In the high velocity oxygen fuel (HVOF) spray process, coating properties are sensitive to the characteristics of in-flight particles, which are mainly determined by process parameters. Obtaining a comprehensive multi-physical model or analysis of the HVOF process remains challenging because of the complex chemical and thermodynamic reactions that occur during the deposition procedure. This study proposes to develop a robust methodology via the artificial neural networks (ANN) to solve this problem for the HVOF sprayed coatings under different operating parameters. Two ANN models were developed and implemented to predict coating’s performances (microhardness, porosity and wear rate) and to analyze the influence of operating parameters (stand-off distance, oxygen flow rate, and fuel flow rate) while considering the intermediate variables (temperature and velocity of in-flight particles). A detailed procedure for creating and optimizing these two ANN models is presented in this work, which encodes the implicitly physical phenomena governing the HVOF process. Results show that the developed implicit models can satisfy the prediction requirements and clarify the interrelationships between the spraying conditions, behaviors of in-flight particles, and the final coating performances, resulting in providing better control of the HVOF sprayed coatings.

在高速氧气燃料（HVOF）喷涂工艺中，涂层性能对飞行中颗粒的特性敏感，而这些特性主要由工艺参数决定。由于在沉积过程中会发生复杂的化学和热力学反应，因此获得全面的多物理场模型或对HVOF过程进行分析仍然具有挑战性。本研究建议通过人工神经网络（ANN）开发一种可靠的方法，以解决HVOF喷涂涂料在不同工作参数下的这一问题。开发并实施了两个ANN模型，以预测涂层的性能（显微硬度，孔隙率和磨损率），并分析操作参数的影响（间隔距离，氧气流速，和燃料流量），同时考虑中间变量（飞行中粒子的温度和速度）。这项工作介绍了创建和优化这两个ANN模型的详细过程，该过程对控制HVOF过程的隐式物理现象进行了编码。结果表明，所开发的隐式模型能够满足预测要求，并阐明喷涂条件，飞行中粒子的行为与最终涂层性能之间的相互关系，从而为HVOF喷涂涂层提供更好的控制。