The chatter that occurs during the turning operation, especially when cutting a slender and flexible shaft, determines the surface quality of the workpiece and the stability of the machining system. However, when building a dynamic model of a slender workpiece with a chuck and tailstock, it is generally regarded as a cantilever or simply supported beam, without consideration of the axial force and supported stiffness effect. In this work, a dynamic model for thin and flexible workpieces with different clamping boundary conditions was first built. Then, a finite element analysis (FEA) was used to study the influence of the axial force and supporting stiffness on the mode frequencies of the workpiece. A further analysis found that the relationship between support stiffness, axial force, and the dynamic response of the workpiece is nonlinear and far more complex than that of the simply supported beam model. The clamping force directly influenced the magnitude of the vibration response with the decrease of shaft stiffness during the turning process. These results were verified experimentally by measuring the vibrational response of slender shafts with different clamping modes using an on-rotor sensing (ORS) system. It proved that the proposed model shows advantages for the identification of dynamic vibration and quality control when machining slender workpieces.

In marine electric propulsion systems (MEPS) driven by variable-frequency drives, motor current signals often exhibit complex modulation components, ambiguous spectra, and severe noise interference, rendering it challenging to extract fault-related modulation components. To address this issue, we propose a zero-crossing tacholess order tracking method based on motor current signals. This method utilizes zero-crossing estimation of the instantaneous frequency to perform angular resampling of stator current signals and demodulates the envelope spectrum to extract fault characteristic spectra, enabling the diagnosis of mechanical faults in MEPS. Given the synchronization of the synchronous motor speed with the inverter fundamental frequency, this method estimates instantaneous frequencies in the time domain without requiring integration or time–frequency representation, which is simple and computationally efficient. Data validation on a small-scale marine electric propulsion test platform demonstrates that the proposed method exhibits good robustness under variable-speed conditions and effectively detects imbalance faults caused by propeller breakages and gear faults resulting from bevel gear tooth defects. Therefore, the proposed method can be applied to diagnose faults in downstream mechanical equipment driven by motors.

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.