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In-process material removal rate monitoring for abrasive belt grinding using multisensor fusion and 2D CNN algorithm

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Abstract

In the abrasive belt grinding process, actual material removal is an important parameter that affects its accuracy. At present, for obtaining the actual material removal, offline measurements are required to establish the mathematical prediction model. To improve the accuracy and efficiency of abrasive belt machine grinding, this paper proposes a novel method for monitoring material removal using multiple sensors and a two-dimensional (2D) convolutional neural network (2D-CNN) learning algorithm. In this method, features of multiple types (color, texture, and shape) are extracted from vision signals, and that of multiple domains (time, frequency, and time–frequency domain) are extracted from sound and tactile signals. These features are constructed into a 2D feature matrix as the input model, and the 2D-CNN prediction model is established between the multisensor features and the material removal rate of the abrasive belt grinding process. An experimental dataset is used to train and verify the established model. The results show that the proposed method can identify that sensor signals are sensitive to the material removal rate. After optimizing and tuning the model parameters, the coefficient of determination of the prediction results is as high as 94.5% and the root mean square error is 0.017. Therefore, the proposed method can be employed for the prediction of material removal rate for different belt specifications and different grinding parameters. Compared to traditional machine learning methods, this method can yield better training results without feature selection and optimization.

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Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the Shaanxi Province key projects (grant number 2017ZDXM-GY-133).

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Authors and Affiliations

  1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China

    Nina Wang, Guangpeng Zhang, Lijuan Ren, Yongchang Li & Zhijian Yang

Authors
  1. Nina Wang
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  2. Guangpeng Zhang
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  3. Lijuan Ren
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  4. Yongchang Li
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  5. Zhijian Yang
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Contributions

Nina Wang performed the analysis and summary of the experimental data, and was a major contributor in writing the manuscript. Lijuan Ren, Yongchang Li and Zhijian Yang participate in carrying out grinding experiments. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Guangpeng Zhang.

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All data in this paper comes from machining grinding experiments and does not involve ethical issues.

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The authors declare no competing interests.

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Wang, N., Zhang, G., Ren, L. et al. In-process material removal rate monitoring for abrasive belt grinding using multisensor fusion and 2D CNN algorithm. Int J Adv Manuf Technol 120, 599–613 (2022). https://doi.org/10.1007/s00170-022-08768-2

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  • DOI: https://doi.org/10.1007/s00170-022-08768-2

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