基于人工智能数据驱动的冷喷涂涂层制造优化

Cold spray additive manufacturing (CSAM) is an effective technique for applying metallic layers to various surfaces, particularly beneficial for thermosensitive materials, such as polymers and composites. However, optimizing coating outcomes remains challenging due to several complex factors influencing process efficacy. Machine learning (ML) offers a powerful solution to enhance the quality of CSAM by predicting key coating properties, such as particle penetration depth and flattening. This study addresses the problem of accurately predicting key coating characteristics, specifically particle penetration depth and flattening, by integrating finite element model (FEM) with supervised ML techniques. A dataset of 132 FEM simulations was generated, covering multiple metal–polymer combinations and a wide range of impact velocities. The study evaluates and compares several ML algorithms, including support vector regression, decision trees, Gaussian process regression (GPR), and neural networks (NNs), with the goal of minimizing prediction error measured via root-mean-square error (RMSE). Results show that GPR achieves the best performance for particle flattening (RMSE = 3.9), while a bilayered NN provides the most accurate prediction of penetration depth (RMSE = 2.3). The findings highlight the need for distinct models due to the differing physical mechanisms governing each output: penetration depth exhibits a more linear and predictable relationship with impact velocity and material density, whereas flattening is influenced by complex local deformation and interfacial dynamics. This study demonstrates the feasibility and efficiency of using ML to generalize FEM results, reducing computational cost and enabling fast prediction of coating behavior across varying process conditions.