Page 10: Research news on Data-driven alloy design

Data-driven alloy design integrates computational methods, machine learning, and advanced manufacturing to create metallic alloys and composites with tailored properties. Emphasis is placed on metal 3D printing and related additive processes as platforms for controlling solidification, microstructure, and defect formation in systems such as aluminum, titanium, magnesium, high-entropy, and shape-memory alloys. By linking processing parameters, atomic-scale mechanisms, and multiscale models to mechanical, thermal, and environmental performance, this area accelerates discovery and optimization of structural and functional materials for demanding applications.

Engineering

Can smoother surfaces prevent hydrogen embrittlement?

Hydrogen is a promising fuel for developing sustainable industrial processes, but its use is hindered by hydrogen embrittlement—a phenomenon that weakens metals and can cause sudden failure. Now, researchers from Japan have ...

Engineering

AlloyGPT: Leveraging a language model to aid alloy discovery

Additive manufacturing of alloys has enabled the creation of machine parts that meet the complex requirements needed to optimize performance in aerospace, automotive, and energy applications. Finding the ideal mix of elements ...

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