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Warpage optimization of a name card holder using neural network model / Ahmad Amiruddin Bin Rosdi

By:

Ahmad Amiruddin Rosdi

Material type: Text

TextPublication details: Kuantan, Pahang : UMP , 2009Description: xvii, 77 p. : ill. (some col.) ; 30 cm. + 1 CD-ROMISBN:

THE0007454(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2009 Abstract: Injection molding has become widely process that used in plastic manufacturing. To produce high quality product, it has to consider the process condition. In this study, optimum parameters for injection molding of a name card holder are determined. Finite element software MoldFlow, statistical design of experiment and artificial neural network are used in finding optimum value. The process parameter influencing warpage is determined using finite element software based on data using full factorial design. By exploiting finite element analysis result, a predictive model using artificial neural network is created. Optimum value is determined by comparing result by using finite element analysis and optimization using artificial neural network and choose the smallest percentage of error.

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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	TP1150 .A45 2009 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000046311
Final Year Report	UMPLIB PEKAN	CD 4456 \| TP1150 .A45 2009 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000046312

Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2009

Injection molding has become widely process that used in plastic manufacturing. To produce high quality product, it has to consider the process condition. In this study, optimum parameters for injection molding of a name card holder are determined. Finite element software MoldFlow, statistical design of experiment and artificial neural network are used in finding optimum value. The process parameter influencing warpage is determined using finite element software based on data using full factorial design. By exploiting finite element analysis result, a predictive model using artificial neural network is created. Optimum value is determined by comparing result by using finite element analysis and optimization using artificial neural network and choose the smallest percentage of error.