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 2 0 1 8 such as twin boundaries in the EBSD grain orientation mapping establishes the direct correlation between micro- structure and the mechanical proper- ties. An example of correlated imaging and EBSDmaps of evolved FCC and BCC phases are presented in Fig. 3b, illus- trating the subsequent change in mi- crostructure and phase transition of Al x CrCuFeNi 2, as a function of varying Al content from x=0.8 to 1.0 [7] . TEM analyses further help obtain nano- to-atomic resolution information, as shown in Fig. 3c. The figure shows a bright-field TEM image of Al 0.8 CrCuFeNi 2 , showing the B2 precipitates embed- ded inside the FCC matrix. Although B2 precipitates reveal faceted morphol- ogy, the interface of B2 and FCC does not show coherency with each other because of the presence of misfit dislo- cations [8] . The atomic resolution imag- ing and spectroscopies further exhibit the localized precipitation and elemen- tal segregation, especially at the defects in the HEA components, such as Cu rich precipitates in CoCrCuFeNiAl [9] (Fig. 3d) that drastically affect the performance of the materials [10,11] . The latest development of the APT technique has significantly improved the scientific understanding of the mi- cro-structure of HEAs, as it provides the 3Dinformationof elemental distribution with a high spatial resolution (0.2 nm) and signal-to-noise ratio for improved minimum detection level. APT works on the concept of reconstructing the ele- mental distribution in a material which was evaporated via field-emission from a needle-shaped sample and collected on a detector. Figure 3e represents the APT maps of Al and Cr of Al 0.3 CoCrFeNi, where the compositional segregation is visible [12] . Overall, high-resolution mi- croscopy significantly aids in identify- ing across-the-scale features, including large-scale metallurgical defects (poro- sity and micro-cracks), microscale sec- ondary phases, and nano/atomic scale segregations, which greatly affect the properties of HEAs. This further assists in designing the post-treatment pro- cesses for achieving desired character- istics in AM components. Despite all of the advances in the microstructural characterizations of AM- HEA materials, there are two challenges requiring persistent efforts: enhancing the predictability of microstructures to reduce the costs of experimentations, and extracting accurate microstructur- al information. In this regard, the data- driven approaches benefit the AM pro- cessed HEAs both by predicting the optimal microstructures through pro- cessing parameters optimization, and by performing a fast and quantitative microstructure analysis, as illustrated in Fig. 4 [13,14] . The multiscale data-driven models are selected based on the gov- erning physics of the AM methods re- quired for the preparation of the HEAs. A proper balance between the provid- ed data set and the data-driven models represents a robust design which ex- hibits the optimized microstructure as an output, as illustrated in Fig. 4. Con- currently, quantitative microstructure characterization is extremely import- ant in analyzing and understanding the complexity in microstructure accurate- ly and efficiently. In this case, an atomic displacement map is obtained depict- ing the strains and lattice distortions as an output after analyzing the atomic resolution high angle annular dark field image utilizing the feature-recognition based machine-learning model. Over- all, the data-driven approaches provide a time-efficient and cost-effective path- way to predict the optimized micro- structure and offers the faster route for quantitative microstructural analysis of AM processed HEAs. In summary, AM technology has the potential to bring a revolution in manufacturing by improving process- ing of novel alloys such as HEAs. Al- though multiple research works are ongoing to establish AM as a viable manufacturing solution for designing HEA-based components, there are still processing-property correlation related Fig. 4 — Schematic illustration of data-driven approaches for predicting the optimized microstructure and performing the quantitative microstructure analysis of AM processed HEAs. The optimized microstructure is obtained by inputting the thermal and microstructure data into the surrogate model, and the atomic distance map is achieved through the quantitative analysis of the high angle annular dark field image using the atom identified model [13,14] .