| 000 | 01948nam a2200265 a 4500 | ||
|---|---|---|---|
| 001 | vtls000052465 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204444.0 | ||
| 008 | 110329t2010 my a f m 000 0 eng d | ||
| 020 | _aTHE0005762(Local) | ||
| 039 | 9 |
_a201905101529 _baida _c201204091033 _dFida _c201110041129 _dFida _c201107140042 _dVLOAD _y201103291641 _zida |
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| 040 | _aUMP | ||
| 090 | _aTA418.7 .A39 2010 rs Bc. | ||
| 100 | 0 | _aMohd Aizuddin Mat Alwi | |
| 245 | 1 | 0 |
_aOptimization of surface roughness in milling by using response surface method (RSM) / _cMohd Aizuddin Mat Alwi |
| 260 |
_aKuantan, Pahang : _bUMP, _c2010 |
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| 300 |
_axiii, 53 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM |
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| 502 | _aProject paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2010 | ||
| 504 | _aBibliography : p. 51-52 | ||
| 520 | 3 | _aAluminium 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. | |
| 650 | 0 | _aSurface roughness | |
| 650 | 0 | _aResponse surfaces (Statistics) | |
| 650 | 0 | _aMachining | |
| 999 |
_aVIRTUA40 _c2335 _d2341 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992 | ||