Supporting the Manufacturing Process of Metal Products with the Methods of Artificial Intelligence

• Autorzy: Wilk-Kołodziejczyk D.

Identyfikatory

DOI

10.1515/amm-2016-0322

• Języki publikacji: EN

Abstrakty

EN

The aim of this research programme was to develop a series of methods and solutions to support the decision-making process in foundry and materials engineering. The specific problems discussed included the selection of methods for data processing and knowledge representation formalisms, backed up by the creation of decision algorithms based on contemporary achievements of artificial intelligence, tailored to the needs of foundry industry and metallurgy. The manufacturing process of metal items is associated with many aspects, which affect the quality of end product. For process engineers responsible for the supervising and planning of production, an important feature is, among others, the diversified nature of numerous aspects of the knowledge acquisition and integration from distributed sources of information which, when made available in an appropriate manner, can support the improvement of manufacturing process.

Słowa kluczowe

EN

manufacturing process of metal products; decision algorithms; implementation of IT solutions

• 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: 2016
• Tom: Vol. 61, iss. 4
• Strony: 1995--1998
• Opis fizyczny: Bibliogr. 13 poz., rys., schem., wzory

Twórcy

autor

Wilk-Kołodziejczyk D.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland
• Foundry Research Institute, Zakopianska 73 Str., 30-418 Kraków, Poland

Bibliografia

• [1] Z. Górny, S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, Attribute-based knowledge representation in the process of defect diagnosis, Archives of Metallurgy and Materials 55 (3), 819-826 (2010)
• [2] K. Regulski, S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, Practical aspects of knowledge integration using attribute tables generated from relational databases, in: Studies in Computational Intelligence, Springer-Verlag 381, 13-22 (2011).
• [3] Z. Górny, S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, K. Regulski, Diagnosis of casting defects using uncertain and incomplete knowledge, Archives of Metallurgy and Materials 55 (3), 827-836 (2010).
• [4] S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, K. Regulski, G. Dobrowolski, Rough Sets Applied to the RoughCast System for Steel Castings, in: Intelligent Information and Database Systems 6592, 52-61(2011).
• [5] D. Boehm-Davis, K. Dontas, R. S. Michalski, A validation and exploration of the Collins-Michalski theory of plausible reasoning. Tech. rep., George Mason University, 1990.
• [6] M. Virvou, B. D. Boulay, Human plausible reasoning for intelligent help. User Modeling and User-Adapted Interaction 9, 321-375 (1999).
• [7] M. Virvou, A cognitive theory in an authoring tool for intelligent tutoring systems, in: A. E. Kamel, K. Mellouli, P. Borne, (Eds.) Proc. of the 2002 IEEE International Conference on Systems, Man and Cybernetics 2002.
• [8] A. Cawsey, Using plausible inference rules in description planning. In: Proc. of the Fifth Conference of the European Chapter of the Association for Computational Linguistics (EACL-91). Congress Hall, Berlin, 1991.
• [9] S. Abedinzadeh, S. Sadaoui, A trust-based service suggestion system using human plausible reasoning. Applied Intelligence, 2014.
• [10] M. R. Hieb, R. S. Michalski, Multitype inference in multistrategy task-adaptive learning: Dynamic interlaced hierarchies. Tech. rep., George Mason University, 1993.
• [11] M. R. Hieb, R. S. Michalski, A knowledge representation system based on dynamically interlaced hierarchies: Basic ideas and examples. Tech. rep., George Mason University, 1993.
• [12] B. Śnieżyński, S. Kluska-Nawarecka, E. Nawarecki, D. Wilk-Kołodziejczyk, D. Intelligent information system based on logic of plausible reasoning, in: Issues and Challenges in Artificial Intelligence, Springer International Publishing, 57-74, 2014.
• [13] S. Kluska-Nawarecka, E. Nawarecki, B. Śnieżyński, D. Wilk-Kołodziejczyk, The recommendation system knowledge representation and reasoning procedures under uncertainty for metal casting. Metalurgija 54 (1), 263-266 (2015).

Uwagi

EN

Financial support of The National Centre for Research and Development LIDER/028/593/L-4/12/NCBR/2013 is gratefully acknowledged.