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Although microscopy is often treated as a quasi‐static exercise for obtaining a snapshot of events and structure, it is clear that a more dynamic approach, involving real‐time decision making for guiding the investigation process, may provide deeper insights, more efficiently. On the other hand, many applications of machine learning involve the interpretation of local circumstances from experience...
Magnesium AZ31B sheets of 2 mm thickness were stretch formed with a 101.6-mm-diameter punch at temperatures from 25°C to 150°C, in 25°C increments. Surface strains were measured with a digital image correlation method. The punch height versus load curve was found to be the same for temperatures of 25°C and for 50°C, whereas at 75°C and above, the load for a given punch height started to decrease,...
To explore the driving forces behind deformation twinning in Mg AZ31, a machine learning framework is utilized to mine data obtained from electron backscatter diffraction (EBSD) scans in order to extract correlations in physical characteristics that cause twinning. The results are intended to inform physics-based models of twin nucleation and growth. A decision tree learning environment is selected...
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