An enhancement of integrated fuzzy-topsis to improve machining surface roughness

Machining is defined as a process to remove material in the form of chips using single or multiple wedge-shaped cutting tools to produce the desired shape. This process has successfully produced a closer dimensional accuracy and surface finish to meet the industrial demands. However, it is difficult...

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Main Author: Mohd. Adnan, Mohd. Ridhwan Hilmi
Format: Thesis
Language:English
Published: 2014
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Online Access:http://eprints.utm.my/id/eprint/41588/1/MohdRidhwanHilmiMFSKSM2014.pdf
http://eprints.utm.my/id/eprint/41588/
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spelling my.utm.415882017-09-05T06:00:12Z http://eprints.utm.my/id/eprint/41588/ An enhancement of integrated fuzzy-topsis to improve machining surface roughness Mohd. Adnan, Mohd. Ridhwan Hilmi TJ Mechanical engineering and machinery Machining is defined as a process to remove material in the form of chips using single or multiple wedge-shaped cutting tools to produce the desired shape. This process has successfully produced a closer dimensional accuracy and surface finish to meet the industrial demands. However, it is difficult to find the optimal machining parameter values that yield the minimum surface roughness (Ra) values to meet technical specifications for end milling and laser assisted machining (LAM). Thus, this research proposed the integration of Fuzzy Logic (FL) and Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) to predict minimum Ra values and find the optimal machining parameters. In the proposed Fuzzy-TOPSIS model, initially FL is used to consider correct membership functions, linguistic terms and rules. Then, TOPSIS uses the weighted values obtained to handle instabilities in FL with advanced inference methods and rank the FL results by applying the obtained fuzzy intervals. The integration of Fuzzy-TOPSIS model has successfully reduced Ra values by 0.066µm for end milling and 0.112µm for LAM. Upon achieving the minimum values, a precise combination of optimal machining parameters can be obtained. These results reveal that the Fuzzy-TOPSIS model is capable of improving the quality of finished products during machining processes. 2014-02 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/41588/1/MohdRidhwanHilmiMFSKSM2014.pdf Mohd. Adnan, Mohd. Ridhwan Hilmi (2014) An enhancement of integrated fuzzy-topsis to improve machining surface roughness. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computing.
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 TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Mohd. Adnan, Mohd. Ridhwan Hilmi
An enhancement of integrated fuzzy-topsis to improve machining surface roughness
description Machining is defined as a process to remove material in the form of chips using single or multiple wedge-shaped cutting tools to produce the desired shape. This process has successfully produced a closer dimensional accuracy and surface finish to meet the industrial demands. However, it is difficult to find the optimal machining parameter values that yield the minimum surface roughness (Ra) values to meet technical specifications for end milling and laser assisted machining (LAM). Thus, this research proposed the integration of Fuzzy Logic (FL) and Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) to predict minimum Ra values and find the optimal machining parameters. In the proposed Fuzzy-TOPSIS model, initially FL is used to consider correct membership functions, linguistic terms and rules. Then, TOPSIS uses the weighted values obtained to handle instabilities in FL with advanced inference methods and rank the FL results by applying the obtained fuzzy intervals. The integration of Fuzzy-TOPSIS model has successfully reduced Ra values by 0.066µm for end milling and 0.112µm for LAM. Upon achieving the minimum values, a precise combination of optimal machining parameters can be obtained. These results reveal that the Fuzzy-TOPSIS model is capable of improving the quality of finished products during machining processes.
format Thesis
author Mohd. Adnan, Mohd. Ridhwan Hilmi
author_facet Mohd. Adnan, Mohd. Ridhwan Hilmi
author_sort Mohd. Adnan, Mohd. Ridhwan Hilmi
title An enhancement of integrated fuzzy-topsis to improve machining surface roughness
title_short An enhancement of integrated fuzzy-topsis to improve machining surface roughness
title_full An enhancement of integrated fuzzy-topsis to improve machining surface roughness
title_fullStr An enhancement of integrated fuzzy-topsis to improve machining surface roughness
title_full_unstemmed An enhancement of integrated fuzzy-topsis to improve machining surface roughness
title_sort enhancement of integrated fuzzy-topsis to improve machining surface roughness
publishDate 2014
url http://eprints.utm.my/id/eprint/41588/1/MohdRidhwanHilmiMFSKSM2014.pdf
http://eprints.utm.my/id/eprint/41588/
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score 13.160551