摘要
GH4175合金是新型难变形高温合金的典型代表,通过成分优化提升其高温单相区变形能力,是避免该合金铸锭开坯过程中开裂的重要前提。综合利用相图热力学计算、高温拉伸实验及机器学习方法,通过成分设计空间逐层筛选优化以及自适应学习策略,获得了影响该合金800℃γ′相体积分数、γ′相完全溶解温度和合金初始液化温度的关键元素,建立了上述元素含量与高温伸长率之间的关系模型,明确了在保证一定热加工温度窗口和800℃γ′相体积分数的前提下,同时具备优异高温塑性的合金成分范围及微观组织特征。
The GH4175 alloy is a typical new difficult-to-deform superalloy.Optimizing its composition to enhance its deformation capability in the high-temperature single-phase region is a crucial prerequisite for preventing cracking during the cogging process of this alloy.By combining thermodynamic calculations,high-temperature tensile experiments,and machine learning methods,the composition of GH4175 was optimized.The key compositional elements that influence theγ′phase volume fraction at 800℃,theγ′phase dissolution temperature,and the alloy melting temperature are identified via design space screening and adaptive learning strategies.A relationship model between the content of these elements and high-temperature elongation is established,clarifying the compositional range that ensures excellent high-temperature ductility while maintaining a reasonable processing window andγ′phase volume fraction at 800℃.
作者
刘宜瑞
刘有云
赵佳军
虎小兵
陈一鸣
赵张龙
李俊杰
王志军
王锦程
LIU Yirui;LIU Youyun;ZHAO Jiajun;HU Xiaobing;CHEN Yiming;ZHAO Zhanglong;LI Junjie;WANG Zhijun;WANG Jincheng(State Key Laboratory of Solidification Processing,Northwestern Polytechnical University,Xi'an 710072,China;93147 Troops of the Chinese People's Liberation Army)
出处
《铸造技术》
CAS
2024年第11期1049-1060,共12页
Foundry Technology
基金
国家重点研发计划(2023YFB4606502)。
关键词
GH4175
机器学习
高温塑性
成分优化
GH4175
machine learning
high-temperature ductility
component optimization
