The Heuristic Model Based on LPR in the Context of Material Conversion

• Autorzy: Wilk-Kołodziejczyk D. , Kluska-Nawarecka S. , Nawarecki E. , Śnieżyński B. , Jaśkowiec K. , Legień G.

Identyfikatory

DOI

10.1515/amm-2017-0245

• Języki publikacji: EN

Abstrakty

EN

High complexity of the physical and chemical processes occurring in liquid metal is the reason why it is so difficult, impossible even sometimes, to make analytical models of these phenomena. In this situation, the use of heuristic models based on the experimental data and experience of technicians is fully justified since, in an approximate manner at least, they allow predicting the mechanical properties of the metal manufactured under given process conditions. The study presents a methodology applicable in the design of a heuristic model based on the formalism of the logic of plausible reasoning (LPR). The problem under consideration consists in finding a technological variant of the process that will give the desired product parameters while minimizing the cost of production. The conducted tests have shown the effectiveness of the proposed approach.

Słowa kluczowe

EN

conversion of a technology; ductile iron; ADI; logic of plausible reasoning

• 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: 2017
• Tom: Vol. 62, iss. 3
• Strony: 1603--1607
• Opis fizyczny: Bibliogr. 26 poz., rys.

Twórcy

autor

Wilk-Kołodziejczyk D.

autor

Kluska-Nawarecka S.

autor

Nawarecki E.

autor

Śnieżyński B.

autor

Jaśkowiec K.

autor

Legień G.

Bibliografia

• [1] P. Szczepaniak, J. Kacprzyk, A. Niewiadomski, Advances in Web Intelligence, Lecture Notes in Computer Science, 3528 (2005).
• [2] G. Stojanov, B. Indurkhya, F. Guerin, T. Veale (Eds.), Modeling Changing Perspectives, Papers from the AAAI Fall Symposium, FS-14-05, 2014.
• [3] N. W. Alkharouf, R. S. Michalski, Multistrategy task-adaptive learning using dynamically interlaced hierarchies, in: R. S. Michalski, J. Wnek (Eds.), Proceedings of the Third International Workshop on Multistrategy Learning, 1996.
• [4] K. Altho, K. Bach, J. Deutch, A. Hanft, J. Manz, T. Muller, R. Newo, M. Reichle, M. Schaaf, K. Weis, Collaborative multi-expert-systems realizing knowledge lines with case factories and distributed learning systems, Proceedings of the 3rd Workshop on Knowledge Engineering and Software Engineering, 2007.
• [5] L. Boon Toh, L. Ho Chung, T. Ah Hwee, T. Hoon Heng, Knowledge and Data Engineering, IEEE Transactions 3 (2), 200-207 (1991).
• [6] A. Collins, R. S. Michalski, Cognitive Science 13, 149 (1989).
• [7] R. Drezewski, J. Sepielak, W. Filipkowski, Information Sciences 295, 18-32 (2015).
• [8] D. M. Gabbay, LDS Labeled Deductive Systems, 1991 Oxford University Press.
• [9] P. Hart, N. J. Nilsson, B. Raphael, IEEE Trans. Syst. Science and Cybernetics 4 (2), 100-107 (1968).
• [10] S. Kluska-Nawarecka, D. Wilk-Kolodziejczyk, K. Regulski, Rough Sets Applied to the RoughCast System for Steel Castings, Edited by: Nguyen, NT; Kim, CG; Janiak, Book Series: Lecture Notes in Artificial Intelligence 6592, 52-61 (2011).
• [11] A. Ligeza, Logical Foundations for Rule-Based Systems, 2006 Springer.
• [12] R.S. Michalski, Inferential theory of learning: Developing foundations for multistrategy learning, in: R.S. Michalski, (Eds.) Machine Learning: A Multistrategy Approach, IV, 1994 Morgan Kaufmann Publishers.
• [13] R. S. Michalski, J. Larson,: Aqval/1 (aq7) user’s guide and program description. Tech. Rep. 731, Department of Computer Science, 1975 University of Illinois, Urbana.
• [14] C. G. Morgan, Autologic. Logique et Analyse 28 (110-111), 257-282 (1985).
• [15] R. E. Neapolitan, Probabilistic Reasoning In Expert Systems: Theory and Algorithms, 2012 CreateSpace Independent Publishing Platform, USA.
• [16] J. Quinlan, C4.5: Programs for Machine Learning, 1993 Morgan Kaufmann, USA.
• [17] G. Riley, Clips an expert system building tool, Proceedings of Technology 2001 Conference, San Jose 1991.
• [18] B. Śniezynski, Probabilistic label algebra for the logic of plausible reasoning, in: M. Kłopotek, et al. (Eds.), Intelligent Information Systems. Advances in Soft Computing, Physica-Verlag, 2002 Springer.
• [19] S. Kluska-Nawarecka, K. Regulski, D. Wilk-Kolodziejczyk, Practical Aspects of Knowledge Integration Using Attribute Tables Generated from Relational Databases, Edited by: Katarzyniak, R; Chiu, TF; Hong, CF; et al. Semantic Methods For Knowledge Management And Communication Book Series: Studies in Computational Intelligence 381, 13-22 (2011).
• [20] T. Szydło, P. Nawrocki, R. Brzoza-Woch, K. Zielinski, Power aware MOM for telemetry-oriented applications using gprs-enabled embedded devices - levee monitoring use case, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems 2, 1059-1064, 2014.
• [21] L. P. Tran, J. P. Hancock, An adaptive-learning expert system for maintenance diagnostics, Proceedings of Aerospace and Electronics Conference, NAECON 3, 1034-1039 (1989).
• [22] S. Wiriyacoonkasem, A.C. Esterline, Adaptive learning expert systems, Proceedings of the South Eastcon 2000, pp. 445-448, 2000.
• [23] L. A. Zadeh, Fuzzy sets. Information and Control 8, 338-353 (1965).
• [24] A. Ligięza, Logical Foundations for Rule-Based Systems, Studies in Computional Intelligence (SCI) 11, 61-63 (2006).
• [25] S. Kluska-Nawarecka, Z. Gorny, D. Wilk-Kolodziejczyk, The logic of plausible reasoning in the diagnosis of castings defects, Archives of Metallurgy and Materials 52, 3, 375-380 (2007).
• [26] 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, 25-31 (2014).

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).