IJE TRANSACTIONS C: Aspects Vol. 29, No. 12 (December 2016) 1726-1733    Article in Press

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M. Mahdavi Jafari and G. R. Khayati
( Received: July 31, 2016 – Accepted in Revised Form: November 11, 2016 )

Abstract    In this study, artificial neural network was used to predict the microhardness of Al2024-multiwall carbon nanotube(MWCNT) composite prepared by mechanical alloying. Accordingly, the operational condition, i.e., the amount of reinforcement, ball to powder weight ratio, compaction pressure, milling time, time and temperature of sintering as well as vial speed were selected as independent input and the mean micro-hardness of composites was selected as model output. To train the model, a Multilayer perceptron neural network structure and feed-forward back propagation algorithm has been employed. After testing many different ANN architectures an optimal structure of the model i.e. 7-25-1 is obtained. The predicted results, with a correlation relation between 0.982 and 0.9952 and 3.26% mean absolute error, show a very good agreement with the experimental values. Furthermore, the ANN model was subjected to a sensitivity analysis and determined the significant inputs affecting hardness of the samples.


Keywords    Al2024 multiwall carbon nanotube composite; Artificial neural network; Microhardness; Mechanical milling


چکیده    در این پژوهش از شبکه عصبی مصنوعی به منظور پیش بینی میکرو سختی کامپوزیت Al2024 تقویت شده با نانو لوله‌های کربنی چند دیواره ساخته شده توسط آلیاژ‌سازی مکانیکی استفاده شده است. بنابراین، مقدار فاز تقویت کننده، نسبت گلوله به پودر، فشار پرس، زمان آسیاب کاری، دما، زمان تفت جوشی و سرعت اسیاکاری به عنوان پارامتر‌های مستقل ورودی و میکروسختی متوسط کامپوزیت به عنوان پارامتر خروجی انتخاب شده‌اند. برای آموزش مدل، از ساختار شبکه عصبی چند لایه پرسپترون و الگوریتم پس انتشار خطا استفاده شده است. بعد از امتحان معماری های ANN متفاوت، ساختار مدل بهینه بصورت 1-25-7 بدست آمد. نتایج پیش بینی شده با نسبت همبستگی بین 982 /0 و 9952/0 و 26/3 درصد خطای میانگین مطلق، سازگاری بسیار خوبی با مقادیر تجربی نشان میدهد. علاوه بر این، مدل شبکه عصبی به منظور پیدا کردن اهمیت پارامترهای ورودی موثر بر خواص مکانیکی نمونهها تحت آنالیز حساسیت قرار گرفت.


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