Additive manufacturing of metal matrix composites

被引:8
作者
Nartu, Mohan Sai Kiran Kumar Yadav [1 ]
Agrawal, Priyanshi [2 ]
机构
[1] Pacific Northwest Natl Lab, Nucl Sci Div, Richland, WA 99354 USA
[2] Idaho Natl Lab, Nucl Sci & Technol Div, Idaho Falls, ID 83410 USA
关键词
Microstructural evolution; In-situ reinforcements; Properties; Metal matrix composites; Sustainability; Additive manufacturing; HIGH-ENTROPY ALLOY; DIRECTED ENERGY DEPOSITION; DIRECT LASER DEPOSITION; IN-SITU FORMATION; MECHANICAL-PROPERTIES; STAINLESS-STEEL; WEAR-RESISTANCE; TITANIUM BORIDE; INCONEL; 625; MICROSTRUCTURAL CHARACTERIZATION;
D O I
10.1016/j.matdes.2025.113609
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Although Metal matrix composites (MMCs) are superior to most sought-after metallic alloys, their challenging fabricability has limited their widespread use in bulk-form applications. Among the many advanced fabrication techniques, Additive Manufacturing (AM), owing to its unique capabilities to produce near-net shapes, has drawn significant traction in the past two decades, especially for materials that are difficult to process using traditional methods. However, unlike pure metal/alloy systems, MMCs are highly sensitive to the processing conditions prevailing in AM techniques due to factors such as the high melting point of reinforcement particles and the potential for in-situ reactions. Therefore, it may be a while before metal matrix composites are commercially produced via AM. This review will discuss the current state-of-the-art design, fabricability, and performance of various additively manufactured Metal matrix composites (AMMCs). A particular focus will be on microstructural evolution and microstructure-property relationships. The most employed AM techniques, such as directed energy deposition, powder bed fusion, binder jetting, sheet lamination, and solid-state friction stir processing, are fundamentally different in terms of thermo-kinetics, forming the perspective for this review. A detailed comparison of microstructural evolution and process parameter optimization, including feedstock preparation methods and the role of machine learning and modeling among the different AM processes, is also presented. Finally, a critical evaluation of emerging AM technologies for MMCs is also provided, highlighting their potential advantages and challenges.
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页数:85
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