An application of the adaptive neuro-fuzzy inference system for prediction of surface roughness in turning Online publication date: Mon, 16-Jul-2007
by Shibendu Shekhar Roy
International Journal of Computer Applications in Technology (IJCAT), Vol. 28, No. 4, 2007
Abstract: Surface roughness is an important parameter in manufacturing engineering. This paper proposes a method using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to establish the relation between cutting parameters and surface roughness in turning, and consequently to predict surface roughness of the work piece using input cutting parameters, namely cutting speed, feed rate and depth of cut. Three different Membership Functions (MF) (i.e., triangular, trapezoidal and bell-shaped) were adopted during the training process of ANFIS in order to compare the prediction accuracy of surface roughness. The comparison indicates that the bell-shaped MF in ANFIS achieves slightly higher prediction accuracy than other MF.
Online publication date: Mon, 16-Jul-2007
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com