摘要
为了满足增材制造专用镍基高温合金成分优化设计需求,采用机器学习(machine learning, ML)和抗裂因子筛选相结合的综合设计策略开发了新型镍基高温合金。热力学计算结果表明,该合金凝固温度范围窄,在临界温度区间内凝固速度快,且收缩总应变及最大应变速率很小,表现出良好的凝固特性。利用选区激光熔化技术制备了新型合金,在成形试样的纵截面和横截面金相中未发现明显裂纹,合金表现出良好的抗裂性能。通过热处理工艺优化,合金在900℃时效后γ′相分数达到44.6%,组织内未见任何TCP相析出,实现了镍基高温合金在增材制造中抗裂性与力学性能的平衡。提出的综合设计策略可为增材制造领域中新材料的快速研发提供新的思路。
To meet the design demand for composition optimization of Ni-based superalloys for additive manufacturing,a novel Ni-based superalloy was developed by an integrated design strategy combining machine learning(ML)and anti-cracking factor screening.Thermodynamic calculations show that the alloy possesses a narrow solidification temperature range.Meanwhile,the alloy solidifies rapidly in the critical temperature interval with small total shrinkage strain and maximum strain rate,showing good solidification characteristics overall.The novel alloy was prepared by using the selective laser melting technique.No obvious cracks are observed in the longitudinal and cross-sectional metallography of the formed specimens,which shows good anti-cracking properties.Through optimization of the heat treatment process,the γ'phase fraction of the alloy reaches 44.6% after aging at 900℃,without any TCP phase precipitation in the structure.The novel alloy developed in this paper achieves a balance between the crack resistance and mechanical properties of Ni-based superalloys in additive manufacturing.The comprehensive design strategy can provide new ideas for the rapid development of novel materials in the field of additive manufacturing.
作者
熊强
连利仙
胡旺
鲍子铭
高振桓
章语
刘颖
XIONG Qiang;LIAN Lixian;HU Wang;BAO Ziming;GAO Zhenhuan;ZHANG Yu;LIU Ying(College of Materials Science and Engineering,Sichuan University,Chengdu 610065,China;School of Computer Science and Engineering,University of Electronic Science and Technology,Chengdu 611731,China;State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment,Deyang 618000,China)
出处
《铸造技术》
CAS
2023年第8期748-755,共8页
Foundry Technology
基金
国家自然科学基金(61976046)
四川省先进材料重大科技专项(2019ZDZX0022)。
关键词
镍基高温合金
机器学习
成分设计
增材制造
抗裂性能
Ni-based superalloy
machine learning
component design
additive manufacturing
anti-cracking performance