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

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.