journal article·2024-04-01Open access

Review on laser directed energy deposited aluminum alloys

Tian-Shu Liu, Peng Chen, Feng Qiu, Hong-Yu Yang, Nicholas Tan Yew Jin, Youxiang Chew, Di Wang, Ruidi Li, Qi-Chuan Jiang, Chaolin Tan

Abstract

Abstract Lightweight aluminum (Al) alloys have been widely used in frontier fields like aerospace and automotive industries, which attracts great interest in additive manufacturing (AM) to process high-value Al parts. As a mainstream AM technique, laser-directed energy deposition (LDED) shows good scalability to meet the requirements for large-format component manufacturing and repair. However, LDED Al alloys are highly challenging due to their inherent poor printability (e.g. low laser absorption, high oxidation sensitivity and cracking tendency). To further promote the development of LDED high-performance Al alloys, this review offers a deep understanding of the challenges and strategies to improve printability in LDED Al alloys. The porosity, cracking, distortion, inclusions, element evaporation and resultant inferior mechanical properties (worse than laser powder bed fusion) are the key challenges in LDED Al alloys. Processing parameter optimizations, in-situ alloy design, reinforcing particle addition and field assistance are the efficient approaches to improving the printability and performance of LDED Al alloys. The underlying correlations between processes, alloy innovation, characteristic microstructures, and achievable performances in LDED Al alloys are discussed. The benchmark mechanical properties and primary strengthening mechanism of LDED Al alloys are summarized. This review aims to provide a critical and in-depth evaluation of current progress in LDED Al alloys. Future opportunities and perspectives in LDED high-performance Al alloys are also outlined.
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Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Aluminum Alloy Microstructure Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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