Piezoelectric Stress Wave Analysis in Rotary Die Scoring Operations
Piezoelectric stress wave monitoring detects cartonboard score cracking in real time by capturing high-frequency elastic strain transients at rotary die nips.
Wavelet Transform
Signal Decomposition
Mathematical analysis techniques decompose a complex time-domain signal into a series of localized waveforms to extract both frequency and temporal information simultaneously. The method known as wavelet transform is particularly useful for analyzing non-stationary signals, such as the transient bursts produced by material fractures or impact events. Unlike traditional Fourier analysis, which loses time information, this approach maps the signal across both time and frequency scales.
Frequency Resolution
The process involves scaling and shifting a mother wavelet along the input signal to measure the correlation between the signal and the wavelet shape. By adjusting the scale, the analysis can focus on high-frequency details over short time intervals or low-frequency trends over longer periods. This variable resolution allows for the precise location of the exact millisecond a fiber break occurs while still identifying the lower frequency vibration of the machinery.
Utilizing different mother wavelets allows the algorithm to be tuned to specific acoustic signatures, increasing the reliability of the defect classification.
Acoustic Characterization
In paper quality testing, researchers apply this mathematical method to the acoustic emission signals captured during tensile tests. The decomposition allows them to distinguish between different fracture mechanisms, such as matrix cracking, fiber pull-out, and fiber breakage. This classification helps in the formulation of stronger recycled paper grades.