UCL researchers advance metal 3D printing with machine learning
AM-SegNet is a smart, lightweight neural network designed to quickly and accurately analyse X-ray images
According to University College London (UCL), mechanical engineering researchers have been using powerful X-rays to monitor the metal 3D printing process, which helps them understand and make improvements. However, these X-ray experiments produce a massive amount of data – too much for humans to analyze by hand.
A team of experts, including UCL’s Prof. Lee and Dr. Leung, created AM-SegNet, a smart, lightweight neural network designed to quickly and accurately analyse the X-ray images from these experiments. The team trained AM-SegNet using a database of more than 10,000 labelled images from top research facilities around the world.
AM-SegNet can analyze an image with about 96% accuracy in less than 4 milliseconds, and it’s more reliable than other advanced models. This means it can rapidly process the data, helping researchers get to the important insights faster.
By speeding up data analysis, AM-SegNet is helping to uncover the detailed physics of the AM process, which in turn helps manufacturers improve their methods and achieve more reliable results. It’s a significant step forward for real-time monitoring and quality control in metal additive manufacturing.
The source code of AM-SegNet is publicly available on GitHub.
