Methods of inspecting a curved object and inspection systems

Abstract

A method of inspecting a curved object is provided. The method includes directing a wavefront onto a surface of a curved object having at least one large scale curvature; acquiring at least one reflected image that includes a distorted reflection of the wavefront from the curved object; and determining distortion correction parameters from the large scale curvature(s) of the object. The method further includes at least one of: i) adjusting the reflected image(s) by correcting the distortion(s) caused by the large scale curvature(s) of the object to create an adjusted image; and analyzing the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object; or ii) analyzing the reflected image(s) using the distortion correction parameters; and adjusting a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object. Inspection systems for curved objects are also provided.

Description

METHODS OF INSPECTING A CURVED OBJECT AND INSPECTION SYSTEMSBACKGROUND

[0001] A selected physical attribute of a material can be analyzed to determine the uniformity of the material, which in turn can provide useful information regarding the appearance and functionality of the material in a particular product application. Methods for analyzing and determining uniformity have relied on pictorial standards and the judgment of human experts, but such qualitative methods lack precision and cannot be utilized in real-time as a product is manufactured.

[0002] Optical methods have been used to measure physical properties of materials in real-time; however, such methods are designed for flat objects. Developments to enable analysis of curved objects would be desirable.SUMMARY

[0003] Large field inspection methods and systems are described for the inspection of curved objects (e.g., painted automotive panels). These could be useful, for instance, in robotic automotive paint repair systems or by smaller automotive shops, which would eliminate the required task of inspecting each painted surface prior to releasing the car back to a customer. Traditional inspection systems either require multiple light sources to illuminate a relatively small area of the surface to be inspected and detect scatter of light from the surface that differs from the surrounding area, or large monitors to project image patterns off the surface under inspection while examining the straightness of the projected patterns. Both methods may be time and cost prohibitive due to the hardware required to perform the inspection over large surfaces. The methods and systems of at least certain embodiments disclosed herein enable large, curved surfaces to be inspected with only one or two images being captured by a stationary inspection system.

[0004] In a first aspect, a method of inspecting a curved object is provided. The method comprises directing a wavefront onto a surface of a curved object having at least one large scale curvature; acquiring at least one reflected image that comprises a distorted reflection of the wavefront from the curved object; and determining distortion correction parameters from the at least one large scale curvature of the object. The method further comprises at least one of: i) adjusting the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyzing the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object; or ii) analyzing the at least one reflected image using the distortion correction parameters; and adjusting a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

[0005] In a second aspect, an inspection system is provided. The inspection system comprises a light source configured to emit a wavefront at a curved object having at least one large scale curvature; and an imaging device configured to acquire at least one reflected image that comprises a distorted reflection of the wavefront from the curved object. The inspection system further comprises one or more processorsconfigured to determine distortion correction parameters from the at least one large scale curvature of the object; and at least one of i) adjust the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyze the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object; or ii) analyze the at least one reflected image using the distortion correction parameters; and adjust a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

[0006] The above Summary is not intended to describe each illustrated embodiment or every implementation of the present certain exemplary embodiments of the present disclosure. The Drawings and the Detailed Description that follow more particularly exemplify certain preferred embodiments using the principles disclosed herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The disclosure may be more completely understood in consideration of the following detailed description of various embodiments of the disclosure in connection with the accompanying figures, in which:

[0008] FIG. 1 is a flow chart of an exemplary method of inspecting a curved object, according to various embodiments disclosed herein;

[0009] FIG. 2A is a generalized schematic perspective view of an exemplary inspection system, according to various embodiments disclosed herein;

[0010] FIG. 2B illustrates a system for modifying a workpiece in which example embodiments can be implemented;

[0011] FIG. 3 is a schematic side view of curved panels and their dimensions in inches;

[0012] FIG. 4 is photographs of Sample Bend #1, Top Left: wavefront projected mottle and defect image. Top Right: dot pattern projected wavefront image. Bottom Left: difference image of top left and top right images used to correct curvature in panel. Bottom Right: distortion corrected image;

[0013] FIG. 5 is photographs of Sample Bend #3. Top left: wavefront projected mottle and defect image, Top right: dot pattern projected wavefront image. Bottom left: difference image of top left and top right images used to correct curvature in panel. Bottom right: distortion corrected image;

[0014] FIG. 6 is photographs of locations of variation in bulk curvature in Sample Bend #3. The ovals indicate regions where the roll bender made complete contact with the panel resulting in localized areas of higher curvature. By contrast, the area highlighted by the rectangle had a business card attached to the black of the panel, which resulted in an impression in the panel;

[0015] FIG. 7 is photographs of a distortion correction region of a convex panel containing a dust nib in an upper region of the image and polished area at a lower region, of each of Sample Bend #1 through Sample Bend #5, with the original image in the top row and the distortion corrected image in the bottom row; and

[0016] FIG. 8 is photographs of a distortion correction region of a concave panel containing a dust nib in an upper region of the image and polished area at a lower region, of each of Sample Bend #6 and Sample Bend #7, with the original image in the top row and the distortion corrected image in the bottom row.

[0017] FIG. 9 illustrates an inspection system in an example network architecture.

[0018] FIGS. 10-12 illustrate example computing devices that can be used in embodiments herein.

[0019] In the drawings, like reference numerals indicate like elements. While the above-identified drawings, which may not be drawn to scale, set forth various embodiments of the present disclosure, other embodiments are also contemplated, as noted in the Detailed Description. In all cases, this disclosure describes the presently disclosed disclosure by way of representation of exemplary embodiments and not by express limitations. It should be understood that numerous other modifications and embodiments can be devised by those skilled in the art, which fall within the scope and spirit of this disclosure.DETAILED DESCRIPTION

[0020] For the following Glossary of defined terms, these definitions shall be applied for the entire application, unless a different definition is provided in the claims or elsewhere in the specification.Glossary

[0021] Certain terms are used throughout the description and the claims that, while for the most part are well known, may require some explanation. It should be understood that:

[0022] As used herein, “majority” refers to greater than 50% of a total 100% of something (e.g., amount, area, volume, etc.).

[0023] As used herein, “minority” refers to less than 50% of a total 100% of something (e.g., amount, area, volume, etc.).

[0024] As used herein, “length” refers to the longest dimension of an object or area on a surface of an object.

[0025] As used herein, “large scale curvature” refers to a curvature that has a length that is at least 4 times greater than a length of a localized variation in textures. Typically, the length is at most 20,000 times greater than a length of a localized variation in textures.

[0026] As used herein, “adjacent” encompasses both in direct contact (e.g., directly adjacent) and having one or more intermediate layers present between the adjacent materials.

[0027] As used herein, “incident” with respect to light refers to the light falling on or striking a material.

[0028] The term “film” or “layer” refers to a single stratum within a multilayer film.

[0029] The term “substrate” encompasses films and layers, including stmctured films / layers.

[0030] By using terms of orientation such as “atop”, “on”, “over,” “covering”, “uppermost”, “underlying” and the like for the location of various elements in the disclosed coated articles, we refer to the relative position of an element with respect to a horizontally -disposed, upwardly -facing substrate. However, unless otherwise indicated, it is not intended that the substrate or articles should have any particular orientation in space during or after manufacture, or in interpreting the claims.

[0031] As used herein, “reflectance” is the measure of the proportion of light or other radiation striking a surface at normal incidence which is reflected off it. Reflectivity typically varies with wavelength and is reported as the percent of incident light that is reflected from a surface (0 percent - no reflected light, 100 - all light reflected). Reflectivity and reflectance are used interchangeably herein.

[0032] As used herein, “reflective” and “reflectivity” refer to the property of reflecting light or radiation, especially reflectance as measured independently of the thickness of a material.

[0033] The terms “about” or “approximately” with reference to a numerical value or a shape means + / - five percent of the numerical value or property or characteristic, but expressly includes the exact numerical value.

[0034] The term “substantially” with reference to a property or characteristic means that the property or characteristic is exhibited to a greater extent than the opposite of that property or characteristic is exhibited. For example, a substrate that is “substantially” transparent refers to a substrate that transmits more radiation (e.g., visible light) than it fails to transmit (e.g., absorbs and reflects). Thus, a substrate that transmits more than 50% of the visible light incident upon its surface is substantially transparent, but a substrate that transmits 50% or less of the visible light incident upon its surface is not substantially transparent.

[0035] As used in this specification and the appended embodiments, the singular forms “a”, “an”, and “the” include plural referents unless the content clearly dictates otherwise. Thus, for example, reference “a compound” includes a mixture of two or more compounds. As used in this specification and the appended embodiments, the term “or” is generally employed in its sense including “and / or” unless the content clearly dictates otherwise.

[0036] Unless otherwise indicated, all numbers expressing quantities or ingredients, measurement of properties and so forth used in the specification and embodiments are to be understood as being modified in all instances by the term “about.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the foregoing specification and attached listing of embodiments can vary depending upon the desired properties sought to be obtained by those skilled in the art utilizing the teachings of the present disclosure. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claimed embodiments, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

[0037] Various exemplary embodiments of the disclosure will now be described. Exemplary embodiments of the present disclosure may take on various modifications and alterations without departing from the spirit and scope of the present disclosure. Accordingly, it is to be understood that the embodiments of the present disclosure are not to be limited to the following described exemplary embodiments but is to be controlled by the limitations set forth in the claims and any equivalents thereof.Method of Inspecting a Curved Article

[0038] In a first aspect, a method of inspecting a curved article is provided. The method comprises:

[0039] directing a wavefront onto a surface of a curved object having at least one large scale curvature;

[0040] acquiring at least one reflected image that comprises a distorted reflection of the wavefront from the curved object;

[0041] determining distortion correction parameters from the at least one large scale curvature of the object; and at least one of:

[0042] i) adjusting the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyzing the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object; or

[0043] ii) analyzing the at least one reflected image using the distortion correction parameters; and adjusting a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

[0044] As used herein, the term “localized variations in small scale textures” refers to a difference in appearance or three-dimensional characteristic of a minority portion of the surface of an object as compared to the appearance or three-dimensional characteristic of a majority portion of the surface of the object. Localized variations are expressly contemplated to include for instance and without limitation, an area of “orange peel” texture adjacent to a majority smooth surface, a smooth area adjacent to a majority rough surface, a scratch, and a dust nib. In some cases, a localized variation in small scale texture is a defect in or on the surface of the object.

[0045] FIG. 1 is a flow chart of such a method, including Operation 110: Directing a wavefront onto a surface of a curved object having at least one large scale curvature; Operation 120: Acquiring at least one reflected image that comprises a distorted reflection of the wavefront from the curved object; and Operation 130: Determining distortion correction parameters from the at least one large scale curvature of the object. It is to be understood that there is more than one suitable way to use the reflected image and / or distortion correction parameters, as described below.

[0046] In one embodiment, the method further comprises Operation 140i: Adjusting the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and Operation 150i: Analyzing the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object. Correcting for distortions present in the system is performed by imaging a known pattern. The resulting pattern is compared to the original and a vector map is defined to correct the position of various regions of the image. The pattern may also contain a known spacing of dots, lines, or a grid from which a vector map can be determined from the distorted image to ensure the dots, lines, or grid is equally spaced in the processed image. Many approaches can be taken for analyzing the small scale textures that may be contained within the image. For point based defects, local or static thresholding methods could be applied to detect the present of bright or dark defects. For texture based analysis, the image could be broken into smaller, equally sized regions called patches which are analyzed to determine the severity of the texture present in each patch, the statistics calculated can include the mean, median, chi-square,standard deviation, variance, range, or interquartile range of the pixel intensities measured within a patch or any combination thereof. The aspect ratio of the patches could be square or rectangular to aide in the detection of certain defects. Alternatively patches of various geometries can be used and may include but not be limited to triangles, hexagons, or circles. Processing of the adjusted image with a Fourier transform filter, wavelets, or convolution filters can be beneficial to isolating a particular texture before its severity is quantified. The methods for calculating the uniformity of regions within an image disclosed in U.S. Patent No. 9,841,383 (Ribnick et al.) or U.S. Application Publication No. 2022 / 0011238 (Caruso et al.) may also be applied.

[0047] Optionally, at least one reflected image is adjusted by correcting the distortion(s) caused by at least one large scale curvature of the object to create an adjusted image. Such a method further comprises analyzing the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object.

[0048] In an alternate embodiment, the method further comprises Operation 140ii: Analyzing the at least one reflected image using the distortion correction parameters; and Operation 150ii: Adjusting a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object. By mapping the distortion in the collected image, the grid-based algorithm can be adjusted such that each patch would measure over the same area of the curved object. For example, if the distortion caused by the object’s curvature caused the image to shrink locally be 50%, the grid size for that patch could be reduced by 50% to ensure the measured area is consistent across the object and is independent of the object’s curvature. The patch geometry may also be distorted to match the distortion cause by the curvature of the curved object, for instance the patch could be modified to match the pincushion, barrel, or keystone distortion in the image. The statistics calculated in each patch can include the mean, median, chi-square, standard deviation, variance, range, or interquartile range of the pixel intensities measured within a patch or any combination thereof. Additionally, the filters applied to each region could be adjusted to ensure their scale is maintained relative to the curved object.

[0049] Often, reflected images are acquired using an imaging device, either directly or off a surface such as a screen. When a reflected image is projected on a screen, in some cases the screen is a diffuse screen, which tends to provide a more even distribution of light in the reflected image.

[0050] Optionally, at least one reflected image is analyzed using the distortion correction parameters. Such a method further comprises adjusting a grid algorithm to rate any localized variations in small scale textures that may be present on the surface of the curved object.

[0051] In some embodiments, determining distortion correction parameters from the large scale curvature(s) of the object comprises: directing a known patterned wavefront that includes a defined pattern onto the surface of the curved object; acquiring one of the at least one reflected images, which comprises a distorted reflection of the defined pattern; and identifying the one or more distortions caused by the at least one large scale curvature of the object. In select embodiments, the wavefront and the known patterned wavefront directed at the curved surface for all of the reflected images are identical. For instance, optionally the wavefront and the known patterned wavefront follow the same optical path orcomprise identical spherical wavefronts emanating from different places. The defined pattern is not particularly limited because it is known, e.g., could be regular or irregular. Often, the defined pattern comprises dots or lines. One way of providing a defined pattern is by employing a mask from which a patterned wavefront is generated.

[0052] In certain embodiments, determining distortion correction parameters from the large scale curvature(s) of the object comprises using a digital model (e.g., CAD model) of the surface to correct the distortions caused by large scale curvature of the object. Multiple formats of CAD model may be available, including STL and OBJ.

[0053] In certain embodiments, determining distortion correction parameters from the large scale curvature(s) of the object comprises measuring the large scale curvature of the surface by profilometry. The type of profilometry is not particularly limited, and may include a stylus profilometer, optical profilometry (e.g., using a line profilometer or a 3D surface scanner), confocal profilometry, and other profilometry techniques known to those skilled in the art.

[0054] In select embodiments, the method further comprises identifying a location of at least one localized variation in small scale texture.

[0055] A uniformity algorithm may be applied to evaluate the severity of the localized variations of texture in contrast associated with the size scale(s) and feature(s) selected by an image processing method. For example, the computerized inspection systems may provide real-time feedback to users, such as process engineers, within manufacturing plants regarding the presence of non-uniformities and their severity, which can allow the users to quickly respond to an emerging non-uniformity by adjusting process conditions to remedy a problem without significantly delaying production or producing large numbers of unusable components. The computerized inspection system may apply algorithms to compute the severity level by ultimately assigning a rating label for the non-uniformity (e.g., “good” or “bad”) or by producing a measurement of non-uniformity severity of a given sample on a continuous scale or more accurately sampled scale.

[0056] By viewing the surface of the curved object in reflection, in some embodiments polarization effects can be used to optimize reflections from selected layers of the curved object, as well as enable the inspection of layers that reside above opaque layers. In reflective geometry, the exact angle of incidence does not affect the sensitivity of the measurement to variations in surface slope, but by choosing the polarization state of the incident beam (or analyzing the reflected beam with a polarizer), unwanted surface reflections within stacked laminates can be eliminated, leaving only reflections from the selected surface of the curved object under test.Inspection System

[0057] In a second aspect, an inspection system is provided. The inspection system comprises:

[0058] a light source configured to emit a wavefront at a curved object having at least one large scale curvature;

[0059] an imaging device configured to acquire at least one reflected image that comprises a distorted reflection of the wavefront from the curved object; and

[0060] one or more processors configured to determine distortion correction parameters from the at least one large scale curvature of the object; and at least one of: i) adjust the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyze the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object; or ii) analyze the at least one reflected image using the distortion correction parameters; and adjust a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

[0061] Referring to FIG. 2A, a generalized schematic perspective view of an exemplary inspection system 200 is provided, according to certain embodiments disclosed herein. The system 200 includes a light source 202 configured to emit a wavefront 203, 205 (only marginal rays shown for clarity) at a curved object 230. In certain embodiments, the wavefront is a known (e.g., predetermined) wavefront. Any suitable light source may be employed, for instance and without limitation, a spatially coherent light source, a point light source, or a polarized point light source. In select embodiments, the light source 202 comprises a programmable projector equipped with an LED light source, collimation optics, and a projection lens.

[0062] The system 200 also includes an imaging device 204 configured to acquire at least one reflected image 240 that comprises a distorted reflection of the wavefront 213, 215 (only marginal rays shown for clarity) from the curved object 230. Any suitable equipment may be employed that captures an image, for instance and without limitation, a red, blue, and green (RGB) camera, a black and white (B&W) camera, or a three-dimensional (3D) image sensor. In certain embodiments, the imaging device 204 comprises a camera (either RGB or B&W). Optionally, the system 200 also includes a screen 212 onto which the reflected image(s) 240 are projected. As noted above, a diffuse screen may be suitable when a screen is employed.

[0063] The system 200 further includes one or more processors 206 configured to determine distortion correction parameters from the at least one large scale curvature of the curved object 230. It is expressly contemplated that all processing operations and portions of operations may be performed by any number of separate processors, such as a single processor, two processors, three processors, four processors, and so forth.

[0064] Optionally, the system 200 includes a mask 210 from which a patterned wavefront is generated. In the embodiment depicted in FIG. 2, the mask 210 is positioned between the light source 202 and the curved object 230, but this is not required. Alternatively, the mask can be disposed between a light source and a projection lens containing an aperture, then light from the light source is passed through the mask and the aperture in the lens forms a spatially coherent source by closing the projection lens aperture. For simpler systems, a colored mask may be used in conjunction with a color camera. In this system a known pattern would be encoded in a wavelength range that would be detected by only a portion of a color camera, for example red. The other portion of the wavelength range, for example blue, would contain anunpattemed wavefront. The single color image would be split where the red channel would map the distortion of the image caused by the part curvature and the blue channel would contain uniformity information of the part. The red channel of the image would then be used to correct the distortion present in the blue channel. In some cases, the mask includes a defined pattern, for instance a pattern that comprises dots or lines, such as forming a grid. Use of a pattern may be advantageous because the position of features of the pattern in the reflected wavefront 213, 215 differs due to the large scale curvature of the curved object 230. The difference in position may be used to correct for the effect of the large scale curvature on the entirety of the reflected wavefront 213, 215. Alternatively to a mask, a known wavefront that includes a defined pattern may be provided by a programmable pattern. For certain systems, having a programmable pattern can be preferred. Control of these patterns can be achieved through the use of a Digital Micromirror Display (DMD), a Spatial Light Modulator (SLM), or a galvo- based laser scanner where the output power or position of the laser can be modulated. For the DMD and SLM systems, either a spatially coherent source can be used directly with the image generation units or a spatially incoherent source may be used in conjunction with a lens and aperture to form a spatially coherent source after the image is formed. Alternatively, for systems where the shape of the curved object (e.g., a part) is known, either through the use of a CAD model of the curved object or measured directly by profilometry methods, the system can be optically modeled to correct for distortions present in the image collected by the camera. To apply this approach, the shape of the wavefront, the shape of the curved object, and the geometry of the imaging system should be known. The distortion can be optically mapped using vector transform of the incident rays on each location of the part. The deflected rays can then be mapped to a location on the collected image. The methods for correcting the distortion that have been previously described can be used to correct the distortion present in the collected image.

[0065] Methods and systems described herein are suitable for inspecting a curved object, which optionally has a radius of curvature of up to 205 millimeters. Typically, an external surface of the curved object comprises an at least partially reflective surface, to be appropriate for inspection by methods according to the present description. In some cases, the curved object comprises an opaque surface (e.g., a painted surface).

[0066] In some embodiments, the inspection system 200 further includes an optional polarizer 250, which polarizes the light 203, 205 emitted from the light source 202 before the emitted light 203, 205 contacts the curved object 230.

[0067] In one embodiment, the reflected wavefront 213, 215 may be at least be partially polarized after reflecting from the surface 232 of the curved object 230. In some embodiments, an optional optical component 260 such as a lens array may be used to condense the reflected wavefront 213, 215 prior to capture of the reflected image(s) 240. In some embodiments, the optical component 260 may be used to analyze at least one of the reflected wavefront 213, 215 to, for example, adjust the polarization of the wavefront, reduce undesirable background reflections from the surface 232 of the curved object 230, and the like.

[0068] Optionally, a digital model (not shown) may be included in the system 200, such as a digital model accessed by processor(s) 206. The digital model is of at least the surface of the curved object 230 to provide information regarding the large scale curvature of the curved object 230 without having to image or measure the curvature separately.

[0069] FIG. 2B illustrates an inspection system for modifying a workpiece in which example embodiments can be implemented. The inspection system 200B includes main components of a light source 202, an imaging device 204, and one or more processors 206. The light source, imaging device, and processor(s) all as described above with respect to FIGS. 1 and / or 2A.

[0070] Inspection system 200B is illustrated in FIG. 2B as in communication with a data store 250. However, it is expressly contemplated that, in some embodiments, data store 250 may be local to, or integrated into inspection system 200B. Similarly, inspection system 200B is illustrated as projecting to a display 220. However, it is expressly contemplated that inspection system 200B may be integrated into a processor of a device that includes display 220. The inspection system 200B may be implemented by one or more suitable computing devices in communication with each of these main components.

[0071] Optional units include one or more of the following: a defined pattern 208, a mask 210, a screen 212, and a GUI (graphical user interface) generator 214. The defined pattern, mask, and screen are all as described above with respect to FIGS. 1 and / or 2 A. The GUI generator 214 may be configured to send information to a display 220. The GUI generator 214 may generate a graphical user interface for display on a display component 220 based on some or all of the information gathered or generated by the processor 206. Suitable displays include for instance and without limitation, a computer screen, a smart phone, or some other user device.

[0072] Other units 216 may further be included in the inspection system 200B.

[0073] In some cases, an optional separate analyzer 270 is present. The analyzer 270 is configured to analyze a reflected image (e.g., using distortion correction parameters) and / or to analyze an adjusted image (e.g., for localized variations in small scale textures). The analyzer may be in communication with the inspection system 200B, the data store 250, and an optional adjustor 280. The optional separate adjustor 280 is configured to adjust at least one reflected image (e.g., by correcting the distortion(s) caused by the large scale curvature of the object) and / or to adjust a grid algorithm (e.g., to rate localized variations in small scale textures that may be present on the surface of the curved object).

[0074] The processor 206 has the functionality of receiving and sending communicable information to and from other devices. This may be done through an application program interface, for example, such that the processor 206 can receive and communicate with any units and / or models within each of the inspection system 200B, the data store 250, the analyzer 270, and the adjustor 280.

[0075] The data store 250 is configured to communicate with the inspection system 200B, the optional analyzer 270, and the optional adjustor 280. The data store 250 may be local to the processor 206 or may be accessible through a cloud-based network. Similarly, while the processor 206 is illustrated in FIG. 2B as local to the inspection system 200B, it is expressly contemplated that the processor 206 may be remotefrom the inspection system 200B and may receive signals, and send commands, using a wireless or cloudbased network.

[0076] Optionally, the data store 250 includes a digital model of the curved object 252. Other optional units in the data store 250 include a grid algorithm 254 and distortion correction parameters 256. The data store 250 optionally further comprises an information database 258 that contains any additional relevant information for access by any of the units in the data store 250 or in the processor 206.

[0077] Other units 260 may further be included in the data store 250.

[0078] FIG. 9 illustrates an inspection system architecture. As an example, architecture 9500 can provide computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various embodiments, remote servers can deliver the services over a wide area network, such as the internet, using appropriate protocols. For instance, remote servers can deliver applications over a wide area network and they can be accessed through a web browser or any other computing component. Software or components shown or described in the present disclosure as well as the corresponding data, can be stored on servers at a remote location. The computing resources in a remote server environment can be consolidated at a remote data center location or they can be dispersed. Remote server infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user. Thus, the components and functions described herein can be provided from a remote server at a remote location using a remote server architecture. Alternatively, they can be provided by a conventional server, installed on client devices directly, or in other ways.

[0079] In the example shown in FIG. 9, some items are similar to those shown in earlier figures. FIG. 9 specifically shows that a processor 9510 can be located at a remote server location 9502. Therefore, a computing device 9520 accesses the processor 9510 through the remote server location 9502. A user 9550 can use the computing device 9520 to access user interfaces 9522 as well. For example, a user 9550 may be a user wanting to check on the progress of inspecting a curved object while sitting in a parking lot, and interacting with an application on the user interface 9522 of their smartphone 9520, or laptop 9520, or other computing device 9520, e.g., an augmented reality (AR) device such as AR glasses.

[0080] FIG. 9 shows that it is also contemplated that some elements of systems described herein are disposed at a remote server location 9502 while others are not. By way of example, each of a data store 9530 and an inspection system 9560 can be disposed at a location separate from the location 9502 and accessed through the remote server at location 9502. Regardless of where it is located, the data store 9530 can be accessed directly by a computing device 9520, through a network (either a wide area network or a local area network), hosted at a remote site by a service, provided as a service, or accessed by a connection service that resides in a remote location. Also, the data can be stored in substantially any location and intermittently accessed by, or forwarded to, interested parties. For instance, physical carriers can be used instead of, or in addition to, electromagnetic wave carriers. This may allow a user 9550 to interact with the processor 9510 through their computing device 9520.

[0081] It will also be noted that the elements of systems described herein, or portions of them, can be disposed on a wide variety of different devices. Some of those devices include servers, desktop computers, laptop computers, imbedded computer, industrial controllers, tablet computers, or other mobile devices, such as palm top computers, cell phones, smart phones, multimedia players, personal digital assistants, etc.

[0082] FIGS. 10-12 illustrate example devices that can be used in the embodiments shown in previous Figures. FIG. 10 illustrates an example mobile device that can be used in the embodiments shown in previous Figures. FIG. 10 is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as either a worker’s device or a supervisor’s device, for example, in which the present system (or parts of it) can be deployed. For instance, a mobile device can be deployed in the operator compartment of computing device for use in generating, processing, or displaying the data.

[0083] FIG. 10 provides a general block diagram of the components of a mobile cellular device 1016 that can run some components shown and described herein. The mobile cellular device 1016 interacts with them or runs some and interacts with some. In the device 1016, a communications link 1013 is provided that allows the handheld device to communicate with other computing devices and under some embodiments provides a channel for receiving information automatically, such as by scanning. Examples of communications link 1013 include allowing communication though one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks.

[0084] In other examples, applications can be received on a removable Secure Digital (SD) card that is connected to an interface 1015. The interface 1015 and communication links 1013 communicate with a processor 1017 (which can also embody a processor) along a bus 1019 that is also connected to a memory 1021 and input / output (I / O) components 1023, as well as clock 1025 and location system 1027.

[0085] I / O components 1023, in one embodiment, are provided to facilitate input and output operations and the device 1016 can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I / O components 1023 can be used as well.

[0086] The clock 1025 illustratively comprises a real time clock component that outputs a time and ate.It can also provide timing functions for the processor 1017.

[0087] Illustratively, the location system 1027 includes a component that outputs a current geographical location of the device 1016. This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. It can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions.

[0088] A memory 1021 stores operating system 1029, network settings 1031, applications 1033, application configuration settings 1035, data store 1037, communication drivers 1039, andcommunication configuration settings 1041. The memory 1021 can include all types of tangible volatile and non-volatile computer-readable memory devices. It can also include computer storage media (described below). Memory 1021 stores computer readable instructions that, when executed by the processor 1017, cause the processor to perform computer-implemented steps or functions according to the instructions. Processor 1017 can be activated by other components to facilitate their functionality as well. It is expressly contemplated that, while a physical memory store 1021 is illustrated as part of a device, that cloud computing options, where some data and / or processing is done using a remote service, are available.

[0089] FIG. 11 shows that the device can also be a smart phone 1171. The smart phone 1171 has a touch sensitive display 1173 that displays icons or tiles or other user input mechanisms 1175. Mechanisms 1175 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, the smart phone 1171 is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone. Note that other forms of the devices are possible. However, while FIG. 11 illustrates an embodiment where a device 1100 is a smart phone 1171, it is expressly contemplated that a display may be presented on another comping device.

[0090] FIG. 12 is one example of a computing environment in which elements of systems and methods described herein, or parts of them (for example), can be deployed. With reference to FIG. 12, an example system for implementing some embodiments includes a general-purpose computing device in the form of a computer 1210. Components of the computer 1210 may include, but are not limited to, a processing unit 1220 (which can comprise a processor), a system memory 1230, and a system bus 1221 that couples various system components including the system memory to the processing unit 1220. The system bus 1221 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Memory and programs described with respect to systems and methods described herein can be deployed in corresponding portions of FIG. 12.

[0091] The computer 1210 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by the computer 1210 and includes both volatile / nonvolatile media and removable / non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile / nonvolatile and removable / non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer 1210. Communication media may embody computer readable instructions, data structures, program modules or other data in a transport mechanismand includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0092] The system memory 1230 includes computer storage media in the form of volatile and / or nonvolatile memory such as read only memory (ROM) 1231 and random-access memory (RAM) 1232. A basic input / output system 1233 (BIOS) containing the basic routines that help to transfer information between elements within the computer 1210, such as during start-up, is typically stored in ROM 1231. RAM 1232 typically contains data and / or program modules that are immediately accessible to and / or presently being operated on by processing unit 1220. By way of example, and not limitation, FIG. 12 illustrates an operating system 1234, application programs 1235, other program modules 1236, and program data 1237.

[0093] The computer 1210 may also include other removable / non-removable and volatile / nonvolatile computer storage media. By way of example only, FIG. 12 illustrates a hard disk drive 1241 that reads from or writes to non-removable, nonvolatile magnetic media, nonvolatile magnetic disk 1252, an optical disk drive 1255, and nonvolatile optical disk 1256. The hard disk drive 1241 is typically connected to the system bus 1221 through a non-removable memory interface such as interface 1240, and optical disk drive 1255 is typically connected to the system bus 1221 by a removable memory interface, such as interface 1250.

[0094] Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (e.g., ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0095] The drives and their associated computer storage media discussed above and illustrated in FIG. 12, provide storage of computer readable instructions, data structures, program modules and other data for the computer 1210. In FIG. 12, for example, a hard disk drive 1241 is illustrated as storing operating system 1244, application programs 1245, other program modules 1246, and program data 1247. Note that these components can either be the same as or different from operating system 1234, application programs 1235, other program modules 1236, and program data 1237.

[0096] A user may enter commands and information into the computer 1210 through input devices such as a keyboard 1262, a microphone 1263, and a pointing device 1261, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite receiver, scanner, or the like. These and other input devices are often connected to the processing unit 1220 through a user input interface 1260 that is coupled to the system bus but may be connected by other interface and bus stmctures. A visual display 1291 or other type of display device is also connected to the system bus 1221 via an interface, such as a video interface 1290. In addition to the monitor, computers may also include other peripheral output devices such as speakers 1297 and printer 1296, which may be connected through an output peripheral interface 1295.

[0097] The computer 1210 is operated in a networked environment using logical connections, such as a Local Area Network (LAN) or Wide Area Network (WAN) to one or more remote computers, such as a remote computer 1280.

[0098] When used in a LAN networking environment, the computer 1210 is connected to the LAN 1271 through a network interface or adapter 1270. When used in a WAN networking environment, the computer 1210 typically includes a modem 1272 or other means for establishing communications over the WAN 1273, such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device. FIG. 12 illustrates, for example, that remote application programs 1285 can reside on a remote computer 1280.

[0099] In the present detailed description of the preferred embodiments, reference is made to the accompanying drawings, which illustrate specific embodiments in which the invention may be practiced. The illustrated embodiments are not intended to be exhaustive of all embodiments according to the invention. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention. The detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims.

[0100] The techniques of this disclosure may be implemented in a wide variety of computer devices, such as servers, laptop computers, desktop computers, notebook computers, tablet computers, hand-held computers, smart phones, and the like. Any components, modules or units have been described to emphasize functional aspects and do not necessarily require realization by different hardware units. The techniques described herein may also be implemented in hardware, software, firmware, or any combination thereof. Any features described as modules, units or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. In some cases, various features may be implemented as an integrated circuit device, such as an integrated circuit chip or chipset. Additionally, although a number of distinct modules have been described throughout this description, many of which perform unique functions, all the functions of all of the modules may be combined into a single module, or even split into further additional modules. The modules described herein are only exemplary and have been described as such for better ease of understanding.

[0101] If implemented in software, the techniques may be realized at least in part by a computer- readable medium comprising instructions that, when executed in a processor, performs one or more of the methods described above. The computer-readable medium may comprise a tangible computer-readable storage medium and may form part of a computer program product, which may include packaging materials. The computer-readable storage medium may comprise random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The computer-readable storage medium may also comprise a non-volatile storage device, such as a hard-disk, magnetic tape, a compactdisk (CD), digital versatile disk (DVD), Blu-ray disk, holographic data storage media, or other nonvolatile storage device.

[0102] The term “processor,” as used herein may refer to any of the foregoing structure or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated software modules or hardware modules configured for performing the techniques of this disclosure. Even if implemented in software, the techniques may use hardware such as a processor to execute the software, and a memory to store the software. In any such cases, the computers described herein may define a specific machine that is capable of executing the specific functions described herein. Also, the techniques could be fully implemented in one or more circuits or logic elements, which could also be considered a processor.

[0103] Listing of Exemplary Embodiments

[0104] In a first embodiment, the present disclosure provides a method of inspecting a curved object. The method comprises directing a wavefront onto a surface of a curved object having at least one large scale curvature; acquiring at least one reflected image that comprises a distorted reflection of the wavefront from the curved object; and determining distortion correction parameters from the at least one large scale curvature of the object. The method further comprises at least one of: i) adjusting the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyzing the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object; or ii) analyzing the at least one reflected image using the distortion correction parameters; and adjusting a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

[0105] In a second embodiment, the present disclosure provides a method of inspecting a curved object according to the first embodiment, comprising adjusting the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyzing the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object.

[0106] In a third embodiment, the present disclosure provides a method of inspecting a curved object according to the first embodiment, comprising analyzing the at least one reflected image using the distortion correction parameters; and adjusting a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

[0107] In a fourth embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through third embodiments, wherein the determining distortion correction parameters from the at least one large scale curvature of the object comprises: directing a known patterned wavefront that includes a defined pattern onto the surface of the curved object; acquiring one ofthe at least one reflected images, which comprises a distorted reflection of the defined pattern; and identifying the one or more distortions caused by the at least one large scale curvature of the object.

[0108] In a fifth embodiment, the present disclosure provides a method of inspecting a curved object according to the fourth embodiment, wherein the wavefront and the known patterned wavefront directed at the curved surface for all of the at least one reflected images are identical.

[0109] In a sixth embodiment, the present disclosure provides a method of inspecting a curved object according to the fourth embodiment or the fifth embodiment, wherein the defined pattern comprises dots or lines.

[0110] In a seventh embodiment, the present disclosure provides a method of inspecting a curved object according to any of the fourth through sixth embodiments, wherein the defined pattern is created by a mask from which a patterned wavefront is generated.

[0111] In an eighth embodiment, the present disclosure provides a method of inspecting a curved object according to the first embodiment, wherein the determining distortion correction parameters from the at least one large scale curvature of the object comprises using a digital model of the surface to correct the distortions caused by large scale curvature of the object.

[0112] In a ninth embodiment, the present disclosure provides a method of inspecting a curved object according to the first embodiment, wherein the determining distortion correction parameters from the at least one large scale curvature of the object comprises measuring the large scale curvature of the surface by profilo metry.

[0113] In a tenth embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through ninth embodiments, wherein the large scale curvature has a length that is at least 4 times greater than a length of the localized variations in small scale textures.

[0114] In an eleventh embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through tenth embodiments, wherein the at least one reflected image is projected on a screen.

[0115] In a twelfth embodiment, the present disclosure provides a method of inspecting a curved object according to the eleventh embodiment, wherein the screen is a diffuse screen.

[0116] In a thirteenth embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through twelfth embodiments, wherein the at least one reflected image is acquired using an imaging device.

[0117] In a fourteenth embodiment, the present disclosure provides a method of inspecting a curved object according to the thirteenth embodiment, wherein the imaging device comprises a camera.

[0118] In a fifteenth embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through fourteenth embodiments, further comprising identifying a location of at least one localized variation in small scale texture.

[0119] In a sixteenth embodiment, the present disclosure provides a method of inspecting a curved object according to the fifteenth embodiment, wherein the at least one localized variation in small scale texture comprises orange peel.

[0120] In a seventeenth embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through sixteenth embodiments, wherein an external surface of the curved object comprises an at least partially reflective surface.

[0121] In an eighteenth embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through seventeenth embodiments, wherein the curved object comprises an opaque surface.

[0122] In a nineteenth embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through eighteenth embodiments, wherein the wavefront is provided by a spatially coherent light source.

[0123] In a twentieth embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through eighteenth embodiments, wherein the wavefront is provided by a point light source.

[0124] In a twenty -first embodiment, the present disclosure provides a method of inspecting a curved object according to the twentieth embodiment, wherein light emitted from the point light source is polarized.

[0125] In a twenty-second embodiment, the present disclosure provides a method of inspecting a curved object according to any of the first through twenty -first embodiments, wherein the curved object comprises a radius of curvature of up to 205 millimeters.

[0126] In a twenty -third embodiment, the present disclosure provides an inspection system. The inspection system comprises a light source configured to emit a wavefront at a curved object having at least one large scale curvature; and an imaging device configured to acquire at least one reflected image that comprises a distorted reflection of the wavefront from the curved object. The inspection system further comprises one or more processors configured to determine distortion correction parameters from the at least one large scale curvature of the object; and at least one of i) adjust the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyze the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object; or ii) analyze the at least one reflected image using the distortion correction parameters; and adjust a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

[0127] In a twenty -fourth embodiment, the present disclosure provides an inspection system according to the twenty -third embodiment, further comprising a known wavefront that includes a defined pattern.

[0128] In a twenty -fifth embodiment, the present disclosure provides an inspection system according to the twenty -fourth embodiment, wherein the defined pattern comprises dots or lines.

[0129] In a twenty-sixth embodiment, the present disclosure provides an inspection system according to the twenty -fourth embodiment or the twenty -fifth embodiment, wherein the defined pattern is created by a mask from which a patterned wavefront is generated.

[0130] In a twenty-seventh embodiment, the present disclosure provides an inspection system according to the twenty -third embodiment, further comprising a digital model of the surface of the curved object.

[0131] In a twenty -eighth embodiment, the present disclosure provides an inspection system according to any of the twenty -third through twenty-seventh embodiments, further comprising a screen.

[0132] In a twenty-ninth embodiment, the present disclosure provides an inspection system according to the twenty -eighth embodiment, wherein the screen is a diffuse screen.

[0133] In a thirtieth embodiment, the present disclosure provides an inspection system according to any of the twenty -third through twenty -ninth embodiments, wherein the imaging device comprises a camera.

[0134] In a thirty-first embodiment, the present disclosure provides an inspection system according to any of the twenty -third through thirtieth embodiments, wherein the light source is a spatially coherent light source.

[0135] In a thirty-second embodiment, the present disclosure provides an inspection system according to any of the twenty -third through thirtieth embodiments, wherein the light source is a point light source.

[0136] In a thirty-third embodiment, the present disclosure provides an inspection system according to the thirty-second embodiment, wherein light emitted from the point light source is polarized.EXAMPLES

[0137] A system was constructed containing a light source projector capable of generating a wavefront of light and a sample holder for positioning a sample containing a surface to be analyzed. The system also included a diffuse imaging screen for capturing the wavefront that reflected off the surface to be analyzed and a camera with an imaging lens focused on the diffuse imaging screen. The diffuse imaging screen was positioned parallel to the sample holder and the camera was positioned perpendicular to the imaging screen.

[0138] A light source projector was built using a red LED (Light Emitting Diode, PN: UHP-T-625) purchased from Prizmatix (Holon, Isreal). An imaging lens (Fujinon HF12XA-5M) from Fujinon (Saitama City, Japan) was affixed to the light to create a projection lens and a space was created so a mask could be placed within the image plane of the projection lens. A glass distortion target (Edmund Optics PN:58-509) from Edmund Optics (Barrington, NJ) was placed between the LED light source and the imaging lens to pattern the wavefront. A flat mirror was placed in the sample holder roughly where the curved sample to be inspected would be positioned. With the projection lens aperture fully open (f=1.4) the focusing ring on the projection lens was adjusted to focus the reflected pattern onto the imaging screen. The aperture on the projection lens was then set to f=12.

[0139] By decreasing the aperture size in the projection lens a wavefront was formed. Illuminating a sample containing surface defects such as orange peel or point defects such as dust nibs or fisheyes with a wavefront, a distorted wavefront can be viewed by projecting it onto a diffuse imaging screen which allows the viewer to quickly examine where such defects may be present in a surface. This approach can be useful for inspecting nominally flat surfaces, but the reflected wavefront can be difficult to interpret when a panel with either convex, concave for a combination of both is viewed as the bulk curvature of the panel will change the relative size and position of the defects of interest. By patterning the wavefront witha mask, the bulk curvature of the panel is able to be mapped making it a useful method for processing, visualizing, rating, and mapping the location of defects in coated curved panels.

[0140] A camera (FLIR BFS-U3-244S8M-C) from Teledyne FLIR (Wilsonville, OR) and lens (Computar 1” 12.5mm F / 1.4) from Computar (Cary, NC) was positioned perpendicular to the diffuse imaging screen and focused on the diffuse screen. The camera was connected to a computer to collect and further analyze the recorded images.

[0141] A painted panel containing a dust nib point defect as well as an area that had been processed with a polishing procedure intended to remove an orange peel paint nonuniformity defect was selected. A slip roll was used to vary the curvature of the painted panel and the panel was inspected using the described approach after each time the panel was processed through the slip roll to generate different bulk curvatures on the same sample.

[0142] With the panel curvature set by the slip roll, the panel was placed in the sample holder. Two images were collected at each panel curvature. The intensity of the light source was varied to ensure the image captured by the camera was not saturated. The first image was collected without the glass distortion target present, the distortion target was fitted and a second image was collected. This process was repeated for all the curvatures shown in FIG. 3.

[0143] To isolate the dot pattern for the distortion correction, the absolute difference was taken in software between the two images collected at each curvature. The isolated dot pattern was then used to correct the distortion present in the first image of the reflected wavefront to create a third distortion corrected image. These processing steps were repeated for all panel curvatures. To correct for the distortion present in the first wavefront image, National Instruments Vision Assistant Software was used to threshold and filter the dots within the isolated dot pattern image using the Niblack local thresholding method, generate the correction coefficients using a kl polynomial Grid distortion model, and apply them to the second wavefront image. The orange peel and dust nib areas were then isolated from the distortion corrected images for each panel curvature and are presented in FIGS. 7 and 8, showing the distortion caused by the panel curvature has been removed. The isolation was performed by manually cropping and aligning the distortion corrected images to compare the orange peel and dust nib areas for each panel.

[0144] More particularly, FIG. 7 provides photographs of a distortion correction region of a convex panel 700 containing a dust nib 710 in an upper region of the image and polished area 720 at a lower region, of each of Sample Bend #1 through Sample Bend #5, with the original image in the top row and the distortion corrected image in the bottom row. FIG. 8 provides photographs of a distortion correction region of a concave panel 800 containing a dust nib 810 in an upper region of the image and polished area 820 at a lower region, of each of Sample Bend #6 and Sample Bend #7, with the original image in the top row and the distortion corrected image in the bottom row.

[0145] Additionally, FIG. 4 provides photographs of Sample Bend #1. The top left is a picture of a wavefront projected mottle and defect image. The top right picture is a dot pattern projected wavefront image. The bottom left picture is a difference image of the top left and top right images used to correct curvature in the panel. The bottom right picture is a distortion corrected image. FIG. 5 providesphotographs of Sample Bend #3. The top left picture is a wavefront projected mottle and defect image. The top right picture is a dot pattern projected wavefront image. The bottom left picture is a difference image of the top left and top right images used to correct curvature in the panel. The bottom right picture is a distortion corrected image.

[0146] Further, FIG. 6 provides photographs of locations of variation in bulk curvature in Sample Bend #3. The ovals indicate regions where the roll bender made complete contact with the panel resulting in localized areas of higher curvature. By contrast, the area highlighted by the rectangle had a business card attached to the black of the panel, which resulted in an impression in the panel.

[0147] Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that a variety of alternate and / or equivalent implementations can be substituted for the specific embodiments shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the specific embodiments discussed herein. Therefore, it is intended that this disclosure be limited only by the claims and the equivalents thereof.

[0148] Furthermore, all publications and patents referenced herein are incorporated by reference in their entirety to the same extent as if each individual publication or patent was specifically and individually indicated to be incorporated by reference. In the event of inconsistencies or contradictions between portions of the incorporated references and this application, the information in the preceding description prevails. Various exemplary embodiments have been described. These and other embodiments are within the scope of the following claims.

Claims

What is claimed is:

1. A method of inspecting a curved object, the method comprising: directing a wavefront onto a surface of a curved object having at least one large scale curvature; acquiring at least one reflected image that comprises a distorted reflection of the wavefront from the curved object; determining distortion correction parameters from the at least one large scale curvature of the object; and at least one of: i) adjusting the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyzing the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object; or ii) analyzing the at least one reflected image using the distortion correction parameters; and adjusting a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

2. The method of claim 1, comprising adjusting the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyzing the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object.

3. The method of claim 1, comprising analyzing the at least one reflected image using the distortion correction parameters; and adjusting a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

4. The method of any of claims 1 to 3, wherein the determining distortion correction parameters from the at least one large scale curvature of the object comprises: directing a known patterned wavefront that includes a defined pattern onto the surface of the curved object; acquiring one of the at least one reflected images, which comprises a distorted reflection of the defined pattern; and identifying the one or more distortions caused by the at least one large scale curvature of the object.

5. The method of claim 4, wherein the wavefront and the known patterned wavefront directed at the curved surface for all of the at least one reflected images are identical.

6. The method of claim 4 or claim 5, wherein the defined pattern comprises dots or lines.

7. The method of any of claims 4 to 6, wherein the defined pattern is created by a mask from which a patterned wavefront is generated.

8. The method of claim 1, wherein the determining distortion correction parameters from the at least one large scale curvature of the object comprises using a digital model of the surface to correct the distortions caused by large scale curvature of the object.

9. The method of claim 1, wherein the determining distortion correction parameters from the at least one large scale curvature of the object comprises measuring the large scale curvature of the surface by profdometry.

10. The method of any of claims 1 to 9, wherein the at least one reflected image is projected on a diffuse screen.

11. The method of any of claims 1 to 10, wherein the at least one reflected image is acquired using an imaging device.

12. The method of any of claims 1 to 11, further comprising identifying a location of at least one localized variation in small scale texture.

13. The method of claim 12, wherein the at least one localized variation in small scale texture comprises orange peel.

14. The method of any of claims 1 to 13, wherein the wavefront is provided by a spatially coherent light source or a point light source.

15. The method of any of claims 1 to 14, wherein the curved object comprises a radius of curvature of up to 205 millimeters.

16. An inspection system comprising: a light source configured to emit a wavefront at a curved object having at least one large scale curvature; an imaging device configured to acquire at least one reflected image that comprises a distorted reflection of the wavefront from the curved object; and one or more processors configured to determine distortion correction parameters from the at least one large scale curvature of the object; and at least one of: i) adjust the at least one reflected image by correcting the one or more distortions caused by the at least one large scale curvature of the object to create an adjusted image; and analyze the adjusted image for localized variations in small scale textures that may be present on the surface of the curved object; or ii) analyze the at least one reflected image using the distortion correction parameters; and adjust a grid algorithm to rate localized variations in small scale textures that may be present on the surface of the curved object.

17. The inspection system of claim 16, further comprising a known wavefront that includes a defined pattern comprising dots or lines.

18. The inspection system of claim 17, wherein the defined pattern is created by a mask from which a patterned wavefront is generated.

19. The inspection system of claim 16, further comprising a digital model of the surface of the curved object.

20. The inspection system of any of claims 16 to 19, further comprising a screen.

Images