Modelling of mechanical properties of Al-Si-Cu cast alloys using the neural network

• Autorzy: Dobrzański L. A. , Maniara R. , Sokolowski J. H. , Krupiński M.
• Konferencja: 12th International Scientific Conference CAM3S'2006, 27-30th November 2006, Gliwice-Zakopane
• Języki publikacji: EN

Abstrakty

EN

Purpose: The paper presents some results of the research connected with the development of new approach based on the neural network to predict the chemical composition and cooling rate to the mechanical properties of Al-Si-Cu cast alloys. The independent variables in the model are chemical composition of Al-Si-Cu cast alloys and cooling rate. The dependent parameters are hardness, microhardness, yield strength and apparent elastic limit. Design/methodology/approach: The experimental alloy used for training of neural network was prepared at the University of Windsor (Canada) in the Light Metals Casting Laboratory, in a 10 kg capacity ceramic crucible. Thermal analysis tests were conducted using the UMSA Technology Platform. Compression tests were conducted at room temperature using a Zwick universal testing machine. Prior to testing an extensometer was used to minimize frame bending strains. Compression specimens were tested corresponding to each of the three cooling rate. Rockwell F-scale hardness tests were conducted at room temperature using a Zwick HR hardness testing machine. Vickers microhardness tests were conducted using a DUH 202 microhardness testing machine. Findings: The results of this investigation show that there is a good correlation between experimental and predicted dates and the neural network has a great potential in mechanical behavior modeling of Al-Si-Cu castings. Practical implications: The worked out model can be applied in computer system of Al-Si-Cu casting alloys selection and designing for Al-Si-Cu casting parts. Originality/value: Original value of the work is applied the artificial intelligence as a tools for designing the required mechanical properties of Al-Si-Cu castings.

Słowa kluczowe

EN

artificial intelligence methods; numerical methods; mechanical properties

PL

sztuczna inteligencja; metody sztucznej inteligencji; techniki numeryczne; właściwości mechaniczne

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2007
• Tom: Vol. 20, nr 1-2
• Strony: 347--350
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Dobrzański L. A.

autor

Maniara R.

autor

Sokolowski J. H.

autor

Krupiński M.

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

Bibliografia

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