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A systematic review on advanced surface coating technologies for high-pressure piston pumps 认领 引用
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作者 Yifei Dong Zhichao Jiao +9 位作者 Yangyang Ma Qing Zhou Ming Yang Xing Ran Zhe Wang Chengjiang Tang Yulong Li Xiner Li Haishan Teng Xiaojiang Lu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期288-309,共22页
Piston pumps are extensively employed in the industrial and aerospace sectors,particularly in applications that demand high-pressure operation and precise flow regulation.However,the inherent structural complexity of ... Piston pumps are extensively employed in the industrial and aerospace sectors,particularly in applications that demand high-pressure operation and precise flow regulation.However,the inherent structural complexity of these components,coupled with their frequent exposure to adverse operating conditions,leads to various forms of surface degradation in friction pair elements.To address these challenges,researchers have pursued the development of advanced coating technologies aimed at enhancing surface hardness,reducing friction coefficients,and improving wear resistance.These coatings typically include metallic coatings,ceramic coatings,and diamond-like carbon(DLC)coatings,along with more cutting-edge yet highly promising high-entropy alloy(HEA)coatings.As a novel class of materials,HEA coatings hold considerable potential to overcome the inherent limitations of conventional coating systems.This review provides a comprehensive overview of recent advances in piston pump coating technologies,with particular emphasis on deposition methodologies,microstructural characteristics,and tribological performance.By establishing microstructure-property relationships within these coating systems,this study proposes future research directions for optimizing surface engineering approaches in hydraulic pump applications. 展开更多
关键词 Tribological performance Advanced coatings Piston pump Multiple-principal element Aeronautics applications
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Transfer learning-enabled performance prediction of metallic materials:Methods,applications and prospects 认领 引用
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作者 Yufan Liu Dexin Zhu +7 位作者 Zhihao Tian Jiayi Liu Xing Ran Zhe Wang Chengjiang Tang Lifei Wang Wei Xu Xin Lu 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第3期749-767,共19页
In the era of materials genome engineering,data-driven machine learning has become a powerful tool for accelerating the re-search and development of metallic materials.However,the predictive accuracy and generalizatio... In the era of materials genome engineering,data-driven machine learning has become a powerful tool for accelerating the re-search and development of metallic materials.However,the predictive accuracy and generalization ability of traditional machine learning models are often limited by the scarcity and heterogeneity of available data,especially in small-sample scenarios.To address these chal-lenges,transfer learning has emerged as an effective strategy to leverage knowledge from related domains,thereby enhancing model per-formance with limited target data.This review systematically summarizes the fundamental concepts,methodologies,and representative applications of transfer learning in the prediction of metallic materials'properties.Transfer learning can be categorized into feature-based,instance-based,parameter-based,and knowledge-based methods.This work discusses their respective mechanisms,advantages,and limit-ations.Case studies demonstrate that transfer learning can significantly improve prediction accuracy,data efficiency,and model inter-pretability in tasks such as mechanical property prediction and alloy design.Furthermore,this work highlights emerging trends including hybrid,multi-task,meta,and adaptive transfer learning,which further expand the applicability of these techniques.Finally,this work out-lines future research directions,emphasizing the need for data standardization,algorithmic innovation,multimodal data fusion,and the in-tegration of physical principles to achieve robust,interpretable,and generalizable models.The perspectives presented aim to advance the intelligent design and discovery of metallic materials,promoting efficient knowledge transfer and collaborative innovation in materials science. 展开更多
关键词 small-sample data machine learning transfer learning performance prediction
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Designing laser powder bed fusion low-alloyed titanium with superior strength-ductility trade-offvia machine learning 认领 引用 被引量：2
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作者 Xiaohang Zhang Xing Ran +7 位作者 Zhe Wang Wei Xu Xiangyu Zhu Zhiheng Du Jiazhen Zhang Runguang Li Yageng Li Xin Lu 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2025年第34期323-330,共8页
1.Introduction Titanium(Ti)and its alloy have become a critical structural material in aerospace,weaponry,and equipment industries due to their high strength,low density,and excellent corrosion resistance[1-3].
关键词 machine learning weaponry high strength structural material aerospace strength ductility trade off laser powder bed fusion equipment industries
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Reinforcement Learning in Materials Science:Recent Advances,Methodologies and Applications 认领 引用
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作者 Jiaye Li Xinyuan Zhang +7 位作者 Chunlei Shang Xing Ran Zhe Wang Chengjiang Tang Xiaohang Zhang Mingshuo Nie Wei Xu Xin Lu 《Acta Metallurgica Sinica(English Letters)》 SCIE EI CAS CSCD 2025年第12期2077-2101,共25页
In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike t... In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike traditional trial-and-error and physics-based approaches,RL agents autonomously identify optimal strategies across high-dimensional and dynamic design spaces by iterative interactions with complex environments.This capability makes RL especially effective for target optimization and sequential decision-making in challenging materials science problems.In this review,we present a comprehensive overview of fundamental RL algorithms,including Q-learning,deep Q-networks(DQN),actor-critic methods,and deep deterministic policy gradient(DDPG).Then,the core mechanisms,advantages,limitations,and representative applications of RL in materials discovery,property optimization,process control,and manufacturing are discussed systematically.Lastly,key future research directions and opportunities are outlined.The perspectives presented herein aim to foster interdisciplinary collaboration and drive innovation at the frontier of AI‑driven materials science. 展开更多
关键词 Reinforcement learning Data-driven Objective optimization Material design Material application
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