Developing a Methodology for Building the Knowledge Base and Application Procedures Supporting the Process of Material and Technological Conversion

• Autorzy: Wilk-Kołodziejczyk Dorota , Jaśkowiec Krzysztof , Bitka Adam , Pirowski Zenon , Grudzień-Rakoczy Małgorzata , Chrzan Konrad , Małysza Marcin , Doroszewski Maciej

Identyfikatory

DOI

10.24425/amm.2022.139707

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

artificial intelligence; material conversion; technological conversion; selection of parameters; prediction of mechanical properties

• Wydawca: Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences
• Czasopismo: Archives of Metallurgy and Materials
• Rocznik: 2022
• Tom: Vol. 67, iss. 3
• Strony: 1085--1091
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Wilk-Kołodziejczyk Dorota

• AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Al. Mickiewicza 30, 30-059 Kraków, Poland
• Centre of Casting Technology, The Łukasiewicz Research Network - Cracow Technology Institute, Poland

• https://orcid.org/0000-0001-5590-0477

autor

Jaśkowiec Krzysztof

• Centre of Casting Technology, The Łukasiewicz Research Network - Cracow Technology Institute, Poland

• https://orcid.org/0000-0002-0569-1192

autor

Bitka Adam

• Centre of Casting Technology, The Łukasiewicz Research Network - Cracow Technology Institute, Poland

• https://orcid.org/0000-0002-2342-5023

autor

Pirowski Zenon

• Centre of Casting Technology, The Łukasiewicz Research Network - Cracow Technology Institute, Poland

• https://orcid.org/0000-0003-4588-3444

autor

Grudzień-Rakoczy Małgorzata

• Centre of Casting Technology, The Łukasiewicz Research Network - Cracow Technology Institute, Poland

• https://orcid.org/0000-0002-5875-9007

autor

Chrzan Konrad

• Centre of Casting Technology, The Łukasiewicz Research Network - Cracow Technology Institute, Poland

• https://orcid.org/0000-0003-2738-4377

autor

Małysza Marcin

• Centre of Casting Technology, The Łukasiewicz Research Network - Cracow Technology Institute, Poland

• https://orcid.org/0000-0002-7484-1393

autor

Doroszewski Maciej

• AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Al. Mickiewicza 30, 30-059 Kraków, Poland

• https://orcid.org/0000-0003-0177-9493

Bibliografia

• [1] D. Wilk-Kołodziejczyk, Reasoning algorithm for creative decision support system integrating inference and machine learning, Computer Science-AGH 18 (3), 317-338 (2017). DOI: https://doi.org/10.7494/csci.2017.18.3.2364
• [2] S. Kluska-Nawarecka, B. Śnieżyński, W. Parada, M. Lustofin, D. Wilk-Kołodziejczyk, The use of LPR (logic of plausible reasoning) to obtain information on innovative casting technologies, Archives of Civil and Mechanical Engineering 14 (1), 25-31 (2014). DOI: https://doi.org/0.1016/j.acme.2013.05.011
• [3] Z. Gorny, S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, K. Regulski, Methodology for the Construction of a Rule-Based Knowledge Base Enabling the Selection of Appropriate Bronze Heat Treatment Parameters Using Rough Sets, Archives of Metallurgy and Materials 60 (1), 309-312 (2015). DOI: https://doi.org/10.1515/amm-2015-0050
• [4] J. David, P. Svec, R. Garzinov ́a, S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, K. Regulski, Heuristic modeling of casting processes under the conditions uncertainty, Archives of Civil and Mechanical Engineering 16, 179-185 (2016). DOI: https://doi.org/10.1016/j.acme.2015.10.006
• [5] D. Wilk-Kołodziejczyk, Supporting the Manufacturing Process of Metal Products with the Methods of Artificial Intelligence, Archives of Metallurgy and Materials 61 (4), 1995-1998 (2016). DOI: https://doi.org/10.1515/amm-2016-0322
• [6] D. Wilk-Kołodziejczyk, S. Kluska-Nawarecka, E. Nawarecki, B. Śnieżyński, K. Jaśkowiec, G. Legień, The Heuristic Model Based on LPR in the Context of Material Conversion, Archives of Metallurgy and Materials 62 (3), 1603-1607 (2017). DOI: https://doi.org/10.1515/amm-2017-0245
• [7] D. Wilk-Kołodziejczyk, K. Regulski, T. Giętka, G. Gumienny, K. Jaśkowiec, S. Kluska-Nawarecka, The selection of Heat Treatment Parameters to Obtain Austempered Ductile Iron with the Required Impact Strength, Journal of Materials Engineering and Performance 27 (11), 5865-5878 (2018).
• [8] D. Wilk-Kolodziejczyk, K. Regulski, G. Gumienny, B. Kacprzyk, S. Kluska-Nawarecka, K. Jaskowiec, Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the content of alloying elements, International Journal of Advanced Manufacturing Technology 95 (9-12), 3127-3139 (2016). DOI: https://doi.org/10.1007/s00170-017-1430-7
• [9] D. Wilk-Kolodziejczyk, K. Regulski, G. Gumienny, Comparative analysis of the properties of the nodular cast iron with carbides and the austempered ductile iron with use of the machine learning and the support vector machine, International Journal of Advanced Manufacturing Technology 87 (1-4), 1077-1093 (2016). DOI: https://doi.org/10.1007/s00170-016-8510-y
• [10] Z. Górny, S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, Heuristic models of the toughening process to improve the properties of non-ferrous metal alloys, Archives of Metallurgy and Materials 58 (3), (849-852) (2013). DOI: https://doi.org/10.2478/amm-2013-0085
• [11] E. Czekaj, S. Pysz, A. Garbacz-Klempka A., R. Żuczek, Wprowadzenie do zagadnień konwersji elementów konstrukcyjnych - w tym wytwarzanych poprzez odlewanie, XVIII Międzynarodowa Konferencja Naukowo-Techniczna Odlewnictwa Metali Nieżelaznych Nauka i Technologia. Monografia. Odlewnictwo Metali Nieżelaznych, Wydawnictwo Naukowe „Akapit”, 45-62 (2015).
• [12] R. Kowalski, Logic for Problem Solving. Oxford, New York (2002).
• [13] A. Collins, R.S. Michalski, The Logic of Plausible Reasoning: a Core Theory, Cognitive Science 13 (1-49), (1989).
• [14] J.R. Quinlan, C4.5, Programs for Machine Learning, Morgan Kaufmann, USA (1993).
• [15] R.S. Michalski, Inferential Theory of Learning: Developing Foundations for Multistrategy Learning. Morgan Kaufmann Publishers (1994).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).