Intelligent real-time monitoring of Computer Numerical Control tool wear based on a fractional-order chaotic self-synchronization system

Tool wear and damage are unavoidable in machines which operate for long periods. The main objective of this study was the prediction and understanding of tool wear ahead of time. To achieve this a real-time monitoring system was needed which incidentally also improves product yield and efficiency. Traditionally, tool wear and working life have been estimated by past experience. To monitor the state of the tool, we used a method employing fractional-order chaotic self-synchronization system matched with extension theory to analyze, extract, and measure tool vibration signals. The fractional-order Chen-Lee chaotic system was used to detect differences in micro vibrations, resulting from different degrees of tool wear, and these were introduced into the master and slave systems. The extreme changes produced by micro differences in the chaotic system, and matter element module design based on the extension theory, were used to accurately determine tool status. Chaos theory, wavelet packet analysis, and Fast Fourier transform were then used to compare the results. This method can reduce the number of sensors and the time required for real-time monitoring of expected life compared to previous determination methods where temperature and current diagnostics must be included.