Study on the Digital Intelligent Diagnosis of Miniature Machine Tools

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 applica...

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Bibliographic Details
Published in:Applied sciences 2021-09, Vol.11 (18), p.8372
Main Authors: Chan, Tzu-Chi, Jian, Ze-Kai, Wang, Yu-Chuan
Format: Article
Language:English
Subjects:
Abnormalities
Automation
Big Data
Communication
Customization
Data analysis
Decision making
Diagnosis
digital production
Efficiency
Eigenvalues
Five axis
health monitoring
Intelligence
intelligent diagnosis
Internet of Things
Machine tools
Machinery
Manufacturing
miniature machine tool
Principal components analysis
principal components analysis (PCA)
Real time
Sensors
Software
Spindles
Technology
Time signals
Timing
Vibration
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11188372