Laser Cladding Surface Roughness Optimisation Driven by SC_ISSA-BP Network
In the process of laser cladding,the process parameters are diversified,resulting in the resultant control showing a nonlinear relationship.By deeply analysing the influence of each parameter on the cladding layer,the optimal process can be obtained quickly to improve the performance of the cladding layer and promote the application of laser cladding technology.In this paper,based on the orthogonal experimental design,the experimental data were analysed in terms of extreme deviation to study the influence of process parameters on the surface roughness of laser melted CoCrFeNiMo0.2 coatings.The experimental samples were analysed microscopically by means of an ultra-depth-of-field scanner to obtain their surface roughness values.The process parameters were optimised according to the algorithm to investigate the effects of laser power,powder feeding speed,scanning rate and lap rate on the surface roughness of the laser melted multi-lap coatings,in order to optimise the best combination of process parameters and obtain the optimal surface roughness at the same time.Comparing the optimised models in terms of the degree of fit,the BP neural network is 94.79％,the SSA-BP neural network is 96.981％,and the SC_ISSABP neural is 98.528％.The Root Mean Square Error RMSE,an error metric for the conventional BP neural network,is 58.385 8 μm,whereas the RMSE for the SSA-BP neural network is 51.297 4 μm,and for the SC_ISSABP neural network is 43.940 8 μm.The optimisation ability of the SC_ISSABP neural network is the most significant.
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