Laser powder bed fusion (LPBF) is an emerging technique for the fabrication of triply periodic minimal surface (TPMS) structures in metals. In this work, different TPMS structures such as Diamond, Gyroid, Primitive, Neovius, and Fisher–Koch S with graded relative densities are fabricated from 316L steel using LPBF. The graded TPMS samples are subjected to sandblasting to improve the surface finish before mechanical testing. Quasi-static compression tests are performed to study the deformation behavior and energy absorption capacity of TPMS structures. The results reveal superior stiffness and energy absorption capabilities for the graded TPMS samples compared to the uniform TPMS structures. The Fisher–Koch S and Primitive samples show higher strength whereas the Fisher–Koch S and Neovius samples exhibit higher elastic modulus. The Neovius type structure shows the highest energy absorption up to 50% strain among all the TPMS structures. The Gibson–Ashby coefficients are calculated for the TPMS structures, and it is found that the C2 values are in the range suggested by Gibson and Ashby while C1 values differ from the proposed range.

This study summarizes the recent progress in thermoplastic drawing of bulk metallic glasses. The integration of drawing with templated embossing enables the fabrication of arrays of high-aspect-ratio nanostructures whereas the earlier drawing methodologies are limited to a single fiber. The two-step drawing can produce metallic glass structures such as, vertically aligned nanowires on substrates, nanoscale tensile specimens, hollow microneedles, helical shafts, and micro-yarns, which are challenging to fabricate with other thermoplastic forming operations. These geometries will open new applications for bulk metallic glasses in the areas of sensors, optical absorption, transdermal drug-delivery, and high-throughput characterization of size-effects. In this article, we review the emergence of template-based thermoplastic drawing in bulk metallic glasses. The review focuses on the development of experimental set-up, the quantitative description of drawing process, and the versatility of drawing methodology.

Additive manufacturing is rapidly evolving and revolutionizing the fabrication of complex metal components with tunable properties. Machine learning and neural networks have emerged as powerful tools for process–property optimization in additive manufacturing. These techniques work well for the prediction of a single property but their applicability in optimizing multiple properties is limited. In the present work, an exclusive neural network is developed to demonstrate the potential of a single neural network in optimizing multiple part properties. The model is used to identify the optimal process parameter values for laser power, scan speed, and hatch spacing for the required surface roughness, relative density, microhardness, and dimensional accuracy in stainless steel parts. In-house-generated experimental data are used to train the model. The model has seven neurons in the hidden layer, which are selected using hyperparameter optimization. K-fold cross-validation is performed to ensure the robustness of the model, which results in a mean squared error of 0.0578 and R2 score of 0.59. The developed model is then used to predict the optimal process parameters corresponding to the user-required part properties. The model serves as a significant pre-processing step to identify the best parameters before printing, thus saving time and costs for repeated part fabrication. The study provides more insights into the usage of a single artificial neural network for the optimization of multiple properties of printed metal parts.

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