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Metals characterization using deep learning image analysis

A talk given in the Warwick Manufacturing Group Seminars, by Dr Alisa Stratulat Head of EMEA Image Analysis at MIPAR, and manages the distributors in EMEA, contributes to the sales and marketing strategy and offers image analysis support to customers. The quick development of materials relies on the understanding of material microstructure (size, porosity, morphology, grains etc.), a reliable manufacturing process, and a thorough analysis of the performance for different applications. To solve some of the research challenges, 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 presentation will overview the advantages of using modern image analysis techniques to analyze grains, defects, and phases with real research applications.