Progress in metal additive manufacturing using innovative solid-state friction stir-based techniques

被引:10
作者
Olhan, Sandeep [1 ]
Antil, Bindu [2 ]
Behera, B. K. [1 ]
机构
[1] Indian Inst Technol Delhi, Dept Text & Fibre Engn, New Delhi 110016, India
[2] Penn State Univ, EMS Energy Inst, John & Willie Leone Family Dept Energy & Mineral E, University Pk, PA 16802 USA
关键词
Metal additive manufacturing; Additive solid-state friction stir-based tech-; niques (ASSFST); Friction stir processing; Microstructure evolution; Fusion-based additive manufacturing; SEVERE PLASTIC-DEFORMATION; MG-SI ALLOYS; ALUMINUM-ALLOY; MECHANICAL-PROPERTIES; PROCESS PARAMETERS; TENSILE-STRENGTH; GRAIN-REFINEMENT; MAGNESIUM ALLOY; MICROSTRUCTURAL CHARACTERIZATION; DYNAMIC RECRYSTALLIZATION;
D O I
10.1016/j.jallcom.2024.177395
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Additive solid-state friction stir-based techniques (ASSFST) represent a cutting-edge advancement in additive manufacturing (AM), offering a compelling alternative to traditional fusion-based methods. These techniques uniquely operate in the solid-state, below the material's melting point, allowing them to leverage friction stir processing (FSP) principles to produce components with refined grain structures and controlled texture. Furthermore, ASSFST significantly reduces material waste, utilizes non-consumable tools, and generates negligible emissions, making it an environmentally sustainable option. This paper provides a comprehensive review of the current state-of-the-art in various ASSFST, with a specific focus on the rationale behind selecting these methods and their suitability for different materials. It delves into an in-depth exploration of emerging ASSFST, covering their working principles, material flow dynamics, heat generation mechanisms, and microstructural evolution. The review also underscores the role of machine learning (ML) in optimizing these processes, offering predictive capabilities that enhance efficiency and consistency. Although ASSFST holds great potential to revolutionize AM, the field is still in its early developmental stages. This review provides a comprehensive overview of the current state of ASSFST, the challenges encountered, and future research directions, aiming to advance the understanding and industrial adoption of these transformative, game-changing techniques.
引用
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页数:31
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