In this work, an artificial neural network (ANN) model was developed to investigate the application of Cr 3 C 2 -25NiCr coatings by HVOF spraying and predict the resulting properties based on flow rates, stand-off distance, and other parameters. HVOF coatings were sprayed and tests were conducted to generate data for training, validating, and testing the model. The model was trained with an R-value of 0.99965 to predict the relationship between spray parameters and coating properties including hardness, porosity, and wear rate. The reliability and accuracy of the model was subsequently verified using independent test sets.

This paper presents a novel approach for predicting cold spray coating thickness. The coating thickness distribution is a collection of single coating profiles associated with different spray angles and spraying distances. 3D geometric models of these profiles are developed and coupled with robotic trajectories and spraying parameters to simulate coating deposition. Based on the results of the simulation, the robot trajectory, operating parameters, and spray strategy can be adjusted by a feedback loop until the desired coating thickness distribution is achieved. Experimental verification shows that the method has good prediction accuracy and wide application potential.

This paper discusses the challenges of constructing mathematical models of physicochemical and heat-mass transfer processes associated with reactive heterogeneous materials used in laser additive manufacturing. The results of calculations of thermocapillary convection induced by laser heating in an aluminum melt with an admixture of nickel particles are presented. Models of interphase and chemical interactions with the formation of intermediate phases and intermetallic compounds on nickel particles added to the melt during laser alloying or cladding are proposed, which make it possible to calculate the composition of intermetallic phases in the trace of the beam after crystallization and cooling.