CAREER: Unveiling the Governing Mechanisms of Fatigue Failure in Additively Manufactured Aluminum

事业：揭示增材制造铝材疲劳失效的控制机制

• 批准号: 1752400
• 负责人: Ashley Spear
• 金额: $ 50万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1752400&HistoricalAwards=false
• 关键词: CAREER; Unveiling; Governing; Mechanisms; Fatigue

Additive manufacturing (AM), often referred to as 3D printing, is an exciting technology that can offer more flexibility and efficiency in the production of complex metal parts compared to conventional manufacturing. However, the path to AM as a viable and safe alternative in applications where structural components must carry loads (in some cases, where components must sustain repetitive loading over long periods of time) is at a critical junction. The widespread incorporation of this transformative manufacturing technology is hampered by the fact that it is currently not possible to predict when and why an additively manufactured metal component might fail, and to design the component accordingly to mitigate risk of failure. This presents a major problem for many industries that are looking to use AM to produce metal load-bearing components. This Faculty Early Career Development Program (CAREER) award supports fundamental research to address this pressing need and to enable the expanded, yet safe, use of metal AM in many industries, including aerospace, automotive, biomedical, manufacturing, and national defense. The research is closely integrated with a unique outreach program that will engage students across different age levels and backgrounds, including middle-school students from rural locations in Utah.The research supported by this CAREER award is a fundamental step toward expanding the use of AM to fatigue-critical applications through the discovery of 3D, microstructure-sensitive, fatigue-crack driving mechanisms in additively manufactured aluminum. Two parallel research thrusts will be carried out. One thrust will focus on experimentally characterizing the microstructural features in 3D neighborhoods of fatigue cracks observed in aluminum specimens produced by laser powder bed fusion. The second thrust will focus on numerically characterizing the local micromechanical fields that evolve in 3D as a function of underlying, manufacturing-induced microstructure and defect distribution, with particular focus on residual-stress incompatibility, porosity, and surface roughness. Data-driven approaches will be leveraged across the experimental and numerical data sets to provide new insights into the mechanisms responsible for fatigue failure among the specimens. While the research focuses on aluminum alloys, it is anticipated that the findings regarding the relative importance of geometrical defects, like pores and surface roughness, versus intrinsic material defects on fatigue failure of additively manufactured parts could be broadly applicable to other metals as well.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.

增材制造 (AM)，通常称为 3D 打印，是一项令人兴奋的技术，与传统制造相比，它可以在复杂金属零件的生产中提供更大的灵活性和效率。然而，在结构部件必须承载负载（在某些情况下，部件必须长时间承受重复负载）的应用中，增材制造作为可行且安全的替代方案的路径正处于关键时刻。这种变革性制造技术的广泛应用受到了以下事实的阻碍：目前无法预测增材制造的金属部件何时以及为何会发生故障，并相应地设计部件以降低故障风险。这对于许多希望使用增材制造技术生产金属承重部件的行业来说是一个主要问题。该学院早期职业发展计划 (CAREER) 奖项支持基础研究，以满足这一迫切需求，并在许多行业（包括航空航天、汽车、生物医学、制造和国防）扩大金属增材制造的安全使用。该研究与一项独特的推广计划紧密结合，该计划将吸引不同年龄层和背景的学生，包括来自犹他州农村地区的中学生。这项职业奖支持的研究是通过在增材制造铝中发现 3D、微观结构敏感、疲劳裂纹驱动机制，将增材制造的使用扩展到疲劳关键应用的根本一步。将开展两项平行的研究工作。其中一项重点是通过实验表征在激光粉末床熔合生产的铝样品中观察到的疲劳裂纹 3D 邻域的微观结构特征。第二个重点是对 3D 中演变的局部微机械场进行数值表征，作为底层、制造引起的微观结构和缺陷分布的函数，特别关注残余应力不相容性、孔隙率和表面粗糙度。将在实验和数值数据集中利用数据驱动的方法，为样本疲劳失效的机制提供新的见解。虽然该研究的重点是铝合金，但预计有关几何缺陷（如孔隙和表面粗糙度）与内在材料缺陷对增材制造零件疲劳失效的相对重要性的研究结果也可以广泛适用于其他金属。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持 标准。

项目成果

Convolutional neural networks for expediting the determination of minimum volume requirements for studies of microstructurally small cracks, part II: Model interpretation

• DOI: 10.1016/j.commatsci.2023.112261
• 发表时间: 2023-06-03
• 期刊: COMPUTATIONAL MATERIALS SCIENCE
• 影响因子: 3.3
• 作者: DeMille，Karen J.;Spear，Ashley D.
• 通讯作者: Spear，Ashley D.

A void descriptor function to uniquely characterize pore networks and predict ductile-metal failure properties

• DOI: 10.1007/s10704-020-00463-1
• 发表时间: 2020-06
• 期刊: International Journal of Fracture
• 影响因子: 2.5
• 作者: J. M. Erickson;Aowabin Rahman;A. Spear

Evaluation of a Modified Void Descriptor Function to Uniquely Characterize Pore Networks and Predict Fracture-Related Properties in Additively Manufactured Metals

• DOI: 10.1016/j.actamat.2021.117464
• 发表时间: 2021-11
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Dillon Watring;J. Benzing;O. L. Kafka;L. Liew;Newell Moser;J. Erickson;N. Hrabe;A. Spear

Mechanisms driving high-cycle fatigue life of as-built Inconel 718 processed by laser powder bed fusion

• DOI: 10.1016/j.msea.2019.06.003
• 发表时间: 2019-07-22
• 期刊: MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
• 影响因子: 6.4
• 作者: Watring, Dillon S.;Carter, Kristen C.;Spear, Ashley D.
• 通讯作者: Spear, Ashley D.

Convolutional neural networks for expediting the determination of minimum volume requirements for studies of microstructurally small cracks, Part I: Model implementation and predictions

• DOI: 10.1016/j.commatsci.2022.111290
• 发表时间: 2022-05
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Karen J. DeMille;A. Spear