SBIR/STTR Award attributes
Under this STTR program, QuesTek Innovations LLC will utilize its knowledge and expertise in Integrated Computational Materials Engineering (ICME) to develop improved part-scale, metal-based additive manufacturing (AM) process models, focusing on thermal history and grain growth prediction. QuesTek will partner with Professor Gregory Wagner at Northwestern University, who has expertise in modeling the thermal history of AM processes. The expertise of the Wagner Research Group will combine with QuesTekrsquo;s knowledge on microstructural prediction to implement improvements to part-scale simulations of AM processes to predict grain structure, which will enable prediction of component-level microstructural anisotropy.Phase I efforts will focus on research and development of methodologies for improving the accuracy and efficiency of higher-scale AM simulations regarding laser powder bed fusion of Inconel 625. Methodologies will begin from the well-established single-track simulations, moving to multi-layer simulations, and finally starting to formulate and develop methodologies for addressing efficiency concerns of simulations at the part-scale by using reduced-order models calibrated by higher accuracy models. The efforts of the Wagner Research Group will synergize with and improve QuesTekrsquo;s efforts, as accurate thermal history predictions are imperative for accurate grain growth predictions, while QuesTekrsquo;s efforts will help further validate the Wagner Grouprsquo;s work.Phase II efforts would focus on further developing methodologies to achieve more efficient and accurate part-scale AM simulations. In tandem with the algorithm development, an emphasis would be placed on further part-scale validation studies. These studies would be used both for calibrating and validating the methods for different AM processing parameter ranges, to extend the versatility and robustness of the tools developed
