Cover Image

Enhancement of the machining surface roughness model with cuckoo algorithm

Quality of machining products is generally associated with the surface roughness (Ra) which is one of the important aspects that could affect machining performances. This study seeks to find the minimum value of Ra for a modern machining process, Abrasive Water Jet (AWJ). This study proposed Cuckoo...

Full description

Saved in:
Bibliographic Details
Main Author: Mohamad, Azizah
Format: Thesis
Language:English
Published: 2014
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/47962/25/AzizahMohamadMFC2014.pdf
http://eprints.utm.my/id/eprint/47962/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.47962
record_format eprints
spelling my.utm.479622017-08-06T09:52:05Z http://eprints.utm.my/id/eprint/47962/ Enhancement of the machining surface roughness model with cuckoo algorithm Mohamad, Azizah Q Science (General) Quality of machining products is generally associated with the surface roughness (Ra) which is one of the important aspects that could affect machining performances. This study seeks to find the minimum value of Ra for a modern machining process, Abrasive Water Jet (AWJ). This study proposed Cuckoo algorithm to improve the surface roughness of the AWJ process because, since 2013, there has only been one study on the algorithm. Ra model in AWJ has five machining parameters comprising traverse speed (V), water jet pressure (P), standoff distance (h), abrasive grit size (D) and abrasive flow rate (m). These parameters in the Ra model have contributed to the best solution for machining performance by achieving the minimum values. In this study, to obtain better results, the parameters in the standard Ra model were enhanced in the modeling process where the number of parameters has been reduced. The enhanced parameters in the model are represented by “Q”. Computational techniques were used to evaluate the model whereas SPSS were used to validate the results. The result shows that minimum Ra value of the proposed model is 2.1709 µm. It was much lower than the results of three computational techniques which are Artificial Neural Network (ANN), Regression analysis and Support Vector Machine (SVM) by about 29.2 %, 23.8 % and 19.3% respectively. 2014-06 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/47962/25/AzizahMohamadMFC2014.pdf Mohamad, Azizah (2014) Enhancement of the machining surface roughness model with cuckoo algorithm. Masters thesis, Universiti Teknologi Malaysia, Faculty of Science.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Mohamad, Azizah
Enhancement of the machining surface roughness model with cuckoo algorithm
description Quality of machining products is generally associated with the surface roughness (Ra) which is one of the important aspects that could affect machining performances. This study seeks to find the minimum value of Ra for a modern machining process, Abrasive Water Jet (AWJ). This study proposed Cuckoo algorithm to improve the surface roughness of the AWJ process because, since 2013, there has only been one study on the algorithm. Ra model in AWJ has five machining parameters comprising traverse speed (V), water jet pressure (P), standoff distance (h), abrasive grit size (D) and abrasive flow rate (m). These parameters in the Ra model have contributed to the best solution for machining performance by achieving the minimum values. In this study, to obtain better results, the parameters in the standard Ra model were enhanced in the modeling process where the number of parameters has been reduced. The enhanced parameters in the model are represented by “Q”. Computational techniques were used to evaluate the model whereas SPSS were used to validate the results. The result shows that minimum Ra value of the proposed model is 2.1709 µm. It was much lower than the results of three computational techniques which are Artificial Neural Network (ANN), Regression analysis and Support Vector Machine (SVM) by about 29.2 %, 23.8 % and 19.3% respectively.
format Thesis
author Mohamad, Azizah
author_facet Mohamad, Azizah
author_sort Mohamad, Azizah
title Enhancement of the machining surface roughness model with cuckoo algorithm
title_short Enhancement of the machining surface roughness model with cuckoo algorithm
title_full Enhancement of the machining surface roughness model with cuckoo algorithm
title_fullStr Enhancement of the machining surface roughness model with cuckoo algorithm
title_full_unstemmed Enhancement of the machining surface roughness model with cuckoo algorithm
title_sort enhancement of the machining surface roughness model with cuckoo algorithm
publishDate 2014
url http://eprints.utm.my/id/eprint/47962/25/AzizahMohamadMFC2014.pdf
http://eprints.utm.my/id/eprint/47962/
_version_ 1643652418166063104
score 13.211869

Similar Items