首页|Studies from IMDEA Materials Institute Update Current Data on Machine Learning (Application of Machine Learning To Assess the Influence of Microstructure On Twin Nucleation In Mg Alloys)
Studies from IMDEA Materials Institute Update Current Data on Machine Learning (Application of Machine Learning To Assess the Influence of Microstructure On Twin Nucleation In Mg Alloys)
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Investigators discuss new findings in Machine Learning. According to news reporting originating in Madrid, Spain, by NewsRx journalists, research stated, "Twin nucleation in textured Mg alloys was studied by means of electron back-scattered diffraction in samples deformed in tension along different orientations in more than 3000 grains. In addition, 28 relevant parameters, categorized in four different groups (loading condition, grain shape, apparent Schmid factors, and grain boundary features) were also recorded for each grain." Financial supporters for this research include Consejeria de Educacion, Juventud y Deporte, Comunidad de Madrid, China Scholarship Council. The news reporters obtained a quote from the research from IMDEA Materials Institute, "This infor- mation was used to train supervised machine learning classification models to analyze the influence of the microstructural features on the nucleation of extension twins in Mg alloys. It was found twin nucleation is favored in larger grains and in grains with high twinning Schmid factors, but also that twins may form in the grains with very low or even negative Schmid factors for twinning if they have at least one smaller neighboring grain and another one (or the same) that is more rigid. Moreover, twinning of small grains with high twinning Schmid factors is favored if they have low basal slip Schmid factors and have at least one neighboring grain with a high basal slip Schmid factor that will deform easily."
MadridSpainEuropeAlloysCyborgsEmerging TechnologiesMachine LearningIMDEA Materials Institute
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