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 ...
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 ...
Hosted on MSN
Real-time technique directly images material failure in 3D to improve nuclear reactor safety and longevity
MIT researchers have developed a technique that enables real-time, 3D monitoring of corrosion, cracking, and other material failure processes inside a nuclear reactor environment. Subscribe to our ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results