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.

金属增材制造（AM），如激光粉末床融合（LPBF），不仅在产品创新方面得到了越来越多的探索，而且在车间生产方面也得到了越来越多的探索，这一点在各个行业都取得了越来越大的成功。然而，缺乏知识的疲劳失效和LPBF部件的性能不确定性构成了一个重大的挑战，并破坏了部署LPBF的高后果应用的潜力。该学院早期职业发展（CAREER）奖支持基础研究，以了解LPBF处理对缺陷和随后的疲劳行为的影响，推进几何形状复杂并承受多轴载荷的LPBF零件的疲劳分散知识。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，为未来金属AM生产动态承载应用奠定技术基础，从而提高美国工业的竞争力。该CAREER项目还将整合教育和推广计划，旨在通过积极吸引K-12学生参与STEM教育并招募妇女和少数民族参与研究，扩大代表性不足的群体的参与，为未来几代多样化的工程师提供制造业创新和大数据时代不可或缺的知识和技能。这种早期职业努力的最终目标是了解疲劳复杂LPBF部件在多轴载荷下的失效数据驱动的疲劳寿命预测。该研究将调查疲劳失效的性质，从塑性变形和裂纹萌生在最高的应力集中和疲劳寿命预测到评估裂纹扩展的脆弱区域使用多尺度的方法。在微观尺度上，临界缺陷与裂纹起始特征（通过X射线计算机断层扫描或光学轮廓术）将根据与疲劳失效的相关性进行识别;临界缺陷及其空间相互作用对裂纹扩展的影响将使用断裂力学和数据密集统计进行检查。在部件尺度上，最高应力集中的薄弱区域将通过解耦多轴加载的应力和应变行为的有限元建模来检查。然后，关键缺陷和脆弱部位的主应力的影响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部件的疲劳寿命。这些发现有望产生与LPBF部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用于高后果应用。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智力价值和更广泛的影响审查标准进行评估来支持。