Wire arc additive manufacturing (WAAM) with high deposition rates has attracted industry interest for the demonstrated economic production of medium-to-large-scale metallic components. The structural integrity and mechanical properties of the built parts depend on the selection of the optimum deposition parameters and the tool path strategy. In this study, an alternate orthogonal deposition strategy was employed. The influence of the beads’ geometry and the associated heat input on the mechanical and structural behavior of mild steel (ER70S-6) were investigated. The influence of the bead width (BW) and the overlapping percentage (OP) between the adjacent beads on the average and layer-by-layer hardness of the blocks along the building direction were evaluated. Tensile strength was also characterized. The alternate orthogonal building strategy enhanced the geometrical uniformity of the built blocks and the microstructural isotropy along the building direction. Increasing the BW increased the total heat input per bead per layer, which significantly reduced the hardness and tensile strength of the built blocks by 19% and 17% compared to 8% and 7% when increasing the OP, respectively. Total heat input, number of heating cycles, and cooling rates triggered the phases formed, and their morphologies along the building direction were also characterized.

In this paper, bimetallic functionally graded structures were fabricated using wire and arc additive manufacturing (WAAM). The bimetallic walls were built by depositing Si-Bronze and high-strength low-alloy (HSLA) steel, successively. The microstructural evolution of the built structures, especially within the fusion zone between the dissimilar alloys, was investigated in relation to their mechanical properties. The built bimetallic walls showed a high level of integrity. An overall interface length of 9 mm was investigated for microstructural evolution, elemental mapping and microhardness measurements along the building direction. Microhardness profiles showed a gradual transition in hardness passing through the diffusion zone with no evidence for intermetallic compounds. Failure of the tensile specimens occurred at the Si-Bronze region, as expected. Bending tests confirmed good ductility of the joint between the dissimilar alloys. Direct shear test results proved a shear strength comparable to that of HSLA steel. The obtained results confirm that it is appropriate to fabricate HSLA steel/Si-Bronze FGMs using WAAM technology.

Machine learning inversion of extended X-ray absorption fine structure (EXAFS) into structural descriptors relies on data sets generated from ab initio molecular dynamics (AIMD), which are computationally expensive and system-specific. Here we evaluate the Objective Neural Network for EXAFS (ONNE), a synthetic training data framework that statistically samples configurational spaces. We compare ONNE datasets with AIMD data in terms of configurational diversity, representativeness, and computational cost. Models trained on ONNE data achieve coordination number predictions comparable to AIMD-trained models while requiring 99.37% lower computational costs. Fine-tuning the pretrained ONNE model with a small number of AIMD configurations further improves structural predictions and reduces uncertainty while maintaining a lower computational expense. We demonstrate that ONNE can serve as an efficient pretraining strategy for ML-EXAFS, reducing the dependence on expensive simulations and thus informing future ML-EXAFS investigations by clarifying the advantages, limitations, and applicability of ONNE relative to MD approaches.

Molten salts are central to advanced clean energy technologies yet elucidating their highly disordered local atomic environments is especially challenging. In this work, we apply a model-agnostic machine learning method, to invert experimental extended X-ray Absorption Fine Structure (EXAFS) of NaF–ZrF₄ (53–47 mol%) directly into pair distribution functions (PDFs). Thereby circumventing the difficulty of conventional analysis that requires structural assumptions or molecular dynamics (MD) simulations, which are limited by the accuracy of force fields and computational expense. Our workflow generates training data from diverse statistical PDFs, enabling robust learning across varied configurations. Our results resolve key structural features for local atomic coordination of Zr directly from experiments, that align with previous MD and X-ray scattering studies providing a rapid and scalable tool for characterizing disordered systems. This workflow offers a pathway to in-situ and operando monitoring of atomic structure evolution for various material classes.