Patent Application
20170109895
This disclosure relates generally to measurement systems. More specifically, this disclosure relates to an apparatus and method for measuring haze of sheet materials or other materials using an off-axis detector.
Many transparent, translucent, or other non-opaque materials are produced in long webs or sheets. One characteristic of these types of materials is haze, which refers generally to the scattering of light passing through the materials. Haze typically reduces the contrast of objects viewed through the materials. For example, haze in a plastic sheet used in product packaging might reduce the clarity of lettering viewed through the plastic sheet. Low haze may be important or essential for certain applications, such as consumer electronics packaging or medical device packaging.
Haze measurements typically occur in laboratory settings. For example, a sample of a material can be positioned against an entrance port of an integrating sphere, which can measure the haze of the sample. However, conventional laboratory instruments for measuring haze are typically not suitable for use in a manufacturing or processing environment. Moreover, conventional laboratory instruments for measuring haze are typically contact-type devices, meaning the devices must be placed in physical contact with a material.
This disclosure provides an apparatus and method for measuring haze of sheet materials or other materials using an off-axis detector.
In a first embodiment, a method includes illuminating a material with first light along an optical path. The method also includes capturing an image of second light transmitted through the material using a first detector, where a first portion of the second light follows the optical path of the first light and a second portion of the second light diverges from the optical path. The method further includes capturing one or more measurements of third light using a second detector. The third light diverges from the optical path by a larger amount than the second portion of the second light, and the second detector is spaced apart from the first detector. In addition, the method includes determining one or more haze values associated with the material based on the image and the one or more measurements and at least one of storing and outputting the one or more haze values.
In a second embodiment, an apparatus includes at least one memory configured to store an image generated by illuminating a material with first light along an optical path. The image includes an image of second light transmitted through the material captured by a first detector, where a first portion of the second light follows the optical path of the first light and a second portion of the second light diverges from the optical path. The apparatus also includes at least one interface configured to receive one or more measurements of third light captured by a second detector that is spaced apart from the first detector. The third light diverges from the optical path by a larger amount than the second portion of the second light. The apparatus further includes at least one processor configured to determine one or more haze values associated with the material based on the image and the one or more measurements.
In a third embodiment, a system includes a light source configured to illuminate a material with first light along an optical path. The system also includes a first detector configured to capture an image of second light transmitted through the material, where a first portion of the second light follows the optical path of the first light and a second portion of the second light diverges from the optical path. The system further includes a second detector spaced apart from the first detector and configured to generate one or more measurements of third light diverging from the optical path by a larger amount than the second portion of the second light. In addition, the system includes an analyzer configured to determine one or more haze values associated with the material based on the image and the one or more measurements.
In a fourth embodiment, a non-transitory computer readable medium contains computer readable program code that, when executed by one or more processors, causes the one or more processors to obtain an image generated by illuminating a material with first light along an optical path. The image includes an image of second light transmitted through the material captured by a first detector, where a first portion of the second light follows the optical path of the first light and a second portion of the second light diverges from the optical path. The medium also contains computer readable program code that, when executed by the one or more processors, causes the one or more processors to obtain one or more measurements of third light captured by a second detector that is spaced apart from the first detector. The third light diverges from the optical path by a larger amount than the second portion of the second light. The medium further contains computer readable program code that, when executed by the one or more processors, causes the one or more processors to determine one or more haze values associated with the material based on the image and the one or more measurements.
Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.
For a more complete understanding of this disclosure, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:
In this example, the system 100 includes a light source 104 and optics 106 that are used to produce a collimated beam 108. The light source 104 represents any suitable source of illumination. For example, the light source 104 could include one or more monochrome or narrow-band sources, such as one or more light emitting diodes (LEDs) or lasers. The light source 104 could also include one or more polychrome sources or multiple narrow-band sources, such as multiple LEDs or lasers. The light source 104 could further include spectrally rich sources, such as xenon or other gas-discharge sources, incandescent sources, black body sources, or wide-band LEDs. The light source 104 could optionally include filters or other components for spectral shaping. The light source 104 could have continuous or pulsed operation, and any pulsing may be regular or irregular in its timing.
The optics 106 create the collimated beam 108 using the illumination provided by the light source 104. Any suitable optics 106 could be used, such as masks, lenses, mirrors, or prisms. The collimated beam 108 represents any suitable collimated beam of light. The collimated beam 108 could, for example, represent a beam of light with a circular cross-section having a diameter of at least 1 mm. The collimated beam 108 may have a radially symmetric intensity distribution and a small divergence angle (such as less than 1°). The collimated beam 108 may strike the material 102 at any suitable angle, such as 90°.
A light trap 110 helps to ensure that ambient light is substantially excluded from the measurement location of the material 102. The light trap 110 could represent, for example, a suitable arrangement of baffles or annular light traps that exclude ambient light while providing an unhindered path for the collimated beam 108. A housing 112 can support the components used to produce the collimated beam 108 and can also block ambient light.
Light from the collimated beam 108 strikes the material 102 and is transmitted through the material 102, emerging as transmitted light. The transmitted light includes light 114 traveling along the same general path as the collimated beam 108, meaning this light was not substantially scattered by the material 102. The transmitted light also includes light 116 traveling along a canonical path that diverges away from the path of the collimated beam 108, meaning this light was scattered by the material 102 to a more significant degree. The canonical path could have a subtending angle of at least 10° at its apex, and the canonical path could intersect the material 102 over a larger area than the collimated beam 108. Note that the amount of light 114 and the amount of light 116 vary depending on the haze of the material 102.
The light 114 and 116 strikes an image detector 118, which captures an image of the light 114 and 116. The image detector 118 could, for example, measure the intensity of the light directly incident on each pixel of the detector 118. The image detector 118 could include pixels directly in line with the collimated beam 108 to measure the light 114 and additional pixels to measure the light 116. The image detector 118 may include an imaging area that is large enough to capture most or all of the light 116 within the canonical path.
The image detector 118 includes any suitable image capturing device or devices, such as a charge-coupled device (CCD), a complementary metal oxide semiconductor (CMOS) device, or a charge injection device (CID). In particular embodiments, the image detector 118 includes a two-dimensional detector array, such as an array of monochrome or RGB detectors. Also, the image detector 118 could have sufficient dynamic range so that pixels are not saturated during image measurements (even if all light from the collimated beam 108 strikes the pixels measuring the light 114). Other components could also be used with the image detector 118, such as an arbitrary spectral filter. Note that the light striking the image detector 118 could be unfocussed, although standard detector micro-optics or other non-focusing optics could be used. Also note that when a pulsed light source 104 is used, the image detector 118 can be synchronized with the light source 104 to capture images at the appropriate times. Because the image detector 118 captures images of light traveling along the optical path of the collimated beam 108, the image detector 118 is said to represent an on-axis detector, which captures on-axis measurements of the light 114 and 116.
A light trap 120 and a housing 122 help to ensure that ambient light is substantially excluded from the measurement location of the material 102. The light trap 120 could exclude ambient light while providing an unhindered path for the light 114 and 116 to the detector 118. The housing 122 could also support the components within the housing.
As shown in
Haze in transparent or other materials can be caused by bubbles or other inclusions of different sizes, as well as by the total amount of bubbles or other inclusions that are present in the materials. The image detector 118 allows the determination of haze measurements in a univariate way by capturing images of the light 114-116. The addition of the off-axis detector(s) 124 allows an analysis that is at least bivariate. This bivariate analysis allows the system 100 to simultaneously detect the haze of the material 102 and the effect of the size of bubbles or other inclusions on the haze.
Each off-axis detector 124 includes any suitable light measuring device(s) or image capturing device(s), which could be the same as or different from the image detector 118. For example, each off-axis detector 124 could denote a zero dimensional (0D) imaging device having a single pixel, a one dimensional (1D) imaging device having a single row or a single column of pixels, or a two dimensional (2D) imaging device having at least one row and at least one column of pixels. An off-axis detector 124 may or may not measure focused light. In some embodiments, only an aggregate or average amount of light received by an off-axis detector 124 is used to measure haze, and the off-axis detector 124 or an external component that receives measurements from the off-axis detector 124 could identify the aggregate or average amount of light. While one off-axis detector 124 is shown here, multiple off-axis detectors 124 could also be used. When multiple off-axis detectors 124 are used, the outputs from those off-axis detectors 124 could be used individually or collectively, such as by averaging or accumulating the outputs from those off-axis detectors 124.
In some embodiments, each off-axis detector 124 is used in conjunction with a target 128. The target 128 can include a highly-reflective surface that is not in the light path of the collimated beam 108. The target 128 can help to direct additional light 126 towards the off-axis detector 124 for use in measuring the light 126. The target 128 includes any suitable structure having a reflective surface. To help to reduce or prevent light reflected from the target 128 impinging on the detector 118, there could be a light baffle or other structure 119 on at least one side of the detector 118, or the detector 118 can be geometrically arranged to minimize the effect of such reflections.
An analyzer 130 receives images captured by the image detector 118 and measurements from the off-axis detector(s) 124. The analyzer 130 analyzes this information to determine one or more haze measurements for the material 102. For example, as described in more detail below, the analyzer 130 could process a captured image by identifying an amount of light striking different regions of the image detector 118. As particular examples, the analyzer 130 could measure the amount of light in a central disc of the image and in one or more concentric regions around the disc, or the analyzer 130 could measure the amount of light in a specified area of the image and in one or more elliptical regions around or next to the specified area. These amounts of light can be used to calculate values such as raw haze, haze blur, and haze fuzz of the material 102. For instance, in some embodiments, measurements of light in different areas can be used to form one or more ratios, and the ratios can be used to calculate the haze measurements.
The analyzer 130 also uses the measurements from the off-axis detector(s) 124. For example, off-axis measurements from the off-axis detector(s) 124 can be combined with on-axis measurements associated with images from the image detector 118 to form two-dimensional or other multi-dimensional datasets. In some embodiments, the multi-dimensional datasets include (i) one or more ratios calculated using multiple regions in one or more images captured by the image detector 118 and (ii) one or more averaged or accumulated off-axis measurements calculated using measurements from the off-axis detector(s) 124. The multi-dimensional datasets can be used to compute haze measurements and optional auxiliary values. The auxiliary values could represent any suitable characteristics of the material 102, such as a typical size of inclusions in the material 102, a dispersion value for scattering of light by the material 102, or a strength of scattering of light by the material 102.
The calculation of the haze measurements and the optional auxiliary values could be done in any suitable manner. For example, a multi-dimensional dataset can be compared to at least one reference dataset to identify a haze measurement and an auxiliary value. The reference dataset can, for instance, be produced from a set of samples of known haze. There are various ways to produce a reference set and to identify a haze measurement and an optional auxiliary value from the reference set. As particular examples, a regular calibration using sample averages or a chemometric method using variations within a sample could be used. Also, as described below, the system could calculate directional haze measurements, meaning haze measurements that are calculated along different directions of the material 102. In these embodiments, there could be different reference datasets for different directions. Again, any suitable technique could be used to generate the different reference datasets for different directions.
In particular embodiments, the analyzer 130 uses a bivariate correlation developed in a laboratory setting to associate (i) ratios of light in different areas of obtained images and average/accumulated off-axis measurements with (ii) haze measurements and optional auxiliary values. The analyzer 130 can receive images from the image detector 118 and measurements from the off-axis detector 124, identify the ratios using the images, and identify averaged/accumulated off-axis measurements. The bivariate correlation can be used to convert this information into haze measurements and optional auxiliary values, which could be output from the analyzer 130.
Note that in this example, there is a single off-axis detector 124 shown in
The analyzer 130 includes any suitable structure for analyzing images and off-axis measurements to determine haze measurements. The analyzer 130 could, for example, include one or more processors 126 and one or more memories 128 storing instructions and data used, generated, or collected by the processors 126 (such as images captured by the image detector 118 and measurements from the off-axis detector 124). The analyzer 130 could also include one or more network interfaces 130 facilitating communication over one or more networks, such as an Ethernet interface.
Outputs from the analyzer 130 could be used in any suitable manner. For example, haze measurements and optional auxiliary values could be provided to a controller 138 in a manufacturing system that produces the material 102. The controller 138 could then adjust the production of the material 102 based on the haze measurements and optional auxiliary values. In this way, the haze measurements and optional auxiliary values provided by the analyzer 130 can be used to adjust pigments or process conditions (like temperature or pressure) used to produce the material 102. This may help to ensure that the haze of the material 102 stays within defined limits.
Note, however, that the system 100 could be used in other types of systems other than web or sheet manufacturing systems. For instance, the system 100 could be used as part of an inspection system for analyzing the material 102 during subsequent processing steps, such as before, during, or after printing operations or before, during, or after formation of the material into manufactured parts. The haze measurements and optional auxiliary values could also be provided to any other suitable destination 138, such as to a database or other memory for storage or to a display for graphical presentation to a user. The haze measurements and optional auxiliary values could be used for any other suitable purpose. Note that the analyzer 130 could represent a stand-alone component or could be incorporated into a component or system that uses the haze measurements and optional auxiliary values, such as when the analyzer 130 is implemented within the controller 132.
As shown in
In this example, the light 214 and 216 emerging from the material 202 is not provided directly to the image detector 218. Rather, the light 214 and 216 illuminates one side of a target surface 240, and the image detector 218 captures an image of the other side of the target surface 240. The target surface 240 can be optically thin so that the illumination by the light 214 and 216 on one side is minimally blurred or distorted when viewed from the other side. The target surface 240 may represent a flat surface that is generally parallel to the material 202. The target surface 240 could be formed from any suitable material(s), such as one or more highly diffuse translucent materials. To help to reduce or prevent light reflected from the target 228 impinging on the target surface 240, there could be a light baffle or other structure 219 on at least one side of the target surface 240, or the target 228 or the target surface 240 can be geometrically arranged to minimize the effect of such reflections.
As shown in
In this example, the light 314 and 316 emerging from the material 302 is not provided directly to the image detector 318. Rather, the light 314 and 316 is reflected off a target surface 340 to the image detector 318. The target surface 340 may represent a generally flat surface that is parallel or at a slight or moderate angle to the material 302. The target surface 340 reflects the light 314 and 316 to the image detector 318, allowing the image detector 318 to be placed in a location that does not obstruct the light 314 and 316. The target surface 340 could be formed from any suitable material(s), such as one or more highly reflective materials. To help to reduce or prevent light reflected from the target 328 impinging on the target surface 340, there could be a light baffle or other structure 319 on at least one side of the target surface 340, or the target 328 or the target surface 340 can be geometrically arranged to minimize the effect of such reflections.
Note that the measurements from the off-axis detector(s) 124, 224, 324 in each system could be processed in any suitable manner to identify some measure of the highly scattering light from the material 102. For example, if a 0D off-axis detector is used, the outputs of the off-axis detector can be used directly as estimates of the strongly-scattered light exiting the material 102. If a 1D or 2D off-axis detector is used, the outputs of the off-axis detector can be used to estimate focused or unfocussed light by summing the outputs from various regions of the detector. These regions could be defined statically or dynamically. As a particular example, a region can be defined based on the centroid of detected light, and a summation can be made in a symmetric or other area around the centroid.
Although
As shown in
Note that while the regions 402-406 are shown as being contiguous, there can optionally be annular or other spaces between these regions 402-406. Also note that while the regions 404-406 are shown as being annular, each of these regions could be replaced by a disc or other shape that includes more-inner regions. For example, the region 404 could be replaced by a disc that covers both the regions 402-404 in
In
Again, while the regions 452-456 are shown as being contiguous, there can optionally be annular or other spaces between these regions 452-456. Also, the regions 454-456 may or may not overlap, meaning the region 454 may or may not include the region 452 and the region 456 may or may not include the regions 452-454. Further, the sizes, shapes, and positions of the regions 452-456 may be static or dynamic, such as when the regions 452-456 can be dynamically centered around the centroid of the image 450, the maximum of the image 450, or the maximum of a smoothed version of the image 450. Also, characteristics of the regions 452-456 could be provided by an external source or calculated based on a radial falloff curve of the pixel values (which can be determined using various heuristics). Other or additional techniques could also be used to define the regions 452-456.
In
Once again, while the regions 482-486 are shown as being contiguous, there can optionally be annular or other spaces between these regions 482-486. Also, the regions 484-486 may or may not overlap, meaning the region 484 may or may not include the region 482 and the region 486 may or may not include the regions 482-484. Further, the sizes, shapes, and positions of the regions 482-486 may be static or dynamic, such as when the regions 482-486 can be dynamically centered around the centroid of the image 480, the maximum of the image 480, or the maximum of a smoothed version of the image 480. While the region 484 is shown as completely encompassing or including the region 482, the region 484 could be located only at one or more (but not all) sides of the region 482, and the same can be true for the region 486 with respect to the region 484 or the region 482. Also, characteristics of the regions 482-486 could be provided by an external source or calculated based on a radial falloff curve of the pixel values (which can be determined using various heuristics). Other or additional techniques could also be used to define the regions 482-486.
In some embodiments, to process the image 400, 450, or 480, an analyzer can add the pixel values in each region 402-406, 452-456, or 482-486 to produce a sum for that region. Pixels that straddle a boundary between two regions could be handled in various ways. For example, those pixels could be omitted from the analysis. As another example, each pixel could be included in the region where the pixel's center is located. As a third example, a pixel can be fractionally included in both regions based on the percentage of the pixel in each region. As a fourth example, weighting factors can be applied to the pixels in a region before summing them. Weighting factors can be used, for instance, to reduce the effect of pixels that are near the boundaries of a region or that are in more than one region. Weighting factors can also be used to increase the effect of pixels that are not close to a region boundary or that are in only one region.
The sums associated with the regions 402-406, 452-456, or 482-486 can then be used to calculate one or more initial haze measurements. For example, raw haze represents the amount of diffused light in at least one non-central region divided by the total amount of light. One example raw haze value can be calculated as:
where Sum (R1) represents the sum of pixel values in the region 402, 452, or 482, Sum(R2) represents the sum of pixel values in the region 404, 454, or 484, and Sum(R3) represents the sum of pixel values in the region 406, 456, or 486. Multiple raw haze values could be computed by omitting one of Sum(R2) and Sum(R3) in the numerator of Equation (1) or by using different definitions of the regions 402-406, 452-456, or 482-486. In particular embodiments, each raw haze value could be converted into a corrected raw haze value using one or more calibration curves, which can be obtained by taking measurements of raw haze using one or more standards of known haze.
Other initial haze measurements could include haze blur and haze fuzz. Haze blur represents a ratio of slightly diffused light to essentially direct light, which can be expressed as:
Haze fuzz represents a ratio of moderately diffused light to slightly diffused light, which can be expressed as:
Any other or additional initial haze measurements could be used here, such as other measurements based on ratios involving single regions or combinations of regions in the image 400, 450, or 480. Calibration curves could also be used to produce corrected haze blur and haze fuzz measurements.
These initial haze measurements can be useful, but as noted above haze can be caused by bubbles or inclusions of different sizes, as well as by the total amount of bubbles or other inclusions that are present in material. In some instances, the initial haze measurements above do not provide an accurate measure of the haze of the material 102 due to larger than expected bubbles or other inclusions in the material 102. These larger than expected inclusions cause greater off-axis scattering of the light from the collimated beam. By using one or more off-axis detectors to measure the off-axis scattering of the light from the collimated beam, the initial haze measurements above could be modified (such as based on laboratory or other known correlations), or the initial haze measurements could be output along with the off-axis measurements. In either case, an external controller or other system (or even human personnel) could use this data to make decisions regarding the material 102, such as how to modify a manufacturing or processing system.
Although
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In either case, the analysis of the material may be performed using images of multiple areas of the material (where the multiple light beams 504 or 604 are transmitted through the material). Also, when directional haze measurements are being determined, different areas of the images being analyzed could be divided into regions oriented in the same direction or in different directions.
In
When used with the technique in
Light 822 associated with a fifth (polychrome) collimated beam is divided by a dichroic mirror 824, which provides part of the light 822 to an unfocussed image detector 826. The other part of the light 822 is reflected off a mirror 828 and provided to another unfocussed image detector 830.
As can be seen in
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An image of light that has interacted with the material is captured at step 904, and at least one off-axis measurement of strongly scattered light is obtained at step 906. This could include, for example, capturing a monochrome or polychrome image of the light that has been transmitted through the sheet of material. The light can be received directly or indirectly from the material. This could also include receiving one or multiple off-axis measurements from one or more off-axis detectors. If multiple off-axis measurements are obtained, the measurements could be averaged, accumulated, or processed in any other suitable manner.
Multiple regions are identified in the captured image at step 908. This could include, for example, identifying static or dynamic regions in the image. The regions could include a central disc as well as outer annual regions (or larger discs) or a specified area and elliptical regions that may or may not include the specified area. However, any other suitable regions could be defined or used.
The regions in the image and the off-axis measurements are analyzed at step 910, and one or more haze values (and optionally one or more auxiliary values) are identified and used at step 912. This could include, for example, summing the values of the pixels in each region to determine a total sum for that region, where pixels spanning multiple regions could be handled in any suitable manner. This could also include using the sums associated with the different regions to calculate initial raw haze, haze blur, and haze fuzz values. This may further include using one or more calibration curves to adjust the initial haze measurements. In addition, this could include using reference datasets or other information to identify one or more final haze measurements and one or more optional auxiliary values based on the off-axis measurements. The optional auxiliary value(s) could represent any suitable characteristic(s) of the material, such as typical inclusion size or a dispersion value or a strength of scattering of light by the material. The haze measurements and auxiliary values could then be stored, output to a process controller or other destination, used to modify or control a production process, or used in any other suitable manner.
Although
In some embodiments, various functions described in this patent document are implemented or supported by a computer program that is formed from computer readable program code and that is embodied in a computer readable medium. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device.
It may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer code (including source code, object code, or executable code). The term “communicate,” as well as derivatives thereof, encompasses both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrase “associated with,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C.
The description in this patent document should not be read as implying that any particular element, step, or function is an essential or critical element that must to be included in the claim scope. Also, none of the claims is intended to invoke 35 U.S.C. §112(f) with respect to any of the appended claims or claim elements unless the exact words “means for” or “step for” are explicitly used in the particular claim, followed by a participle phrase identifying a function. Use of terms such as (but not limited to) “mechanism,” “module,” “device,” “unit,” “component,” “element,” “member,” “apparatus,” “machine,” “system,” “processor,” “processing device,” or “controller” within a claim is understood and intended to refer to structures known to those skilled in the relevant art, as further modified or enhanced by the features of the claims themselves, and is not intended to invoke 35 U.S.C. §112(f).
While this disclosure has described certain embodiments and generally associated methods, alterations and permutations of these embodiments and methods will be apparent to those skilled in the art. Accordingly, the above description of example embodiments does not define or constrain this disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this disclosure, as defined by the following claims.
