CAREER: Manufacturing USA: Deep Learning to Understand Fatigue Performance and Processing Relationship of Complex Parts by Additive Manufacturing for High-consequence Applications

职业：美国制造：通过深度学习了解复杂零件的疲劳性能和加工关系，通过增材制造实现高后果应用

• 批准号: 2239307
• 负责人: Jia Liu
• 金额: $ 50万
• 依托单位: Auburn 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: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239307&HistoricalAwards=false
• 关键词: CAREER; Manufacturing; USA; Deep; Learning

Metal additive manufacturing (AM) such as laser powder-bed fusion (LPBF) has been increasingly explored not only for product innovation, but also shop-floor production, demonstrated by growing success from a variety of industries. However, the lack of knowledge in both fatigue failure and the performance uncertainty of LPBF parts poses a significant challenge and undermines the potential of deploying LPBF for high-consequence applications. This Faculty Early Career Development (CAREER) award supports fundamental research to understand the effects of LPBF processing on defects and subsequent fatigue behavior, advance the knowledge of fatigue scattering of LPBF parts that are complex in geometry and subject to multiaxial loading. The effort will establish a physics-centric, machine learning framework for fatigue life predictions, serving as a technological foundation for future metal AM production of dynamic load-bearing applications, and thus, enhance the competitiveness of U.S. industry. This CAREER project will also integrate education and outreach programs designed to broaden the participation from underrepresented groups through actively engaging K-12 students for STEM education and recruiting women and minorities into research, priming future generations of diverse engineers with the knowledge and skills indispensable in the age of manufacturing innovation and big data.The ultimate goal of this early career effort is to understand fatigue failures of complex LPBF parts under multiaxial loading for data-driven fatigue life predictions. The research will investigate the nature of fatigue failures from plastic deformation and crack initiation at the highest stress concentrations and translate fatigue life predictions into evaluating the crack growth at the vulnerable zones using a multiscale approach. On the micro-scale, critical defects with crack-initiating features (by x-ray computed tomography or optical profilometry) will be identified based on the correlation with fatigue failures; both the effects of critical defects and their spatial interactions on crack growth will be examined using fracture mechanics and data-intense statistics. On the part scale, the weak regions of the highest stress concentrations will be examined by finite element modeling of stress and strain behaviors through decoupling multiaxial loading. The effects of critical defects and the principal stresses at vulnerable localities will then be incorporated into a hierarchical graph convolutional network of deep learning to model their synergistic impacts on crack growth and calculate the fatigue life of LPBF parts with advanced data analytics. The findings are expected to generate new knowledge of defect formation relevant to fatigue performance of LPBF parts, uncover the synergistic impacts of multiscale factors on fatigue fractures, and further LPBF adoption for high-consequence applications.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.

激光粉末床熔合 (LPBF) 等金属增材制造 (AM) 已得到越来越多的探索，不仅用于产品创新，还用于车间生产，各行业的不断成功证明了这一点。然而，缺乏对 LPBF 零件的疲劳失效和性能不确定性的了解，带来了重大挑战，并削弱了将 LPBF 部署用于高后果应用的潜力。该学院早期职业发展 (CAREER) 奖支持基础研究，以了解 LPBF 加工对缺陷和后续疲劳行为的影响，增进对几何形状复杂且承受多轴载荷的 LPBF 零件的疲劳散射的了解。这项工作将建立一个以物理为中心的机器学习框架，用于疲劳寿命预测，为未来动态承载应用的金属增材制造生产奠定技术基础，从而增强美国工业的竞争力。该职业项目还将整合教育和外展计划，旨在通过积极吸引 K-12 学生接受 STEM 教育并招募女性和少数族裔参与研究，扩大代表性不足群体的参与，为未来几代不同的工程师提供不可或缺的知识和技能。制造创新和大数据的时代。这一早期职业生涯的最终目标是了解多轴载荷下复杂 LPBF 零件的疲劳失效，以进行数据驱动的疲劳寿命预测。该研究将调查最高应力集中下塑性变形和裂纹萌生引起的疲劳失效的性质，并将疲劳寿命预测转化为使用多尺度方法评估易损区域的裂纹扩展。在微观尺度上，将根据与疲劳失效的相关性来识别具有裂纹萌生特征的关键缺陷（通过 X 射线计算机断层扫描或光学轮廓测量法）；将使用断裂力学和数据密集型统计来检查关键缺陷的影响及其对裂纹扩展的空间相互作用。在零件尺度上，将通过解耦多轴载荷对应力和应变行为进行有限元建模来检查最高应力集中的薄弱区域。然后，关键缺陷的影响和易损部位的主应力将被纳入深度学习的分层图卷积网络中，以模拟它们对裂纹扩展的协同影响，并通过高级数据分析计算 LPBF 零件的疲劳寿命。研究结果预计将产生与 LPBF 零件疲劳性能相关的缺陷形成的新知识，揭示多尺度因素对疲劳断裂的协同影响，并进一步将 LPBF 用于高后果的应用。该奖项反映了 NSF 的法定使命，并被视为值得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来支持。