Data-Driven Model Selection for Compacted Graphite Iron Microstructure Prediction

Abstract

Compacted graphite iron (CGI), having a specific graphite form with a large matrix contact surface, is a unique casting material. This type of cast iron tends to favor direct ferritization and is characterized by a complex of very interesting properties. Intelligent computing tools such as artificial neural networks (ANNs) are used as predictive modeling tools, allowing their users to forecast the microstructure of the tested cast iron at the level of computer simulation. This paper presents the process of the development of a metamodel for the selection of a neural network appropriate for a specific chemical composition. Predefined models for the specific composition have better precision, and the initial selection provides the user with automation of reasoning and prediction. Automation of the prediction is based on the rules obtained from the decision tree, which classifies the type of microstructure. In turn, the type of microstructure was obtained by clustering objects of different chemical composition. The authors propose modeling the prediction of the volume fraction of phases in the CGI microstructure in a three-step procedure. In the first phase, k-means, unsupervised segmentation techniques were used to determine the metamodel (DT), which in the second phase enables the selection of the appropriate ANN submodel (third phase).