Predictive mapping of surface roughness in turning of hardened AISI 4340 using carbide tools

Abstract

This study presents a novel approach to predict surface roughness in the hard turning of AISI 4340 steel using carbide tools, aimed to develop a comprehensive predictive map. The hypothesis that surface roughness can be accurately predicted using a linear regression model was tested and confirmed. Experimental results showed surface roughness in the range of 1.946 to 5.636 microns. Statistical analysis revealed a normal distribution of surface roughness data with linear regression as the best-fit model, significantly determined by feed rate and explaining 98.41% of the variance. Machine learning validated this model, achieving high prediction accuracy (R² = 96.91%, MSE = 0.058, RMSE = 0.242). The innovative predictive map, created using a full factorial design, demonstrated a strong agreement between predicted and validated values. This work highlights the potential of integrating statistical and machine learning techniques for precise surface roughness prediction, recommending industrial validation to enhance machining productivity.