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Surface Roughness Prediction in End-Milling Process

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posted on 2016-02-04, 15:41 authored by Ibrahem MaherIbrahem Maher

Surface roughness prediction for the end-milling process, which is one of the major cutting processes, is a very important economical consideration in order to increase machine operation and decrease production cost in an automated manufacturing environment.

In this study; prediction of surface roughness (Ra) for Brass (60/40) material based on cutting parameters: cutting speed, feed rate, and depth of cut; was studied. 

 

Adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in the end milling process. Surface roughness was used as dependant variable while cutting speed of range (750 - 1750rpm), feed rate of range (50 - 250mm/min) and depth of cut of range (0.3 - 0.7mm) were used as predictor variables. Normal and feed forces were used as predictor variables to verify the ANFIS model. Different membership functions were adopted during the training process of ANFIS.

 

Surface roughness was measured in an off line manner using stylus based profile-meter (surtronic 3+). The normal and feed forces were measured in an on-line manner using two components dynamometer.

 

The effects of cutting parameters on the normal force, feed force and surface roughness were discussed.  Experimental test data were used to examine the ANFIS model by defining the reliability and percentage error of the model. Experimental results demonstrate the effectiveness of the proposed model. While the predicted surface roughness was compared with measured data; the mean square error has been found equal to 8.5 % hence the achieved accuracy is equal to 91.5 %.

 

Although this work focuses on prediction of surface roughness for end-milling operation, the concepts introduced are general; ie., prediction of surface roughness using ANFIS can be applied to many other cutting and machining processes.

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