Optimization of rib-groove filling of titanium alloy rib-web eigenstructure based on RSM-GA and BP-PSO
Taking TA15 titanium alloy rib-web eigenstructure as the research object,firstly,the real-time monitoring method of image perception was used to capture the filling state of the rib-groove at any time,and the material flow and filling rule in the rib-groove were studied by finite element simulation.Secondly,the dual response surface regression model was constructed based on the RSM-GA to opti-mize and design billet size parameters,and GA optimization algorithm was used to carry out intelligent decision-making.At the same time,the BP neural network intelligent prediction model of different billets for rib-grooves filling was established,and the robust optimiza-tion solution was carried out with the intelligent decision of PSO algorithm.Then,by comparing the optimal solutions of the two models,it is concluded that the RSM-GA model has higher precision and better filling effect of the optimal solutions.Finally,the physical simulation experiment validation of the RSM-GA optimization results was carried out using image perception.It is confirmed that the influence of un-certain factor fluctuating in the optimization design of billet while obtaining the optimum rib-groove filling.
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