Leveraging Intelligent Machines for Sustainable and Intelligent Manufacturing Systems

This study presents an intelligent machine developed for real-time quality monitoring during CNC turning, aimed at improving cutting efficiency and reducing production energy. A dynamometer integrated into the CNC machine captures decomposed cutting forces using the Daubechies wavelet transform. These force ratios are correlated with key workpiece dimensions: surface roughness, average roughness, straightness, and roundness. Two predictive models—nonlinear regression and a feed-forward neural network with Levenberg–Marquardt backpropagation—are employed to estimate these parameters under varying cutting conditions. Experimental results indicate that nonlinear regression models outperform neural networks in predictive accuracy. The proposed system offers effective in-process control of machining quality, contributing to shorter cycle times, lower defect rates, and more sustainable manufacturing practices.