Lyle Levine, Brandon Lane, Chandler Becker, James Belak, Robert Carson, David Deisenroth, Edward Glaessgen, Thomas Gnaupel-Herold, Michael Gorelik, Gretchen Greene, Saadi Habib, Callie Higgins, Michael Hill, Nik Hrabe, Jason Killgore, Jai Won Kim, Gerard Lemson, Kalman Migler, Shawn Moylan, Darren Pagan, Thien Phan, Maxwell Praniewicz, David Rowenhorst, Edwin Schwalbach, Jonathan Seppala, Brian Simonds, Mark Stoudt, Jordan Weaver, Ho Yeung, Fan Zhang
{"title":"2022 年调幅工作台测量、挑战问题、模型提交和会议的成果与结论","authors":"Lyle Levine, Brandon Lane, Chandler Becker, James Belak, Robert Carson, David Deisenroth, Edward Glaessgen, Thomas Gnaupel-Herold, Michael Gorelik, Gretchen Greene, Saadi Habib, Callie Higgins, Michael Hill, Nik Hrabe, Jason Killgore, Jai Won Kim, Gerard Lemson, Kalman Migler, Shawn Moylan, Darren Pagan, Thien Phan, Maxwell Praniewicz, David Rowenhorst, Edwin Schwalbach, Jonathan Seppala, Brian Simonds, Mark Stoudt, Jordan Weaver, Ho Yeung, Fan Zhang","doi":"10.1007/s40192-024-00372-4","DOIUrl":null,"url":null,"abstract":"<p>The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. 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引用次数: 0
摘要
增材制造基准测试系列(AM Bench)为验证各种增材制造(AM)技术和材料系统的增材制造(AM)模拟提供了严格的测量数据。AM Bench 包括广泛的原位和非原位测量、针对 AM 建模社区的模拟挑战以及相应的系列会议。2022 年,第二轮 AM Bench 测量、挑战问题和会议已经完成,主要侧重于金属的激光粉末床熔融 (LPBF) 加工以及聚合物的材料挤压加工和大桶光聚合。来自美国国家标准与技术研究院(NIST)10 个部门和另外 21 个组织的 100 多人直接参与了 AM Bench 2022 的测量、数据管理和会议组织工作。国际AM界提交了138套盲法建模模拟,用于与原位和离场测量进行比较,比2018年第一轮AM Bench提交的46套有所增加。对这些提交数据的分析为了解当前的 AM 建模能力提供了宝贵的信息。AM Bench 数据永久存档,可免费在线访问。AM Bench 会议还举办了关于 AM 材料和组件的资格认证和认证的嵌入式研讨会。
Outcomes and Conclusions from the 2022 AM Bench Measurements, Challenge Problems, Modeling Submissions, and Conference
The Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. The AM Bench conference also hosted an embedded workshop on qualification and certification of AM materials and components.
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.