Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining

The machining operation can be generally classified into two types which are traditional machine and non-traditional (modem) machine. There are two types of machining employed in this research, end milling (traditional machining) and abrasive waterjet machining (non-traditional machining). Optimizin...

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Main Author: Yusup, Norfadzlan
Format: Thesis
Language:English
Published: 2012
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Online Access:http://eprints.utm.my/id/eprint/29267/5/NorfadzlanYusupMFSKSM2012.pdf
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spelling my-utm-ep.292672018-05-27T06:54:45Z Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining 2012 Yusup, Norfadzlan TJ Mechanical engineering and machinery The machining operation can be generally classified into two types which are traditional machine and non-traditional (modem) machine. There are two types of machining employed in this research, end milling (traditional machining) and abrasive waterjet machining (non-traditional machining). Optimizing the process parameters is essential in order to provide a better quality and economics machining. This research develops an optimization algorithm using artificial bee colony (ABC) algorithm to optimize the process parameters that will lead to minimum surface roughness (Ra) value for both end milling and abrasive waterjet machining. In end milling, three process parameters that need to be optimized are the cutting speed, feed rate and radial rake angle. For abrasive waterjet, five process parameters that need to be optimized are the traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. These machining process parameters significantly impact on the cost, productivity and quality of machining parts. The ABC simulations are developed to achieve the minimum Ra value in both end milling and abrasive waterjet machining. The results obtained from the simulation are compared with experimental, regression modelling, Genetic Algorithm (GA) and Simulated Annealing (SA). In end milling, ABC reduced the Ra by 10% and 8% compared to experimental and regression. In abrasive waterjet, the performance was much better where the Ra value decreased by 28%, 42%, 2% and 0.9% compared to experimental, regression, GA and SA respectively. 2012 Thesis http://eprints.utm.my/id/eprint/29267/ http://eprints.utm.my/id/eprint/29267/5/NorfadzlanYusupMFSKSM2012.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information Systems
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Yusup, Norfadzlan
Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining
description The machining operation can be generally classified into two types which are traditional machine and non-traditional (modem) machine. There are two types of machining employed in this research, end milling (traditional machining) and abrasive waterjet machining (non-traditional machining). Optimizing the process parameters is essential in order to provide a better quality and economics machining. This research develops an optimization algorithm using artificial bee colony (ABC) algorithm to optimize the process parameters that will lead to minimum surface roughness (Ra) value for both end milling and abrasive waterjet machining. In end milling, three process parameters that need to be optimized are the cutting speed, feed rate and radial rake angle. For abrasive waterjet, five process parameters that need to be optimized are the traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. These machining process parameters significantly impact on the cost, productivity and quality of machining parts. The ABC simulations are developed to achieve the minimum Ra value in both end milling and abrasive waterjet machining. The results obtained from the simulation are compared with experimental, regression modelling, Genetic Algorithm (GA) and Simulated Annealing (SA). In end milling, ABC reduced the Ra by 10% and 8% compared to experimental and regression. In abrasive waterjet, the performance was much better where the Ra value decreased by 28%, 42%, 2% and 0.9% compared to experimental, regression, GA and SA respectively.
format Thesis
qualification_level Master's degree
author Yusup, Norfadzlan
author_facet Yusup, Norfadzlan
author_sort Yusup, Norfadzlan
title Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining
title_short Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining
title_full Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining
title_fullStr Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining
title_full_unstemmed Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining
title_sort artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining
granting_institution Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department Faculty of Computer Science and Information Systems
publishDate 2012
url http://eprints.utm.my/id/eprint/29267/5/NorfadzlanYusupMFSKSM2012.pdf
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