Integrating numerical techniques and predictive diagnosis for precision enhancement in roller cam rotary table

Abstract

Currently, various industries are increasingly trending towards multi-axis machining and Industry 4.0. As a result, the demand for machining flexibility, speed, and precision in machine tools is also rising. Different manufacturers provide many components of machine tools, and the alignment precision, main structure, component rigidity, and dynamic performance of these components all impact the machining precision of multi-axis machine tools.Therefore, the structural design of each component is crucial. In this study, a roller cam rotary table is selected for analysis using the Finite Element Method, considering the constraints of the machine environment. The investigation includes Vibration mode shape, gravitational load, External loading, transient, spectral, and topology optimization inspection. The static analysis reveals weak points in the machine's structure. Modal analysis helps understand the natural frequencies that affect the machine's structure, resulting in mitigation of resonance during the machining process and enhancing machining precision. Finally, the modal impact test is conducted using accelerometers and an impact hammer whereas ME'scope curve fitting is employed to determine the mode shapes and natural frequencies. The results are compared to validate the accuracy of the analysis, with a deviation of 8.5%, 8.3%, and 1.4%. Topological optimization analysis is performed on machine components, aiming to reduce weight and improve natural frequencies as compared to the original design. Precision tests such as repeatability and segmentation accuracy are essential for rotary tables. These tests provide information about the errors at different angles, ensuring accuracy, reliability, and quality during the machining process. In this study, vibration signals from the rotary table are captured using a smart predictive diagnosis performance system (PDPS). An algorithm is used to analyze the normal vibration signals and establish a healthy model for continuous monitoring of the rotary table's health status. Principal Component Analysis (PCA) is applied to identify fault features and diagnose mechanical problems. This enables early detection of anomalies in the rotary table, reducing downtime and maintenance costs due to damage and improving the machine's efficiency.