سال انتشار: ۱۳۸۴

محل انتشار: اولین کنفرانس بین المللی و هفتمین کنفرانس ملی مهندسی ساخت و تولید

تعداد صفحات: ۷

نویسنده(ها):

R Madoliat – Assistant professor, Iran University of Science and Technology
F jafarvand – MSc Student of Manufacturing engineering
R Raei – MSc Student of Manufacturing engineering

چکیده:

Surface roughness is one of the essential quality characteristics that must be precisely controlled. Artificial neural network modeling is a method of surface roughness prediction. In this study, two different networks are used for surface roughness prediction in turning processes. Experimental data for turning of 6061- T6511 Aluminum alloy, obtained from literature were employed to train the ANN models. These ANNs were trained by Levenberg – Marquardt and Bayesian regularization algorithms. Results show that the Bayesian regularization network has better prediction accuracy in comparison with Levenberg-Marquardt algorithm. A comparison of ANN model with regression model was also carried out.