The use of modern casting materials allows the achievement of higher product quality indices. The conducted experimental studies of new materials allow obtaining alloys with high performance properties while maintaining low production costs. Studies have shown that in certain areas of applications, the expensive to manufacture austempered ductile iron (ADI) can be replaced with ausferritic ductile iron or bainitic nodular cast iron with carbides, obtained without the heat treatment of castings. The dissemination of experimental results is possible through the use of information technologies and building applications that automatically compare the properties of materials, as the machine learning tools in comparative analysis of the properties of materials, in particular ADI and nodular cast iron with carbides.

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This paper presents assumptions for a system of automatic cataloging and semantic text documents searching. As an example, a document repository for metals processing technology was used. The system by using ontological model provides the user with a new approach to the exploration of database resources -easier and more intuitive information search. In the current document storage systems, searching is often based only on keywords and descriptions created manually by the system administrator. The use of text mining methods, especially latent semantic indexing, allows automatic clustering of documents with respect to their content. The result of this clustering is integrated with the ontological model, making navigation through documents resources intuitive and does not require the manual creation of directories. Such an approach seems to be particularly useful in a situation where we are dealing with large repositories of unstructured documents from such sources as the Internet. This situation is very typical for cases of searching information and knowledge in the area of metallurgy, for example with regard to innovation and non-traditional suppliers of materials and equipment.Keywords: knowledge engineering, documents processing, ontologies, semantic integration, technological knowledge, metallurgy Artykuł prezentuje założenia systemu umożliwiającego automatyczne katalogowanie i przeszukiwanie merytoryczne dokumentów tekstowych na przykładzie repozytorium dokumentów dotyczących technologii przetwórstwa metali. System dzięki zastosowaniu modelu ontologicznego ma umożliwić użytkownikowi nowe podejście do eksploracji zasobów bazodanowychprostsze i bardziej intuicyjne wyszukiwanie informacji. W obecnych systemach przechowywania dokumentów często jedyną formą wyszukiwania jest wyszukiwanie na podstawie katalogu słów kluczowych i deskrypcji tworzonych ręcznie przez administratora systemu. Zastosowanie metod eksploracji tekstu, w szczególności ukrytego indeksowania semantycznego umożliwia automatyczne grupowanie dokumentów pod względem ich zawartości. Wynik takiego grupowania zostaje zintegrowany z modelem ontologicznym, przez co nawigacja poprzez zasoby dokumentów staje się intuicyjna i nie wymaga tworzenia ręcznie katalogów. Takie podejście wydaje się szczególnie przydatne w sytuacji, gdy mamy do czynienia z dużymi repozytoriami nieuporządkowanych dokumentów pochodzących m.in. z sieci Internet.

Product properties for innovative materials, e.g. dual phase steels, require precise control of production processes. Difficulties in optimization of process parameters correspond with large number of control variables, which should be considered in the technology design. Sensitivity analysis allows evaluating the importance of all process inputs on the final properties of material. Information on the most important inputs is crucial for further design of the process. Application of sensitivity analysis requires detailed knowledge of the process phenomena as well as the definition of the mathematical model of the thermomechanical process. Furthermore, some sensitivity analysis algorithms are of the high computational cost. Presented work concerns possibility of the application of data exploration approach in evaluation of the importance of process inputs as the alternative for sensitivity analysis. Use of data mining algorithms eliminates necessity of mathematical model development, it also does not require any apriori knowledge about the process. Authors presents the comparison of sensitivity analysis and data exploration approach in evaluating relationships between inputs and outputs of the hot rolling for dual phase steel strips. The presented approach and the perspectives of the practical application could lead to significant decrease of time necessary for the computations of process design. The theoretical considerations are supplemented with the results of both types of analysis.

The application of data mining techniques in the design of modern foundry materials allows achieving higher product quality indicators. Designing of a new product always requires thorough knowledge of the effect of alloying elements on the microstructure and hence also on the properties of the examined material. The conducted experimental studies allow for a qualitative assessment of the indicated relationships, but it is the use of intelligent computational techniques that enables building an approximation model of the microstructure and, owing to this, make predictions with high precision. The developed model of prediction supports the technology-related decisions as early as at the stage of casting design and is considered the first step in selecting the type of material used.