Comprehensive characterization of the structure of Zr-based metallic glasses.

Structure of metallic glasses fascinates as the generic amorphous structural template for ubiquitous systems. Its specification necessitates determination of the complete hierarchical structure, starting from short-range-order (SRO) → medium-range-order (MRO) → bulk structure and free volume (FV) distribution. This link has largely remained elusive since previous investigations adopted one-technique-at-a-time approach, focusing on limited aspects of any one domain. Reconstruction of structure from experimental data inversion is non-unique for many of these techniques. As a result, complete and precise structural understanding of glass has not emerged yet. In this work, we demonstrate the first experimental pathway for reconstruction of the integrated structure, for Zr 67 Ni 33 and Zr 52 Ti 6 Al 10 Cu 18 Ni 14 glasses. Our strategy engages diverse (× 7) multi-scale techniques [XAFS, 3D-APT, ABED/NBED, FEM, XRD, PAS, FHREM] on the same glass. This strategy complemented mutual limitations of techniques and corroborated common parameters to generate complete, self-consistent and precise parameters. Further, MRO domain size and inter-void separation were correlated to identify the presence of FV at MRO boundaries. This enabled the first experimental reconstruction of hierarchical subset: SRO → MRO → FV → bulk structure. The first ever image of intermediate region between MRO domains emerged from this link. We clarify that determination of all subsets is not our objective; the essence and novelty of this work lies in directing the pathway towards finite solution, in the most logical and unambiguous way.

Machine learning based de-noising of electron back scatter patterns of various crystallographic metallic materials fabricated using laser directed energy deposition.

A novel machine learning (ML) method of refining noisy Electron Back Scatter Patterns (EBSP) is proposed. For this, conditional generative adversarial networks (c-GAN) have been employed. The problem of de-noising the EBSPs was formulated as an image translation task conditioned on the input images to get refined/denoised output of EBSPs which can be indexed using conventional Hough transform based indexing algorithms. The ML model was trained using 10,000 EBSPs acquired under different settings for additively manufactured FCC, BCC and HCP alloy samples ensuring enough diversity and complexity in training data set. Pairs of noisy and corresponding optimal EBSPs were acquired by suitable tweaking of the EBSP acquisition parameters such as beam defocus, pattern binning and EBSD camera exposure duration. The trained model has brought out significant improvement in EBSD indexing success rate on test data, accompanied by betterment of indexing accuracy, quantified through 'pattern fit'. Complete automation of the EBSP refinement was demonstrated where in entire EBSD scan data can be fed to the model to get the refined EBSPs from which high quality EBSD data can be obtained.