Develop 1st and 2nd mathematical model for torque prediction by using response surface methodology when milling modified AISI P20 tool steel / Mohd Zamri Bin Ja'afar
Material type:
TextPublication details: Kuantan, Pahang : UMP, 2009Description: xv, 49 p. : ill. (some col.) ; 30 cm. + 1 computer discISBN: - THE0006615(Local)
- Develop 1st and 2nd mathematical model for torque prediction by using response surface methodology when milling modified AISI P20 tool steel [electronic resource]
| Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|
Final Year Report
|
UMPLIB PEKAN | TJ1225 .Z36 2009 rs Bc. (Browse shelf(Opens below)) | 1 | Not for loan | 0000044411 | ||
Final Year Report
|
UMPLIB PEKAN | CD 4229 | TJ1225 .Z36 2009 rs Bc. (Browse shelf(Opens below)) | 1 | Not for loan | 0000044412 |
Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2009
Bibliography : p. 43-44
The present paper discusses the development of the first and second order models for predicting the cutting torque produced in end-milling operation of modified AISI P20 tool steel. The first and second order cutting torque 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 power. The cutting torque 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 increase in the cutting speed, feed rate, axial and radial depths of cut will cause the cutting torque to become larger. The received second order equation shows, based on the variance analysis, that the cutting torque decreased when cutting speed, federate, axial and radial depth of cut is reduced. The predictive models in this study are believed to produce values of the longitudinal component of the cutting torque close to those readings recorded experimentally with a 95% confident interval.