| 000 | 02371nmm a2200313 a 4500 | ||
|---|---|---|---|
| 001 | vtls000098461 | ||
| 003 | KUKTEM | ||
| 005 | 20251117113333.0 | ||
| 008 | 161214s2016 my fq d 001 0 eng d | ||
| 020 | _aTHE0005124(Local) | ||
| 039 | 9 |
_a201905131525 _bhanafiah _c201712150934 _dfateeha _y201612141239 _zsaini |
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| 040 | _aUMP | ||
| 090 | _aFKP .S46 2016 r Bc. | ||
| 090 | _aCD 10455 | ||
| 100 | 1 | _aSeow, Xiang Yuan | |
| 245 | 1 | 0 |
_aPrediction of remaining useful life of an end mill cutter _h[electronic resource] / _cSeow Xiang Yuan |
| 260 |
_aKuantan, Pahang : _bUMP, _c2016 |
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| 300 |
_a1 computer disc : _bdigital data ; _c12 cm. |
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| 500 | _aFaculty of Manufacturing Engineering | ||
| 500 | _aTheses Gred A | ||
| 502 | _aProject Paper (Bachelor of Engineering in Manufacturing Engineering (Hons.)) -- Universiti Malaysia Pahang – 2016 | ||
| 520 | 3 | _aThis thesis presents a comparison between methods for tool life prediction. The main objective of the thesis is to have an accurate prediction of the RUL and select the best method for prediction. An experiment has been conducted using Kistler dynamometer and Olympus metallurgical microscope on a HASS VF-6 milling machine to acquire the sensor force signals and actual tool wear respectively. The force signal gives the significant statistical features of the data. The features are extracted using statistical measure and reduced using a stepwise regression model. The prediction methods are Support Vector Regression and Neural Network. Both the models are trained using the MATLAB software. The results of the models are compared against each other to select the best method. Moreover, the methods are also applied on data taken from PHM Society. This data serves as a preliminary result and fundamental knowledge for my own experiment. The models trained in this project are compared with the existing models. These results show that the proposed methods are suitable for predicting the remaining useful life. | |
| 538 | _aItem in PDF format | ||
| 610 | 2 | 0 |
_aFaculty of Manufacturing Engineering _xDissertations |
| 650 | 0 |
_aUniversities and Colleges _xDissertations |
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| 650 | 0 | _aTheses | |
| 856 | 4 | 0 |
_uhttp://ecollib.ump.edu.my/id/eprint/4031 _zAccess in library only |
| 999 |
_aVIRTUA40 _c6960 _d6966 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*0901*1000*2450*2600*3000*5000*5001*5020*5200*5380*6100*6500*6501*8560*9992 | ||