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- Title
Analysis of Critical Velocity of Cold Spray Based on Machine Learning Method with Feature Selection.
- Authors
Wang, Ziyu; Cai, Shun; Chen, Wenliang; Ali, Raneen Abd; Jin, Kai
- Abstract
Cold spraying has a potential application prospect in the field of repairing and additive manufacturing. The critical velocity of the cold spray is a key factor that determines the adhesion of particles during the cold spraying process, and it only depends on the particle parameters under the same working conditions. In the present study, the relationship between particle parameters and critical velocity is investigated using a feature selection method to obtain the influence weight of different particle parameters. Based on the results of feature selection, linear and nonlinear artificial neural networks are established to predict the critical velocity, respectively. The results of the feature selection show that the mechanical parameters of the material have a higher influence weight on the critical velocity than thermal parameters. In the prediction model, the ANN (artificial neural network) method shows a good prediction, and the nonlinear ANN model achieves better generalization ability than the linear ANN model and empirical formula with 95.24% prediction accuracy on the original data set and 96.45% prediction accuracy on the new data set.
- Subjects
CRITICAL velocity; METAL spraying; MACHINE learning; FEATURE selection; ARTIFICIAL neural networks; CRITICAL analysis
- Publication
Journal of Thermal Spray Technology, 2021, Vol 30, Issue 5, p1213
- ISSN
1059-9630
- Publication type
Article
- DOI
10.1007/s11666-021-01198-8