This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence Newcastle University ePrints-eprint.ncl.ac.uk Tan JH, Wong WLE, Dalgarno KW. An overview of powder granulometry on feedstock and part performance in the selective laser melting process.

We present a fast and precise deep-learning architecture, which we term O-Net, for obtaining super-resolved images from conventional phase-modulated optical microscopical techniques, such as phase-contrast microscopy and differential interference contrast microscopy. O-Net represents a novel deep convolutional neural network that can be trained on both simulated and experimental data, the latter of which is being demonstrated in the present context. The present study demonstrates the ability of the proposed method to achieve super-resolved images even under poor signal-to-noise ratios and does not require prior information on the point spread function or optical character of the system. Moreover, unlike previous state-of-the-art deep neural networks (such as U-Nets), the O-Net architecture seemingly demonstrates an immunity to network hallucination, a commonly cited issue caused by network overfitting when U-Nets are employed. Models derived from the proposed O-Net architecture are validated through empirical comparison with a similar sample imaged via scanning electron microscopy (SEM) and are found to generate ultra-resolved images which came close to that of the actual SEM micrograph.

In this study, a new magnesium (Mg) alloy containing 0.4% Ce was developed using the technique of disintegrated melt deposition followed by hot extrusion. The tensile and compressive properties of the developed Mg–0.4Ce alloy were investigated before and after heat treatment with an intention of understanding the influence of cerium on the deformation and corrosion of magnesium. Interestingly, cerium addition has enhanced the strength (by 182% and 118%) as well as the elongation to failure of Mg (by 93% and 8%) under both tensile and compressive loadings, respectively. After heat treatment, under compression, the Mg–0.4Ce(S) alloy exhibited extensive plastic deformation which was 80% higher than that of the as-extruded condition. Considering the tensile and compressive flow curves, the as-extruded Mg–0.4Ce and the heat treated Mg–0.4Ce(S) alloys exhibited variation in the nature and shape of the curves which indicates a disparity in the tensile and compressive deformation behavior. Hence, these tensile and compressive deformation mechanisms were studied in detail for both as-extruded as well as heat treated alloys with the aid of microstructural characterization techniques (scanning electron microscope (SEM), transmission electron microscope (TEM), selective area diffraction (SAD), and X-ray diffraction (XRD) analysis. Furthermore, results of immersion tests of both as-extruded and heat treated alloys revealed an improved corrosion resistance (by ∼3 times in terms of % weight loss) in heat treated state vis-a-vis the as-extruded state.

Growing concerns like depleting mineral resources, increased materials wastage, and structural light-weighting requirements due to emission control regulations drive the development of sustainable metal matrix composites. Al and Mg based alloys with relatively lower melting temperatures qualify for recycling applications and hence are considered as the matrix material for developing sustainable composites. The recent trend also explores various industrial by-products and agricultural wastes as green reinforcements, and this article presents insights on the properties of Al and Mg based sustainable metal matrix composites with special emphasis on green reinforcements and processing methods.