In Laser Powder Bed Fusion technology, if the process parameters are not selected properly, it can lead to the problem of excessive deformation, which affects the quality of formed parts. Therefore, this paper presents an in-depth study on the optimisation and compensation of deformation for parts fabricated by Laser Powder Bed Fusion (LPBF).
The prediction model of BP neural network on the deformation of 316 L stainless steel forming parts is established, and the prediction model is used as the fitness function in the grey wolf algorithm to achieve the optimisation of LPBF forming process parameters. After that, based on the triangular faceted sheet inverse deformation method, a compensation strategy for the deformation amount of LPBF-fabricated parts is proposed.
The results of deformation optimization and compensation were verified through simulation and experimental means. The results show that the effect is quite obvious.
By coupling the three methods of surface correspondence, BP neural network and grey Wolf algorithm, better optimization is achieved for deformation.
