  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.
