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- 电子邮箱：masoudinejad@uestc.edu.cn
- 入职时间：2019-07-03
- 所在单位：机械与电气工程学院
- 学历：博士研究生毕业
- 办公地点：Room 313, KeYanLou A, Qingshuihe Campus
- 性别：男
- 联系方式：(+86)19141004306
- 学位：工学博士学位
- 在职信息：博士后出站
- 主要任职：Postdoctoral Research Fellow
- 毕业院校：马什哈德菲尔多西大学
- 曾获荣誉：-International Postdoctoral Exchange Fellowship, Office of China Postdoc Council (OCPC), China, Summer 2019.
-Distinguished researcher selected by University of Electronic Science and Technology of China-Postdoctoral fellowship, Summer 2019.
论文成果
Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters
- 点击次数：
- 所属单位：University of Electronic Science and Technology of China
- 刊物所在地：International Journal of Fatigue
- 关键字：Friction stir welding; Artificial neural network; Fatigue life; Aluminum alloy; Fracture toughness
- 摘要：In this paper, the fracture behavior and fatigue crack growth rate of the 2024-T351 aluminum alloy has been investigated. At first, the 2024-T351 aluminum alloys have been welded using friction stir welding procedure and the fracture toughness and fatigue crack growth rate of the CT specimens have been studied experimentally based on ASTM standards. After that, in order to predict fatigue crack growth rate and fracture toughness, artificial neural network is used. To obtain the best neuron number in the hidden layer of the artificial neural network, different neuron numbers are tested and ...
- 全部作者：,,,,
- 论文类型：Research Paper
- 论文编号：106840
- 学科门类：工学
- 一级学科：机械工程
- 卷号：160
- 页面范围：106840
- ISSN号：0142-1123
- 是否译文：否
- 发表时间：2022-03-05