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Mathematical modeling to predict surface roughness in milling process / Gan Sin Yi

By:

Gan, Sin Yi

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2008Description: 71 p. : ill. (some col.) ; 30 cm. + 1 computer discISBN:

THE0006504(Local)

Other title:

Mathematical modeling to predict surface roughness in milling process / [electronic resource]

Subject(s):

Dissertation note: Project paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 2008 Abstract: Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.

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Holdings ( 2 )
Title notes ( 2 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB PEKAN	TJ1185 .G36 2008 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000038059
Final Year Report	UMPLIB PEKAN	CD 3383 \| TJ1185 .G36 2008 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000038060

Project paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 2008

Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.