模拟裸铝板激光熔化的吸收和熔池动力学的能力:2022 年异步 AM-Bench 挑战赛的结果与启示

IF 2.4 3区 材料科学 Q3 ENGINEERING, MANUFACTURING Integrating Materials and Manufacturing Innovation Pub Date : 2024-02-01 DOI:10.1007/s40192-023-00336-0
Brian J. Simonds, Jack Tanner, Alexandra Artusio-Glimpse, Niranjan Parab, Cang Zhao, Tao Sun, Paul A. Williams
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摘要

2022 年异步 AM-Bench 挑战赛旨在测试模拟准确预测激光功率吸收的能力,以及静止和扫描激光照射固体金属时激光熔化过程中各种熔池行为(宽度、深度和凝固)的能力。在这项挑战中,参赛者需要预测一系列实验结果。实验数据来自 2019 年在阿贡国家实验室先进光子源进行的一系列实验。这些实验将积分球辐射测量与高速 X 射线成像相结合,可同时记录绝对激光功率吸收和熔池的二维投影图像。所有挑战问题均基于使用裸铝固体金属的实验。参与者可获得相关的实验信息,如激光功率、扫描速度、激光光斑大小和材料成分。此外,还向参赛者提供了固体 Ti-6Al-4V 上固定和扫描激光实验的吸收率和 X 射线成像数据,这些数据可用于在尝试挑战问题之前测试他们的模型。本次挑战赛共收到来自 8 个不同研究小组的 56 份提交材料,涉及 8 个单项挑战问题。本次挑战赛的数据和相关信息可从 NIST 公共数据存储库下载。本文总结了 2022 年异步 AM-Bench 挑战赛的结果,并讨论了从中吸取的经验教训,以便为今后的挑战赛提供参考。
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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

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.

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来源期刊
Integrating Materials and Manufacturing Innovation
Integrating Materials and Manufacturing Innovation Engineering-Industrial and Manufacturing Engineering
CiteScore
5.30
自引率
9.10%
发文量
42
审稿时长
39 days
期刊介绍: The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.
期刊最新文献
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