Advanced microstructure classi fi cation by data mining methods

The mechanical properties of modern multi-phase materials significantly depend on the distribution, the shape and the size of the microstructural constituents. Thus, quantification and classification of the microstructure are decisive in identifying the underlying structure-property relationship of a specific material. Due to the complexity of the microstructure in modern materials, a reliable classification of microstructural constituents remains one of the biggest challenges in metallography. The present study demonstrates how data mining methods can be used to determine varying steel structures of two-phase steels by evaluating their morphological parameters. A data mining process was developed by using a support vector machine as classifier to build a model that is able to distinguish between different microstructures of the two-phase steels. The impact of preprocessing and feature selection methods on the classification result was tested.