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A WOA-BP neural network microstructure evolution prediction model of TC11 titanium alloy and application in hollow shaft during cross wedge rolling with mandrel
https://orcid.org/http://orcid.org/0009-0009-0052-289X
The mechanical properties of TC11 titanium alloy parts are closely related to their microstructure, and a study of the microstructure evolution during cross wedge rolling (CWR) can be beneficial for mechanical property regulation. In this paper, a WOA-BP neural network prediction model was developed and utilized to investigate the microstructure evolution of the TC11 titanium alloy hollow shaft during CWR with mandrel. Firstly, the effect of process parameters on the alpha phase grain size of TC11 titanium alloy was investigated through hot compression tests at temperatures ranging from 850 to 950 ℃, and the grain size under different conditions was obtained. Accordingly, a WOA-BP microstructure prediction model was established utilizing the whale optimization algorithm. Then, the microstructure prediction model was embedded into the software Simufact through the secondary development techniques to forecast the alpha phase grain size in the hot compression experiment and the CWR hollow shaft experiment. Finally, the CWR TC11 alloy hollow shaft experiments were conducted. The maximum error between the experiment and the predicted results is 11.07%, which means the model can accurately predict the alpha phase grain size of TC11 alloy. The results indicated that the established model can effectively predict the alpha phase grain size during CWR. Additionally, the model was used to predict the alpha phase grain size of TC11 alloy hollow shaft at different stages of CWR with mandrel.
A WOA-BP neural network microstructure evolution prediction model of TC11 titanium alloy and application in hollow shaft during cross wedge rolling with mandrel
https://orcid.org/http://orcid.org/0009-0009-0052-289X
The mechanical properties of TC11 titanium alloy parts are closely related to their microstructure, and a study of the microstructure evolution during cross wedge rolling (CWR) can be beneficial for mechanical property regulation. In this paper, a WOA-BP neural network prediction model was developed and utilized to investigate the microstructure evolution of the TC11 titanium alloy hollow shaft during CWR with mandrel. Firstly, the effect of process parameters on the alpha phase grain size of TC11 titanium alloy was investigated through hot compression tests at temperatures ranging from 850 to 950 ℃, and the grain size under different conditions was obtained. Accordingly, a WOA-BP microstructure prediction model was established utilizing the whale optimization algorithm. Then, the microstructure prediction model was embedded into the software Simufact through the secondary development techniques to forecast the alpha phase grain size in the hot compression experiment and the CWR hollow shaft experiment. Finally, the CWR TC11 alloy hollow shaft experiments were conducted. The maximum error between the experiment and the predicted results is 11.07%, which means the model can accurately predict the alpha phase grain size of TC11 alloy. The results indicated that the established model can effectively predict the alpha phase grain size during CWR. Additionally, the model was used to predict the alpha phase grain size of TC11 alloy hollow shaft at different stages of CWR with mandrel.
A WOA-BP neural network microstructure evolution prediction model of TC11 titanium alloy and application in hollow shaft during cross wedge rolling with mandrel
https://orcid.org/http://orcid.org/0009-0009-0052-289X
The mechanical properties of TC11 titanium alloy parts are closely related to their microstructure, and a study of the microstructure evolution during cross wedge rolling (CWR) can be beneficial for mechanical property regulation. In this paper, a WOA-BP neural network prediction model was developed and utilized to investigate the microstructure evolution of the TC11 titanium alloy hollow shaft during CWR with mandrel. Firstly, the effect of process parameters on the alpha phase grain size of TC11 titanium alloy was investigated through hot compression tests at temperatures ranging from 850 to 950 ℃, and the grain size under different conditions was obtained. Accordingly, a WOA-BP microstructure prediction model was established utilizing the whale optimization algorithm. Then, the microstructure prediction model was embedded into the software Simufact through the secondary development techniques to forecast the alpha phase grain size in the hot compression experiment and the CWR hollow shaft experiment. Finally, the CWR TC11 alloy hollow shaft experiments were conducted. The maximum error between the experiment and the predicted results is 11.07%, which means the model can accurately predict the alpha phase grain size of TC11 alloy. The results indicated that the established model can effectively predict the alpha phase grain size during CWR. Additionally, the model was used to predict the alpha phase grain size of TC11 alloy hollow shaft at different stages of CWR with mandrel.
A WOA-BP neural network microstructure evolution prediction model of TC11 titanium alloy and application in hollow shaft during cross wedge rolling with mandrel
Archiv.Civ.Mech.Eng
Yin, Jian (author) / Yang, Cuiping (author)
2024-03-27
Article (Journal)
Electronic Resource
English
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