Transactions of Materials and Heat Treatment

Title：Mold wear analysis and prediction on fine forging process for titanium alloy blade based on GA-ELM

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ClassificationCode：TG156

year,vol(issue):pagenumber：2020,45(10):130-136

Abstract：

In order to improve the efficiency and accuracy of analysis on mold wear amount in the fine forging process of titanium alloy blade, taking the fine forging process of TC11 titanium alloy blade as the research object, the numerical simulation was carried out by the finite element analysis software Deform-3D. Combined with the modified Archard wear model situation, the data of mold wear sample for blade in the fine forging process was established, and the mold wear amount was predicted by the genetic algorithm-extreme learning machine (GA-ELM) model. Taking the mold wear amount as output parameters, taking the related blade fine forging process parameters as input parameters, the mold wear amount was predicted. Combined with GA-BP neural network model optimized by genetic algorithm and original ELM model, the prediction results were compared. Finally, the mold wear amount analyzed by finite element software Deform verifies the accuracy and reliability of GA-ELM model prediction result. The results show that the mold wear amount predicted by the GA-ELM model has a higher accuracy, and it is superior to other algorithms.

Funds：

贵州省科技支撑计划(黔科合支撑［2019］2019)

梅益（1974-），男，博士，教授 E-mail：me@gzu.edu.cn 通讯作者：罗宁康（1987-）,男，硕士，讲师 E-mail：luoningkang@163.com

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