Meaning
Spectroscopic data gathered from in-line extruder sensors often contain high-frequency noise from mechanical vibrations and electrical interference. This raw data can be smoothed using a savitzky golay filter, which fits local polynomial regressions to sequential windows of data points. It reduces noise while preserving the shape, height and position of the underlying spectral peaks.
The algorithm is used in color and composition sensors to ensure stable readings from the polymer melt.
Noise Reduction
Smoothing spectral scans prevents random fluctuations from being interpreted as changes in polymer composition or additive concentration. By applying the savitzky golay algorithm, the control system can isolate the real baseline shifts and absorption features of the material. This filtering improves the reliability of the chemometric models used to calculate the dosing ratio of pigments and modifiers.
It prevents the feeding systems from over-correcting in response to transient signal spikes.
Spectral Fidelity
Traditional moving average filters flatten sharp peaks and distort the resolution of the spectrum, which can hide subtle changes in polymer degradation or additive levels. The polynomial fitting used by the savitzky golay method matches the curvature of the peaks, maintaining the integrity of the data. This precision is required for accurate quantification of low-level additives such as slip agents or UV stabilizers.
Computation Speed
Process control computers require rapid signal processing to make adjustments in real time during continuous extrusion. Because the algorithm uses pre-calculated convolution coefficients, the filtering process takes very little time, allowing it to keep pace with high-speed sensor scans. This speed ensures that the feedback loop has zero latency.
