A mechanistic understanding of phase transformation behaviour under extreme thermal conditions is essential for controlling microstructure and performance of additively manufactured metallic components. However, direct experimental evidence capturing phase evolution during rapid solidification remains limited. In this study, phase transformations in M50 tool steel processed by …

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Electron beam directed energy deposition (EB-DED) has significant potential for rapid near-net-shape manufacturing of large components with its high deposition rate and favorable mechanical properties. However, process stability and dimensional accuracy are hindered by complex transferred-metal behaviors and layer-accumulated geometric errors, making real-time monitoring and co…
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Digital Glass Forming (DGF) is a new manufacturing process for low-batch glass fabrication. The work zone temperature in DGF processes must be maintained in the glass’s working range to ensure good fabrication. If the temperature is too low, the filament will not wet to the substrate or previously deposited material and, if the temperature is too high, the filament may disengag…
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This paper presents a homogenized topology optimization (TO) method for spatially optimizing cell-size distribution of triply-periodic minimal surface (TPMS) structures, with high accuracy in the optimized structural response after de-homogenization. To achieve this, we introduce a novel de-homogenization technique that directly minimizes the difference between the wavenumbers …
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This study investigates the coalescence behavior of bisphenol A polycarbonate droplets in the Arburg Plastic Freeforming additive manufacturing process through a computational framework that integrates thermal, rheological, and mechanical analyses. Using FLOW-3D simulations, the influence of extrusion temperature on droplet morphology, thermal field evolution, and interfacial b…
Comprehensive, real-time characterization of curing evolution remains a critical challenge for ensuring the geometrical accuracy and mechanical integrity of vat photopolymerization (VPP). Here, we present a physics-informed, machine-learning-enhanced in-situ ultrasonic monitoring (IUM) framework that simultaneously estimates three key in-process part properties—degree of conver…
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