Improving Turning Machining Processes through Integration of Micro-Textured Tools: A Modeling Approach

Abstract

This study explores the enhancement of machining performance through incorporation of micro-textured cutting tools and machine learning based modelling techniques. Two different textured tool geometries in the form of micro-drilled holes and micro-channel textures were created on the rake surface of carbide cutting tools. The performance of these textured tools was compared experimentally with standard dry turning and lubricated turning conditions for AISI 1014 mild steel on a normal lathe for different cutting speeds (26 and 39m/min) and depths of cut (0.75 and 1mm). Experimental results indicated that textured tools substantially enhanced the machining quality as compared with standard turning conditions. The importance of machining variables was evaluated using Random Forest (RF) feature importance analysis. The study revealed that surface roughness was the most significant parameter followed by sawtooth distance, cutting force and power consumption. Additionally, machine learning regression and classification models were built using Support Vector Regression (SVR), Support Vector Classification (SVC) and RF techniques. When compared, the RF model resulted in higher performing model with higher R² and other statistical values. The RF classification model yielded better results with an accuracy of 0.954, F1-score of 0.936, precision of 0.943, and recall of 0.943. The results demonstrate that the integration of micro-textured tools and machine learning algorithms can significantly improve the machining efficiency, reduce the frictional and thermal effects, improve the surface quality and provide accurate predictions of the machining responses.