journal article·2023-11

Revealing bulk metallic glass crystallization kinetics during laser powder bed fusion by a combination of experimental and numerical methods

Zerong Yang, Florian Huber, Anna Krapf, Benoit Merle, Matthias Markl, Michael Schmidt, Carolin Körner

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Abstract

Laser powder bed fusion (PBF-LB/M) has been used to fabricate bulk metallic glass (BMG) parts. However, there is still a lack of understanding about the crystallization behavior of BMGs during PBF-LB/M. This article aims to combine experimental and numerical methods to gain a more comprehensive understanding of the BMG crystallization kinetics during PBF-LB/M. The material used in this work is an industrial-grade BMG commercially known as AMZ4 (Zr 59.3Cu 28.8Al 10.4Nb 1.5 , at.%). The samples prepared by PBF-LB/M were characterized by optical microscopy, scanning electron microscopy, and nanoindentation. Simulations were performed using our in-house developed software with setups mimicking the PBF-LB/M experiment. The experimental and numerical results are compared and discussed in detail. The most important finding is that BMG crystallization in PBF-LB/M is a very localized phenomenon, mainly caused by short-range in situ heat treatment. Here, short-range in-situ heat treatment refers to concomitant heat treatment that occurs during the processing of the current melt line, very few subsequent melt lines, and very few subsequent layers. This work provides insight into the BMG crystallization phenomenon that occurs during PBF-LB/M processing, especially its underlying crystallization kinetics. Furthermore, this work demonstrates the usefulness of our in-house developed simulation software in guiding PBF-LB/M process development for BMGs and BMG composites.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Metallic Glasses and Amorphous Alloys · Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies

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

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