Intelligent optimization of forging forming process of TC18 titanium alloy landing gear based on machine learning
In view of the problems of narrow processing window of TC18 titanlum alloy,and the difficulties in predicting the forming quality and performance of parts and optimizing the design of process parameters,the thermal deformation behaviors and microstructure e-volution laws of TC18 titanium alloy were analyzed by hot compression expenments.The deep neural network(DNN)model for the ther-mal deformation flow stress and microstructure evolution of TC18 titanium alloy was constructed based on Bayesian algorithm optimization.Through the secondary development of UG and Deform,the automatic modeling and simulation of landing gear forging with different process parameters were completed,and the size-process-quality database of pre-forging parts was established.Combined with DNN,ge-netic algorithm(GA),particle swarm optimization(PSO)and fast non-dominated sorting genetic algorithm(NSGA2),the optimal pre-forging process parameters were determined.The results show that the maximum forming force of forging is reduced by 40.6％after NSGA2 optimization,and the content of primary a phase in the target cross-section is 0.207,which is close to the optimal content of 20.0％.
NETL
NSTL
万方数据
维普
