The study presented here is related with one of the components of a hybrid decision support system called CAPCAST (Computer Aided Process - CAST), developed under a research project at the Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology. This is a module for rule generation to serve the knowledge base operating in an expert system. The scope of the system operation involves the selection of technological parameters for the manufacture of machine parts from ductile iron. However, it can be extended to include other materials and technologies.

Stereological description of dispersed microstructure is not an easy task and remains the subject of continuous research. In its practical aspect, a correct stereological description of this type of structure is essential for the analysis of processes of coagulation and spheroidisation, or for studies of relationships between structure and properties. One of the most frequently used methods for an estimation of the density Nv and size distribution of particles is the Scheil - Schwartz – Saltykov method. In this article, the authors present selected methods for quantitative assessment of ductile iron microstructure, i.e. the Scheil - Schwartz – Saltykov method, which allows a quantitative description of three-dimensional sets of solids using measurements and counts performed on two-dimensional cross-sections of these sets (microsections) and quantitative description of three-dimensional sets of solids by X-ray computed microtomography, which is an interesting alternative for structural studies compared to traditional methods of microstructure imaging since, as a result, the analysis provides a three-dimensional imaging of microstructures examined.

A mathematical model of austenite - bainite transformation in austempered ductile cast iron has been presented. The model is based on a model developed by Bhadeshia [1, 2] for modelling the bainitic transformation in high-silicon steels with inhibited carbide precipitation. A computer program has been developed that calculates the incubation time, the transformation time at a preset temperature, the TTT diagram and carbon content in unreacted austenite as a function of temperature. Additionally, the program has been provided with a module calculating the free energy of austenite and ferrite as well as the maximum driving force of transformation. Model validation was based on the experimental research and literature data. Experimental studies included the determination of austenite grain size, plotting the TTT diagram and analysis of the effect of heat treatment parameters on the microstructure of ductile iron. The obtained results show a relatively good compatibility between the theoretical calculations and experimental studies. Using the developed program it was possible to examine the effect of austenite grain size on the rate of transformation.

The article presents two modules operating in a hybrid CAPCAST system implemented in the Department of Applied Computer Science

and Modelling, AGH University of Science and Technology, Cracow. These are the modules: CAPCAST-base of producers and

CAPCAST-base of materials. Registered producers may benefit from other modules of the system, the base can also be an independent

source of knowledge about Polish foundries and their production capacity, and can serve as a kind of platform for the implementation of

the basic functions of e-business. The base of materials can also be a source of knowledge about materials, and it allows searching and

filtering the lists of materials in terms of user-selected attributes using a multi-level search engine. This module is integrated with the rest

of the system and can be used by other modules. The system has been developed at the AGH Department of Applied Computer Science

and Modelling in Cracow.

This article presents the methodology for exploratory analysis of data from microstructural studies of compacted graphite iron to gain

knowledge about the factors favouring the formation of ausferrite. The studies led to the development of rules to evaluate the content of

ausferrite based on the chemical composition. Data mining methods have been used to generate regression models such as boosted trees,

random forest, and piecewise regression models. The development of a stepwise regression modelling process on the iteratively limited

sets enabled, on the one hand, the improvement of forecasting precision and, on the other, acquisition of deeper knowledge about the

ausferrite formation. Repeated examination of the significance of the effect of various factors in different regression models has allowed

identification of the most important variables influencing the ausferrite content in different ranges of the parameters variability.