Experimentally-Validated Model for Heat Treatment of 3D-Printed Titanium

Ti-6Al-4V is a commonly-used alloy in aerospace and defense applications. Additive manufacturing or AM (particularly laser powder-bed fusion, or LPBF) has successfully been used to manufacture Ti-6Al-4V to lower material usage, reduce lead times, and create parts with complex geometries. ASTM standards dictate heat treatment procedures for traditionally manufactured Ti-6Al-4V, but the resulting microstructural changes on AM parts have not yet been modeled and are not completely understood. It is desired to create an experimentally-validated computational model that quantifies structure-property-process relationships for LPBF Ti-6Al-4V.  In the Phase I project, Lynntech team demonstrated feasibility of a first principles based computational scheme to calculate microstructural properties of LPBF Ti-6Al-4V as a function of different heat treatment procedures.  Simultaneous experimental evaluation of these properties was also conducted with commercial LPBF Ti-6Al-4V samples.  Based on the information gathered about the effect of post-fabrication heat treatment on microstructure, a deep understanding has been developed to serve as a foundation upon which advanced modeling techniques can be employed to predict materials property changes due to heat-treatment.  During the proposed Phase II project, Lynntech team will combine the computational and experimental approaches learned in the Phase I project, with machine learning tools to create a robust computational tool for predicting microstructural and mechanical properties of the LPBF Ti-6Al-4V samples after heat treatment procedures.  The results will be validated through heat treatment experiments followed by tension and fatigue tests of commercial LPBF Ti-6Al-4V samples.  During Phase III, a plan will be generated to transition the results of the study to end-use applications in the additive manufacturing of near-net-shape parts for the US Army. An optimized heat-treatment process for L-PBF Ti-6Al-4V will be proposed based on computational optimization.