Prediction of surface morphology in laser processing of stainless steel based on BP neural network
Abstract In order to process surfaces with different wettability, it is necessary to control the laser processing parameters to change the size and morphology of the texture. In response to the difficulty in predicting the size of surface texture in laser processing, this study established a groove texture depth and width prediction model based on the BP neural network. The minimum average error of this model can be as low as 3.27%, which can accurately predict the depth and width of the eroded groove.
Authors
- Xinjie Gu (ORCID: https://orcid.org/0000-0003-0838-462X)
- Changfeng Xu (ORCID: https://orcid.org/0009-0002-7259-8091)
- Fuping Tang
- Mao Shu (ORCID: https://orcid.org/0000-0001-7563-6116)
- Jianping Zhou (ORCID: https://orcid.org/0000-0002-2141-681X)
- Lusheng Li (ORCID: https://orcid.org/0000-0002-7783-9635)
- Z. C. Ling
- Chunyang Pan
Institutions
- University of Nottingham Ningbo China (CN)
- Ningbo University of Technology (CN)
- China University of Mining and Technology (CN)
- Ningbo City College of Vocational Technology (CN)
- Jiangsu University of Science and Technology (CN)
- Weichai Power (China) (CN)
- Ningbo College of Health Sciences (CN)
Publication Details
- Journal
- Journal of Physics Conference Series
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1088/1742-6596/3068/1/012093
- Primary Topic
- Surface Roughness and Optical Measurements
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China