Prediction of cutting force in end-milling operation of modified AISI P20 tool steel

This thesis discuss the development of the first and second order models for predicting the cutting force produced in end-milling operation of modified AISI P20 tool steel. The first and second order cutting force equations are developed using the response surface methodology (RSM) to study the effe...

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Bibliographic Details
Main Author: Muhamad Nazumi, Basuki
Format: Undergraduates Project Papers
Language:English
Published: 2009
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/1007/1/Prediction%20of%20cutting%20force%20in%20end-milling%20operation%20of%20modified%20AISI%20P20%20tool%20steel.pdf
Description
Summary:This thesis discuss the development of the first and second order models for predicting the cutting force produced in end-milling operation of modified AISI P20 tool steel. The first and second order cutting force equations are developed using the response surface methodology (RSM) to study the effect of four input cutting parameters which is cutting speed, feed rate, radial depth and axial depth of cut on cutting force. The cutting force contours with respect to input parameters are presented and the predictive models analyses are performed with the aid of the statistical software package Minitab. The separate affect of individual input factors and the interaction between these factors are also investigated in this study. In first order model, the decrease of cutting speed along with the increase in the cutting speed, feed rate, axial and radial depths of cut will cause the cutting force to become larger. The received second order equation shows, based on the variance analysis, that the cutting force increased when federate and radial depth of cut is raised. However, the cutting force increased with the slightly reduce of axial depth and cutting speed value. The predictive models in this study are believed to produce values of the longitudinal component of the cutting power close to those readings recorded experimentally with a 95% confident interval.