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.

冷喷涂在维修和增材制造领域具有潜在的应用前景。冷喷涂的临界速度是决定冷喷涂过程中颗粒附着力的关键因素，并且仅取决于相同工作条件下的颗粒参数。在本研究中，使用特征选择方法研究粒子参数与临界速度之间的关系，以获得不同粒子参数的影响权重。基于特征选择的结果，分别建立了线性和非线性人工神经网络来预测临界速度。特征选择的结果表明，材料的机械参数对临界速度的影响权重大于热参数。