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Volume 13 | Issue 4 | Year 2026 | Article Id. IJME-V13I4P101 | DOI : https://doi.org/10.14445/23488360/IJME-V13I4P101

Hybrid Nanofluid-Assisted Minimum Quantity Lubrication (MQL) for Turning AISI 1040 Steel (EN8): An Experimental and Machine Learning Approach

Dattatraya Popat Kshirsagar, Amol Dnyanwshwar Wable, Swapnil Dnyadeo Galande, Atul Bhausaheb Pawar, Vishnu Damodhar Wakchaure

Received Revised Accepted Published
01 Jan 2026 08 Feb 2026 10 Mar 2026 29 Mar 2026

Citation :

Dattatraya Popat Kshirsagar, Amol Dnyanwshwar Wable, Swapnil Dnyadeo Galande, Atul Bhausaheb Pawar, Vishnu Damodhar Wakchaure, "Hybrid Nanofluid-Assisted Minimum Quantity Lubrication (MQL) for Turning AISI 1040 Steel (EN8): An Experimental and Machine Learning Approach," International Journal of Mechanical Engineering, vol. 13, no. 4, pp. 1-15, 2026. Crossref, https://doi.org/10.14445/23488360/IJME-V13I4P101

Abstract

The study presents an experimental and predictive investigation of bio-degradable nanofluids for sustainable machining under Small Quantity Lubrication (SQL). Palm oil, selected for its biodegradable and eco-friendly characteristics, was blended with Silicon Carbide (SiC), Titanium Dioxide (TiO₂), and their hybrid mixtures at different concentrations. Turning tests were carried out on AISI 1040 (EN8) steel using a Taguchi L27 design, covering 81 trials with varying surface speed, feed rate, radial depth of cut, and nanofluid parameters. Adding the nanoparticles improves thermal conductivity, viscosity, and stability of the nano fluid, showing the machining efficiency and Material Removal Rate (MRR). To predict MRR, Artificial Neural Networks (ANN) and multiple deep learning techniques were used. The decision Tree, Support Vector Regression, Random Forest, AdaBoost, XGBoost, Gradient Boosting, and CatBoost were implemented. Among them, ensemble learning methods such as Gradient Boosting (R² = 0.9944) and XGBoost (R² = 0.9876) demonstrated the highest accuracy, while ANN also achieved strong generalization (R² = 0.9598). The results confirm that hybrid nanofluid-based MQL significantly improves machining sustainability and that ensemble machine learning approaches provide reliable prediction of machining performance.

Keywords

Minimum Quantity Lubrication, Hybrid Nanoparticles, Machine Learning Models, Predictive Modeling.

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