000			03391ntm a2200373 i 4500
999			_c97661 _d97667
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005			20251125110115.0
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007			ta
008			220915t20222022my a|||fr|||| 000 0 eng d
020			_aTHE0009438 (Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aKK .N35 2022 r Thesis
100	1		_aNajwa Wahida Zainal Abidin _eauthor.
245	1	0	_aOptimization of multi-holes drilling path using particle swarm optimization / _cNajwa Wahida Zainal Abidin
264		1	_aPahang : _bUMP, _c2022
264		4	_c© 2022
300			_axii, 96 pages: _bIllustration ; _c30 cm.+ _e1 CD ROM.
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aCollege of Engineering
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2022
504			_aIncludes bibliographical reference
520	3		_aMulti-hole drilling is a manufacturing process that is commonly used in industries. In this process, the tool movement and switching, on average, take 70% of the total machining time. There are many applications of multi-hole drilling, such as in mould, die-making and printed circuit board (PCB). One way to improve the multi-hole drilling is by optimising the tool path in the process. This research aims to model and optimise multi-hole drilling problems using Particle Swarm Optimisation (PSO) algorithm. The study begins by modelling the multi-hole drilling problems using the Travelling Salesman Problem (TSP) concept. The objective function was set to minimise the total tool path distance. Then, the PSO was formulated to minimise total length in multi-hole drilling. The main issue in this stage was to convert the continuous encoding in PSO to permutation problems as in multi-hole drilling. For this purpose, a topological sorting procedure based on the most prominent particle rule was implemented. The algorithm was tested on 15 test problems where between 10 to 150 holes were randomly generated. The performance of PSO was then compared with other meta-heuristic algorithms, including Genetic Algorithm (GA) and Ant Colony Optimisation (ACO), Whale Optimisation Algorithm (WOA), Ant Lion Optimiser (ALO), Dragonfly Algorithm (DA), Grasshopper Optimisation Algorithm (GOA), Moth Flame Optimisation (MFO) and Sine Cosine Algorithm (SCA). Then, a validation experiment was conducted by implementing the PSO generated tool path against the commercial CAD-CAM path. In this stage, the machining time was measured. The results from the computational experiment indicated that the proposed PSO algorithm came out with the best solution in 10 out of the 15 test problems. In the meantime, the validation experiment result proved that the PSO generated tool path provides faster machining time compared with the commercial CAD-CAM path by 5% on average. The results clearly showed that PSO has a great potential to be applied in the multi-hole drilling process. The findings from this research could benefit the manufacturing industry to improve their productivity using existing resources.
610	2	0	_aCollege of Engineering
650		0	_aUniversities and colleges
650		0	_aThesis
942			_2lcc _cTHESIS