Understanding material failure by machine learning analysis of pattern strains

Metals are made of small crystals - i.e., atoms are arranged in a particular geometric arrangement, which are typically in the range of a few 10s of microns (0.01 mm). The arrangement of these crystals greatly affects the performance of the metal and hence the performance of components where metals are used - such as in aeroplanes, gas turbine engines, cars, etc. The manner in which such materials deform, crack and fail under a variety of conditions is an important area in terms of cost and safety. When a metal is made - i.e., the liquid is cooled down and it becomes a solid material - crystals start forming within the liquid, which grow during cooling until they come in contact with each other, where they form crystal boundaries. Hence, we have formed a polycrystalline material, in which all the different crystals might have different orientations. As metals are typically used for load-bearing applications, it is imperative to understand the mechanical performance of such materials. Initially, metals deform in a fully elastic (fully reversable) manner but once the elastic limit has been exceeded, they deform plastically, which means any additional strain is irreversible - i.e., permanent. Such plastic deformation can lead to failure of the material. At a crystal level, the plastic strain - or shearing - happens along particular planes associated with the crystal. Hence, the plasticity in a crystal is not random but can only happen along particular directions. However, as the material is made up of many crystals, and they all have different orientations, the deformation process of a polycrystalline material is highly complex.

For the proposed PhD project, experimental data are already available that bring together maps of orientations of such crystals together with the deformation pattern generated during mechanical loading of such samples. The focus of the PhD project will be to use machine learning techniques to better understand the interplay between the crystal orientations and deformation patterns in a polycrystalline material during plastic deformation in order to eventually predict the manner in which materials deform and fail.

As a first step, we wish to infer a distribution of the directions of deformation displacement and the length of such deformations (or displacements).

As later steps, we will then look at time series data (or movies of images) and also at out-of-plane 3-dimensional deformations.

This will enable us to better understand deformation - with a view to anticipation, remedy and possibly prevention.

The PhD project is a collaboration between the Faculties of IT and Engineering with the student being based in the Faculty of IT.

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