Ability to Simulate Absorption and Melt Pool Dynamics for Laser Melting of Bare Aluminum Plate: Results and Insights from the 2022 Asynchronous AM-Bench Challenge

• Published in: Integrating materials and manufacturing innovation Vol. 13; no. 1; pp. 175 - 184
• Main Authors: Simonds, Brian J., Tanner, Jack, Artusio-Glimpse, Alexandra, Parab, Niranjan, Zhao, Cang, Sun, Tao, Williams, Paul A.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2024
• Subjects: Additive Manufacturing Benchmarks 2022; Characterization and Evaluation of Materials; Chemistry and Materials Science; Materials Science; Metallic Materials; Nanotechnology; Structural Materials; Surfaces and Interfaces; Thematic Section: Additive Manufacturing Benchmarks 2022; Thin Films; Laser powder bed fusion; Absorption; Additive manufacturing; Simulation
• ISSN: 2193-9764; 2193-9772
• DOI: 10.1007/s40192-023-00336-0

The 2022 Asynchronous AM-Bench challenge was designed to test the ability of simulations to accurately predict laser power absorption as well as various melt pool behaviors (width, depth, and solidification) during laser melting of solid metal during stationary and scanned laser illumination. In this challenge, participants were asked to predict a series of experimental outcomes. Experimental data were obtained from a series of experiments performed at the Advanced Photon Source at Argonne National Laboratories in 2019. These experiments combined integrating sphere radiometry with high-speed X-ray imaging, allowing for the simultaneous recording of absolute laser power absorption and two-dimensional, projected images of the melt pool. All challenge problems were based on experiments using bare aluminum solid metal. Participants were provided with pertinent experimental information like laser power, scan speed, laser spot size, and material composition. Additionally, participants were given absorptance and X-ray imaging data from stationary and scanned laser experiments on solid Ti–6Al–4V that could be used for testing their models before attempting challenge problems. In total, this challenge received 56 submissions from eight different research groups for eight individual challenge problems. The data for this challenge, and associated information, are available for download from the NIST Public Data Repository. This paper summarizes the results from the 2022 Asynchronous AM-Bench challenge as well as discusses the lessons learned to help inform future challenges.