A step towards intelligent EBSD microscopy: machine-learning prediction of twin activity in MgAZ31.

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 gained over many observations; that is, machine learning potentially provides an ideal solution for more efficient microscopy. This paper explores the potential for informing the microscope's observation strategy while characterising critical events. In particular, the identification of regions likely to experience twin activity (twin interaction with grain boundary) in AZ31 magnesium is attempted, from only local information. EBSD-based observations in the neighbourhoods of twin activity are fed into a machine-learning environment to inform the future search for such events, and the accuracy of the resultant decisions is quantified relative to the number of prior observations. The potential for utilising different types of local information, and their resultant value in the prediction process, is also assessed. After applying an attribute selection filter, and various other machine-learning tools, a decision-tree model is able to classify likely neighbourhoods of twin activity with 85% accuracy. The resultant framework provides the first step towards an intelligent microscopy for efficient observation of stochastic events during in situ microscopy campaigns.