A Crystal Plasticity Finite Element—Machine Learning Combined Approach for Phase Transformation Prediction in High Entropy Alloy

材料科学 电子背散射衍射 有限元法 微观结构 晶体孪晶 合金 延展性(地球科学) 转化(遗传学) 相(物质) 高熵合金 晶体塑性 实验数据 机器学习 人工智能 计算机科学 结构工程 复合材料 数学 工程类 生物化学 蠕动 化学 统计 有机化学 基因
作者
Mehrzad Soltani,Sanjida Ferdousi,Ravi Sankar Haridas,Rajiv S. Mishra,Yijie Jiang
出处
期刊:International Journal of Applied Mechanics [World Scientific]
卷期号:16 (02) 被引量:5
标识
DOI:10.1142/s1758825124500248
摘要

The mechanical properties of an alloy depend on its microstructure. The strength-ductility trade-off is a paradigm that existed for a long time. Advanced alloys, such as high entropy alloys (HEAs), utilize a dual-phase strengthening mechanism, which originates from the microstructural phenomena consisting of twinning and phase transformation, to significantly improve their mechanical properties. To understand the impact of phase transformation mechanism on stress–strain response, developments of crystal plasticity finite element models (CPFEM) and machine learning (ML) together with experimental methods have potential to capture the relationships between descriptive features and targeted phenomena. Here, ML models on local crystallography, local stresses, and energy-based driving forces are leveraged for phase transformation prediction in a HEA. The ML model (XGBoost classification model) uses a hybrid training data combining electron backscatter diffraction (EBSD) experimental data and CPFEM simulation results. This approach enhances prediction performance at optimum data sizes. This predictive model is implemented in multiple experimental measurements to validate our models and evaluates importance of different physical quantities on phase transformation phenomenon. The prediction accuracy reached over 95% compared to experimental data. The CPFEM-ML framework used in this study is expected to be applicable to other HEA systems to facilitate the understanding and prediction of the phase transformation.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
卡萨卡萨完成签到,获得积分10
3秒前
青松果完成签到,获得积分10
5秒前
yousen完成签到,获得积分20
5秒前
Sid应助Sunwenrui采纳,获得60
5秒前
赘婿应助lw777采纳,获得10
7秒前
8秒前
小蘑菇应助张大英采纳,获得10
8秒前
华仔应助2889580752采纳,获得10
10秒前
嘻嘻完成签到,获得积分10
10秒前
11秒前
丘比特应助科研通管家采纳,获得10
11秒前
领导范儿应助科研通管家采纳,获得10
11秒前
慕青应助科研通管家采纳,获得10
11秒前
11秒前
斯文败类应助科研通管家采纳,获得10
11秒前
11秒前
思源应助科研通管家采纳,获得10
11秒前
田様应助科研通管家采纳,获得10
11秒前
Volcano完成签到,获得积分10
11秒前
无花果应助俏皮的白柏采纳,获得10
12秒前
单薄的夜南应助宁学者采纳,获得10
12秒前
13秒前
13秒前
13秒前
14秒前
空禅yew发布了新的文献求助10
14秒前
华仔应助云辞忧采纳,获得10
16秒前
16秒前
Sunwenrui完成签到,获得积分10
16秒前
12完成签到,获得积分10
18秒前
whoami发布了新的文献求助10
19秒前
19秒前
搜集达人应助TTT0530采纳,获得10
20秒前
张大英发布了新的文献求助10
21秒前
21秒前
22秒前
tassssadar完成签到,获得积分10
22秒前
隐形曼青应助小猴子采纳,获得10
25秒前
26秒前
whoami完成签到,获得积分10
26秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988868
求助须知:如何正确求助?哪些是违规求助? 3531255
关于积分的说明 11253071
捐赠科研通 3269858
什么是DOI,文献DOI怎么找? 1804822
邀请新用户注册赠送积分活动 881994
科研通“疑难数据库(出版商)”最低求助积分说明 809035