Intelligent information technology of visual information processing for metals diagnostics

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Authors:

V.A. Yemelyanov, Cand. Sci. (Tech.), Sevastopol Banking Institute of the Banking University of the National Bank of Ukraine, Senior Instructor of the Information Technologies and Systems Department, Sevastopol, Ukraine

Abstract:

Purpose. To develop intelligent information technology for processing visual information for the metals state diagnostics. As against the already existing technologies it will allow diagnosing the state of metal by all characteristics (chemical composition, structure, properties).

Methodology. The methods of comparative study, scientific abstraction and mathematical simulation have been used in the study.

Findings. The basic stages of the intelligent information technology have been described. The neural networks choice to solve the problem of automation metallographic analysis at all its stages has been substantiated. The neural networks results for metallographic images recognition to determine quantitative information about metal have been shown. The neural network results to determine the metals properties by samples of steel of different grades have been described.

Originality. We have developed the intelligent information technology of visual information processing for the metals state diagnostics based on the neural networks and the precedents theory. It can diagnose the metals state by all its characteristics (chemical composition, structure, properties).

Practical value. Scientific results of the work allowed us to develop the intelligent information technology of the visual information processing for determination of metals properties. The software which implements the methods and the stages of the developed information technology have been created.

References:

1. Bramfitt, B.L. and Benscoter, Arlan O. (2002), Metallographer’s Guide. Practices and Procedures for Iron and Steels, ASM International.

2. Hosseini, H., Shamaniana, M. and Kermanpura, A. (2011), “Characterization of microstructures and mechanical properties of Inconel 617/310 stainless steel dissimilar welds”, Materials Characterization, Vol. 62, Issue 4, Apr., pp. 425–431.

3. Wang Zhiping, Lu Yang, Wu Chenwed, Xu Jianlin and Yang Xinzhuang (1997), “Cast-iron metallographic structure by computer picture processing system”, Journal of Cansu University of Technology, Vol. E-1, No. 1. Dec., pp. 29–32.

4. Ємельянов В.О.Інтелектуальна інформаційна технологія оцінки характеристик сплавів у металографічному аналізі: автореф. дис. на здобуття. наук. степеня канд. техн. наук: спец.  05.13.06 „Інформаційні технології“ / Ємельянов Віталій Олександрович // Національний аерокосмічний університет ім.М.Є.Жуковського, „ХАІ“. – Харків, 2011. – 20с.

Iemelianov, V.A. (2011), “Intellectual information technology of evaluation of the characteristics of alloys in the metallographic analysis”, Abstract of Cand. Sci. (Tech.) dissertation, Informational Technologies, National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, Ukraine.

5. Pratt, W.K. (2001), Digital image processing, John Wiley & Sons, USA.

6. Gonzalez, R.S. and Woods, R.E. (2002), Digital image processing, Prentice, USA.

7. Suzuki Kenji (2013), Artificial Neural Networks: Architectures and Applications, InTech.

8. База данных микроструктур металлов и сплавов [Электронный ресурс] – Режим доступа: http://www.microstructure.ru/rudbview

Metals and Alloys Microstructures Database (2007), available at: http://www.microstructure.ru/rudbview

9. Aamodt, A. and Plecza, E. (1994), “Case-Based Reasoning: Foundation issues, methodological variations a system approaches”, A.I. Communications, pp. 39–59.

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ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

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