@ARTICLE{Wilk-Kołodziejczyk_D._Developing_2022,
 author={Wilk-Kołodziejczyk, D. and Jaśkowiec, K. and Bitka, A. and Pirowski, Z. and Grudzień-Rakoczy, M. and Chrzan, K. and Małysza, M. and Doroszewski, M.},
 volume={vol. 67},
 number={No 3},
 journal={Archives of Metallurgy and Materials},
 pages={1085-1091},
 howpublished={online},
 year={2022},
 publisher={Institute of Metallurgy and Materials Science of Polish Academy of Sciences},
 publisher={Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences},
 abstract={The article presents the developed IT solutions supporting the material and technological conversion process in terms of the possibility of using the casting technology of selected alloys to produce products previously manufactured with the use of other methods and materials. The solutions are based on artificial intelligence, machine learning and statistical methods. The prototype module of the information and decision-making system allows for a preliminary assessment of the feasibility of this type of procedure. Currently, the selection of the method of manufacturing a product is based on the knowledge and experience of the technologist and constructor. In the described approach, this process is supported by the proprietary module of the information and decision-making system, which, based on the accumulated knowledge, allows for an initial assessment of the feasibility of a selected element in a given technology. It allows taking into account a large number of intuitive factors, as well as recording expert knowledge with the use of formal languages. Additionally, the possibility of searching for and collecting data on innovative solutions, supplying the knowledge base, should be taken into account. The developed and applied models should allow for the effective use and representation of knowledge expressed in linguistic form. In this solution, it is important to use methods that support the selection of parameters for the production of casting. The type, number and characteristics of data have an impact on the effectiveness of solutions in terms of classification and prediction of data and the relationships detected.},
 type={Article},
 title={Developing a Methodology for Building the Knowledge Base and Application Procedures Supporting the Process of Material and Technological Conversion},
 URL={http://www.czasopisma.pan.pl/Content/124063/PDF/AMM-2022-3-38-Kolodziejczyk.pdf},
 doi={10.24425/amm.2022.139707},
 keywords={artificial intelligence, material and technological conversion, selection of parameters, prediction of mechanical properties},
}