Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant

Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant

Response surface methodology with Box–Benhken (BB) design of experiment is utilized to discuss about the development of first and second order model for coefficient of friction (COF) and specific wear rate (SWR) for engine oil treatment added with waste palm oil blended with SAE40. The designs utili...

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Main Authors: A. K., Amirruddin, K., Kadirgama, M., Samykano, D., Ramasamy, M. M., Rahman, M. M., Noor
Format: Conference or Workshop Item
Language:English
English
Published: 2018
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/22676/
http://umpir.ump.edu.my/id/eprint/22676/1/28.%20Coefficient%20of%20friction%20and%20specific%20wear%20rate%20prediction.pdf
http://umpir.ump.edu.my/id/eprint/22676/2/28.1%20Coefficient%20of%20friction%20and%20specific%20wear%20rate%20prediction.pdf
id ump-22676
recordtype eprints
spelling ump-226762019-04-09T02:32:02Z http://umpir.ump.edu.my/id/eprint/22676/ Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant A. K., Amirruddin K., Kadirgama M., Samykano D., Ramasamy M. M., Rahman M. M., Noor TJ Mechanical engineering and machinery Response surface methodology with Box–Benhken (BB) design of experiment is utilized to discuss about the development of first and second order model for coefficient of friction (COF) and specific wear rate (SWR) for engine oil treatment added with waste palm oil blended with SAE40. The designs utilize the factors (rotational speeds (200 RPM to 300 RPM), volume concentration and applied loads (2kg to 9kg)) with the response (COF and SWR), evaluated using piston ring-liner contact tribology tester. Other than that, the effect of the factors can be investigated from the equation develop. The contour plot also can be generated to predict the COF and SWR at any experimental zone. The model generated shows that the COF and SWR increases when load, speed and volume concentration are increased. The second-order is more accurate compare with the first order for COF while first order model is more accurate for wear rate. The COF increases with load to cause more wear. 2018 Conference or Workshop Item NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22676/1/28.%20Coefficient%20of%20friction%20and%20specific%20wear%20rate%20prediction.pdf pdf en http://umpir.ump.edu.my/id/eprint/22676/2/28.1%20Coefficient%20of%20friction%20and%20specific%20wear%20rate%20prediction.pdf A. K., Amirruddin and K., Kadirgama and M., Samykano and D., Ramasamy and M. M., Rahman and M. M., Noor (2018) Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant. In: 7th International Conference on Design and Concurrent Engineering IDECON 7.0, 17 - 18 September 2018 , Kuching, Sarawak. pp. 1-14..
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
A. K., Amirruddin
K., Kadirgama
M., Samykano
D., Ramasamy
M. M., Rahman
M. M., Noor
Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant
description Response surface methodology with Box–Benhken (BB) design of experiment is utilized to discuss about the development of first and second order model for coefficient of friction (COF) and specific wear rate (SWR) for engine oil treatment added with waste palm oil blended with SAE40. The designs utilize the factors (rotational speeds (200 RPM to 300 RPM), volume concentration and applied loads (2kg to 9kg)) with the response (COF and SWR), evaluated using piston ring-liner contact tribology tester. Other than that, the effect of the factors can be investigated from the equation develop. The contour plot also can be generated to predict the COF and SWR at any experimental zone. The model generated shows that the COF and SWR increases when load, speed and volume concentration are increased. The second-order is more accurate compare with the first order for COF while first order model is more accurate for wear rate. The COF increases with load to cause more wear.
format Conference or Workshop Item
author A. K., Amirruddin
K., Kadirgama
M., Samykano
D., Ramasamy
M. M., Rahman
M. M., Noor
author_facet A. K., Amirruddin
K., Kadirgama
M., Samykano
D., Ramasamy
M. M., Rahman
M. M., Noor
author_sort A. K., Amirruddin
title Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant
title_short Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant
title_full Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant
title_fullStr Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant
title_full_unstemmed Coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant
title_sort coefficient of friction and specific wear rate prediction model using response surface methodology for waste cooking oil blended lubricant
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/22676/
http://umpir.ump.edu.my/id/eprint/22676/1/28.%20Coefficient%20of%20friction%20and%20specific%20wear%20rate%20prediction.pdf
http://umpir.ump.edu.my/id/eprint/22676/2/28.1%20Coefficient%20of%20friction%20and%20specific%20wear%20rate%20prediction.pdf
first_indexed 2023-09-18T22:33:54Z
last_indexed 2023-09-18T22:33:54Z
_version_ 1777416467773390848

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