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.
The aim of this study is to design and implement a computer system, which will allow the semantic cataloging and data retrieval in the field of cast iron processing. The intention is to let the system architecture allow for consideration of data on various processing techniques based on the information available or searched by a potential user. This is achieved by separating the system code from the knowledge of the processing operations or from the chemical composition of the material being processed. This is made possible by the creation and subsequent use of formal knowledge representation in the form of ontology. So, any use of the system is associated with the use of ontologies, either as an aid for the cataloging of new data, or as an indication of restrictions imposed on the data which draw user attention. The use of formal knowledge representation also allows consideration of semantic meaning, a consequence of which may be, for example, returning all elements in subclasses of the searched process class or material grade.
The objective of studies presented in this publication was structuring of research knowledge about the ADI functional properties and changes in these properties due to material treatment. The results obtained were an outcome of research on the selection of a format of knowledge representation that would be useful in further work aiming at the design, application and implementation of an effective system supporting the decisions of a technologist concerning the choice of a suitable material (ADI in this case) and appropriate treatment process (if necessary). ALSV(FD) logic allows easy modelling of knowledge, which should let addressees of the target system carry out knowledge modelling by themselves. The expressiveness of ALSV (FD) logic allows recording the values of attributes from the scope of the modelled domain regarding ADI, which is undoubtedly an advantage in the context of further use of the logic. Yet, although the logic by itself does not allow creating the rules of knowledge, it may form a basis for the XTT format that is rule-based notation. The difficulty in the use of XTT format for knowledge modelling is acceptable, but formalism is not suitable for the discovery of rules, and therefore the knowledge of technologist is required to determine the impact of process parameters on values that are functional properties of ADI. The characteristics of ALSV(FD) logic and XTT formalism, described in this article, cover the most important aspects of a broadly discussed, full evaluation of the applicability of these solutions in the construction of a system supporting the decisions of a technologist.