Optimization of Aluminum Alloy Surface Treatment Process Parameters Based on BP Network

Aiming to the problems such as the complication of aluminum alloy surface treatment process, the dynamic changes of conditions of processing equipment and the complete dependance on the artificial experience of the parameter setting and control optimization was studied. Treatment process control optimization expert system was investigated and developed based on actual production data as the training samples of a neural network model. The aluminum alloy surface treatment process parameters neural network intelligent recognition model was build, in which the input was process parameters and the output was the predicted value of qualified product rate. And then the neural network model replaced the actual production system to optimize the process parameters by orthogonal design within the range of process parameters. The results indicated that the trained neural network model could well map the complex non-linear relationship between the process parameters and the optimization indicators. Combined with orthogonal design, the model can accurately optimize process parameters and forecast qualified product under arbitrary conditions.