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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Abstract
Dry hard turning of heat-treated SKD11 tool steel offers a sustainable alternative to grinding but poses significant challenges in maintaining surface integrity and tool life due to severe wear and high cutting temperatures. This study develops a physically interpretable, AI-assisted modeling framework that integrates empirical and machine learning methods to predict and optimize surface roughness ([ ]), tool wear ([ ]), and material removal rate (MRR). Seventeen controlled experiments were conducted under varying cutting speed ([ ]), feed rate ([ ]), and depth of cut ([ ]) conditions using CBN inserts (TNGA160404–CBN1020) in dry cutting. A hierarchical modeling approach was established, where [ ] acts as an intermediate variable linking process parameters to [ ], while MRR was computed analytically. The Response Surface Methodology (RSM) provided the baseline regression models with [ ] for [ ] and [ ] for [ ] when wear was included. Support Vector Regression (SVR) further enhanced prediction accuracy to [ ], demonstrating strong nonlinear capability and physical consistency. Multi-objective optimization using the MOPSO–TOPSIS framework achieved a balanced solution at [ ]–160[ ]m/min, [ ]–0.07[ ]mm/rev, and [ ]–0.15[ ]mm, resulting in approximately 10% reduction in surface roughness, 12% reduction in tool wear, and 8% improvement in productivity compared with baseline conditions. The proposed framework bridges physical modeling and AI-based prediction, offering a robust, explainable tool for adaptive control in precision hard turning and a reliable foundation for intelligent machining under limited-data environments.

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