Metals characterization using deep learning image analysis @bhadeshia123

bhadeshia123 | Metals characterization using deep learning image analysis @bhadeshia123 | Uploaded May 2023 | Updated October 2024, 7 hours ago.
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.

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$Atomic mechanism of acicular ferrite transformation in steels Non-metallic inclusions are anathema when it comes to the design of strong steels because they become the initiation sites for fracture. Huge efforts have been made devoted to making clean steels - the oxygen concentration of a hard bearing steel is routinely less than 10 ppm. However, there are other structural steels that have to be welded where the localised heat input generates microstructures in the heat-affected zone that are undesirable. The alloys that are used to deposit the weld must have good properties in the as-cast state. In both of these circumstances, specific non-metallic inclusions are a positive boon in that they provide substrates for the intragranular nucleation of bainite. As a consequence, highly organised sheaves of bainite are altered into a more chaotic arrangement that frequently deflects propagating cracks and hence enhances the toughness. This is the so-called acicular ferrite that is the subject of this lecture.$

Some steels research at the Universidad Federal Fluminense, Brazil