BetaCitation Atlas

RESEARCH PAPER · npj Computational Materials · 2020

Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimization

Pei Liu, Haiyou Huang, Stoichko Antonov, Cheng Wen, Dezhen Xue, Houwen Chen, Longfei Li, Qiang Charles Feng, Toshihiro Omori, Yanjing Su

Read original paper ↗

See where this research leads.

120 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimizationOriginalAccelerated design of L12-strengthened Co-base superalloys based on machine learning of experimental data44Temperature dependence of single crystal elastic constants in a CoNi-Base alloy: A new methodology17Evaluating yield strength of Ni-based superalloys via high throughput experiment and machine learning20Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Machine Learning for High-Entropy Alloys10Machine learning-guided design and development of metallic structural materials27Prediction of the Electronic Work Function by Regression Algorithm in Machine Learning6Investigation of melt-growth alumina/aluminum titanate composite ceramics prepared by directed energy deposition51Machine Learning Boosts the Design and Discovery of Nanomaterials138Machine learning assisted screening of non-rare-earth elements for Mg alloys with low stacking fault energy23Machine learning assisted empirical formula augmentation21Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies65Automated stopping criterion for spectral measurements with active learning28Low cycle fatigue of a single crystal CoNi-base superalloy12Progress and perspectives in laser additive manufacturing of key aeroengine materials520A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness335A mini review of machine learning in inorganic phosphors10Advanced High-Temperature Structural Materials in Petrochemical, Metallurgical, Power, and Aerospace Sectors—An Overview29Interplay between Solidification Microsegregation and Complex Precipitation in a Γ/Γ’ Cobalt-Based Superalloy Elaborated by Directed Energy Deposition11Machine learning-accelerated first-principles predictions of the stability and mechanical properties of L12-strengthened cobalt-based superalloys21Composition design of high yield strength points in single-phase Co–Cr–Fe–Ni–Mo multi-principal element alloys system based on electronegativity, thermodynamic calculations, and machine learning13Hardness prediction of high entropy alloys with machine learning and material descriptors selection by improved genetic algorithm97Automated pipeline for superalloy data by text mining83The CALPHAD approach for HEAs: Challenges and opportunities68A property-oriented design strategy of high-strength ductile RAFM steels based on machine learning38Explore the full temperature-composition space of 20 quinary CCAs for FCC and BCC single-phases by an iterative machine learning + CALPHAD method21Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures11Accelerated Design of High γ′ Solvus Temperature and Yield Strength Cobalt-Based Superalloy Based on Machine Learning and Phase Diagram5Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures21Recent progress in nanocrystalline Sm–Co based magnets35Deep-neural-network solution of piezo-phototronic transistor based on GaN/AlN quantum wells4An intelligent design for Ni-based superalloy based on machine learning and multi-objective optimization54CALPHAD informed design of multicomponent CoNiCr-based superalloys exhibiting large lattice misfit and high yield stress15Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning4Machine Learning Captures Synthetic Intuitions for Hollow Nanostructures4Rapid Discovery of Efficient Long-Wavelength Emission Garnet:Cr NIR Phosphors via Multi-Objective Optimization56Alloying effects and effective alloy design of high-Cr CoNi-based superalloys via a high-throughput experiments and machine learning framework83Development of a Diffusion Mobility Database for Co-Based Superalloys6Machine learning-assisted data-driven optimization and understanding of the multiple stage process for extraction of polysaccharides and secondary metabolites from natural products23Data‐Driven “Cross‐Component” Design and Optimization of γ′‐Strengthened Co‐Based Superalloys12Machine Learning Customized Novel Material for Energy‐Efficient 4D Printing91Data-driven design of Ni-based turbine disc superalloys to improve yield strength56Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations40Key Parameters and Optimal Design of a Split Induction Coil for T-Shaped Pipe Brazing4A framework to predict binary liquidus by combining machine learning and CALPHAD assessments21Influence of the γ′ Volume Fraction on the High-Temperature Strength of Single Crystalline Co–Al–W–Ta Superalloys3Exploring the impact of configurational entropy on the design and development of CoNi-based superalloys for sustainable applications13Artificial intelligence inspired design of non-isothermal aging for γ–γ′ two-phase, Ni–Al alloys8The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization23NiTiHfX Shape Memory Alloy Actuation Strain Prediction1Machine learning-based quantitative analysis of metal ductile fracture surface16Composition, heat treatment, microstructure and loading condition based machine learning prediction of creep life of superalloys26Alloy synthesis and processing by semi-supervised text mining28Multi‐objective optimization and its application in materials science46Developing and optimizing novel Cr 3+ -activated inorganic NIR phosphors by combining triple-objective optimization and crystal field engineering25Alloy design for laser powder bed fusion additive manufacturing: a critical review117Accelerated design of L1 2 -strengthened single crystal high entropy alloys based on machine learning and multi-objective optimization9Physics-Assisted Machine-Learning Approach for Modeling the Temperature-Dependent Ultimate Strengths of Superalloys, with Comparison to Medium- and High-Entropy Alloys5Novel Ni–Co-based superalloys with high thermal stability and specific yield stress discovered by directed energy deposition14Machine learning-assisted composition design of W-free Co-based superalloys with high γ′-solvus temperature and low density25Machine learning assisted analysis and prediction of rubber formulation using existing databases14Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte143Prediction of creep properties of Co–10Al–9W superalloys with machine learning10Synchronously enhancing the strength, toughness, and stress corrosion resistance of high-end aluminum alloys via interpretable machine learning156Experimentally validated inverse design of multi-property Fe-Co-Ni alloys30Novel Cast Ni‐Based Superalloys with Superb Weldability and Mechanical Properties Screened by a Multiperformance‐Oriented Criterion3Pre-softening HIP treatment enabled crack-healing and superior mechanical properties for René 142 superalloy fabricated via laser powder bed fusion32Machine learning-enabled prediction and optimization of hardness for Nb-Ti-V-Zr refractory high entropy alloy12Machine learning studies for magnetic compositionally complex alloys: A critical review33Compositional design of compounds with elements not in training data using supervised learning1Unsupervised learning-aided extrapolation for accelerated design of superalloys21Machine-Learning-Assisted Compositional Design of Refractory High-Entropy Alloys with Optimal Strength and Ductility43Predicting actuation strain in quaternary shape memory alloy NiTiHfX using machine learning11Applications of machine learning method in high-performance materials design: a review35A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning18Empowering the Sustainable Development of High‐End Alloys via Interpretive Machine Learning52Creep rafting fracture and strain properties of overheating Co-based superalloy: Crystal plasticity phase-field simulation8Machine-Learning-Driven Design of High-Elastocaloric NiTi-Based Shape Memory Alloys9Multi-objective design of Ni-B-Al master alloy by adaptive machine learning-driven aluminothermic reduction experiment3Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization17Facilitated the discovery of new γ/γ′ Co-based superalloys by combining first-principles and machine learning16Fast parameter optimization for high-fidelity crystal plasticity simulation using active learning5Multi-Properties prediction of perovskite materials using Machine learning and Meta-Heuristic feature selection9Robustness of machine learning predictions for Fe-Co-Ni alloys prepared by various synthesis methods8Developing novel low-density high-entropy superalloys with high strength and superior creep resistance guided by automated machine learning43Improving electrolyte performance for target cathode loading using an interpretable data-driven approach2Construction of material phase diagram using different uncertainty estimation strategies2Machine Learning-Based Computational Design Methods for High-Entropy Alloys49Interpretable machine learning study on the relationship between microstructural features and properties of GH4742 alloy1Phase-field-informed machine learning on creep behavior of Ni-based single-crystal superalloys5Application of Machine Learning in Nano Research — A Review2Machine learning-driven optimization of hardness and toughness in high-entropy alloy coatings based on composition and descriptor10Design of superalloys with multiple properties via multi-task learning8Automated system for high-throughput process-structure-property dataset generation of structural materials: A γ/γ′ superalloy case study10Machine learning for obtaining values of thermomechanical processing parameters of high-entropy alloys with desirable strength and ductility13Enhanced extrapolative machine learning for designing high‐performance multi‐principal‐element superalloys9Interpretable machine learning-guided design of Fe-based soft magnetic alloys12Machine learning assisted design of CrTaTiV-based refractory high entropy alloys with enhanced hardness7Composition design and process optimization of refractory boride coatings based on high-throughput preparation and machine learning2From Algorithms to Applications: A Comprehensive Review of Machine Learning in Computational Materials Science5A new multi-objective optimization design framework for high-temperature alloy steel via machine learning4Explainable machine learning-enabled dual-objective design of γ' phase characteristic parameters in γ'-strengthened Co-based superalloys11Predicting dislocation patterns and discovering the law of similitude: Machine learning based on fully reversed fatigue of FCC metals3Rapid Discovery of High-Performance Additive Manufacturing Superalloys Using High-Throughput and Artificial Intelligence Methods0Machine learning prediction of precipitates guides the discovery of Ni-based single crystal superalloys with high microstructural stability3Preparation, Characterization, and Application of Additive Manufacturing Superalloy Data0Compositional design of Ni-based superalloys for additive manufacturing: Progress and perspectives9Machine learning-driven new paradigm for Co-based superalloys3Composition design and optimization of a Ni-Co based wrought superalloy for 750 °C service2Effect of Co/Ni ratio on precipitation phase evolution and element distribution behavior in as-cast Co-Ni-based superalloy2A comprehensive review of machine learning for superalloys: from data-driven prediction to intelligent design2Data-driven parameters design for laser powder bed fusion of high-performance nickel-based superalloys2Interpretable machine learning for intrinsic mechanical properties of γ′ phase in cobalt-based superalloys3Alloy design of γ′-strengthened Co-Ni-base superalloys with low crack susceptibility suitable for additive manufacturing0Integrated PredRNN++ and phase-field for microstructure prediction and simulation acceleration1Unveiling bootstrap uncertainty bias: understanding bagging efficiency over Gaussian processes in materials active learning0Impact of Co/Ni Ratio on Solidification Characteristics and As-Cast Microstructure of Co-Al-W-Based Superalloys0Effect of Re and Mo on the microstructural evolution and hardness of CoNiAlCr-based superalloys at 1100 °C and 1150 °C0Knowledge-guided descriptors for multi-property prediction and candidate screening of superalloys0Predicting glass-forming ability in binary alloys by machine learning0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

120 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Accelerated design of L12-strengthened Co-base superalloys based on machine learning of experimental data

    Materials & Design · 2020-07-26

    Jinxin Yu, Chenglei Wang, Chenglei Wang, Yuechao Chen, Cuiping Wang, Cuiping Wang, Xingjun Liu

    Read original paper ↗ ·

  2. Temperature dependence of single crystal elastic constants in a CoNi-Base alloy: A new methodology

    Materials Science and Engineering A · 2020-11-12

    Brent R. Goodlet, Sean P. Murray, Ben Bales, Jeff Rossin, Chris J. Torbet, Tresa M. Pollock

    Read original paper ↗ ·

  3. Evaluating yield strength of Ni-based superalloys via high throughput experiment and machine learning

    Journal of Micromechanics and Molecular Physics · 2020-12-01

    Feng Liu, Zexin Wang, Zi Wang, Zijun Qin, Zihang Li, Liang Jiang, Lan Huang, Liming Tan, Yong Liu

    Read original paper ↗ ·

  4. Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys

    Acta Materialia · 2021-01-01

    Hongtao Zhang, Huadong Fu, Shuaicheng Zhu, Wei Yong, Jianxin Xie

    Read original paper ↗ ·

  5. Machine Learning for High-Entropy Alloys

    Springer series in materials science · 2021-01-01

    Shuai Chen, Yuan Cheng, Huajian Gao

    Read original paper ↗ ·

  6. Machine learning-guided design and development of metallic structural materials

    Journal of Materials Informatics · 2021-01-01

    Jinxin Yu, Shengkun Xi, Shaobin Pan, Yongjie Wang, Qinghua Peng, Rongpei Shi, Cuiping Wang, Xingjun Liu

    Read original paper ↗ ·

  7. Prediction of the Electronic Work Function by Regression Algorithm in Machine Learning

    IEEE International Conference on Big Data Analytics (ICBDA) · 2021-03-05

    Na Li, Tianxin Zong, Zhigang Zhang

    Read original paper ↗ ·

  8. Investigation of melt-growth alumina/aluminum titanate composite ceramics prepared by directed energy deposition

    International Journal of Extreme Manufacturing · 2021-04-14

    Yunfei Huang, Dongjiang Wu, Dake Zhao, Fangyong Niu, Guangyi Ma

    Read original paper ↗ ·

  9. Machine Learning Boosts the Design and Discovery of Nanomaterials

    ACS Sustainable Chemistry & Engineering · 2021-04-27

    Yuying Jia, Xuan Hou, Zhongwei Wang, Xiangang Hu

    Read original paper ↗ ·

  10. Machine learning assisted screening of non-rare-earth elements for Mg alloys with low stacking fault energy

    Computational Materials Science · 2021-04-29

    M. Wang, Hulei Yu, Y. Chen, Mingxin Huang

    Read original paper ↗ ·

  11. Machine learning assisted empirical formula augmentation

    Materials & Design · 2021-08-09

    Bin Xiong, Xinpeng Zhao, Yunfeng Hu, Haiyou Huang, Yang Liu, Yanjing Su

    Read original paper ↗ ·

  12. Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies

    Journal of Chemical Information and Modeling · 2021-08-10

    Pengcheng Xu, Dongping Chang, Tian Lu, Long Li, Minjie Li, Wencong Lu

    Read original paper ↗ ·

  13. Automated stopping criterion for spectral measurements with active learning

    npj Computational Materials · 2021-08-25

    Tetsuro Ueno, Hideaki Ishibashi, Hideitsu Hino, Kanta Ono

    Read original paper ↗ ·

  14. Low cycle fatigue of a single crystal CoNi-base superalloy

    Materials Science and Engineering A · 2021-09-09

    Sean P. Murray, Alice Cervellon, Jonathan Cormier, Tresa M. Pollock

    Read original paper ↗ ·

  15. Progress and perspectives in laser additive manufacturing of key aeroengine materials

    International Journal of Machine Tools and Manufacture · 2021-09-29

    Chaolin Tan, Fei Weng, Shang Sui, Youxiang Chew, Guijun Bi

    Read original paper ↗ ·

  16. A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness

    Acta Materialia · 2021-11-01

    Yang Chen, Chang Ren, Yuefei Jia, Gang Wang, Minjie Li, Wencong Lu

    Read original paper ↗ ·

  17. A mini review of machine learning in inorganic phosphors

    Journal of Materials Informatics · 2022-01-01

    Lipeng Jiang, Xue Jiang, Guocai Lv, Yanjing Su

    Read original paper ↗ ·

  18. Advanced High-Temperature Structural Materials in Petrochemical, Metallurgical, Power, and Aerospace Sectors—An Overview

    Springer eBooks · 2022-01-01

    Pradyut Sengupta, I. Manna

    Read original paper ↗ ·

  19. Interplay between Solidification Microsegregation and Complex Precipitation in a Γ/Γ’ Cobalt-Based Superalloy Elaborated by Directed Energy Deposition

    Materials Characterization · 2022-01-01

    Thibaut Froeliger, Arthur Després, Louïse Toualbi, Didier Locq, Muriel Veron, Guilhem Martin, Rémy Dendievel

    Read original paper ↗ ·

  20. Machine learning-accelerated first-principles predictions of the stability and mechanical properties of L12-strengthened cobalt-based superalloys

    Journal of Materials Informatics · 2022-01-01

    Shengkun Xi, Jinxin Yu, Longke Bao, Liuping Chen, Zhou Li, Rongpei Shi, Cuiping Wang, Xingjun Liu

    Read original paper ↗ ·

  21. Composition design of high yield strength points in single-phase Co–Cr–Fe–Ni–Mo multi-principal element alloys system based on electronegativity, thermodynamic calculations, and machine learning

    Tungsten · 2022-01-03

    Jiaohui Yan, Zijing Song, Wei Fang, Xinbo He, Ruobin Chang, Shao-Wu Huang, Jiaxin Huang, Haoyang Yu, Fuxing Yin

    Read original paper ↗ ·

  22. Hardness prediction of high entropy alloys with machine learning and material descriptors selection by improved genetic algorithm

    Computational Materials Science · 2022-01-18

    Shuai Li, Shu Li, Shu Li, Shu Li, Dong-Rong Liu, Rui Zou, Zhiyuan Yang

    Read original paper ↗ ·

  23. Automated pipeline for superalloy data by text mining

    npj Computational Materials · 2022-01-19

    Weiren Wang, Xue Feng Jiang, Shaohan Tian, Pei Liu, Depeng Dang, Yanjing Su, Turab Lookman, Jianxin Xie

    Read original paper ↗ ·

  24. The CALPHAD approach for HEAs: Challenges and opportunities

    MRS Bulletin · 2022-02-01

    Chuan Zhang, Ying Yang

    Read original paper ↗ ·

  25. A property-oriented design strategy of high-strength ductile RAFM steels based on machine learning

    Materials Science and Engineering A · 2022-02-26

    Xiaochen Li, Mingjie Zheng, Xinyi Yang, Pinghan Chen, Wenyi Ding

    Read original paper ↗ ·

  26. Explore the full temperature-composition space of 20 quinary CCAs for FCC and BCC single-phases by an iterative machine learning + CALPHAD method

    Acta Materialia · 2022-03-22

    Yingzhi Zeng, Mengren Man, Kewu Bai, Yong‐Wei Zhang

    Read original paper ↗ ·

  27. Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures

    Integrating materials and manufacturing innovation · 2022-04-06

    Mahmudul Hasan, Yuwei Mao, Kamal Kumar Choudhary, Francesca Tavazza, Alok Choudhary, Ankit Agrawal, Pınar Acar

    Read original paper ↗ ·

  28. Accelerated Design of High γ′ Solvus Temperature and Yield Strength Cobalt-Based Superalloy Based on Machine Learning and Phase Diagram

    Frontiers in Materials · 2022-05-16

    Cuiping Wang, Xin Chen, Yuechao Chen, Jinxin Yu, Wensu Cai, Zhongfeng Chen, Xiang Yu, Yingju Li, Yuansheng Yang, Xingjun Liu

    Read original paper ↗ ·

  29. Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures

    Journal of Materials Engineering and Performance · 2022-05-24

    Hamid Reza Abedi, Keyvan Safaei Baghbaderani, Ala’aldin Alafaghani, Mohammadreza Nematollahi, Fatemeh Kordizadeh, Moataz M. Attallah, Ala Qattawi, Mohammad Elahinia

    Read original paper ↗ ·

  30. Recent progress in nanocrystalline Sm–Co based magnets

    Materials Today Chemistry · 2022-06-03

    Kai Guo, H. Lu, Guojing Xu, D. Liu, H.B. Wang, X.M. Liu, Xiaoyan Song

    Read original paper ↗ ·

  31. Deep-neural-network solution of piezo-phototronic transistor based on GaN/AlN quantum wells

    Nano Energy · 2022-07-06

    Yuankai Zhou, Minjiang Dan, Yicheng Shao, Yan Zhang

    Read original paper ↗ ·

  32. An intelligent design for Ni-based superalloy based on machine learning and multi-objective optimization

    Materials & Design · 2022-07-11

    Yuedan Deng, Yu Zhang, Xiufang Gong, Hu Wang, Yucheng Wang, Ying Liu, Lixian Lian

    Read original paper ↗ ·

  33. CALPHAD informed design of multicomponent CoNiCr-based superalloys exhibiting large lattice misfit and high yield stress

    Materials Science and Engineering A · 2022-08-27

    Zhida Liang, Steffen Neumeier, Ziyuan Rao, Mathias Göken, Florian Pyczak

    Read original paper ↗ ·

  34. Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning

    Computer Modeling in Engineering & Sciences · 2022-10-27

    Zihang Li, Zexin Wang, Zi Wang, Zijun Qin, Feng Liu, Liming Tan, Xiaochao Jin, Xueling Fan, Lan Huang

    Read original paper ↗ ·

  35. Machine Learning Captures Synthetic Intuitions for Hollow Nanostructures

    ACS Applied Nano Materials · 2022-10-28

    Xue Feng Jiang, Yongzhi Zhao, Jianfang Liu, Baorui Jia, Xuanhui Qu, Mingli Qin

    Read original paper ↗ ·

  36. Rapid Discovery of Efficient Long-Wavelength Emission Garnet:Cr NIR Phosphors via Multi-Objective Optimization

    ACS Applied Materials & Interfaces · 2022-11-09

    Lipeng Jiang, Xue Feng Jiang, Changxin Wang, Pei Liu, Yan Zhang, Guocai Lv, Turab Lookman, Yanjing Su

    Read original paper ↗ ·

  37. Alloying effects and effective alloy design of high-Cr CoNi-based superalloys via a high-throughput experiments and machine learning framework

    Acta Materialia · 2022-11-12

    Xiaoli Zhuang, Stoichko Antonov, Wendao Li, Song Lu, Longfei Li, Qiang Charles Feng

    Read original paper ↗ ·

  38. Development of a Diffusion Mobility Database for Co-Based Superalloys

    Journal of Phase Equilibria and Diffusion · 2022-11-27

    Greta Lindwall, Kil-Won Moon, Maureen E. Williams, Whitney Tso, Carelyn E. Campbell

    Read original paper ↗ ·

  39. Machine learning-assisted data-driven optimization and understanding of the multiple stage process for extraction of polysaccharides and secondary metabolites from natural products

    Green Chemistry · 2023-01-01

    Jiamu Ma, Jianling Yao, Xueyang Ren, Ying Dong, Ruolan Song, Xiangjian Zhong, Yuan Zheng, Dongjie Shan, Fang Lv, Xianxian Li, Qingyue Deng, Yingyu He, Ruijuan Yuan, Gaimei She

    Read original paper ↗ ·

  40. Data‐Driven “Cross‐Component” Design and Optimization of γ′‐Strengthened Co‐Based Superalloys

    Advanced Engineering Materials · 2023-01-26

    Song Lu, Min Zou, Xiaorui Zhang, Stoichko Antonov, Wendao Li, Longfei Li, Qiang Charles Feng

    Read original paper ↗ ·

  41. Machine Learning Customized Novel Material for Energy‐Efficient 4D Printing

    Advanced Science · 2023-02-05

    Chaolin Tan, Qian Li, Xiling Yao, Lequn Chen, Jinlong Su, Fern Lan Ng, Yuchan Liu, Tao Yang, Youxiang Chew, C.T. Liu, T. DebRoy

    Read original paper ↗ ·

  42. Data-driven design of Ni-based turbine disc superalloys to improve yield strength

    Journal of Material Science and Technology · 2023-03-17

    Bin Xu, Haiqing Yin, Xue Feng Jiang, Cong Zhang, Ruijie Zhang, Yongwei Wang, Xuanhui Qu, Zhenghua Deng, Guoqiang Yang, Dil Faraz Khan

    Read original paper ↗ ·

  43. Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

    Science and Technology of Advanced Materials · 2023-04-11

    Jianbao Gao, Jing Zhong, Guangchen Liu, Shaoji Zhang, Jiali Zhang, Zuming Liu, Bo Song, Lijun Zhang

    Read original paper ↗ ·

  44. Key Parameters and Optimal Design of a Split Induction Coil for T-Shaped Pipe Brazing

    Coatings · 2023-05-17

    Zhenlin Zhang, Ziheng Yang, Pu Xie, Yue Zhao, Jiguo Shan, Yan Liu, Aiping Wu, Sida Ma, Lei Zhang, Hui Chen

    Read original paper ↗ ·

  45. A framework to predict binary liquidus by combining machine learning and CALPHAD assessments

    Materials & Design · 2023-06-24

    Guillaume Deffrennes, Kei Terayama, Taichi Abe, Etsuko Ogamino, Ryo Tamura

    Read original paper ↗ ·

  46. Influence of the γ′ Volume Fraction on the High-Temperature Strength of Single Crystalline Co–Al–W–Ta Superalloys

    Crystals · 2023-07-13

    Fei Xue, Andreas Bezold, Nicklas Volz, Andreas Kirchmayer, Christopher H. Zenk, Steffen Neumeier, Mathias Göken

    Read original paper ↗ ·

  47. Exploring the impact of configurational entropy on the design and development of CoNi-based superalloys for sustainable applications

    Journal of Alloys and Compounds · 2023-07-15

    Ahad Mohammadzadeh, Akbar Heidarzadeh, Hailey Becker, Jorge Valilla Robles, Alberto Meza, M. Avella, Miguel Alberto Monclús, Damien Tourret, J. M. Torralba

    Read original paper ↗ ·

  48. Artificial intelligence inspired design of non-isothermal aging for γ–γ′ two-phase, Ni–Al alloys

    Scientific Reports · 2023-08-04

    Vickey Nandal, Sae Dieb, Dmitry S. Bulgarevich, Toshio Osada, Toshiyuki Koyama, Satoshi Minamoto, Masahiko Demura

    Read original paper ↗ ·

  49. The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization

    npj Computational Materials · 2023-08-10

    Pei Liu, Haiyou Huang, Cheng Wen, Turab Lookman, Yanjing Su

    Read original paper ↗ ·

  50. NiTiHfX Shape Memory Alloy Actuation Strain Prediction

    Research Square · 2023-08-28

    Hossein Abedi, Ala Qattawi

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.