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Real-time prediction of grinding surface roughness based on multi-sensor signal fusion

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Abstract  

In this study, aiming at problems that the existing surface roughness prediction models fail to consider, the time-varying characteristics of the rough grinding process, and the difficulty in feature selection, a real-time surface roughness prediction model based on multi-sensor signal fusion is developed. Firstly, features are extracted from the time domain, frequency domain, and time–frequency domain of force and vibration signal, and then features that do not reflect the time-varying characteristics of the machining process are eliminated. Finally, a multi-sensor signal fusion method is proposed based on the principal component analysis (PCA). Results show that fused features are capable of retaining the physical meaning of original features, and achieving stable and high-precision prediction of surface roughness when they are input into the BP neural network (BPNN). Specifically, the mean absolute percentage error (MAPE) and maximum percentage error (MPE) of a single sample achieved using the fused signal from the first stage are 3.25% and 8.77%, respectively, while those from the second stage are 2.77% and 6.80%, respectively. These values are lower than those obtained from a single force signal (11.2% and 28.4%, 9.87% and 36.64%) or a single vibration signal (13.68% and 36.5%, 8.13% and 28.76%). Additionally, it is found that the surface roughness at different stages of grinding processing is dominated by different factors. For example, during the first stage, the fourth principal component PC4 is the redundant information for the model. However, this feature significantly affects the performance of the model during the second stage. Overall, this paper lays the foundation for understanding mechanisms of time-varying surface roughness in an actual grinding process and realizing accurate monitoring.

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Acknowledgements

The authors appreciate the financial support from the National Natural Science Foundation of China (51875078, 51991372).

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Authors and Affiliations

  1. State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China

    Yuhang Pan, Yonghao Wang, Xubao Liu & Ping Zhou

  2. China Telecom Corporation Limited Research Institute, Beijing, 102209, China

    Yajuan Qiao

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  1. Yuhang Pan
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  2. Yajuan Qiao
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  3. Yonghao Wang
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  4. Xubao Liu
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  5. Ping Zhou
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Contributions

Yuhang Pan: conceptualization, methodology, writing—original draft. Yajuan Qiao: writing (review and editing), resources. Yonghao Wang: validation, experiment. Xubao Liu: Investigation, experiment. Ping Zhou: Project administration, supervision.

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Correspondence to Ping Zhou.

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Pan, Y., Qiao, Y., Wang, Y. et al. Real-time prediction of grinding surface roughness based on multi-sensor signal fusion. Int J Adv Manuf Technol 127, 5847–5861 (2023). https://doi.org/10.1007/s00170-023-11886-0

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  • DOI: https://doi.org/10.1007/s00170-023-11886-0

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