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April 1, 2026Surface Review and Letters

AI-Based Modeling and Analysis of Surface Roughness, Tool Wear, and Material Removal Rate in Dry Hard Turning of Skd11 for Adaptive Control Insights

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Authors

VNVAN-CANH NGUYENHanoi University of IndustryPLPham Ngoc LinhHanoi University of IndustryANAnh-Thang NguyenHanoi University of Industry

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Overview

Adaptive control insights improve surface integrity and tool life in dry hard turning of SKD11 steel.

Key Points

  • The aim is to develop an AI-assisted model to predict surface roughness, tool wear, and material removal rate in dry hard turning.
  • Conducted seventeen experiments varying cutting speed, feed rate, and depth of cut.
  • Utilized CBN inserts for effective dry cutting.
  • Employed Response Surface Methodology for baseline regression models.
  • Enhanced accuracy with Support Vector Regression.
  • Applied multi-objective optimization for balancing multiple outcomes.
  • Achieved approximately 10% reduction in surface roughness.
  • Realized 12% reduction in tool wear.
  • Improved productivity by 8% compared with baseline.
  • Optimal cutting conditions identified for enhanced performance.

Cite This Study

NGUYEN et al. (2026) studied this question.

synapsesocial.com/papers/69cd7af55652765b073a8856https://doi.org/10.1142/s0218625x26410040
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