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Research on the Model of Scale Removal under Slurry Impact Based on BP Neural Network

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Abstract

The cracking behavior of oxides on 304 stainless steel plate surfaces under slurry impact has been investigated for the problem of the lack of an accurate model for the removal of oxide scale in slurry impact descaling. A backpropagation (BP) neural network model was established using experimental data of oxide removal by shotcrete. The results show that: (1) the ratio of the impact of the spray pressure and spray distance on the removal of oxide scale on the substrate surface is 3.186:2.726; (2) the maximum deviation rate between the predicted values of injection pressure and injection distance and the sample values is 8.53%; and (3) the descaling process parameters provided by the BP neural network model were used in the experiment, and the maximum deviation rate between the scale removal amount and the target value was 7.14%. This study provides guidance for the promotion and application of impact descaling technology.

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Acknowledgements

Funds: Central guidance for local scientific and technological development funds (Z135050009017); Key R&D Plan of Shanxi Province (201903D421046); Shanxi Province Graduate Education Innovation Project (BY2022010); Shanxi Province Higher Education Science and Technology Innovation Plan Project (2022L308)

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Authors and Affiliations

  1. Shanxi Provincial Key Laboratory of Metallurgical Equipment Design and Technology, College of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Ze-lin Chai, Cun-long Zhou, Rui Guo, Hao Qi & Chunyang Li

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  1. Ze-lin Chai
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  2. Cun-long Zhou
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  3. Rui Guo
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  4. Hao Qi
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All the authors contributed equally to this work.

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Correspondence to Cun-long Zhou.

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Chai, Zl., Zhou, Cl., Guo, R. et al. Research on the Model of Scale Removal under Slurry Impact Based on BP Neural Network. JOM 76, 445–451 (2024). https://doi.org/10.1007/s11837-023-06206-6

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  • DOI: https://doi.org/10.1007/s11837-023-06206-6

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