Aerospace and Mechanical Insider on MSN
AI and machine learning transform materials testing
Materials testing remains a cornerstone of engineering and manufacturing, ensuring that components and structures—from ...
Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...
A team of researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time -- a development that could lead to the creation of stronger, more ...
Lawrence Livermore National Laboratory (LLNL) scientists have developed a new approach that can rapidly predict the structure and chemical composition of heterogeneous materials. In a new study in ...
Tungsten's superior performance in extreme environments makes it a leading candidate for plasma-facing components (PFCs) in fusion reactors, but the ultra-high heat can damage its microscopic ...
How can artificial intelligence (AI) machine learning models be used to identify new materials? This is what a recent study published in Nature hopes to address as a team of researchers investigated ...
Programmable material systems are emerging architectural structures but the co-design of structure, material, and external stimuli present grand challenges. A team with Northwestern Engineering’s Wei ...
More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...
How additive manufacturing advanced the development of functionally graded materials. Why compositionally graded materials present a greater challenge to materials engineers. How computational ...
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