Predictive simulation of bulk metallic glass crystallization during laser powder bed fusion

Yang Z, Markl M, Körner C (2022)


Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2022

Journal

Book Volume: 59

Article Number: 103121

DOI: 10.1016/j.addma.2022.103121

Open Access Link: http://dx.doi.org/10.1016/j.addma.2022.103121

Abstract

Laser powder bed fusion (L-PBF) has been employed to fabricate bulk metallic glass (BMG) parts. However, traditional experimental trial-and-error methods to determine process parameters for specific materials and L-PBF machines are time-consuming and expensive. In this paper, a phenomenological crystallization model, namely the Nakamura model, is coupled with L-PBF process simulation. A convenient approach for the crystallization parameter determination and a two-step Euler method for the numerical implementation has been developed. Numerical simulations are performed using the material parameters of a Zr-based BMG Zr59.3Cu28.8Al10.4Nb1.5 (at.%, trade name: AMZ4). The numerical results are validated by comparing with experimental results from different perspectives. Based on the numerical findings, a comprehensive understanding of BMG crystallization behavior during L-PBF is gained. In the end, the crystallization model is implemented in our in-house developed software SAMPLE2D. SAMPLE2D simulation results are presented, whereby the L-PBF process window for fully amorphous AMZ4 parts is explored. Thereby, it is believed that the developed numerical software can be applied to aid process development for BMGs by taking the crystallization phenomenon into account.

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APA:

Yang, Z., Markl, M., & Körner, C. (2022). Predictive simulation of bulk metallic glass crystallization during laser powder bed fusion. Additive Manufacturing, 59. https://dx.doi.org/10.1016/j.addma.2022.103121

MLA:

Yang, Zerong, Matthias Markl, and Carolin Körner. "Predictive simulation of bulk metallic glass crystallization during laser powder bed fusion." Additive Manufacturing 59 (2022).

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