• Technical Name
  • Application of Advanced Intelligent Feature Capture for Machine Tool Cutting States Monitoring and Prediction
  • Operator
  • National Chin-Yi University of Technology
  • Booth
  • Online display only
  • Contact
  • 姚賀騰
  • Email
  • htyau@ncut.edu.tw
Technical Description By utilizing the chaotic attractor in dynamic error mapping conversion technique that the research team applies with, the lack of accuracy and immediacy on the existing technology can be further improved. The details of the technique can be described in two aspects. First, as in the feature extraction of tool wear condition, dynamic errors of fractional-order chaos system is being used to detect the minor signal variations in different levels of tool wear condition. The measured signals transferred to images by chaotic map attractor noticeably shows the variations in different wear patterns. The second aspect of the technique is the smart diagnosis and prediction. To identify the manufacturing and tool wear status, the transferred images are applied to deep learning CNN. The tool life prediction is then carried out by the center of gravity for chaotic behavior coordinate system figure which is transferred and mapped through chaotic dynamic error by the Grey System Theory.
Scientific Breakthrough Products with this technique will be launch in 2020. It's expected to assist factories in enhancing their smart technology and competitiveness. In addition, the research team led by Professor Yau is the first to propose the research of Feature Extraction in Electrical and Mechanical System Fault with Fractional-order Chaos Dynamic Error Conversion. Currently, relevant researches have been published in international journals.
Industrial Applicability The Technology of Intelligent Fractional-order Chaos System for Dynamic Error Conversion Feature Extract has been applied to solve the processing states monitoring and prediction in Intelligent Machinery and Intelligent Manufacturing. It can enhance the international competitiveness that rely on domestic machine tool factory and traditional processing industry.