CAREER: Reduced-scale Additively Manufactured Models for Quantifying the Behavior of Large Structural Steel Castings

职业：用于量化大型结构钢铸件行为的缩小比例增材制造模型

• 批准号: 2329562
• 负责人: Ravi Yellavajjala
• 金额: $ 53.51万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329562&HistoricalAwards=false
• 关键词: CAREER; Reduced; scale; Additively; Manufactured

This Faculty Early Career Development (CAREER) grant will support research to extend the applicability of reduced-scale physical models to quantify the failure behavior of full-scale structural steel castings under seismic loads. The modular construction of steel structures using castings reduces the structural weight, construction time, and erection costs. Steel castings also offer exceptional architectural freedom and high seismic and blast resistance. However, they are often large and geometrically complex, hence traditional mechanical testing is not commonly feasible, which prevents structural engineers from exploring their full potential. The outcome of this project will enable engineers to perform multiple tests on scaled-models using testing laboratories with limited capabilities instead of fewer tests on full-scale prototypes in sprawling facilities with expensive equipment, providing a better understanding of system-level behavior at a relatively meager cost. The research will also facilitate the development of design guidelines for structural steel castings, increase their market share, create new jobs in the US manufacturing sector, and reduce the carbon footprint. As part of the grant, an extensive set of education and outreach activities will also be pursed to increase the participation of Native Americans in STEM disciplines and improve their retention and graduation rates, including undergraduate internships, multi-day workshops, and competitions and debates.Satisfying the geometrical and material strength similitude requirements and deriving scaling relationships are the two challenges that accurate reduced-scale physical models are required to overcome. This research aims to quantify the fundamental scaling relationships between strength, ultra-low cycle fatigue fracture (ULCF) initiation strain, and life of additively manufactured, reduced-scale physical models and full-scale steel castings subject to seismic loads. The approach is centered on the additive manufacturing of reduced-scale models with the help of data-driven process and build parameters and post-heat treatments to satisfy geometric and material strength similitudes. It also comprises development of a deep neural network framework to be trained with large quantities of fracture data, numerically generated from realistic microstructures, to learn the damage scaling relationship that will relate the fatigue fracture behavior of additive manufactured models to full-scale steel castings. The experimental validation plan includes characterization of the microscopic fatigue damage using x-ray tomography and structural testing of two full-scale components. Overall, the similitude theory based on mechanics, additively manufactured physical models, and machine-learned scaling relationships will advance the use of reduced-scale physical modeling beyond large deformations into the realm of fatigue fracture.This project is jointly funded by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) and the Established Program to Stimulate Competitive Research (EPSCoR).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.

该学院的早期职业发展（CAREER）拨款将支持研究，以扩展缩小比例的物理模型的适用性，以量化地震载荷下全尺寸结构钢铸件的失效行为。使用铸件的钢结构模块化结构减少了结构重量、施工时间和安装成本。铸钢件还具有卓越的建筑自由度以及高抗震和防爆性能。然而，它们通常很大且几何形状复杂，因此传统的机械测试通常不可行，这阻碍了结构工程师充分发挥其潜力。该项目的成果将使工程师能够使用能力有限的测试实验室对比例模型进行多次测试，而不是在拥有昂贵设备的庞大设施中对全尺寸原型进行较少的测试，从而以相对较低的成本更好地了解系统级行为。该研究还将促进结构钢铸件设计指南的制定，增加其市场份额，为美国制造业创造新的就业机会，并减少碳足迹。作为赠款的一部分，还将开展一系列广泛的教育和推广活动，以增加美国原住民对 STEM 学科的参与，并提高他们的保留率和毕业率，包括本科生实习、多日研讨会以及竞赛和辩论。满足几何和材料强度相似性要求以及推导比例关系是精确的缩小比例物理模型需要克服的两个挑战。本研究旨在量化增材制造、缩小尺寸物理模型和承受地震载荷的全尺寸铸钢件的强度、超低周疲劳断裂 (ULCF) 起始应变和寿命之间的基本比例关系。该方法以缩小尺寸模型的增材制造为中心，借助数据驱动的工艺和构建参数以及后热处理来满足几何和材料强度的相似性。它还包括开发深度神经网络框架，使用大量断裂数据进行训练，这些数据是从现实微观结构中数字化生成的，以学习损伤缩放关系，将增材制造模型的疲劳断裂行为与全尺寸铸钢件联系起来。实验验证计划包括使用 X 射线断层扫描表征微观疲劳损伤以及对两个全尺寸部件进行结构测试。总体而言，基于力学、增材制造物理模型和机器学习比例关系的相似理论将推动大变形之外的缩小规模物理建模在疲劳断裂领域的应用。该项目由土木、机械和制造创新部 (CMMI) 和刺激竞争研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并已被 通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。