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RESEARCH PAPER · Computational Materials Science · 2022

Mapping the creep life of nickel-based SX superalloys in a large compositional space by a two-model linkage machine learning method

Hongyong Han, Wendao Li, Stoichko Antonov, Longfei Li

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Mapping the creep life of nickel-based SX superalloys in a large compositional space by a two-model linkage machine learning methodOriginalLinking Stress-Rupture Properties to Processing Parameters of HAYNES® 718 Nickel Superalloy Using Machine Learning2Improving machine learning based phase and hardness prediction of high-entropy alloys by using Gaussian noise augmented data96Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9Cr-1Mo Steel20Simple Data Analytics Approach Coupled with Larson–Miller Parameter Analysis for Improved Prediction of Creep Rupture Life13An explainable machine learning model for superalloys creep life prediction coupling with physical metallurgy models and CALPHAD27Deep learning accelerates the development of Ni-based single crystal superalloys: A physical-constrained neural network for creep rupture life prediction37A data-driven low-cycle fatigue life prediction model for nickel-based superalloys22Prediction of High-Temperature Creep Life of Austenitic Heat-Resistant Steels Based on Data Fusion10Composition, heat treatment, microstructure and loading condition based machine learning prediction of creep life of superalloys26A Method for Predicting the Creep Rupture Life of Small-Sample Materials Based on Parametric Models and Machine Learning Models16Interpretable Predicting Creep Rupture Life of Superalloys: Enhanced by Domain‐Specific Knowledge37Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine learning12Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks71Transfer learning enables the rapid design of single crystal superalloys with superior creep resistances at ultrahigh temperature27Creep behaviour investigation of additively manufactured IN738LC superalloy based on Materials Genome approach22Machine learning assisted prediction of creep life in Ni-based alloys: An ensemble-based stacking regressor approach0Predicting creep failure life in adhesive-bonded single-lap joints using machine learning12Machine Learning Models for Creep Rupture Prediction of Inconel 6172Design of novel interpretable deep learning framework for microstructure–property relationships in nickel and cobalt based superalloys11High-throughput investigation of elemental substitution effects on precipitation of TCP phases in Ni-based superalloys containing Re and Ru5Designing nickel-based superalloys with high creep life and low thermal expansion coefficient using machine learning assisted by multi-objective optimization6Machine learning approaches for creep rupture life prediction of metallic materials: A comprehensive review11A framework for fatigue life prediction of fiber reinforced composites with limited testing data3Compositional design of Ni-based superalloys for additive manufacturing: Progress and perspectives9An energy-based physics–data fusion model for thermomechanical fatigue life prediction of directionally solidified superalloys5Graph representation learning for predicting creep rupture life in Nickel-Based single crystal superalloys0Factors influencing creep life and deformation mechanisms in Ni-based single crystal superalloys: A review0Application of Machine Learning in the Study of Creep Properties of Alloys0Optimization of nickel-based superalloys by an efficient hot consolidation method and machine learning0AI-Assisted Creep Time Prediction Using Creep Strain Curves of AISI 316 Austenitic Stainless Steel: Effects of Data Transformation and Hyperparameter Optimisation1Material insights for supercapacitor designs: A shapley additive explanatory study of critical temperature dependence on atomic properties0

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  1. Linking Stress-Rupture Properties to Processing Parameters of HAYNES® 718 Nickel Superalloy Using Machine Learning

    ˜The œminerals, metals & materials series · 2023-01-01

    David E. Farache, George Maxwell Nishibuchi, Sebastian Elizondo, John G. Gulley, Alex Post, Kyle Stubbs, Keith L. Kruger, Arun Mannodi‐Kanakkithodi, Michael S. Titus

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  2. Improving machine learning based phase and hardness prediction of high-entropy alloys by using Gaussian noise augmented data

    Computational Materials Science · 2023-03-21

    Yicong Ye, Yahao Li, Runlong Ouyang, Zhouran Zhang, Yu Tang, Shuxin Bai

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  3. Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9Cr-1Mo Steel

    Applied Sciences · 2023-04-15

    Mengyu Chai, Yuhang He, Yongquan Li, Yan Song, Zaoxiao Zhang, Quan Duan

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  4. Simple Data Analytics Approach Coupled with Larson–Miller Parameter Analysis for Improved Prediction of Creep Rupture Life

    Metals and Materials International · 2023-04-20

    Chang-Ho Lee, Taejoo Lee, Yoon Suk Choi

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  5. An explainable machine learning model for superalloys creep life prediction coupling with physical metallurgy models and CALPHAD

    Computational Materials Science · 2023-06-01

    Yuyu Huang, Jide Liu, Chongwei Zhu, Xinguang Wang, Yizhou Zhou, Xiaofeng Sun, Jinguo Li

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  6. Deep learning accelerates the development of Ni-based single crystal superalloys: A physical-constrained neural network for creep rupture life prediction

    Materials & Design · 2023-07-18

    Fan Yang, Wenyue Zhao, Yi Ru, Yanling Pei, Shusuo Li, Shengkai Gong, Huibin Xu

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  7. A data-driven low-cycle fatigue life prediction model for nickel-based superalloys

    Computational Materials Science · 2023-08-13

    Luopeng Xu, Rulun Zhang, Mengquan Hao, Lei Xiong, Qin Jiang, Zhixin Li, Qingyuan Wang, Xiaopeng Wang

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  8. Prediction of High-Temperature Creep Life of Austenitic Heat-Resistant Steels Based on Data Fusion

    Metals · 2023-09-21

    Limin Wei, Shuo Wang, Weixun Hao, Jingtao Huang, Nan Qu, Yong Liu, Jingchuan Zhu

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  9. Composition, heat treatment, microstructure and loading condition based machine learning prediction of creep life of superalloys

    Mechanics of Materials · 2023-10-04

    Ronghai Wu, Zeng Lei, Jiangkun Fan, Zichao Peng, Yunsong Zhao

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  10. A Method for Predicting the Creep Rupture Life of Small-Sample Materials Based on Parametric Models and Machine Learning Models

    Materials · 2023-10-22

    Xu Zhang, Jianyao Yao, Yulin Wu, Xuyang Liu, Changyin Wang, Hao Liu

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  11. Interpretable Predicting Creep Rupture Life of Superalloys: Enhanced by Domain‐Specific Knowledge

    Advanced Science · 2024-01-02

    Jiawei Yin, Ziyuan Rao, Dayong Wu, Huiting Lv, Haikun Ma, Teng Long, Jie Kang, Qian Wang, Yandong Wang, Ru Su

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  12. Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine learning

    Materials Today Communications · 2024-04-05

    Zhiyang Qin, Hongliang Zhao, Shuya Zhang, Yuheng Fan, Xianglei Dong, Zishuo Lan, Xiaobing Hu, Yang Song, Chunwen Guo

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  13. Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

    Scientific Reports · 2024-05-08

    Longze Li, Jiang Chang, Aleksandar Vakanski, Yachun Wang, Tiankai Yao, Min Xian

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  14. Transfer learning enables the rapid design of single crystal superalloys with superior creep resistances at ultrahigh temperature

    npj Computational Materials · 2024-07-14

    Fan Yang, Wenyue Zhao, Yi Ru, Siyuan Lin, Jiapeng Huang, Boxuan Du, Yanling Pei, Shusuo Li, Shengkai Gong, Huibin Xu

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  15. Creep behaviour investigation of additively manufactured IN738LC superalloy based on Materials Genome approach

    Materials Science and Engineering R Reports · 2024-12-10

    Zhen Xu, Xiaogang Hu, Chuan Guo, Zhiwei Lv, Zhiyuan Wang, Zhuoyu Li, Zhifang Shi, Zhennan Chen, Qiang Zhu

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  16. Machine learning assisted prediction of creep life in Ni-based alloys: An ensemble-based stacking regressor approach

    International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · 2025-01-16

    Shaik Arshid Banu, Gaytri Mohapatra, Sanya Kushwaha, Himani Himani, Lavanya Tanwar, Subhash Singh

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  17. Predicting creep failure life in adhesive-bonded single-lap joints using machine learning

    Scientific Reports · 2025-02-26

    Faizullah Jan, Marcin Kujawa, Piotr Paczos, Victor A. Eremeyev

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  18. Machine Learning Models for Creep Rupture Prediction of Inconel 617

    Journal of Pressure Vessel Technology · 2025-03-07

    Mohammad Shafinul Haque, Zakai Al Kadri

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  19. Design of novel interpretable deep learning framework for microstructure–property relationships in nickel and cobalt based superalloys

    Computational Materials Science · 2025-03-31

    Aditya Gollapalli, Abhishek Kumar Singh

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  20. High-throughput investigation of elemental substitution effects on precipitation of TCP phases in Ni-based superalloys containing Re and Ru

    Journal of Alloys and Compounds · 2025-04-19

    Hongyong Han, Wendao Li, Fan Xiu Lu, Fan Xiu Lu, Jinghui Xu, Longfei Li, Qiang Feng

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  21. Designing nickel-based superalloys with high creep life and low thermal expansion coefficient using machine learning assisted by multi-objective optimization

    Journal of Materials Science · 2025-06-01

    Yao Zhu, Wangjun Peng, Dafan Du, Anping Dong, Baode Sun, Pingguo Jiang

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  22. Machine learning approaches for creep rupture life prediction of metallic materials: A comprehensive review

    International Journal of Pressure Vessels and Piping · 2025-10-22

    Muhammad Bilal Jan, Mengyu Chai

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  23. A framework for fatigue life prediction of fiber reinforced composites with limited testing data

    International Journal of Fatigue · 2025-12-21

    Yubin Lu, Zhen Wu

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  24. Compositional design of Ni-based superalloys for additive manufacturing: Progress and perspectives

    Materials Science and Engineering R Reports · 2026-01-14

    Bingbing Yin, Yong Wei, Jiarui Zhu, Haoyu Ge, Shuaicheng Zhu, Yunwei Gui, Wenjing Zhang, Huadong Fu, Jinghe Xie

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  25. An energy-based physics–data fusion model for thermomechanical fatigue life prediction of directionally solidified superalloys

    International Journal of Fatigue · 2026-01-25

    Jundong Wang, Yuanmin Tu, Song Wu, Pengfei He, Zhixun Wen

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  26. Graph representation learning for predicting creep rupture life in Nickel-Based single crystal superalloys

    Measurement · 2026-04-09

    Wenbo Gao, X.M. Wang, Shouyi Sun, Zhuo Xue, Tao Zhang, Bowen Qi

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  27. Factors influencing creep life and deformation mechanisms in Ni-based single crystal superalloys: A review

    Journal of Materials Research and Technology · 2026-05-01

    Liankai Zhang, Jieshi Chen, Shanglei Yang, H Y Lu, Chenlong Qiu, Yongzhi Zhang, Yi Zheng, Renyao Qin

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  28. Application of Machine Learning in the Study of Creep Properties of Alloys

    cScience · 2026-06-01

    Kai Zhou, Jingjin He, H B Wu, Yan Wei, Xiaoyu Chong

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  29. Optimization of nickel-based superalloys by an efficient hot consolidation method and machine learning

    Journal of materials research/Pratt's guide to venture capital sources · 2026-06-10

    Wu-qiang He, Ming-xue Wang, Feng Liu, Caihe Fan

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  30. AI-Assisted Creep Time Prediction Using Creep Strain Curves of AISI 316 Austenitic Stainless Steel: Effects of Data Transformation and Hyperparameter Optimisation

    Applied Sciences · 2026-06-23

    Arsalan Nazim, Andrea Tonti, Elisabetta Gariboldi

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  31. Material insights for supercapacitor designs: A shapley additive explanatory study of critical temperature dependence on atomic properties

    Next Materials · 2026-08-20

    Hussein Ali Kadhim Kyhoiesh, Islam H. El Azab, Hassan E. Abd Elsalam, Mahmoud Elsayed

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