Analysis of the Possibility of Using Selected Artificial Intelligence Algorithms for the Assessment of the Microstructure of Vermicular Cast Iron

This paper presents an analysis of artificial intelligence algorithms in the context of their applicability to the automatic analysis of microstructure images. In the example presented, reference is made to exemplary images of the microstructure of vermicular cast iron. A characteristic feature of this alloy is the shape of the graphite separations. The microstructure consists of elements that humans can learn to recognise quite simply. Developing an application that recognises ‘dark colour’ and ‘worm shape’ is no longer so straightforward. The determination of the ‘dark’ colour in the algorithm becomes problematic, because depending on the conditions under which the photo was taken (e.g. time: day/night), the actual intensity values are altered. A similar situation occurs in the determination of shape, which varies from case to case. Such a classification is very general and results in large differences between instances of the class. Even a term like ‘relatively large’ can change depending on the size of the graphite separation itself. A dark colour can be represented as a sudden change in image intensity, i.e. large values of the gradient modulus. The question arises: what happens if ‘dark’ can be more than one microcomponent, for example graphite and perlite. A good solution would be to define an associated set of features that would more precisely define just this component of the microstructure - that is, its shape, colour and surroundings. The paper uses the local feature paradigm to do this. Referring to the literature, it can be pointed out that [1] local features are referred to as non-small and specific parts of an image. Distinctive image features need to be distinguished in order to detect these places of interest. In this case, they are: edges, spots and ridges.