International Journal of Engineering، جلد ۳۱، شماره ۱، صفحات ۸۸-۹۵

عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله

عنوان انگلیسی Predicting Force in Single Point Incremental Forming by Using Artificial Neural Network
چکیده انگلیسی مقاله In this study, an artificial neural network was used to predict the minimum force required to single point incremental forming (SPIF) of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, the feed rate of the tool, spindle speed, wall angle, thickness of metal sheets and type of material were selected as input and the minimum vertical force component was selected as the model output. To train the model, a Multilayer perceptron neural network structure and feed-forward backpropagation algorithm have been employed. After testing many different artificial neural network (ANN) architectures, an optimal structure of the model i.e. 6-14-1 was obtained. The results, with a correlation relation between experiments to predicted force,-0.215 mean absolute error, show a very good agreement.
کلیدواژه‌های انگلیسی مقاله SPIF, Input variables, ANOVA, Vertical force component

نویسندگان مقاله manish oraon |
Production Engineering, BIT Mesra

Vinay Sharma |
Production Engineering, BIT Mesra

نشانی اینترنتی
فایل مقاله اشکال در دسترسی به فایل - ./files/site1/rds_journals/409/article-409-2061939.pdf
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات