Application of artificial neural networks for modelling correlations in age hardenable aluminium alloys

• Autorzy: Musharavati F. , Hamouda A. S. M.
• Języki publikacji: EN

Abstrakty

EN

Purpose: This paper discusses some of the preliminary results of an ongoing research on the applications of artificial neural networks (ANNs) in modelling, predicting and simulating correlations between mechanical properties of age hardenable aluminium alloys as a function of alloy composition. Design/methodology/approach: Appropriate combinations of inputs and outputs were selected for neural network modelling. Multilayer feedforward networks were created and trained using datasets from public literature. Influences of alloying elements, alloy composition and processing parameters on mechanical properties of aluminium alloys were predicted and simulated using ANNs models.Two sample t-tests were used to analyze the prediction accuracy of the trained ANNs. Findings: Good performances of the neural network models were achieved. The models were able to predict mechanical properties within acceptable margins of error and were able to provide relevant simulated data for correlating alloy composition and processing parameters with mechanical properties. Therefore, ANNs models are convenient and powerful tools that can provide useful information which can be used to identify desired properties in new aluminium alloys for practical applications in new and/or improved aluminium products. Research limitations/implications: Few public data bases are available for modelling properties. Minor contradictions on the experimental values of properties and alloy compositions were also observed. Future work will include further development of simulated data into property charts. Practical implications: Correlations between mechanical properties and alloy compositions can help in identifying a suitable alloy for a new or improved aluminum product application. In addition, availability of simulated structure-process-property data or charts assists in reducing the time and costs of trial and error experimental approaches by providing near-optimal values that can be used as starting point in experimental work. Originality/value: Since the simulated data provides near-optimal values, manufacturers of new and/or improved aluminum alloys can use the simulated data as guidelines for narrowing down extensive experimental work. This in turn reduces the process design cycle times. Designers of new and/or improved aluminum products can also use the simulated data as a guideline for correlating property-application information, which is useful in preliminary design phase.

Słowa kluczowe

EN

artificial intelligence method; artificial neural networks; aluminium alloy; mechanical properties

PL

metoda sztucznej inteligencji; sztuczne sieci neuronowe; stop aluminium; właściwości mechaniczne

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2010
• Tom: Vol. 41, nr 1-2
• Strony: 140--146
• Opis fizyczny: Bibliogr. 12 poz., rys., tabl.

Twórcy

autor

Musharavati F.

autor

Hamouda A. S. M.

• Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713 Doha, Qatar,

Bibliografia

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