摘要
基于修正的Archard磨损模型,对G3镍基合金热挤压成形工艺中挤压模具磨损行为进行了有限元分析。采用BP神经网络建立热挤压模具形状和磨损深度的映射关系。以模具表面磨损深度均匀分布为目标,结合遗传算法(genetic algorithm,GA),提出了一种集三者为一体的G3镍基合金热挤压模具型腔优化设计方法。计算结果表明,模具型腔经过优化后,最大磨损深度值降低约30%,磨损深度沿锥模表面分布更均匀,表明这种优化设计方法可以提高挤压模具的耐磨性能和使用寿命。
The hot extrusion process of G3 nickel-base alloy was modeled and analyzed by the finite-element method（FEM） to obtain the wear depth of nodes on the die surface based on the modified Archard wear theory.BP neutral network was applied to identify the relationship between the die profile and wear depth of the extrusion die.A genetic algorithm（GA） was used to optimize the die profile which yields more uniform wear distribution on die surface.Thus,the FEM,neutral network,and genetic algorithm were combined to develop a method for the design of the optimal shape of a hot extrusion die.The result shows that the maximum wear depth of the extrusion die is decreased by about 30%,and the wear distribution along the die profile surface is more uniform,indicating that the above approach can improve the wear resistance and the life of extrusion die in hot extrusion of G3 alloy.
出处
《稀有金属材料与工程》
SCIE
EI
CAS
CSCD
北大核心
2011年第7期1157-1162,共6页
Rare Metal Materials and Engineering
基金
国家自然科学基金
上海宝钢集团公司重点资助项目(50831008)
关键词
遗传算法
神经网络
热挤压
模具型腔
优化
genetic algorithm
neutral network
hot extrusion
die profile
optimization
