9 JUNE 2021, 1pm-1.45pm(CET)
Metals characterization using deep learning image analysis
The quick development of metals relies on the understanding of material microstructure (grain size, porosity, morphology, etc.), a reliable manufacturing process, and a thorough analysis of the performance for different applications. To solve some of the industrial challenges (increase throughput and deliver flawless products) and stay ahead of the competitors, automated, reliable and intelligent analysis techniques are needed.
Using deep learning and a powerful image analysis engine, MIPAR (www.mipar.us) allows users to perform a fast, accurate and automated analysis of images. In three simple steps: trace, train and apply, researchers can create a model that identifies the features of interest and run personalized recipes on new images to detect complex features.
This webinar will overview the advantages of using deep learning image segmentation analysis in metals to analyze complex microstructures, perform automated grains and inclusions analysis, investigate failures and defects and analyze particles and satellites in additive manufactured powders, with example applications in titanium, nickel, steel and copper.
Alisa Stratulat, PhD
Program Manager - MIPAR Image Analysis
CEO - SciSpot Scientific Solutions