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008			250505t20232023my a|||fs|||| 000 0 eng d
020			_aTHE0010026 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aPSM .A45 2023 r Bc.
100	0		_aMuhammad Amir Aminuddin, _eauthor.
245	1	0	_aTrend analysis on machine downtime for preventive maintenance of computer numerical control (CNC) machine / _cMuhammad Amir Bin Aminuddin
264		1	_aKuantan Pahang : _bUMPSA, _c2023
264		4	_c© 2023
300			_axviii, 80 pages : _billustrations ; _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCenter for Mathematical Sciences
502			_aBachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical references
520	3		_aComputer Numerical Control machining is a subtractive manufacturing technique that removes layers of material from a blank or workpiece to create a specific product. It is widely used in numerous industries including electronics. Electronic manufacturing organizations are facing an ever-increasing level of competition on a global scale making it an absolute requirement to decrease the amount of downtime that occurs during production operations to maximize machine availability and productivity. To ensure that customers' demands are met on time, downtime should be evaluated to identify the underlying causes of the issue and mitigate its effects. The study aims to analyze and forecast the trend of Computer Numerical Control (CNC) machine downtime through predictive modelling using machine learning models. In this study, eXtreme Gradient Boosting and Random Forest have been used to forecast the trend of future downtime occurrences. Based on the results of comparative performance analysis, the study reveals that eXtreme Gradient Boosting model outperforms Random Forest in forecasting future CNC downtime, demonstrating lower prediction errors. The outcome of this research is an interactive dashboard that integrates the analysis of historical and forecasted CNC machine downtime trends. This effort aims to provide valuable support to the industry by enhancing their preventive maintenance.
610	2	0	_aCenter for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aFinal Year Report _xDissertations
942			_2lcc _cPSM