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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