Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process

This paper aims to improve a sustainable cutting process through the integration of energy and cost modeling. The solution is based on the multi-objective optimisation of cutting parameters, including cutting speed, feed rate and cutting depth, based energy, cost and quality processes. The modeling...

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Main Authors: Bagaber, Salem Abdullah, A. R., Yusoff
Format: Article
Language:English
Published: Elsevier Ltd 2018
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/24053/1/Energy%20and%20Cost%20Integration%20for%20Multi-Objective1.pdf
http://umpir.ump.edu.my/id/eprint/24053/
https://doi.org/10.1016/j.measurement.2018.12.096
https://doi.org/10.1016/j.measurement.2018.12.096
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Institution: Universiti Malaysia Pahang
Language: English
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spelling my.ump.umpir.240532019-10-15T08:01:03Z http://umpir.ump.edu.my/id/eprint/24053/ Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process Bagaber, Salem Abdullah A. R., Yusoff TS Manufactures This paper aims to improve a sustainable cutting process through the integration of energy and cost modeling. The solution is based on the multi-objective optimisation of cutting parameters, including cutting speed, feed rate and cutting depth, based energy, cost and quality processes. The modeling approach has several notable merits, namely direct energy and indirect energy consumption calculation; a machining cost model for all machining tools including energy cost, production operation cost, cutting tool cost, and cutting fluid cost (dry and wet). Quality is represented by surface roughness. The multi-objective optimisation using Response Surface Methodology (RSM) was compared with the Non-Sorted Genetic Algorithm II (NSGA II) before experimental confirmation tests were made. From the multi-objective optimisation it was found that energy saved can be 9.2% and machining cost can be reduced by 4.6% using RSM. Moreover, the second-generation results of optimisation using NSGA II showed an improvement of more than 70% compared to RSM optimisation. A two-confirmation method validated the optimum point and dry cutting showed lower energy and cost with acceptable quality compared to wet conditions. The model proposed in this study is effective in terms of machining energy, cost and environment so as to be integrated with the sustainable machining. Elsevier Ltd 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24053/1/Energy%20and%20Cost%20Integration%20for%20Multi-Objective1.pdf Bagaber, Salem Abdullah and A. R., Yusoff (2018) Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process. Measurement. pp. 1-35. ISSN 0263-2241 (In Press) https://doi.org/10.1016/j.measurement.2018.12.096 https://doi.org/10.1016/j.measurement.2018.12.096
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TS Manufactures
spellingShingle TS Manufactures
Bagaber, Salem Abdullah
A. R., Yusoff
Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process
description This paper aims to improve a sustainable cutting process through the integration of energy and cost modeling. The solution is based on the multi-objective optimisation of cutting parameters, including cutting speed, feed rate and cutting depth, based energy, cost and quality processes. The modeling approach has several notable merits, namely direct energy and indirect energy consumption calculation; a machining cost model for all machining tools including energy cost, production operation cost, cutting tool cost, and cutting fluid cost (dry and wet). Quality is represented by surface roughness. The multi-objective optimisation using Response Surface Methodology (RSM) was compared with the Non-Sorted Genetic Algorithm II (NSGA II) before experimental confirmation tests were made. From the multi-objective optimisation it was found that energy saved can be 9.2% and machining cost can be reduced by 4.6% using RSM. Moreover, the second-generation results of optimisation using NSGA II showed an improvement of more than 70% compared to RSM optimisation. A two-confirmation method validated the optimum point and dry cutting showed lower energy and cost with acceptable quality compared to wet conditions. The model proposed in this study is effective in terms of machining energy, cost and environment so as to be integrated with the sustainable machining.
format Article
author Bagaber, Salem Abdullah
A. R., Yusoff
author_facet Bagaber, Salem Abdullah
A. R., Yusoff
author_sort Bagaber, Salem Abdullah
title Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process
title_short Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process
title_full Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process
title_fullStr Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process
title_full_unstemmed Energy and Cost Integration for Multi-Objective Optimisation in a Sustainable Turning Process
title_sort energy and cost integration for multi-objective optimisation in a sustainable turning process
publisher Elsevier Ltd
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/24053/1/Energy%20and%20Cost%20Integration%20for%20Multi-Objective1.pdf
http://umpir.ump.edu.my/id/eprint/24053/
https://doi.org/10.1016/j.measurement.2018.12.096
https://doi.org/10.1016/j.measurement.2018.12.096
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