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RESEARCH PAPER · Computational Materials Science · 2018

Machine leaning aided study of sintered density in Cu-Al alloy

Zhenghua Deng, Haiqing Yin, Xue Feng Jiang, Cong Zhang, Kaiqi Zhang, Tong Zhang, Bin Xu, Qingjun Zheng, Xuanhui Qu

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42 direct citing works

Machine leaning aided study of sintered density in Cu-Al alloyOriginalUltra-high temperature ceramics melting temperature prediction via machine learning79Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening310Machine-learning-assisted prediction of the mechanical properties of Cu-Al alloy69Machine learning in materials genome initiative: A review231Transfer learning aid the prediction of sintering densification12Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus78Machine learning-assisted discovery of strong and conductive Cu alloys: Data mining from discarded experiments and physical features86Reliability evaluation method for squeeze casting process parameter data4Recent machine learning guided material research - A review12Imputation Method Based on Collaborative Filtering and Clustering for the Missing Data of the Squeeze Casting Process Parameters7Deep learning modeling strategy for material science: from natural materials to metamaterials29Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments36Machine Learning Based Sintered Density Prediction of Bronze Processed by Powder Metallurgy Route22Dimensionality reduction for machine learning using statistical methods: A case study on predicting mechanical properties of steels14New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning15Machine Learning Sintering Density Prediction Model for MOX Fuel Pellet0Experimental Feedback to Improve the Extrapolation of Machine Learning: Application to Design of PM Cu–Al–Fe–Ni Alloys1Design Strategy for Art Copper Alloys’ Colors Through Machine Learning and Oxidation Treatment3Prediction of sintered density of binary W(Mo) alloys using machine learning27Modeling of the Sintered Density in Cu-Al Alloy Using Machine Learning Approaches14Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles9Machine learning aided prediction of sintering induced swelling in powder metallurgical Cu-Sn alloys17An Investigation of the Mechanical Property Behaviour of Copper/AA7075 Nanocomposites Based on Machine Learning1Research on the prediction of component activity silicon-based binary melt using the MIVM and machine learning algorithms2Facilitating Recycling of 6xxx Series Aluminum Alloys by Machine Learning-Based Optimization6Hybrid machine learning and finite element modeling for accurate prediction of sintering-induced deformation in material extrusion additive manufacturing6Machine Learning-Based Prediction of Young’s Modulus in Ti-Alloys6X-Ray Imaging of Solid-State Sintering and Laser Powder Bed Fusion: A Review of Process Monitoring and Defect Evolution0Machine learning-driven hardness modeling and interpretable analysis of constitutive characteristics for thin copper films0Hybrid constitutive law with machine learning for sintering of advanced ceramics0

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  1. Ultra-high temperature ceramics melting temperature prediction via machine learning

    Ceramics International · 2019-06-11

    Nan Qu, Yong Liu, Mingqing Liao, Zhonghong Lai, Fei Zhou, Puchang Cui, Tianyi Han, Danni Yang, Jingchuan Zhu

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  2. Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening

    Acta Materialia · 2020-01-01

    Hongtao Zhang, Huadong Fu, Xingqun He, Changsheng Wang, Lei Jiang, Long‐Qing Chen, Jianxin Xie

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  3. Machine-learning-assisted prediction of the mechanical properties of Cu-Al alloy

    International Journal of Minerals Metallurgy and Materials · 2020-03-01

    Zhenghua Deng, Haiqing Yin, Xue Feng Jiang, Cong Zhang, Guofei Zhang, Bin Xu, Guoqiang Yang, Tong Zhang, Mao Wu, Xuanhui Qu

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  4. Machine learning in materials genome initiative: A review

    Journal of Material Science and Technology · 2020-05-03

    Yingli Liu, Chen Xu Niu, Zhuo Wang, Yong Gan, Yan Hong Zhu, Shuhong Sun, Tao Shen

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  5. Transfer learning aid the prediction of sintering densification

    Ceramics International · 2020-07-04

    Wu Zhouzhi, Zhang Xiaomin, Zhipeng Zhao, Zhang Hengjia, Tang Hongwu, Liang Yuan

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  6. Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus

    npj Computational Materials · 2020-07-21

    Fei Yang, Zhen Li, Qing Wang, Beibei Jiang, Biaojie Yan, Pengcheng Zhang, Wei Xu, Chuang Dong, Peter Kaehuei Liaw

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  7. Machine learning-assisted discovery of strong and conductive Cu alloys: Data mining from discarded experiments and physical features

    Materials & Design · 2020-10-20

    Qingkun Zhao, Huiya Yang, Jiabin Liu, Haofei Zhou, Hongtao Wang, Wei Tian Yang

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  8. Reliability evaluation method for squeeze casting process parameter data

    The International Journal of Advanced Manufacturing Technology · 2021-08-06

    Jianxin Deng, Zhixing Ye, Rui Tang, Dongdong You, Bin Xie

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  9. Recent machine learning guided material research - A review

    Computational Condensed Matter · 2021-09-20

    Mohammad Asaduzzaman Chowdhury, Nayem Hossain, Md. Bengir Ahmed Shuvho, Mohamad Fotouhi, Md Sakibul Islam, Md. Ramjan Ali, Mohammod Abul Kashem

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  10. Imputation Method Based on Collaborative Filtering and Clustering for the Missing Data of the Squeeze Casting Process Parameters

    Integrating materials and manufacturing innovation · 2022-01-27

    Jianxin Deng, Zhixing Ye, Lubao Shan, Dongdong You, Guangming Liu

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  11. Deep learning modeling strategy for material science: from natural materials to metamaterials

    Journal of Physics Materials · 2022-02-28

    Wenwen Li, Pu Chen, Bo Xiong, Guandong Liu, Shuliang Dou, Yaohui Zhan, Zhiyuan Zhu, Tao Chu, Yao Li, Wei Ma

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  12. Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments

    Journal of Materials Research and Technology · 2022-06-08

    B. Li, Yong Du, Zhongyan Zheng, Xiaofan Ye, Dangqi Fang, X.D. Si, Y.Q. Wang

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  13. Machine Learning Based Sintered Density Prediction of Bronze Processed by Powder Metallurgy Route

    Metals and Materials International · 2022-11-26

    Tafzeelul Kamal, Gouthama, Anish Upadhyaya

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  14. Dimensionality reduction for machine learning using statistical methods: A case study on predicting mechanical properties of steels

    Materials Today Communications · 2022-12-15

    Xiaoan Yang, G.M.A.M. El-Fallah, Qing Tao, Jun Fu, Chenlei Leng, Jennifer H. Shepherd, Hongbiao Dong

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  15. New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning

    Fatigue & Fracture of Engineering Materials & Structures · 2022-12-28

    Dominik Sebastian Leininger, Felix-Christian Reissner, Jörg Baumgartner

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  16. Machine Learning Sintering Density Prediction Model for MOX Fuel Pellet

    Transactions of the Atomic Energy Society of Japan · 2023-01-01

    Masato Kato, Shinya Nakamichi, Shun Hirooka, Masashi Watanabe, Tatsutoshi Murakami, Katsunori ISHII

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  17. Experimental Feedback to Improve the Extrapolation of Machine Learning: Application to Design of PM Cu–Al–Fe–Ni Alloys

    Transactions of the Indian Institute of Metals · 2023-02-18

    Bin Xu, Haiqing Yin, Ruijie Zhang, Zhenghua Deng, Wei Li

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  18. Design Strategy for Art Copper Alloys’ Colors Through Machine Learning and Oxidation Treatment

    JOM · 2023-03-14

    Shuang Zhou, Qian Lei, Jie Yin, Yuxin Liang, Qianye Ren, Can Dai, Zhou Li

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  19. Prediction of sintered density of binary W(Mo) alloys using machine learning

    Rare Metals · 2023-05-02

    He-Xiong Liu, Yunfei Yang, Yongfeng Cai, Chang-hao Wang, Chen Lai, Yaowu Hao, Jinshu Wang

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  20. Modeling of the Sintered Density in Cu-Al Alloy Using Machine Learning Approaches

    ACS Omega · 2023-07-25

    Saleh Asnaashari, Mohammadhadi Shateri, Abdolhossein Hemmati‐Sarapardeh, Shahab S. Band

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  21. Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles

    JOM · 2023-08-22

    Tanu Tiwari, Sadegh Jalalian, Chamini Lakshi Mendis, Dmitry G. Eskin

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  22. Machine learning aided prediction of sintering induced swelling in powder metallurgical Cu-Sn alloys

    Computational Materials Science · 2024-02-01

    Tafzeelul Kamal, Gouthama, Anish Upadhyaya

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  23. An Investigation of the Mechanical Property Behaviour of Copper/AA7075 Nanocomposites Based on Machine Learning

    Mathematical Modelling and Engineering Problems · 2024-08-28

    Raquel Virginia Colcha Ortíz, María Del Carmen Moreno Albuja, Eugenia Mercedes Naranjo Vargas, Mónica Alexandra Moreno Barriga, Miguel Escobar, Ximena Rashell Cazorla Vinueza, Jessica Paola Arcos Logroño, Paúl Montúfar, Paul Marcelo Tacle Humanante

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  24. Research on the prediction of component activity silicon-based binary melt using the MIVM and machine learning algorithms

    International Communications in Heat and Mass Transfer · 2025-05-08

    Fen Zou, Zuhan Shan, Qunzhi Wang, Jijun Wu, Kuixian Wei, Zhengjie Chen, Wenhui Ma

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  25. Facilitating Recycling of 6xxx Series Aluminum Alloys by Machine Learning-Based Optimization

    Journal of Sustainable Metallurgy · 2025-05-12

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  26. Hybrid machine learning and finite element modeling for accurate prediction of sintering-induced deformation in material extrusion additive manufacturing

    Acta Materialia · 2025-06-07

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  27. Machine Learning-Based Prediction of Young’s Modulus in Ti-Alloys

    Metals · 2026-02-19

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  28. X-Ray Imaging of Solid-State Sintering and Laser Powder Bed Fusion: A Review of Process Monitoring and Defect Evolution

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  29. Machine learning-driven hardness modeling and interpretable analysis of constitutive characteristics for thin copper films

    Engineering Computations · 2026-05-08

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  30. Hybrid constitutive law with machine learning for sintering of advanced ceramics

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