Study on the Digital Intelligent Diagnosis of Miniature Machine Tools

Abstract

Several industries are currently focusing on smart technologies, high customization, and the integration of solutions. This study focuses on the intelligent diagnosis of digital small machine tools. Furthermore, the main technology processes and cases for smart manufacturing for machine tool applications are introduced. Owing to the requirements of automated processing to determine the quality of a process in advance, the health status of a machine should be monitored in real time, and machine abnormalities should be detected periodically. In this study, we captured the real-time signals of temperature, spindle current, and the vibration of three small five-axis machine tools. Moreover, we used a principal component analysis to diagnose and compare the health status of the spindles and machines. We developed a miniature machine tool health monitoring application to avoid time delays and loss from damage, and used the application to monitor the machine health online under an actual application. Therefore, the technology can also be used in an online diagnosis of machine tools through modeling technology, allowing the user to monitor trends in the machine health. This research provides a feasible method for monitoring machine health. We believe that the intelligent functions of machine tools will continue to increase in the future.

Keywords

• miniature machine tool
• intelligent diagnosis
• digital production
• principal components analysis (PCA)
• health monitoring