03391ntm a2200373 i 4500 0 0 lcc 0 1 REF 10000 10000 2022-09-15 0 KK .N35 2022 r Thesis T000001920 2022-09-15 00:00:00 1 2022-09-15 THESIS 0 0 lcc 0 1 REF 10000 10000 2022-09-15 0 CD13124 T000001921 2023-01-30 00:00:00 1 2022-09-15 THESIS 97661 97667 MY-KuUP 20251125110115.0 t||||fr|||| 000 0 ta 220915t20222022my a|||fr|||| 000 0 eng d THE0009438 (Local) hardback UMP eng UMP rda KK .N35 2022 r Thesis Najwa Wahida Zainal Abidin author. Optimization of multi-holes drilling path using particle swarm optimization / Najwa Wahida Zainal Abidin Pahang : UMP, 2022 © 2022 xii, 96 pages: Illustration ; 30 cm.+ 1 CD ROM. text rdacontent text rdacontent unmediated rdamedia computer rdamedia volume rdacarrier computer disc rdacarrier text file PDF rda College of Engineering Thesis (Master of Science) -- Universiti Malaysia Pahang – 2022 Includes bibliographical reference Multi-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. College of Engineering Universities and colleges Thesis lcc THESIS