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Semantic segmentation of end mill wear area based on transfer learning with small dataset

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Abstract

In the milling process, the wear area of the tool is often segmented using the traditional image processing method to quantify the tool wear value. However, these methods have the disadvantages of having weak anti-noise capabilities and low segmentation accuracy. Although the semantic segmentation network can achieve excellent segmentation accuracy, obtaining enough end mill wear images to support the semantic segmentation network’s training is challenging due to the high acquisition cost of wear images. As a result, this paper suggests a small sample end mill wear area segmentation method based on transfer learning and generative adversarial networks to address the issue of insufficient samples of end mill wear images. In this paper, WGAN is used to generate wear images to expand the dataset with a few samples, and the transfer learning method is used to improve the generalization ability of the segmentation network and finally achieve small sample training. This approach increases mPA by 4.46% and mIOU by 8.97% when compared to merely using the semantic segmentation network for small sample training. According to experimental findings, this method not only has high stability and segmentation accuracy but also solves the problem of insufficient end mill wear image samples. The method proposed in this paper can be effectively applied to the intelligent detection of the tool wear state, improving the accuracy and stability of the measurement of the tool wear value.

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Funding

This research was supported by the National Natural Science Foundation of China (No. U21A20134) and the Key Research and Development Program of Zhejiang Province (No. 2021C01148).

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Authors and Affiliations

  1. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Chang Chen, Chen Lin, Zhen Meng, Jing Ni, Jiteng Sun & Zuji Li

Authors
  1. Chang Chen
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  2. Chen Lin
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  3. Zhen Meng
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  4. Jing Ni
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  5. Jiteng Sun
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  6. Zuji Li
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Contributions

Chang Chen and Chen Lin contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Chen Lin and Zuji Li. The experimental design and implementation were completed by Chen Lin. The first draft of the manuscript was written by Chang Chen and Chen Lin. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jing Ni.

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Chen, C., Lin, C., Meng, Z. et al. Semantic segmentation of end mill wear area based on transfer learning with small dataset. Int J Adv Manuf Technol 127, 3599–3609 (2023). https://doi.org/10.1007/s00170-023-11725-2

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  • DOI: https://doi.org/10.1007/s00170-023-11725-2

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