Methodology of analysis of casting defects

• Autorzy: Dobrzański L. A. , Krupiński M. , Sokolowski J. H. , Zarychta P. , Włodarczyk-Fligier A.
• Języki publikacji: EN

Abstrakty

EN

Purpose: The goal of this publication is to present the methodology of the automatic supervision and control of the technological process of manufacturing the elements from aluminium alloys and of the methodology of the automatic quality assessment of these elements basing on analysis of images obtained with the X-ray defect detection, employing the artificial intelligence tools. The methodologies developed will make identification and classification of defects possible and the appropriate process control will make it possible to reduce them and to eliminate them - at least in part. Design/methodology/approach: The methodology is presented in the paper, making it possible to determine the types and classes of defects developed during casting the elements from aluminium alloys, making use photos obtained with the flaw detection method with the X-ray radiation. It is very important to prepare the neural network data in the appropriate way, including their standardization, carrying out the proper image analysis and correct selection and calculation of the geometrical coefficients of flaws in the X-ray images. The computer software was developed for this task. Findings: Combining of all methods making use of image analysis, geometrical shape coefficients, and neural networks will make it possible to achieve the better efficiency of class recognition of flaws developed in the material. Practical implications: The presented issues may be essential, among others, for manufacturers of car subassemblies from light alloys, where meeting the stringent quality requirements ensures the demanded service life of the manufactured products. Originality/value: The correctly specified number of products enables such technological process control that the number of castings defects can be reduced by means of the proper correction of the process.

Słowa kluczowe

EN

aluminium alloys; neural networks; images analysis; cast defects

PL

stopy aluminium; sieci neuronowe; analiza obrazów; wady odlewów

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2006
• Tom: Vol. 18, nr 1-2
• Strony: 267--270
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Dobrzański L. A.

• Division of, Materials Processing Technology and Computer Techniquesin Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland

autor

Krupiński M.

• Division of, Materials Processing Technology and Computer Techniquesin Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland

autor

Sokolowski J. H.

• Industrial Research Chair in Light Metals Casting Technology, University of Windsor, 401 Sunset Ave.,N9B 3P4, Windsor, Ontario, Canada

autor

Zarychta P.

• Division of, Materials Processing Technology and Computer Techniquesin Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland

autor

Włodarczyk-Fligier A.

• Division of, Materials Processing Technology and Computer Techniquesin Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland

Bibliografia

• [1] K.W. Dolan: Design and Produkt Optimization for Cast Ligot Metals, Livermore, 2000
• [2] I.J. Polmear: Light Alloys. Metallurgy of the Light Metals.
• [3] S.H. Mousavi Anijdan, A. Bahrami, H.R. Madaah Hosseini, A. Shafyei, Using genetic algorithm and artificial neural network analyses to design an Al-Si casting alloy of minimum porosity, Materials and Design, 27, 2006, pp. 605–609.
• [4] S. Fox, J. Campbell: Visualisation of oxide film defects during solidification of aluminium alloys, Scripta materialia, 43 (2000), pp.881–886.
• [5] L. Wojnar, K.J. Kurzydłowski, J. Szala: Practice of image analysis, PTS, Kraków 2002 (in Polish)
• [6] M. Nałęcz: Neural network, AOW EXIT, Warszawa 2000 (In Polish).
• [7] L.A. Dobrzański, M. Kowalski, J. Madejski: Methodology of the mechanical properties prediction for the metallurgical products from the engineering steels using the artificial intelligence methods, Journal of Materials Processing Technology, Vol. 164–165, 2005, pp. 1500–1509.
• [8] L.A. Dobrzański, M. Krupiński, P. Zarychta, J.H. Sokolowski, W. Kasprzak: Computer assisted classification of flaws identified with the radiographical methods in castings from the aluminium alloys, „Achievements in Mechanical and Materials Engineering” AMME’2005, Gliwice-Wisła, 2005, pp. 155-160.
• [9] L.A. Dobrzański, M. Krupinski, J.H. Sokolowski, Computer aided classification of flaws occurred during casting of aluminum, Journal of Materials Processing Technology, Vol. 167, Is. 2-3, 2005, pp. 456-462.
• [10] L.A. Dobrzański, Fundamentals of Materials Science and Physical Metallurgy. Engineering Materials with Fundamentals of Materials Design, WNT, Warszawa, 2002 (in Polish).
• [11] C.H. Caceres, M.B. Djurdjevic, T.J. Stockwell, J.H. Sokolowski: The effect of Cu content on the level of microporosity in Al-Si-Cu-Mg casting alloys, Elsevier Science, Scripta Materialia, Vol. 40, No. 5, (1999), pp. 631–637.