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September 10, 2025Engineering Technology & Applied Science ResearchOpen Access

Development of a Multi-Objective Optimization Model for the Hard Turning of SKD11 Steel with Nanofluid-Al₂O₃ Minimum Quantity Lubrication Using RSM and PSO

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Authors

TDThe Vinh DoNLNguyen-Anh-Vu Le

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Overview

This analysis demonstrates improvements in machining efficiency and surface quality in SKD11 steel, suggesting effective parameter optimization using MQL.

Key Points

  • The hybrid RSM-PSO approach achieved a minimum surface roughness of 0.43 µm with 3% Al₂O₃.
  • Maximum material removal rate reached 9000 mm³/min at 1.5% Al₂O₃ under optimized conditions.
  • Using a total of 31 experiments, high R² value of 97.69% indicates reliable model accuracy.
  • The findings emphasize the benefits of optimized lubrication strategies for sustainable machining processes.

Cite This Study

Do et al. (2025) studied this question.

synapsesocial.com/papers/68c1ac0954b1d3bfb60e4a33https://doi.org/10.48084/etasr.11351
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Also Consider

Synapse has enriched one closely related paper. Consider it for comparative context:

  1. 1Multi-objective optimization for balancing surface roughness and material removal rate in milling hardened SKD11 alloy steel with SIO2 nanofluid MQL2024 · 10 citations