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RESEARCH PAPER · Tribology International · 2025

Accelerating tribological material discovery: A machine learning framework with predictive insights

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Accelerating tribological material discovery: A machine learning framework with predictive insightsOriginalDevelopment of an Artificial Neural Network-Based Tool for Predicting Failures in Composite Laminate Structures2Physics-informed machine learning for explainable prediction of friction and wear in carbon-ceramic composites16Integrated experimental and machine learning analysis of Al-Si/CSA/MoS2 hybrid composites for enhanced mechanical and tribological performance1Friction and wear prediction of copper-free resin-based brake materials: A hybrid PSO-FPA-BP neural network approach38Machine learning in tribology: A review on framework, case studies, and future perspectives11Machine Learning Prediction of Tribological Properties of Graphene‐Reinforced Perfluoroelastomer Composites Based on Molecular Dynamics Simulations and Experimental Research1Applications of classical and quantum machine learning in manufacturing: predictive maintenance, scheduling and tribology8Impact of metallurgical parameters on wear rate and COF of DP steels using ML-driven methods1Machine learning applications in metallic materials: Recent advances and future perspectives8Future research directions and applications of artificial intelligence in tribology1Recent advances and optimization strategies in tribological performance of high entropy alloys10Experimental Investigation and Predictive Modelling of Tribological Behaviour in Fly Ash Reinforced Polymer Composites Fabricated via Stereolithography1Accelerating inverse design of wear resistant high entropy alloys via multiscale informatics2A small-sample prediction framework for fretting wear rate of Zr alloy based on response surface methodology and transfer learning0Deconstructing Triboinformatics: A Conceptual Framework for the Intersection of Machine Learning and Tribology1Solid Lubrication0Machine learning for cracking the molecular code of room-temperature organic phosphorescent materials: From structure-property relationship to performance prediction0TriboBERT: A pre-training language model tailored for tribology0Machine learning-assisted analysis of friction and transfer films in Cu-CNTs-Graphite composites0A hybrid machine learning framework for predicting thermal response, wear and lubrication regime evolution in graphene-enhanced grease0

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  1. Development of an Artificial Neural Network-Based Tool for Predicting Failures in Composite Laminate Structures

    Biomimetics · 2025-08-08

    Milica Milić, Jelena Svorcan, Ivana D. Atanasovska

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  2. Physics-informed machine learning for explainable prediction of friction and wear in carbon-ceramic composites

    Ceramics International · 2025-09-27

    Yadong Li, Bing Liu, Zhaofu Zhang, Zhaofu Zhang, Wengen Ouyang, Weiwei Shi, Zhenghua Chang, Zheng Hu, Kun Zhang, Bingchen Wei

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  3. Integrated experimental and machine learning analysis of Al-Si/CSA/MoS2 hybrid composites for enhanced mechanical and tribological performance

    Next Materials · 2026-01-09

    Sivasankara Raju Rallabandi, Pandi Thimothy, Gandi Ramarao, Devi Prasad Pilla, Bondala Ramakrishna, Azad Duppala

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  4. Friction and wear prediction of copper-free resin-based brake materials: A hybrid PSO-FPA-BP neural network approach

    Wear · 2026-01-12

    Kaikui Zheng, Qing Le, Ling Pan, Jianmeng Huang

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  5. Machine learning in tribology: A review on framework, case studies, and future perspectives

    Journal of Manufacturing Processes · 2026-01-18

    Yadong LI, Bing Liu, Zhenghua Chang, Zhaofu Zhang, Wengen Ouyang, Zishang Liu, Quanyu Jiang, Yizhe Liu, A. W. Huang, Yuxin Wu, Zheng Hu, Hai Lan, Ming Han, Yunjian Bai, Kun Zhang, Bingchen Wei

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  6. Machine Learning Prediction of Tribological Properties of Graphene‐Reinforced Perfluoroelastomer Composites Based on Molecular Dynamics Simulations and Experimental Research

    Polymer Composites · 2026-01-22

    Shengbo Jin, Jing Zhao, Xueying Tan, Bowen Jiang

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  7. Applications of classical and quantum machine learning in manufacturing: predictive maintenance, scheduling and tribology

    Next research. · 2026-02-24

    Abdullah Jawad, Ethan Stefan-Henningsen, Amirkianoosh Kiani

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  8. Impact of metallurgical parameters on wear rate and COF of DP steels using ML-driven methods

    Results in Engineering · 2026-03-17

    Masoud Safarpour, Aliakbar Ekrami, M. Kazeminezhad

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  9. Machine learning applications in metallic materials: Recent advances and future perspectives

    Journal of Alloys and Compounds · 2026-03-24

    Bin Miao, Guan-Qi Lin, Yuan Zhang, Yu-Wei Cheng, Huan Yang

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  10. Future research directions and applications of artificial intelligence in tribology

    Discover Mechanical Engineering · 2026-04-23

    Amrinder Mehta, Hitesh Vasudev, S. Sabareshwaran, B. V. Suma, Jashanpreet Singh, Brijesh Prasad, Saravjeet Singh, Oda Tadese Katelo

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  11. Recent advances and optimization strategies in tribological performance of high entropy alloys

    Tribology International · 2026-04-25

    Z Chen, Chenguang Gao, Xuhui Pei, Mingchuan Hu, Yin Du, Tao Yang, Benyebka Bou‐Saïd, H P Wang

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  12. Experimental Investigation and Predictive Modelling of Tribological Behaviour in Fly Ash Reinforced Polymer Composites Fabricated via Stereolithography

    Journal of Bio- and Tribo-Corrosion · 2026-05-16

    R. Keshavamurthy, V. Suma, C.S.Ramesh, Ali A. Rajhi, Alaauldeen A. Duhduh, Y. P. Ravitej

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  13. Accelerating inverse design of wear resistant high entropy alloys via multiscale informatics

    Journal of Alloys and Compounds · 2026-05-19

    Haobing Hu, Xiaoliang Shi, Chaohua Wu, Yuxuan Chen, Haowen Qin, Wenzheng Zhai, Xiyao Liu, Ahmed Mohamed Mahmoud Ibrahim

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  14. A small-sample prediction framework for fretting wear rate of Zr alloy based on response surface methodology and transfer learning

    Tribology International · 2026-05-23

    Qi Sun, Haowen Tang, Zhihua Lü, Mingyang Zuo, Pengfei Yang, Xiaoqiang Fan, Minhao Zhu

    Read original paper ↗ ·
  15. Deconstructing Triboinformatics: A Conceptual Framework for the Intersection of Machine Learning and Tribology

    Journal of Tribology · 2026-05-29

    Amir Kordijazi

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  16. Solid Lubrication

    Elsevier eBooks · 2026-06-15

    Xudong Sui, Carsten Gachot, Edoardo Marquis

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  17. Machine learning for cracking the molecular code of room-temperature organic phosphorescent materials: From structure-property relationship to performance prediction

    Green Energy & Environment · 2026-08-01

    Yan Hu, Senlin Tian, Yingjie Li

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  18. TriboBERT: A pre-training language model tailored for tribology

    Tribology International · 2026-08-27

    Wenhao He, Zhibin Lu

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  19. Machine learning-assisted analysis of friction and transfer films in Cu-CNTs-Graphite composites

    Materials Chemistry and Physics · 2026-09-18

    Xin Zhang, Jiashang Li, Hanhan Yang, Xiongwei Shi, Meiling Xie, Liuchen Wu

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  20. A hybrid machine learning framework for predicting thermal response, wear and lubrication regime evolution in graphene-enhanced grease

    Results in Engineering · 2026-10-07

    Ethan Stefan-Henningsen, Amirkianoosh Kiani

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