Material selection and performance analysis based on machine learning in mold structure design and manufacturing
By constructing the evaluation model of mold material performance based on back propagation neural network(back propagation neural network,BPNN),the material selection process in the design and manufacture of new mold structure is optimized,and the accuracy of mold performance analysis is improved.To achieve this goal,we employ advanced neural network algorithms and train and optimize the model using a large amount of training data.During the experiment,key indicators such as training loss,response speed,and accuracy are recorded in detail to comprehensively evaluate the model's performance.The results show that the constructed BPNN model can quickly converge with an accuracy rate of over 95％.It exhibits excellent stability and generalization ability when dealing with different types of mold materials.This model can provide scientific decision support for material selection in mold design and manufacturing,significantly improving mold performance and service life.It also demonstrates the promising application prospects of artificial intelligence technology in the field of materials science.
万方数据
维普
