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...

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Published in:INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING
Main Authors: Zulkifli, Zulaika; Halim, Nurul Hayati Abdul; Solihin, Zainoor Hailmee; Fauzee, Nur Fatini Mohamad; Hadi, Musfirah Abdul
Format: Article
Language:English
Published: UNIV TUN HUSSEIN ONN MALAYSIA 2024
Subjects:
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
spellingShingle 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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url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001419206000006
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