RII Track-2 FEC: Rapid Qualification for Additively Manufactured Safety-Critical Components

增材制造（AM），如基于金属粉末的3D打印部件资格已被美国国家标准协会（ANSI）增材制造标准化合作组织（AMSC）确定为我国高度优先的标准化差距。为了使3D打印的金属零件在商业上更加可行，必须进行快速的零件鉴定过程。这项研究基础设施改进轨道-2重点EPSCoR合作（RII轨道-2 FEC）奖将允许来自两个EPSCoR管辖区（LA和AL）机构的研究人员，南方大学（SU），路易斯安那州的路易斯安那州立大学（LSU）和亚拉巴马的奥本大学（Au）的国家增材制造卓越中心（NCAME）建立增材制造认证联盟（CAM-Q）。CAM-Q将利用材料科学和数据分析交叉领域的研究机会，技术重点是激光束粉末床融合（LB-PBF）增材制造（AM），从根本上改变AM零件鉴定过程。该项目的主要成果将是AM工艺设计和快速疲劳性能鉴定的整体框架，以及对教育，培训和发展高技能，多学科和多样化劳动力的重大贡献，以支持美国的行业。计划中的研究代表了当前AM资格实践的重大变化。该项目的成功实施将使金属/合金零件的制造具有显著加快的认证周期，使AM工艺具有商业可行性，并将美国行业推向全球领先地位。为了使LB-PBF AM更具商业可行性，必须进行快速零件认证过程。通过将多尺度模拟和表征与数据分析相结合，并通过构建中尺度力学测试框架，CAM-Q将为中尺度构建策略建立处理策略-结构-性能（PSP）关系，并随后展示LB-PBF部件结构完整性的新鉴定方法。CAM-Q的目标是建立一个科学驱动的、基于无损评估（NDE）的框架，用于快速鉴定AM部件的安全关键应用。将生成一个新的PSP数据库，用于离散建筑策略集（DBSS）的关键集合，以通过结合实验，数据分析和基于物理的建模来支持资格认证过程。对于每个离散构建策略集，该数据库将包括：1）位置特定的过程控制参数; 2）详细的微观结构图，包括通过X射线计算机断层扫描（XCT）扫描收集的孔隙/裂纹的体积分布、结合数据分析和重建的三维（3D）“切片和视图”数据立方体，以及将缺陷与疲劳性能联系起来的基于物理的模拟;和3）微观到中尺度的机械测试，以精确定位特定位置的机械性能，重点是疲劳性能。数据分析将用于评估组件的性能，并为制造性能更好的部件的加工策略提供指导，从而实现快速量化。研究成果将被纳入教育和劳动力培训计划，包括为期一年的本科生项目和研究生论文，直接参与行业。研究、教育、劳动力发展和培训计划之间的紧密整合和协调将得到协调良好的项目管理、评估和评估计划的支持。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Additive Manufacturing (AM) such as metal powder based 3D printing part qualification has been identified by the America Makes & American National Standard Institute (ANSI) Additive Manufacturing Standardization Collaborative (AMSC) as a standardization gap with high priority in our nation. To make 3D printed metal parts more commercially viable, a rapid parts qualification process is a must. This Research Infrastructure Improvement Track-2 Focused EPSCoR Collaboration (RII Track-2 FEC) award will permit researchers from two EPSCoR jurisdictions (LA and AL) institutions, Southern University (SU), Louisiana State University (LSU), in Louisiana and the National Center for Additive Manufacturing Excellence (NCAME) at Auburn University (AU) in Alabama to establish a Consortium for Additive Manufacturing Qualification (CAM-Q). CAM-Q will exploit research opportunities at the intersection of materials science and data analytics, with a technological focus on Laser Beam Powder Bed Fusion (LB-PBF) additive manufacturing (AM), to radically transform the AM parts qualification process. The major outcomes of this project will be an overall framework for AM process design and rapid fatigue performance qualification, together with significant contributions to the education, training, and development of a highly-skilled, multidisciplinary, and diverse workforce to support the industry in the United States. The planned research represents a significant change from the current AM qualification practices. Successful execution of this project will enable the fabrication of metal/alloy parts with a significantly accelerated qualification cycle, make the AM process commercially viable, and propel the US industry to a global leadership position.To make LB-PBF AM more commercially viable, a rapid parts qualification process is a must. By coupling multiscale simulations and characterization with data analytics and by structuring a meso-scale mechanical testing framework, CAM-Q will establish processing strategy-structure-performance (PSP) relationships for meso-scale building strategies and subsequently demonstrate a new qualification approach on the structural integrity for LB-PBF parts. CAM-Q’s goal is to establish a science-driven, non-destructive evaluation (NDE) based framework for the rapid qualification of AM parts for safety-critical applications. A new PSP database for a crucial collection of Discrete Building Strategy Sets (DBSS) will be generated to support the qualification process by combining experimentation, data analysis, and physics-based modeling. For each Discrete Building Strategy set, this database will include: 1) location-specific process control parameters; 2) detailed microstructure maps, including volumetric distributions of pores/cracks collected via X-ray computed tomography (XCT) scans, three-dimensional (3D) "slice and view" data cubes in combination with data analysis and reconstruction, and physics-based simulations linking defects to fatigue performance; and 3) micro-to-meso scale mechanical testing to pinpoint location-specific mechanical performance, with emphasis on fatigue performance. Data analytics will be utilized to evaluate the performance of components and provide guidance on processing strategies for making parts with improved performance, thus achieving rapid quantification. The research outcomes will be incorporated in the education and workforce training program, consisting of year-long projects for undergraduates and dissertations for graduate students with direct industry participation. Tight integration and coordination between research, education, and workforce development and training programs will be supported by well-coordinated project management, evaluation, and assessment plans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.