July_August_AMP_Digital

A D V A N C E D M A T E R I A L S & P R O C E S S E S | J U L Y / A U G U S T 2 0 1 9 2 0 at the local level, of the AM process conditions that will yield successfully fabricated parts with the required com- position and microstructure. As men- tioned previously, this research team has already developed a robust frame- work for combining advanced ther- mal models and their surrogates for efficient predictions, in situ/post mor- tem characterization approaches, and model calibration methods to obtain quantitatively predictive models of lo- cal thermal histories during AM (Fig. 5). These models have already been suc- cessfully used to generate printability maps in the process parameter space and predict the effect of thermal histo- ries on the evolution of second phase precipitates during AM of NiTi SMAs. BEYOND 4D PRINTING OF NiTi SHAPE MEMORY ALLOYS Additive manufacturing has at- tracted significant attention over the past decade due to advantages such as complex part features, short lead times, and material savings. Howev- er, challenges remain with regard to a lack of consistency in performance and properties of the printed parts due to the complicated nature of the process. While thermal history is an important parameter in dictating the overall prop- erties of metallic materials fabricated by conventional processing techniques, it deserves special attention for AM: This is because it is significantly affect- ed by processing parameters such as la- ser power, scanning speed, and hatch spacing. This makes it an even bigger challenge for AM of metallic function- al materials, including a wide range of SMAs and magnetic materials, proper- ties of which are much more sensitive to thermal history compared to com- mercial engineering alloys. In an effort to minimize the exper- imentation time for selecting optimal processing parameters to successful- ly 4D print metallic functional parts, it is necessary to use a robust framework like the one described here. Such a framework would incorporate thermal models based on finite element anal- ysis to pinpoint the defect-free print- ability region in the process parameter space. In addition, a coupled precipita- tion model or more robust phase-field models would be used to predict micro- structural features such as phase frac- tion and distribution in the printability region (Fig. 5). With these capabilities, costly trial and error processes can be minimized—enabling a straightforward procedure for tailoring the functional response of metallic functional materi- als for 4D printing applications. ~AM&P For more information: Ibrahim Kara- man, MSEN department, Texas A&M Uni- versity, 3003 TAMU, College Station, TX 77843-3003, 979.862.3923, ikaraman@ tamu.edu. Fig. 5 — Combining experiments andmodels to predict location-dependent shape memory response in LPBF NiTi shape memory alloys.