Toward sustainable machining of hardened SKD11: Machine learning-based evaluation and optimization of surface roughness, tool wear, and CO2 emissions

Toward sustainable machining of hardened SKD11: Machine learning-based evaluation and optimization of surface roughness, tool wear, and CO2 emissions

This research proposes an integrated methodology combining Random Forest regression modeling with the NSGA-III evolutionary algorithm for simultaneous optimization of surface quality and environmental performance during fine turning of SKD11 steel under hybrid cooling conditions merging Minimum Quan...

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Bibliographic Details
Main Authors: Van-Canh Nguyen, Dung Hoang Tien, Van-Hung Pham, Thinh-Viet Nguyen, Thuy-Duong Nguyen
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Results in Engineering
Subjects:
Random forest
NSGA-III
Multi-objective optimization
Carbon emission
Surface roughness
MQL
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025013192
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Summary:This research proposes an integrated methodology combining Random Forest regression modeling with the NSGA-III evolutionary algorithm for simultaneous optimization of surface quality and environmental performance during fine turning of SKD11 steel under hybrid cooling conditions merging Minimum Quantity Lubrication (MQL) with Vortex tube cooling. Input variables comprise cutting speed (Vc), feed rate (fz), and cutting depth (ap), while output parameters include surface roughness (Ra), carbon emission (CE), and tool wear (Vb). Experimental data collected according to a Box-Behnken matrix were utilized to develop linear and non-linear regression models. Results indicated that the second-order model demonstrated superior predictive accuracy for Ra (R² = 0.997) and CE (R² = 0.994), whereas the Vb prediction model failed to achieve sufficient reliability. Consequently, the multi-objective optimization problem focused on minimizing Ra and CE, implemented through the NSGA-III algorithm. The Pareto solutions obtained were ranked using the TOPSIS method with Entropy-calculated weights. The leading optimal solution achieved Ra = 0.264 μm and CE = 0.032 g/min at optimal cutting parameters: Vc = 81.54 m/min, fz = 0.060 mm/rev, and ap = 0.070 mm. This hybrid optimization approach demonstrates high potential for promoting sustainable manufacturing, particularly in high-precision machining with advanced cooling technologies.
ISSN:2590-1230

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