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Optimization of surface roughness in milling by using response surface method (RSM) / Mohd Aizuddin Mat Alwi

By:

Mohd Aizuddin Mat Alwi

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2010Description: xiii, 53 p. : ill. (some col.) ; 30 cm. + 1 CD-ROMISBN:

THE0005762(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2010 Abstract: Aluminium Alloys are attractive materials due to their unique high strength-weight ratio that is maintained at elevated temperatures and their exceptional corrosion resistance. Face mill is used as cutting tool for experiment in milling machine.So in this study, the optimum of surface roughness is optimize by using response surface method. The experiments were carried out using CNC milling machine. The experiment was run with 15 experiment test. All the data was analyzed by using Response Surface Method (RSM) and Neural Network (NN). The result have shown that the feed gave the more affect on the both prediction value of Ra compare to the cutting speed and depth of cut r as P-values is less than 0.05. From the prediction data that shown, the different between both software is smaller and the value is acceptable to get the optimum value of surface roughness.

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Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB PEKAN	TA418.7 .A39 2010 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000054067
Final Year Report	UMPLIB PEKAN	CD 5049 \| TA418.7 .A39 2010 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000054068

Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2010

Bibliography : p. 51-52

Aluminium Alloys are attractive materials due to their unique high strength-weight ratio that is maintained at elevated temperatures and their exceptional corrosion resistance. Face mill is used as cutting tool for experiment in milling machine.So in this study, the optimum of surface roughness is optimize by using response surface method. The experiments were carried out using CNC milling machine. The experiment was run with 15 experiment test. All the data was analyzed by using Response Surface Method (RSM) and Neural Network (NN). The result have shown that the feed gave the more affect on the both prediction value of Ra compare to the cutting speed and depth of cut r as P-values is less than 0.05. From the prediction data that shown, the different between both software is smaller and the value is acceptable to get the optimum value of surface roughness.