Forecasting the tension of casting aluminum parts using artificial neural network
Publish place: The 6th International Conference on Exhibitiion on Metallurgy and Materials Engineering
Publish Year: 1396
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
IMES11_259
تاریخ نمایه سازی: 7 اسفند 1396
Abstract:
As Aluminum alloys have high strength to weight ratio, resistance to abrasion and corrosion and good electrical and thermal conductivity, are suitable for special attention in the industry. Nowadays, many aluminum castings parts are commercial alternative for other parts several factors have played a role in the quality of the casting produced that review and study of experimental in industrial scale, requires much time and cost. One of the ways that had known to accurately predict the behavior of these components, is usage of neural network theory. In this study, stress of casting aluminum parts has been studied and predicted by Artificial Neural Networks due to factors such as: melting temperature of the chemical composition. Training artificial neural network was carried through Multilayer Perceptron procedure. The comparison shows the proper implementation of the predicted values and experimental results and confirm the effectiveness of the method used.
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Authors
Hassan Koohestani
Assistant Professor, Materials Engineering
Meysam Bayat
Bachelor’s degree, Faculty of Materials and Metallurgical Engineering, Semnan University,
Setare Ahmadi
Bachelor’s degree, Faculty of Materials and Metallurgical Engineering, Semnan University,
Parisa Ghanbari
Bachelor’s degree, School of Metallurgy and Materials Engineering, Iran University of Science and Technology,