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.
Highlights
The machinery industry must move toward higher efficiency, precision, and customization, as well as greater intelligence and the integration of complete plants
This study proposed a digital intelligent diagnosis of miniature machine tools
This study introduced the main technology processes and cases for industrial smart manufacturing applications for machine tools
Summary
The machinery industry must move toward higher efficiency, precision, and customization, as well as greater intelligence and the integration of complete plants. In addition to developing key technologies and products, intelligent technologies, such as the Internet of things (IoT), smart robots, and big data analysis, should be introduced. Tao et al [3] presented advances in the IoT, big data, and artificial intelligence which have impacted manufacturing. Lee et al [7] presented advanced information analytics which can be used to increase the performance of networked machines. Farid et al [15] provided measures for the key characteristics of reconfigurability, i.e., integrability and customization, which have driven the intuitive design of these technological advances. These measures were demonstrated for reconfigurability measurements in automated and intelligent manufacturing systems. This research uses three small five-axis machine tools as objects to present an example of an intelligent diagnosis
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