AI-Driven Image Analysis in Materials and Manufacturing
About the Webinar
There is is no shortage of hype surrounding deep learning and AI. It can be difficult to cut through the "pie-in-the-sky promises" and find actual solutions to real-world problems, especially in the scientific image analysis space where in practice, images are far from the ideal cases often seen in demos.
No technology is without limitations, and key barriers to practical deep learning solutions have included vast amounts of requisite training data and a strong dependence on programming expertise. However, the latest developments in both the core technology, and in its commercial deployment, have dramatically reduced the amount of training data required, and placed deep learning's capabilities within reach of anyone capable of annotating images.
In this webinar, we will present the latest in deep learning for materials analysis, and how its commercial deployment enables researchers to evolve their manual annotations into intelligent automation, eliminating the need to annotate any further.
✔︎ Recognizing Shapes Like the Human Brain
Twinned grain size measurement from traditional optical microscopy
✔︎ Separating Signal from Noise
Accurate sizing of subtle melt pools in additively manufactured parts
✔︎ Teaching Software to See Texture
Fully automated analysis of prior beta grains and alpha colonies in titanium
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