An Integrated Computational Framework for the Accelerated Development of Tailored Additively Manufactured Metals

Abstract

Additive manufacturing (AM) is a disruptive technology leveraging innovations of the past and present to enable the design and fabrication of the new standard for components across industries. However, the successful application of the AM process to achieve desired results is in part made possible through the exploitation of inherent material properties and characteristics. Consequently, this process-structure-property-performance relationship must be understood and leveraged within AM to reliably and effectively continue development for improved performance through materials design. The improved performance and functionality of AM components thus necessitates a framework for accelerated materials design and development. For this purpose, a physics-based and data-driven integrated computational materials engineering (ICME) framework is developed, leveraging the utility and efficiency of simulations with experimentation to drive forward materials design and discovery. This is achieved by querying the complex AM PSPP relationships to inform and guide experiments for the cost-effective design of NiTi-based shape memory alloys (SMAs).

In this regard, NiTi-based SMAs are prone to Ni loss under the conditions afforded by the AM process and are subject to a strong correlation between Ni content and transformation temperature (TT). Additionally, these materials suffer from difficulty in fabrication through standard manufacturing processes while exhibiting desirable functional properties. For this reason, the first study of this work takes a critical inspection on the vaporization of alloys during the welding and AM process. This is followed by a second study where an ICME framework consisting of a thermal model, a multi-layer model, and a differential evaporation model, is developed to screen for PSPP trends and inform experiments for the laser powder bed fusion (LPBF) AM of metal alloys. This framework is calibrated and validated against experiments for NiTi SMA, utilizing process parameters to predict Ni content and TT in agreement with experimental measurements and trends. The third study leverages optimization techniques alongside the ICME framework to solve the inverse design problem and predict design parameters required for desired component specifications. The fourth study expands the utility of the framework to the NiTiHf system where, after calibration and validation, model predictions for TT were found to be in good agreement with experiments. The fifth study provides a summary of the work and its contributions towards the accelerated development and design of LPBF AM metals, as well as an outlook on future work for expanded utility and application of the ICME framework.