Condition Monitoring of Milling Tool Wear Based on Fractal Dimension of Vibration Signals

The development of flexible automation in the manufacturing industry is concerned with production activities performed by unmanned machining systems. A major topic relevant to metal-cutting operations is monitoring tool wear, which affects process efficiency and product quality, and implementing automatic tool replacements. In this paper, pattern recognition is described for the milling tool wear conditions by means of chaotic theory. Factors influencing the consistency of the calculated fractal dimension based on fractal dimension of vibration signals are analyzed. Angle domain tracing method is adopted during acquisition of vibration signals to minimize the effect from spindle speed. A new method for calculating the fractal unscale range is proposed in determining fractal dimension. The experiment results show that the fractal theory is leaded into monitoring field for milling tool wear to be practicable.