Page 4: 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

Ultrahigh solid loading enables high precision ceramic parts

National Taiwan University researchers have developed an ultrahigh-solid-loading (83 vol%) yet highly flowable suspension for 3D printing that produces ceramic parts with extremely low shrinkage and 100% density, overcoming ...

Robotics

A geometric twist boosts the power of robotic textiles

By rethinking how thin metal threads are woven into a flexible textile, EPFL researchers have created a lightweight fabric capable of lifting over 400 times its own weight. The work advances the development of wearables that ...

Engineering

Atomistic model explains how memory metals can change their shape

Shape memory alloys are exotic materials that can be deformed at room temperature and return to their "remembered," pre-deformed shape when heated. They are used in a broad range of applications, such as heart stents, dental ...

page 4 from 14