Taoming Chen

Workpiece surface quality is a critical metric for assessing machining quality. However, due to the complex coupling characteristics of cutting factors, accurately predicting surface roughness remains challenging. Typically, roughness is measured post-machining using specialized instruments, which delays feedback and hampers timely problem detection, ultimately resulting in cutting resource wastage. To address this issue, this paper introduces a predictive model for workpiece surface roughness based on the finite element (FE) method and advanced image processing techniques. Initially, an orthogonal turning experiment was designed, and an FE cutting model was constructed to assess the distribution of cutting forces and temperatures under varying cutting parameters. Image processing methods (including mesh calibration, edge extraction, and contour fitting) were then applied to extract surface characteristics from the FE simulation outputs, yielding preliminary estimates of surface roughness. By employing range and regression analyses methods, this study quantitatively evaluates the interdependencies among cutting parameters, forces, temperatures, and roughness, subsequently formulating a multivariate regression model to predict surface roughness. Finally, a turning experiment under actual working conditions was conducted, confirming the model’s capacity to predict the $R_a$ trend with an accuracy of 85.07%. Thus, the proposed model provides a precise predictive tool for surface roughness, offering valuable guidance for optimizing machining parameters and supporting proactive control in the turning process, ultimately enhancing machining efficiency and quality.