Optimization of superplastic forming parameters of solid cage based on artificial neural network

The BP network predicting models of lead brass among superplastic tension temperature, initial strain rate and elongation, flow stress were established using the Levenberg-Marquardt method. The relationships among superplastic tension temperature, initial strain rate and elongation, flow stress were analyzed also. Then optimal superplastic forming parameters were found. According to the parameters, the test of superplastic forming of the solid cage was performed. Results show that the artificial neural network is an effective way of optimizing the process parameters, the parameters meet well the demands of superplastic forming of the solid cage. And this forming process has obviously economic benefits under the optimal superplastic conditions.