Methods of Computational Intelligence in the Context of Quality Assurance in Foundry Products

• Autorzy: Rojek G. , Regulski K. , Wilk-Kołodziejczyk D.

Identyfikatory

DOI

10.1515/afe-2016-0018

• Języki publikacji: EN

Abstrakty

EN

One way to ensure the required technical characteristics of castings is the strict control of production parameters affecting the quality of the finished products. If the production process is improperly configured, the resulting defects in castings lead to huge losses. Therefore, from the point of view of economics, it is advisable to use the methods of computational intelligence in the field of quality assurance and adjustment of parameters of future production. At the same time, the development of knowledge in the field of metallurgy, aimed to raise the technical level and efficiency of the manufacture of foundry products, should be followed by the development of information systems to support production processes in order to improve their effectiveness and compliance with the increasingly more stringent requirements of ergonomics, occupational safety, environmental protection and quality. This article is a presentation of artificial intelligence methods used in practical applications related to quality assurance. The problem of control of the production process involves the use of tools such as the induction of decision trees, fuzzy logic, rough set theory, artificial neural networks or case-based reasoning.

Słowa kluczowe

EN

application of information technology; foundry industry; quality management; casting defects; computational intelligence; artificial intelligence

PL

zastosowanie technologii informatycznych; przemysł odlewniczy; zarządzanie jakością; wady odlewnicze; inteligencja obliczeniowa; sztuczna inteligencja

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2016
• Tom: Vol. 16, iss. 2
• Strony: 11--16
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Rojek G.

• AGH – University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Regulski K.

• AGH – University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Wilk-Kołodziejczyk D.

• AGH – University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland

Bibliografia

• [1] Mrzygłód B., Kowalski A., Olejarczyk-Wożeńska I., Adrian H., Głowacki M. & Opaliński A. (2015). Effect of heat treatment parameters on the formation of ADI microstructure with additions of Ni, Cu, Mo. Archives of Metallurgy and Materials. 60(3A), 1941–1948.
• [2] Mrzygłód B., Adrian A., Regulski K., Olejarczyk-Wożeńska I. & Kluska-Nawarecka S. (2013). The exploitation of TTT diagrams in the CAPCAST system, Transactions of the Foundry Research Institute. 53(4), 45–56.
• [3] David J., Svec P., Frischer R. & Stranavova M. (2012). Usage of RFID wireless identification technology to support decision making in steel works In 21st International Conference on Metallurgy and Materials, 1734-1738.
• [4] Breiman L., Friedman J., Stone C. J. & Olshen R. A. (1984). Classification and regression trees. CRC press.
• [5] Lewicki P., Hill T. (2006). Statistics: methods and applications. Tulsa, OK. Statsoft.
• [6] Kass G.V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 119-127.
• [7] Quinlan J.R. (1986). Induction of decision trees. Machine learning. 1(1), 81-106.
• [8] Speybroeck N. (2012). Classification and regression trees. International journal of public health. 57(1), 243-246.
• [9] Regulski K., Rojek G., Skóra M. & Kusiak J. (2012). Data exploration approach in control of metal forming manufacturing chain: example of fasteners production In Metal Forming 2012: Proceedings of the 14th International Conference on Metal Forming: September 16–19, 2012, Krakow, Poland, J. Kusiak, J. Majta, D. Szeliga, (Eds.) Steel Research International; spec. ed., 1319-1322.
• [10] Regulski K., Szeliga D. & Kusiak J., (2014). Data Exploration Approach Versus Sensitivity Analysis for Optimization of Metal Forming Processes. Key Engineering Materials. 611–612, 1390-1395.
• [11] Zadeh L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy sets and systems. 90(2), 111-127.
• [12] Colomo-Palacios R., González-Carrasco I., López-Cuadrado J.L. & García-Crespo Á. (2012). ReSySTER: A hybrid recommender system for Scrum team roles based on fuzzy and rough sets. International Journal of Applied Mathematics and Computer Science. 22(4), 801-816.
• [13] Warmuzek M., Regulski K. (2011). A procedure of in situ identification of the intermetallic AlTMSi phase precipitates in the microstructure of the aluminum alloys. Practical Metallography. 48(12), 660-683.
• [14] Pawlak Z. (1982). Rough sets. International Journal of Computer & Information Sciences. 11(5), 341-356.
• [15] Kluska-Nawarecka S., Wilk-Kołodziejczyk D., Regulski K. & Dobrowolski G. (2011). Rough sets applied to the RoughCast system for steel castings. Intelligent Information and Database Systems . Part II, Springer Lecture Notes in Computer Science, Volume 6592/2011, 52-61, DOI: 10.1007/978-3-642-20042-7_6, Conf. Proc. In Third International Conference, ACIIDS 2011, Daegu, Korea, April 20-22, 2011, Nguyen, Ngoc Thanh; Kim, Chong-Gun; Janiak, Adam (Eds.).
• [16] Kluska-Nawarecka S., Górny Z., Regulski K., Wilk-Kołodziejczyk D., Jančíková Z. & David J. (2014). A Method to Make Classification of the Heat Treatment Processes Performed on Bronze Using Incomplete Knowledge. Archives of Foundry Engineering. 14(2), 69-72.
• [17] Górny Z., Kluska-Nawarecka S., Wilk-Kołodziejczyk D. & Regulski K. (2015). 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), 309-315, DOI: 10.1515/amm-2015-0050.
• [18] Sztangret Ł., Szeliga D., Kusiak J. & Pietrzyk M. (2012). Application of inverse analysis with metamodelling for identification of metal flow stress. Canadian Metallurgical Quarterly. 51(4), 440-446.
• [19] Rauch Ł., Sztangret Ł. & Pietrzyk M. (2013). Computer system for identification of material models on the basis of plastometric tests. Archives of Metallurgy and Materials, 58(3), 737-743.
• [20] Mrzygłód B., Olejarczyk-Wożeńska I. & Regulski K. (2015). The integration of knowledge about the manufacturing process of ADI with the use of artificial intelligence methods, Archives of Foundry Engineering. 15(2), 59-64.
• [21] Kolodner J. (2014). Case-based reasoning. Morgan Kaufmann.
• [22] Regulski K., Rojek G., Jaśkowiec K., Wilk-Kołodziejczyk D. & Kluska-Nawarecka S. (2016). Computer-Assisted Methods of the Design of New Materials in the Domain of Copper Alloys. Key Engineering Materials. 682, 143-150.
• [23] Rojek G., Regulski K., Jarosz P., Gabryel J. & Kusiak J. 2015). Control of lead refining process with the use of Case-Based Reasoning approach. Computer Methods in Materials Science. 15(1), 78-74, 1641-8581.
• [24] Rojek G., Regulski K., Szeliga D. & Kusiak J. (2015). Intelligent advisory system for support of production process design in the domain of metal forming. Key Engineering Materials. (651-653), 1375-1380, DOI: 10.4028/www.scientific.net/KEM.651-653.1375.
• [25] Glowacz A, (2014). Diagnostics of Synchronous Motor Based on Analysis of Acoustic Signals with the use of Line Spectral Frequencies and K-nearest Neighbor Classifier. Archives of Acoustics. 39(2), 189-194.
• [26] David J., Švec P., Frischer R. & Garzinová R. (2014). The computer support of diagnostics of circle crystallizers. Metalurgija. 53(2), 193-196.
• [27] Tomar D., Agarwal S. (2015). A comparison on multi-class classification methods based on least squares twin support vector machine. Knowledge-Based Systems. 81, 131-147.
• [28] Li Y., Gao Y., Guo J. & Yu X. J. (2013). Fault Diagnosis of Metallurgical Machinery Based on Spectral Kurtosis and GA-SVM. Advanced Materials Research. 634, 3958-3961.
• [29] Peng S., Hu Q., Chen Y. & Dang J. (2015). Improved support vector machine algorithm for heterogeneous data. Pattern Recognition. 48(6), 2072-2083.
• [30] Widodo A., Yang B. (2007). Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Systems with Applications. (33), 241–250.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.