The orientations of these infinitesimally small separations between individual “grains” of a polycrystalline material have big effects. In a material such as aluminum, these collections of grains ...
Hosted on MSN
Predicting material failure: Machine learning spots early abnormal grain growth signs for safer designs
A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time—a development that could lead to the creation of ...
Using state-of-the-art microscopy and simulation techniques, an international research team systematically observed how iron atoms alter the structure of grain boundaries in titanium. They were in for ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results