| About this Abstract |
| Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Symposium
|
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Presentation Title |
Process-Structure-Properties Simulations for Predicting Fatigue Indicator Parameters of Additive Manufactured Ti-6Al-4V with Quantified Uncertainty |
| Author(s) |
Joshua D. Pribe, Saikumar R. Yeratapally, Brodan Richter, Patrick E. Leser, George Weber, Edward H. Glaessgen |
| On-Site Speaker (Planned) |
Joshua D. Pribe |
| Abstract Scope |
Metals produced by additive manufacturing (AM) have complex, spatially heterogeneous microstructures. Microstructural details depend on the build process and can lead to significant variability in the mechanical properties. Understanding and quantifying uncertainty in process-structure-property relationships is thereby an important step in qualifying metal AM parts. This work presents process-structure-properties simulations of AM Ti-6Al-4V with the goal of relating fatigue indicator parameters to process variability. Dimensions of the melt pool and surrounding heat-affected zone during an AM build are predicted using an analytical temperature solution. The results are combined with a Monte Carlo Potts model to predict two- and three-dimensional microstructures. Mechanical loading of the microstructures is simulated using an elasto-viscoplastic fast Fourier transform formulation that determines the micromechanical stress and strain fields in each grain. This in turn enables prediction of microstructure-sensitive fatigue indicator parameters. Distributions of the fatigue indicator parameters and their dependence on selected process parameters are presented. |
| Proceedings Inclusion? |
Undecided |