| 000 | 01816nam a2200253 a 4500 | ||
|---|---|---|---|
| 001 | vtls000045977 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204428.0 | ||
| 008 | 100604t2009 da f m 000 0 eng d | ||
| 020 | _aTHE0007454(Local) | ||
| 039 | 9 |
_a201905161149 _bfawwaz _c201204051142 _dFida _c201107132323 _dVLOAD _y201006040843 _zFida |
|
| 040 | _aUMP | ||
| 090 | _aTP1150 .A45 2009 rs Bc. | ||
| 100 | 0 | _aAhmad Amiruddin Rosdi | |
| 245 | 1 | 0 |
_aWarpage optimization of a name card holder using neural network model / _cAhmad Amiruddin Bin Rosdi |
| 260 |
_aKuantan, Pahang : _bUMP , _c2009 |
||
| 300 |
_axvii, 77 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM |
||
| 502 | _aProject paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2009 | ||
| 520 | 3 | _aInjection 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. | |
| 650 | 0 | _aInjection molding of plastics | |
| 650 | 0 |
_aPlastics _xMoulding |
|
| 650 | 0 | _aFinite element model | |
| 999 |
_aVIRTUA40 _c1863 _d1869 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5200*6500*6501*6502*9992 | ||