Enhancing CNC Machining with Wavelet Analysis for Accurate Overcut Detection

In the realm of CNC machining, precision and efficiency are paramount, especially in complex applications like mold manufacturing.

However, challenges such as overcutting—an abnormal cutting phenomenon where the tool end intersects with the workpiece surface—pose significant risks,

including material waste, product rejection, and economic loss. This issue is particularly pronounced in the machining of free-form surfaces, 

where traditional monitoring techniques fall short. To address this, wavelet analysis emerges as a powerful tool,

offering time-frequency localization to detect and prevent overcutting in real-time.

Understanding Overcut in Free-Form Surface Machining

Overcut occurs due to sudden changes in cutting force, resulting in fluctuations in the motor current. Detecting these anomalies in real-time requires advanced signal analysis. Traditional methods like acoustic emission and vibration monitoring often struggle with the complexity of free-form surface machining. The intricate geometry of molds necessitates a more refined approach to signal processing, which is where wavelet analysis excels.

What is Wavelet Analysis?

Wavelet analysis builds on Fourier analysis, offering a dynamic time-frequency window that adjusts based on the characteristics of the signal. It employs a wavelet basis function to analyze signals across different scales and frequencies. By adapting the time window for high-frequency signals and expanding it for low-frequency ones, wavelet analysis provides a "focused scan" of the signal, enabling precise localization of anomalies like overcutting.

Application of Wavelet Analysis in Overcut Detection

Wavelet analysis can process motor current signals to detect overcutting during CNC machining. Using a magnetic balance Hall current sensor, the method ensures accurate signal capture without altering motor characteristics. The sensor detects fluctuations in motor current, converting these changes into signals suitable for wavelet transformation.

By decomposing the signal into various frequency scales, wavelet analysis isolates overcut features. For instance, small-scale (high-frequency) signals may not reveal overcutting clearly, but larger scales often highlight these features distinctly. This makes it possible to set thresholds for real-time monitoring, effectively identifying and localizing overcutting events in both the time and frequency domains.

Experimental Validation

Tests conducted on TRIAC ATC vertical machining centers validated the efficacy of wavelet analysis. Conditions such as tool diameter, cutting depth, spindle speed, and feed rate were controlled to simulate overcut scenarios. Results demonstrated that while time-domain signals alone were insufficient, wavelet decomposition revealed distinct overcut features, especially on the fourth scale. This underscores the utility of wavelet analysis in detecting and preventing machining errors.

Benefits of Wavelet Analysis in CNC Machining

Enhanced Precision: Wavelet analysis provides detailed insights into signal variations, ensuring accurate detection of overcutting.

Real-Time Monitoring: By localizing anomalies in both time and frequency, the method enables immediate corrective actions.

Adaptability: The technique’s ability to analyze signals across multiple scales makes it suitable for complex free-form surface machining.

Wavelet analysis represents a transformative approach to CNC machining, addressing critical challenges like overcutting with unmatched precision. By integrating this advanced signal processing technique, manufacturers can enhance the quality and reliability of their machining processes, particularly in the production of complex molds and free-form surfaces.