Cold spray is a newly developed solid-state metal deposition technology, which allows for preparing various functional coatings and repairing damaged metal components, as well as fabricating freestanding parts. In order to obtain the deposits with the desired shape and accuracy, the coating profile, including its thickness and distribution, is an important indicator to monitor and control. In this work, an artificial neural network (ANN) model has been employed to describe and predict the multi-layer profile of cold-sprayed deposits. Compared to conventional feature-based modeling methods, the ANN model is capable of simulating a complete track profile on defined substrate morphologies. The superiority of the ANN approach is further emphasized by its ability to simulate a multi-layer profile, which differs from previous works that focus on single-layer profiles. It is essential for guiding the coating formation and fabrication of near-net-shape parts. The results imply that the ANN model is well trained and capable of predicting multi-layer profiles with acceptable accuracy. It can be used for profile control during cold spray additive manufacturing.

冷喷涂是一种新开发的固态金属沉积技术，可用于制备各种功能涂层和修复损坏的金属部件，以及制造独立零件。为了获得具有所需形状和精度的沉积物，涂层轮廓，包括其厚度和分布，是需要监测和控制的重要指标。在这项工作中，人工神经网络 (ANN) 模型已被用于描述和预测冷喷涂沉积物的多层剖面。与传统的基于特征的建模方法相比，ANN 模型能够在定义的基底形态上模拟完整的轨迹轮廓。ANN 方法的优越性通过其模拟多层剖面的能力得到进一步强调，这与以前专注于单层配置文件的工作不同。它对于指导近净成形零件的涂层形成和制造至关重要。结果表明 ANN 模型训练有素，能够以可接受的精度预测多层轮廓。它可用于冷喷涂增材制造过程中的剖面控制。