A research team affiliated with UNIST has unveiled an innovative, high-precision AI-powered quality inspection system that reduces inspection time from 12 minutes to just under 3 seconds. This cutting ...
A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.
Hosted on MSN
AI-based model measures atomic defects in materials
In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during ...
Researchers built an AI system that adapts to process changes, maintaining defect detection accuracy and lowering retraining costs in smart factories. (Nanowerk News) Artificial intelligence is ...
The AI model rapidly maps boundary conditions to molecular alignment and defect locations, replacing hours of simulation and enabling fast exploration and inverse design of advanced optical materials.
A research team led by Dr. Jeong Min Park of the Nano Materials Research Division at the Korea Institute of Materials Science (KIMS), in collaboration with Dr. Jaemin Wang and Prof. Dierk Raabe of the ...
A new AI framework predicts microscopic defects in metal 3D printing, forecasting part strength in seconds and cutting trial-and-error in materials design. (Nanowerk ...
Results that may be inaccessible to you are currently showing.
Hide inaccessible results