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; 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.

金属添加剂制造（AM），例如激光粉末床融合（LPBF），不仅越来越多地探索产品创新，而且还越来越多地探索了商店地板生产，这表明了各种行业的成功越来越多。但是，在疲劳失败和LPBF零件的性能不确定性方面缺乏知识构成了重大挑战，并破坏了用于高分后应用的LPBF的潜力。这项教师早期职业发展（职业）奖支持基本研究，以了解LPBF处理对缺陷和随后的疲劳行为的影响，促进了解LPBF零件的疲劳散射，这些LPBF零件在几何形状上很复杂并受到多用途负载。这项努力将建立一个以物理性的，机器学习的框架，用于疲劳生活预测，为未来金属AM生产动态负载应用的生产是一个技术基础，从而增强了美国行业的竞争力。该职业项目还将整合旨在扩大代表性不足的群体的参与，通过积极参与K-12学生进行STEM教育的学生的参与，并将妇女和少数群体招募参与研究，从而使多元化工程师的后代具有知识和技能的知识和技能，这是在制造早期努力和大数据中的最终努力，以理解成果的最终努力。数据驱动的疲劳生活预测。该研究将研究最高应力浓度下的塑性变形和裂纹启动疲劳失败的性质，并将疲劳寿命预测转化为使用多尺度方法评估脆弱区域的裂纹生长。在微尺度上，将基于与疲劳失败的相关性确定具有裂纹发射特征（通过X射线计算机断层扫描或光学术法）的临界缺陷；临界缺陷及其空间相互作用对裂纹生长的影响都将使用断裂力学和数据强统计量进行检查。在零件尺度上，应通过将多轴负荷解耦的应力和应变行为的有限元模型来检查最高应力浓度的弱区域。然后，危害缺陷和弱势局部区域的主要应力的影响将纳入深度学习的分层图卷积网络中，以模拟其对裂纹增长的协同影响，并通过先进的数据分析来计算LPBF部分的疲劳寿命。这些发现有望产生与LPBF部分疲劳性能相关的缺陷形成知识，发现多尺度因素对疲劳性骨折的协同影响，并进一步采用了高度影响力应用的LPBF。该奖项反映了NSF的法定任务，并通过评估范围来评估范围，并通过评估范围来进行评估和范围的范围。