Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis
Minimum Quantity Lubricant (MQL) is a sustainable machining method offering better lubrication and cooling efficiency for high machining performances. To further enhance its sustainability, this study is conducted based on the Computational Fluid Dynamic (CFD) analysis on an MQL delivery model using...
Published in: | INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
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UNIV TUN HUSSEIN ONN MALAYSIA
2024
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Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001419206000006 |
author |
Zulkifli Zulaika; Halim Nurul Hayati Abdul; Solihin Zainoor Hailmee; Fauzee Nur Fatini Mohamad; Hadi Musfirah Abdul |
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Zulkifli Zulaika; Halim Nurul Hayati Abdul; Solihin Zainoor Hailmee; Fauzee Nur Fatini Mohamad; Hadi Musfirah Abdul Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis Engineering |
author_facet |
Zulkifli Zulaika; Halim Nurul Hayati Abdul; Solihin Zainoor Hailmee; Fauzee Nur Fatini Mohamad; Hadi Musfirah Abdul |
author_sort |
Zulkifli |
spelling |
Zulkifli, Zulaika; Halim, Nurul Hayati Abdul; Solihin, Zainoor Hailmee; Fauzee, Nur Fatini Mohamad; Hadi, Musfirah Abdul Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING English Article Minimum Quantity Lubricant (MQL) is a sustainable machining method offering better lubrication and cooling efficiency for high machining performances. To further enhance its sustainability, this study is conducted based on the Computational Fluid Dynamic (CFD) analysis on an MQL delivery model using Treated Recycled Cooking Oil (TRCO) to simulate a high-speed cutting process. The main aim is to optimise the mist flow velocity which leads to optimum penetration of lubrication deep into the cutting zone. Through the design of experiment approach under the Box-Behnken Response Surface Methodology (RSM) method, 13 sets of parameters were simulated with controlled factors of oil flow rate (50-150 ml/hr), nozzle distance (20-60 mm), and nozzle diameter (1-2 mm). Then, Analysis of Variance (ANOVA) was applied to investigate how the controlled factors influence the response. The simulation works resulted in the MQL mist flow reaching velocity that varied from 15.43 to 115.52 m/s. The ANOVA revealed that the response is significantly influenced by nozzle distance, nozzle diameter, and the interaction between them. The highest velocity was generated at minimum nozzle distance and maximum nozzle diameter. Contrary, maximum nozzle distance and minimum nozzle diameter generated the lowest value. The flowback or rebound conditions of the mist flow at different flow velocities were also visualized and discussed with the aid of CFD contour images. Through optimization, the optimum MQL mist flow velocity at 115.34 m/s is predicted at; oil flow rate: 100 ml/hr, nozzle diameter: 2 mm, and nozzle distance: 20 mm from the tool edge. This optimum MQL mist flow is vital for high-speed cutting due to massive generation of friction and heat that require deep penetration of the lubricant into the cutting for maximum heat UNIV TUN HUSSEIN ONN MALAYSIA 2229-838X 2024 16 6 10.30880/ijie.2024.16.06.028 Engineering WOS:001419206000006 https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001419206000006 |
title |
Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis |
title_short |
Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis |
title_full |
Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis |
title_fullStr |
Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis |
title_full_unstemmed |
Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis |
title_sort |
Predictive Model and Optimisation of MQL Mist Flow Velocity Through CFD Analysis |
container_title |
INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING |
language |
English |
format |
Article |
description |
Minimum Quantity Lubricant (MQL) is a sustainable machining method offering better lubrication and cooling efficiency for high machining performances. To further enhance its sustainability, this study is conducted based on the Computational Fluid Dynamic (CFD) analysis on an MQL delivery model using Treated Recycled Cooking Oil (TRCO) to simulate a high-speed cutting process. The main aim is to optimise the mist flow velocity which leads to optimum penetration of lubrication deep into the cutting zone. Through the design of experiment approach under the Box-Behnken Response Surface Methodology (RSM) method, 13 sets of parameters were simulated with controlled factors of oil flow rate (50-150 ml/hr), nozzle distance (20-60 mm), and nozzle diameter (1-2 mm). Then, Analysis of Variance (ANOVA) was applied to investigate how the controlled factors influence the response. The simulation works resulted in the MQL mist flow reaching velocity that varied from 15.43 to 115.52 m/s. The ANOVA revealed that the response is significantly influenced by nozzle distance, nozzle diameter, and the interaction between them. The highest velocity was generated at minimum nozzle distance and maximum nozzle diameter. Contrary, maximum nozzle distance and minimum nozzle diameter generated the lowest value. The flowback or rebound conditions of the mist flow at different flow velocities were also visualized and discussed with the aid of CFD contour images. Through optimization, the optimum MQL mist flow velocity at 115.34 m/s is predicted at; oil flow rate: 100 ml/hr, nozzle diameter: 2 mm, and nozzle distance: 20 mm from the tool edge. This optimum MQL mist flow is vital for high-speed cutting due to massive generation of friction and heat that require deep penetration of the lubricant into the cutting for maximum heat |
publisher |
UNIV TUN HUSSEIN ONN MALAYSIA |
issn |
2229-838X |
publishDate |
2024 |
container_volume |
16 |
container_issue |
6 |
doi_str_mv |
10.30880/ijie.2024.16.06.028 |
topic |
Engineering |
topic_facet |
Engineering |
accesstype |
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id |
WOS:001419206000006 |
url |
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001419206000006 |
record_format |
wos |
collection |
Web of Science (WoS) |
_version_ |
1825722599141801984 |