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Image-based tool condition monitoring based on convolution neural network in turning process

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Abstract

Tool wear has a significant impact on machining quality, efficiency, and cost, so it is vitally important for manufacturing systems. The current work of Tool Condition Monitoring (TCM) mainly processes the time series signals from multisensory using intelligent algorithms. However, the limits of these methods are as follows: (1) the image information is not integrated into the time series signals, and (2) the traditional methods face the problems of poor generalization and fast convergence. Thus, a novel integrated model based on the multisensory feature fusion and neural network is presented. The sensor data is first pre-processed using Piecewise Aggregate Approximation (PAA) and then recoded into images using Gramian Angular Field (GAF). The images, together with the tool infrared images, are inputs to the Convolutional Neural Network (CNN) model, which realizes the output of flank wear value. Both time series signals and tool infrared images are used to achieve the classification, and the final classification accuracy in the test set is 91%. The results show the high computation efficiency and the good generalization performance of the presented methodology.

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Funding

This work was supported by National Key R&D Program of China (2018YFB1700902).

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Authors and Affiliations

  1. School of Mechanical Engineering, Tongji University, Shanghai, 201804, China

    Rui Kou & Xue-mei Liu

  2. Sino-German College of Applied Science, Tongji University, Shanghai, 201804, China

    Shi-wei Lian, Nan Xie & Bei-er Lu

Authors
  1. Rui Kou
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  2. Shi-wei Lian
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  3. Nan Xie
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  4. Bei-er Lu
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  5. Xue-mei Liu
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Contributions

Kou Rui: experiments and writing—algorithm and original draft preparation. Lian Shi-wei: data processing and writing—reviewing. Xie Nan: scheme development and overall experimental design. Lu Bei-er: experimental platform construction. Liu Xue-mei: validation.

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Correspondence to Nan Xie.

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Kou, R., Lian, Sw., Xie, N. et al. Image-based tool condition monitoring based on convolution neural network in turning process. Int J Adv Manuf Technol 119, 3279–3291 (2022). https://doi.org/10.1007/s00170-021-08282-x

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  • DOI: https://doi.org/10.1007/s00170-021-08282-x

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