CDS&E/Collaborative Research: In-Situ Monitoring-Enabled Multiscale Modeling and Optimization for Environmental and Mechanical Performance of Advanced Manufactured Materials

这一计算和数据支持的科学与工程(CDS&amp；E)项目将有助于实时控制和优化添加剂制造(AM)金属部件，以改善它们的环境和机械性能。由于其在制造复杂几何形状方面的出色性能，金属AM在生产高价值部件方面已逐渐被行业接受。然而，缺乏有效的工艺-结构-性能(PSP)模型，特别是环境辅助失效性能模型的缺乏，阻碍了金属AM的广泛应用质量保证在很大程度上依赖于试错，这是昂贵、耗时和容易出错的。该奖项将建立一个物理受限人工智能(PCAI)框架，以促进对AM工艺引入的独特功能和缺陷如何影响竣工部件的环境辅助性能的基本了解。开发的工具将提供给学术界和工业界。此外，还将为本科生和研究生开设先进制造专业计算机辅助教学的新课程，培养具有人工智能、物理模拟和先进制造技能的未来劳动力。该项目将建立一个现场加工数据驱动框架，能够有效地将制造过程与部分规模激光粉床融合的环境相关性能联系起来，并实现工艺优化，以提高环境辅助失效性能。技术途径包括：1)建立基于PCAI的替代模型，结合现场监测数据预测零件尺度的残余应力和微观组织；2)建立基于物理的降阶模型，将残余应力和组织与成品零件的腐蚀疲劳性能定量关联；3)建立工艺优化方法，以达到零件尺度L板材的目标腐蚀疲劳性能。这项工作也可能适用于其他制造过程，如直接能量沉积、生物制造和纳米制造。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Computational and Data-Enabled Science and Engineering (CDS&E) project will contribute to the real-time control and optimization of additive manufactured (AM) metal components to improve their environmental and mechanical performance. Metal AM has gradually gained acceptance in industries for producing high-value components, thanks to its excellent performance in fabricating complex geometries. However, the lack of efficient process-structure-performance (PSP) models, particularly for environment- assisted failure performance, hinders the broad application of metal AM. Quality assurance heavily depends on trial-and-error, which is expensive, time-consuming, and error-prone. This award will establish a physics-constrained artificial intelligence (PCAI) framework to promote the fundamental understanding of how the unique features and defects introduced by the AM process affect the environmental-assisted performances of as-built parts. The tools developed will be made available to the academic and industrial communities. Furthermore, new courses of the PCAI for advanced manufacturing will be created for both undergraduate and graduate students, cultivating future workforce with skills in AI, physical simulation, and advanced manufacturing.This project will establish an in-situ processing data-driven framework that can effectively link manufacturing process to environmental-related performance for part-scale laser powder bed fusion (L-PBF) and enable process optimization for improved environment-assisted failure performance. The technical approaches involve 1) Establish a PCAI-based surrogate model that can incorporate in-situ monitoring data to predict part-scale residual stress and microstructures; 2) Build a physics-based reduced-order model that can quantitatively correlate the residual stress and microstructures to the corrosion fatigue properties of as-built parts; 3) Establish a process optimization method to achieve the targeted corrosion fatigue properties for part-scale L-PBF. This work may also be applicable to other manufacturing processes such as direct energy deposition, biomanufacturing, and nanomanufacturing.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.