Online grade selection for weight measurements of composite sheets

1. A method, comprising:

measuring a first signal representative of an intensity in a spectral marker region within a selected spectral range and a second signal representative of an intensity in a common region within the selected spectral range from a measurement apparatus comprising at least a first sensor including a transmitter and a receiver of a composite sheet comprising a sheet material base layer and a high-z material in at least one of a coating on the sheet material base layer or as embedded particles within the sheet material base layer, wherein the measuring is performed while the composite sheet is online;

determining a relative signal level that compares a signal level of the first signal to a signal level of the second signal;

identifying a current composite sheet material grade for the composite sheet selected from a plurality of different composite sheet material grades using the relative signal level;

based on the current composite sheet material grade automatically choosing a current sensor calibration from a plurality of different sensor calibrations, and

determining at least one physical parameter for the composite sheet using the current sensor calibration.

2. The method of claim 1 , wherein the physical parameter comprises at least one of a weight per unit area, a thickness, or a density of the high-z material, or a weight per unit area, a thickness, or a density of the sheet material base layer.

3. The method of claim 1 , wherein the selected spectral range is within a visible spectral range or within an infrared (IR) spectral range.

4. The method of claim 1 , wherein the common region comprises a region in the selected spectral range between a peak for the sheet material base layer and the spectral marker region.

5. The method of claim 1 , further comprising using a first bandpass filter for selectively passing radiation in the spectral marker region and a second bandpass filter for selectively passing radiation in the common region.

6. The method of claim 1 , wherein the first sensor comprises an infrared (IR) or a visible light sensor, further comprising a second sensor comprising an x-ray sensor, wherein the physical parameter comprises a weight of the coating and a weight of the sheet material base layer.

7. The method of claim 1 , further comprising generating the plurality of different composite sheet material grades, comprising:

recording infrared (IR) spectral data or visible spectral data from a plurality of samples of the composite sheet;

grouping together respective ones of the plurality of samples into the plurality of different composite sheet material grades by identifying at least one of the-spectral marker region in the respective IR or visible spectral data, and

for each of the composite sheet material grades generating a ratio between an amplitude in their spectral marker-region with respect to an amplitude in the common region.

8. The method of claim 7 , wherein the recording IR spectral data or visible spectra data comprises Fourier transform-derived IR data from 1 to 20 microns.

9. The method of claim 1 , wherein the analyzing is performed entirely automatically.

10. The method of claim 9 , wherein machine learning or a classification algorithm is used for the identifying.

11. A measurement apparatus, comprising:

at least a first sensor comprising a transmitter and a receiver, and

a scanner head for moving the first sensor;

a computing device coupled to receive data from the first sensor, the computing device comprising a processor having an associated memory that stores a plurality of different relative signal level ranges at one or more selected spectral marker regions compared to at least one common region both being within a selected spectral range, each of the different relative signal level ranges corresponding to a composite sheet material grade and an associated sensor calibration;

the computing device for automatically:

measuring a first signal representative of an intensity in the spectral marker region and a second signal representative of an intensity in the common region of a composite sheet comprising a sheet material base layer and a high-z material in at least one of a coating on the sheet material base layer or as embedded particles within the sheet material base layer;

determining a current relative signal level that compares a current signal level of the first signal to a current signal level of the second signal;

identifying a current composite sheet material grade for the composite sheet selected from the composite sheet material grades using the current relative signal level;

based on the current composite sheet material grade automatically choosing a current sensor calibration from the sensor calibrations, and

determining at least one physical parameter for the composite sheet using the current sensor calibration.

12. The measurement apparatus of claim 11 , wherein the physical parameter comprises at least one of an area weight, a thickness, or a density of the high-z material, or an area weight, a thickness, or a density of the sheet material base layer.

13. The measurement apparatus of claim 11 , wherein the selected spectral range is within a visible spectral range or within an infrared (IR) spectral range.

14. The measurement apparatus of claim 11 , wherein the common region comprises a region in the selected spectral range between a peak for the sheet material base layer and the spectral marker region.

15. The measurement apparatus of claim 13 , further comprising a first bandpass filter for selectively passing radiation in the spectral marker regions and a second bandpass filter for selectively passing radiation in the common region.

16. The measurement apparatus of claim 11 , wherein the first sensor comprises an infrared (IR) or visible light sensor, further comprising a second sensor comprising x-ray sensor, wherein the physical parameter comprises a weight of both the coating and a weight of the sheet material base layer.

17. The measurement apparatus of claim 11 , wherein machine learning or a classification algorithm is used for the identifying.