|About this Abstract
||MS&T22: Materials Science & Technology
||Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
||Uncertainty Quantification in Process-Structure-Properties Simulations of Additive Manufactured Ti-6Al-4V
||Joshua D. Pribe, Brodan Richter, Patrick E. Leser, Saikumar R. Yeratapally, George Weber, Andrew R. Kitahara, Edward H. Glaessgen
|On-Site Speaker (Planned)
||Joshua D. Pribe
Metal additive manufacturing (AM) enables rapid fabrication of parts with complex geometries. However, AM parts typically have heterogeneous microstructures that depend on a range of processing parameters. The resulting mechanical property variations inhibit confidence in the structural performance of AM parts, particularly in fatigue where local material behavior is critical. This work leverages process-structure-properties simulations and uncertainty quantification to predict fatigue indicators in AM Ti-6Al-4V. Microstructures are generated using kinetic Monte Carlo simulations that include a calibrated analytical solution for the temperature field and predict crystallographic texture. Micromechanical stress and strain fields are predicted using an elasto-viscoplastic fast Fourier transform formulation. Uncertainty in the fatigue indicators is analyzed with respect to build parameter variations, material property uncertainty, and inherent randomness in microstructure and texture. The key outcome is more robust AM process-structure-properties linkages, supporting development of a computational materials-informed approach to qualification and certification of structural AM parts.