Closed loop image aided optimization for cold spray process based on molecular dynamics

Abstract

This study proposed a closed loop image aided optimization (CLIAO) method to improve the quality of deposition during the cold spray process. Some recent research shows that the quality of deposition measured by flattening ratio of the bonded particle is associated with impact velocity, angle and particle size. Therefore, the original idea of CLIAO is to improve the quality of deposition by obtaining the maximum flattening ratio which is extracted from the molecular dynamics (MD) simulation snapshots directly. To complete this strategy, a Python script is suggested to generate the required snapshots from result files automatically and the image processing technique is used to evaluate the flattening ratio from the snapshots. Moreover, three optimization methods including surrogate optimization (Efficient Global Optimization) and heuristic algorithms (Particle Swarm Optimization, Different Evolution algorithm) are engaged. Then a back propagation neural network (BPNN) is used to accelerate the process of optimization, where the BPNN is used to build the meta-model instead of the forward calculation. The optimization result demonstrates that all the above methods can obtain the acceptable solution. The comparison between those methods is also given and the selection of them should be determined by the trade-off between efficiency and accuracy.