492
T. Scherzer
0
20
40
60
80 100 120 140 160 180 200 220 240
0
20
40
60
80
100
Predicted Data
Actual Data
100 m/min
80 m/min
60 m/min
40 m/min
20 m/min
Conversion [%]
Time [sec]
12
13
14
15
16
17
18
Thickness [µm]
Fig. 21.9 Simultaneous monitoring of thickness and conversion of a clear acrylate coating at various
line speeds of a roll coating machine. Quantitative data were predicted with a PLS2 calibration
model. Gray lines represent reference values that were determined off-line by FTIR transmission
spectroscopy or a thickness gauge, respectively, after the end of the coating experiment. Reprinted
with permission from Ref. [24]. Copyright 2010 American Chemical Society
inclination of the web, local variation of the viscosity (e.g., due to temperature gradients) etc. In order to include the spatial distribution of the parameter of interest in
process control, two-dimensional monitoring of the process is required. This is the
domain of hyperspectral monitoring.
Traditionally, hyperspectral cameras have been mainly used for three-dimensional
or at least rather thick objects, which resulted in applications in waste sorting, food
monitoring (meat, fish, vegetables, fruits etc.) and the characterization of other objects
in agriculture (e.g., logs and boards), art conservation etc. However, today’s highclass hyperspectral cameras have sufficient sensitivity to monitor even thin samples
such as coatings, laminates, finished textiles (see paragraph 21.3.3.), printed conductive polymer layers, pages in medieval illuminated manuscripts [28] and similar twodimensional objects. Therefore, hyperspectral imaging was also used for monitoring
thickness, conversion and other parameters of coatings. With an adequate calibration, each of these parameters can be predicted quantitatively. However, the main
intention of the use of hyperspectral cameras in coating and lamination technology
is monitoring the spatial distribution of these parameters across the samples as well
as the detection of possible inhomogeneities.
T. Scherzer
0
20
40
60
80 100 120 140 160 180 200 220 240
0
20
40
60
80
100
Predicted Data
Actual Data
100 m/min
80 m/min
60 m/min
40 m/min
20 m/min
Conversion [%]
Time [sec]
12
13
14
15
16
17
18
Thickness [µm]
Fig. 21.9 Simultaneous monitoring of thickness and conversion of a clear acrylate coating at various
line speeds of a roll coating machine. Quantitative data were predicted with a PLS2 calibration
model. Gray lines represent reference values that were determined off-line by FTIR transmission
spectroscopy or a thickness gauge, respectively, after the end of the coating experiment. Reprinted
with permission from Ref. [24]. Copyright 2010 American Chemical Society
inclination of the web, local variation of the viscosity (e.g., due to temperature gradients) etc. In order to include the spatial distribution of the parameter of interest in
process control, two-dimensional monitoring of the process is required. This is the
domain of hyperspectral monitoring.
Traditionally, hyperspectral cameras have been mainly used for three-dimensional
or at least rather thick objects, which resulted in applications in waste sorting, food
monitoring (meat, fish, vegetables, fruits etc.) and the characterization of other objects
in agriculture (e.g., logs and boards), art conservation etc. However, today’s highclass hyperspectral cameras have sufficient sensitivity to monitor even thin samples
such as coatings, laminates, finished textiles (see paragraph 21.3.3.), printed conductive polymer layers, pages in medieval illuminated manuscripts [28] and similar twodimensional objects. Therefore, hyperspectral imaging was also used for monitoring
thickness, conversion and other parameters of coatings. With an adequate calibration, each of these parameters can be predicted quantitatively. However, the main
intention of the use of hyperspectral cameras in coating and lamination technology
is monitoring the spatial distribution of these parameters across the samples as well
as the detection of possible inhomogeneities.