Normal view MARC view ISBD view

Optimization of surface roughness in milling using neural network (NN) / Ruzaimi Zainon

By:

Ruzaimi Zainon

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2010Description: xv, 74 p. : ill. (some col.) ; 30 cm. + 1 computer discISBN:

THE0005785(Local)

Other title:

Optimization of surface roughness in milling using neural network (NN) [computer file]

Subject(s):

Dissertation note: Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2010 Abstract: This thesis discuss the Optimization of surface roughness in milling using Artificial Neural Network (ANN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using coated carbide TiN as the cutting tool and aluminium 6061 as material due to predict the resulting of surface roughness. The parameters of the variables are feed, cutting speed and depth of cut while the output is surface roughness. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found where RSM approaches show 83.64% accuracy which reliable to be use in Ra prediction and state the feed parameter is the most significant parameter followed by depth of cut and cutting speed influence the surface roughness. ANN technique shows 96.68% of accuracy which is feasible and applicable in the prediction value of Ra. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB PEKAN	CD 5067 \| TA418.7 .R89 2010 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000054104
Final Year Report	UMPLIB PEKAN	TA418.7 .R89 2010 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000054103

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

Bibliography : p. 63-64

This thesis discuss the Optimization of surface roughness in milling using Artificial Neural Network (ANN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using coated carbide TiN as the cutting tool and aluminium 6061 as material due to predict the resulting of surface roughness. The parameters of the variables are feed, cutting speed and depth of cut while the output is surface roughness. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found where RSM approaches show 83.64% accuracy which reliable to be use in Ra prediction and state the feed parameter is the most significant parameter followed by depth of cut and cutting speed influence the surface roughness. ANN technique shows 96.68% of accuracy which is feasible and applicable in the prediction value of Ra. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining.