Minimization of tool path length of drilling process using particle swarm optimization (PSO)

Abdullah, Haslina and Zaman, Nizam Nurehsan and Talib, Norfazillah and Lee, Woon Kiow and Saleh, Aslinda and Zakaria, Mohamad Shukri (2020) Minimization of tool path length of drilling process using particle swarm optimization (PSO). In: Mechanical, Materials and Manufacturing Engineering: Case Studies. Penerbit UTHM, pp. 48-56. ISBN 978-967-2389-56-9

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In the era of challenging economic, the industry in our country has been forced to produce a good quality product and increase the productivity of machining process simultaneously in order to compete with other countries. Drrilling process is one of a very important cutting process in industry. In a drilling for machining by Computer Numerical Control (CNC) such as drilling machines, the parameter of the tool routing path for the machining operation plays a very important role to minimize the machining time (Tiwari 2013, Rao and Kalyankar 2012) . This machine can be used with procedures for drilling, spreading, weaning and threading with a lot of the holes precisely. In order to increase the efficiency and productivity of drilling process, optimization on parameters of process can lead to better performance. Optimization of holes drilling operations will lead to reduction in time order and better productivity of manufacturing systems. Optimizing the tool path has played an important role, especially in mass production because reducing the time to produce one piece eventually lead to a significant reduction in the cost of the entire series (Pezer, 2016). In various publications and articles, scientists and researchers adapted several methods of artificial intelligence (AI) or hybrid optimization method for tool path artificial immune system (AIS), genetic algorithms (GA), Artificial Neural networks (ANN) Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) (Narooei and Ramli, 2014). These methods were been proven that can produce better performance and increase the productivity of drilling process. Therefore, in this study, the Particle Swarm Optimization (PSO) algorithm was develop in order to minimizing the tool path length in the drilling process which can produce the better results for the required machining time process. For this study, the main purpose is to apply the Particle Swarm Optimization (PSO) algorithm for use in searching for the optimal tool routing path for in simulation of drilling process

Item Type: Book Section
Uncontrolled Keywords: Mechanical engineering--Case studies; Materials--Case studies; Manufacturing processes--Case studies; Production engineering--Case studies; Government publications--Malaysia
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Mechanical Engineering
Depositing User: Mrs. Siti Noraida Miskan
Date Deposited: 01 Nov 2021 03:22
Last Modified: 01 Nov 2021 03:22

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