further improves the performance by adaptively altering the level of denoising based on the performance of the network. The two modules could be optimized with the backward gradient, yielding an end-to-end learning framework. On the basis of the dataset, CMCNet performed exceptionally well when categorizing photos intelligently. After 50 training epochs, the model outperformed the original classification network by 3.250%, achieving the top-1 accuracy of 95.375% on the test set. The NJUST-CCTD can be downloaded at https://github.com/paddy112233/PADDY.

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Availability of data and materials

The NJUST-CCTD is available from the Github website The parameters of the milling tools and acquisition systems are listed in Tables and figures.

Code availability

The algorithm (code) designed for the framework is clearly expressed in the flow chart.

Notes

  1. MMClassification is an open source classification toolbox based on PyTorch.

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Acknowledgements

The authors would also like to thank professor Shiming Xiang, Dr. Xin Zhang and Dr. Xinbang Zhang for their diligent advice in terms of essay writing, as well as General Manager Guoqiang Guo for the wear milling tools from Shanghai Spaceflight Precision Machinery Institute.

Funding

This study was supported by the National Key Research and Development Program of China (2021YFB2012104), National Natural Science Foundation of China (52075267, 52005332).

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Authors and Affiliations

  1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Yi Pan, Guoda Xu, Zhe Xiong, Bowen Hu, Yuxin Sun & Yulin Wang

  2. Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, Beijing, China

    Yi Pan & Chunhong Pan

  3. Beijing Research Institute of Mechanical & Electrical Technology Ltd., Beijing, 100083, Beijing, China

    Fengjiao Li

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  1. Yi Pan
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Contributions

Yi Pan, Guoda Xu, Zhe Xiong, Bowen Hu and Yulin Wang have generated the new research idea, designed methodology, done investigations, and conducted experiments; Yi Pan, Guoda Xu and Bowen Hu have established the dataset; Yi Pan, Yuxin Sun, Fengjiao Li and Chunhong Pan have designed algorithms and network frameworks. All authors have actively participated in the revision and approved the manuscript.

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Correspondence to Yulin Wang.

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Pan, Y., Xu, G., Xiong, Z. et al. NJUST-CCTD: An Image Database for Milling Tool Wear Classification with Deep Learning. Int J Adv Manuf Technol 127, 3681–3698 (2023). https://doi.org/10.1007/s00170-023-11418-w

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