| 000 | 02235nam a2200277 a 4500 | ||
|---|---|---|---|
| 001 | vtls000052554 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204447.0 | ||
| 008 | 110331t2010 my a f m 000 0 eng d | ||
| 020 | _aTHE0005785(Local) | ||
| 039 | 9 |
_a201905101625 _baida _c201110041141 _dFida _c201107140042 _dVLOAD _c201103311720 _dida _y201103311639 _zida |
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| 040 | _aUMP | ||
| 090 | _aTA418.7 .R89 2010 rs Bc. | ||
| 100 | 0 | _aRuzaimi Zainon | |
| 245 | 1 | 0 |
_aOptimization of surface roughness in milling using neural network (NN) / _cRuzaimi Zainon |
| 246 | 3 |
_aOptimization of surface roughness in milling using neural network (NN) _h[computer file] |
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| 260 |
_aKuantan, Pahang : _bUMP, _c2010 |
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| 300 |
_axv, 74 p. : _bill. (some col.) ; _c30 cm. + _e1 computer disc |
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| 502 | _aProject paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2010 | ||
| 504 | _aBibliography : p. 63-64 | ||
| 520 | 3 | _aThis 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. | |
| 650 | 0 | _aSurface roughness | |
| 650 | 0 | _aResponse surfaces (Statistics) | |
| 650 | 0 | _aMachining | |
| 999 |
_aVIRTUA40 _c2414 _d2420 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*6502*9992 | ||