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

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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.

[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).

Archives of Metallurgy and Materials

The Journal of Institute of Metallurgy and Materials Science and Commitee on Metallurgy of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2016: 0.571
5-year IMPACT FACTOR: 0.776

CiteScore 2016: 0.85

SCImago Journal Rank (SJR) 2016: 0.347
Source Normalized Impact per Paper (SNIP) 2016: 0.740


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