Methods and systems for inspecting tires and for generating tire quality inspection data

Abstract

Example methods and systems for inspecting vehicular tires for irregularities. An example method comprises: rotating a tire relative to sensors, obtaining profile data comprising radial and axial spatial locations of a plurality of points on a surface of the tire, obtaining a reference curve representative of at least a portion of the tire surface, processing the set of profile data relative to the reference and identifying irregularities in the surface of the using the processed data. The method may involve identifying tire features and adjusting the profile data based on those features. The method may involve generating height maps to assist with identifying irregularities. The method may involve identifying the tread surface portions of the profile data, matching the tread surfaces to tread pattern template and adjusting the profile data accordingly.

Description

METHODS AND SYSTEMS FOR INSPECTING TIRES AND FOR GENERATING TIREQUALITY INSPECTION DATARelated Applications

[0001] This application claims priority from, and for the purposes of the United States of America the benefit under 35 USC 119 in connection with, United States patent application No. 63 / 611148 filed 16 December 2023 which is hereby incorporated by reference herein.Technical Field

[0002] This application relates in a general way to the field of tire manufacture, and more specifically to the process of quality inspection for irregularities and / or defects in manufactured tires. Particular embodiments provide methods and systems for inspecting tires and for generating tire quality inspection data and / or the like.Background

[0003] Manufactured tires may have irregularities, which, after identification and further inspection, may be classified as defects. By way of non-limiting example, irregularities may include: excess material present in some locations, insufficient material present in some locations, air entrapment between components which may cause blisters, incorrect assembly of components; presence of contaminants and / or the like. Such irregularities may occur on the sidewalls of tires, on the tread portion of tires or on the interior surface of tires.

[0004] Defects associated with such irregularities may cause tires to perform sub-optimally or to fail prematurely. Consequently, there is a desirability to identify irregularities during the manufacturing process or otherwise prior to the tires being delivered to OEMs or used by consumers.

[0005] Identification of tire irregularities and / or defects may be done manually; however, this is a time and labour intensive process, which is subject to human error and as such there is a preference that quality inspection for irregularities is done automatically or with technological assistance.

[0006] Measurement systems such as cameras, laser scanners, x-ray machines, force measurement devices, balance assessment devices, and tactile sensors may be used toaid in the inspection of tires. Measurement data acquired by such measurement systems can be challenging to use for identification of tire irregularities, in particular and without limitation, because of relative movement between the tire and the measurement systems (e.g. variable alignment) which can introduce inconsistencies, errors and / or imperfections into the measured data.

[0007] There is a general desire for the automated generation of tire inspection data which is capable of accounting for inconsistencies, errors and / or imperfections in the raw (e.g. measured) data.

[0008] The foregoing examples of the related art and limitations related thereto are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the drawings.Summary

[0009] The following embodiments and aspects thereof are described and illustrated in conjunction with systems, tools and methods which are meant to be exemplary and illustrative, not limiting in scope. In various embodiments, one or more of the abovedescribed problems have been reduced or eliminated, while other embodiments are directed to other improvements.

[0010] One aspect of the invention provides a method for inspecting vehicular tires for irregularities. The method comprises: obtaining, during relative rotation between a tire and one or more sensors about a tire axis and at each of a plurality of relative angular orientations between the tire and the one or more sensors, a set of profile data, wherein each set of profile data comprises radial and axial spatial locations of a plurality of points on a surface of the tire at the corresponding relative angular orientation between the tire and the one or more sensors; obtaining a reference curve representative of at least a portion of the tire surface at an angular orientation about the tire axis; for each set of profile data, processing the set of profile data and the reference curve to thereby obtain a corresponding set of relative profile data based on the set of profile data and the reference curve; and facilitate identification of one or more irregularities in the surface of the tire based at least in part on the sets of relative profile data.

[0011] Processing the profile data to mitigate variable alignment of the tire as between the plurality of relative angular orientations and to thereby obtain, for each set of profile data, a corresponding set of alignment-adjusted profile data.

[0012] Obtaining the reference curve may comprise generating the reference curve based at least in part on the alignment-adjusted profile data.

[0013] Obtaining the reference curve representative of an angular profile of the tire may comprise generating a reference curve based on a tire-specific model of the tire.

[0014] Processing the set of profile data and the reference curve to thereby obtain the corresponding set of relative profile data may comprise comparing the corresponding set of alignment-adjusted profile data to the reference curve to thereby obtain the corresponding set of relative profile data.

[0015] Processing the set of profile data and the reference curve to thereby obtain the corresponding set of relative profile data may comprise determining distances between the corresponding set of alignment-adjusted profile data and the reference curve at a number of locations along the reference curve, the corresponding set of relative profile data may comprise the distances.

[0016] Processing the profile data to mitigate variable alignment of the tire may comprise, for each set of profile data, adjusting a position of the set of profile data.

[0017] Adjusting the position of the set of profile data may comprise: determining a location of a tire feature in one or more sets of profile data; and adjusting the position of the set of profile data based at least in past on the location of the tire feature.

[0018] Adjusting the position of the set of profile data may comprise one or more of: adjusting an axial position of the set of profile data along the tire axis; adjusting a radial position of the set of profile data in a radial direction orthogonal to the tire axis; and adjusting an angular orientation of the set of profile data about an axis orthogonal to both the tire axis and the radial direction.

[0019] Adjusting the axial position of the set of profile data along the tire axis may comprise: determining an axial location of a tire feature in one or more sets of profile data; and adjusting the axial position of the set of profile data along the tire axis based at least in past on the axial location of the tire feature.

[0020] Adjusting the radial position of the set of profile data in the radial direction may comprise: determining a radial location of a tire feature in one or more sets of profile data; and adjusting the radial position of the set of profile data along the tire axis based at least in past on the radial location of the tire feature.

[0021] Adjusting the angular orientation of the set of profile data around the axis orthogonal to both the tire axis and the radial direction may comprise: determining an angular rotation of a tire feature in one or more sets of profile data; and adjusting the angular orientation of the set of profile data about the axis orthogonal to both the tire axis and the radial direction based at least in past on the angular orientation of the tire feature.

[0022] The tire feature may comprise any one or more of: a tread rib; a tread pattern; a tire bead; and a tread bottom.

[0023] Determining the location of a tire feature may comprise: identifying tread bottom points; and identifying a tread feature within the bottom points.

[0024] Determining the location of a tire feature may comprise generating a binary image of tread bottom points.

[0025] Generating the binary image of the tread bottom points may comprise applying a standard deviation filter to the one or more sets of profile data.

[0026] Determining the location of a tire feature may comprise smoothing the binary image to identify a circumferentially contiguous tire feature.

[0027] Determining the location of a tire feature may comprise matching the binary image to a tread pattern template.

[0028] Determining the location of a tire feature may comprise matching one of the one or more sets of profile data to a tread profile template.

[0029] Processing the profile data to mitigate variable alignment of the tire may comprise: matching one or more sets of profile data to a tread pattern; identifying a tire feature in the one or more sets of profile data based at least in part on the tread pattern; determining an axial location of the tire feature in an axial direction parallel with the tire axis; determining radial locations of the tire feature in a radial direction orthogonal to the axial direction; and determining an angular orientation of the tire feature about an axis orthogonal to both the axial direction and the radial direction.

[0030] The one or more sets of profile data may comprise averaged sets of profile data.

[0031] Generating the reference curve may comprise fitting a series of arcs to data points of one or more of the plurality of sets of alignment-adjusted profile data.

[0032] Generating the reference curve may comprise: grouping the profile data of each of the plurality of sets of alignment adjusted profile data into bins based on their axial and radial values; identifying bins containing a threshold amount of alignment adjusted data points; and fitting a curve through the identified bins.

[0033] Each bin may represent a rectangular area.

[0034] The reference curve may be a g1 PolySegArc.

[0035] Comparing the set of alignment-adjusted profile data to the reference curve to thereby obtain the corresponding set of relative profile data may comprise: dividing the reference curve into a plurality of segments; for each of the plurality of segments: determining a point on the set of alignment-adjusted profile data closest to the reference curve within the segment; assigning a distance between the determined point and the reference curve to be an element of the corresponding set of relative profile data.

[0036] Facilitating identification of one or more irregularities based at least in part on the sets of relative profile data may comprise generating a height map of a surface of the tire based on the sets of relative profile data.

[0037] The height map may be encoded as a two-dimensional image wherein a luminosity of each pixel represents an element of relative profile data.

[0038] The two-dimensional image may comprise an 8-bit image, and the 8 bit image has a first bit increment which corresponds to a distance between 10 and 50 microns.

[0039] Areas of the tire which exceed a range encodable using the first bit increment may be encoded using a second bit increment, the second bit increment corresponding to a larger distance than the first bit increment.

[0040] Areas of the tire which exceed a range encodable using the first bit increment may be encoded using a second bit increment wherein a magnitude of relative profile data encoded by the second bit increment is offset from a magnitude of profile irregularity encoded by the first bit increment.

[0041] Facilitating identification of irregularities may comprise: identifying features in the relative profile data; processing the relative profile data based on the identified features toobtain normalized relative profile data; and identifying irregularities based at least in part on the normalized relative profile data.

[0042] The identified features may comprise one of more of: ponds, walls surrounding ponds, and lattice structures.

[0043] Processing the relative profile data based on the identified features to obtain normalized relative profile data may comprise: identifying a tire structure located at a plurality of locations comprises identifying local smooth regions; fitting a curve between the identified local smooth regions; and determining distances between the relative profile data and the fitted curve to be the normalized relative profile data.

[0044] Identifying local smooth regions may comprise identifying regions with a standard deviation in relative profile data of less than an threshold distance.

[0045] The tread profile template may be generated by fitting lines and arcs to one or more representative sets of profile data.

[0046] The method may comprise creating the relative rotation between the tire and the one or more sensors about the tire axis.

[0047] Creating the relative rotation between the tire and the one or more sensors about the tire axis may comprise: providing one or more rollers, each roller having a roller axis about which the roller is rotatable and the roller axes of the one or more rollers oriented in parallel; bringing the surface of the tire into contact with the one or more rollers, such that the tire is at least approximately oriented with the tire axis parallel to the roller axes of the one or more rollers; and driving at least one of the one or more rollers to rotate the tire.

[0048] The one or more sensors may comprise one or more camera-laser pairs, and wherein obtaining the plurality of sets of profile data comprises measuring a distance from the camera-laser pairs to the surface of the tire.

[0049] Another aspect of the invention provides a system for inspecting vehicular tires for irregularities. The system comprises: one or more sensors for measuring locations of points on a surface; an apparatus for creating relative rotation between a tire and the one or more sensors; and a processor connected to receive signals from the one or more sensors, the processor configured to: obtain, during relative rotation between the tire and the one or more sensors about the tire axis and at each of a plurality of relative angular orientations between the tire and the one or more sensors, a set of profile data, wherein each set of profile data comprises radial and axial spatial locations of a plurality of points on a surfaceof the tire at the corresponding relative angular orientation between the tire and the one or more sensors; obtain a reference curve representative of at least a portion of the tire surface at an angular orientation about the tire axis; for each set of profile data, process the set of profile data and the reference curve to thereby obtain a corresponding set of relative profile data based on the set of profile data and the reference curve; and facilitate identification of one or more irregularities in the surface of the tire based at least in part on the sets of relative profile data.

[0050] In addition to the exemplary aspects and embodiments described above, further aspects and embodiments will become apparent by reference to the drawings and by study of the following detailed descriptions.

[0051] It is emphasized that the invention relates to all combinations of the above features, even if those are recited in different claims.Brief Description of the Drawings

[0052] Exemplary embodiments are illustrated in referenced figures of the drawings. It is intended that the embodiments and figures disclosed herein are to be considered illustrative rather than restrictive.

[0053] Figure 1 is a flow chart illustrating a method for tire inspection and for generating tire inspection data according to a particular embodiment.

[0054] Figure 2 is a schematic diagram of a computing system which may be used to perform a number of the methods described herein.

[0055] Figure 3 is a schematic depiction of a method for mitigating variable alignment of the tire as between the plurality of profiles which may be used with the Figure 1 method according to a particular embodiment.

[0056] Figure 4A is a flow chart illustrating a method for landmark detection which may be used to in the variable alignment mitigation method of Figure 3 according to a particular embodiment.

[0057] Figure 4B is a flow chart illustrating a method for determining tread landmark parameters which may be used in the landmark detection method of Figure 4A according to a particular embodiment.

[0058] Figure 4C is a flow chart illustrating a method for determining tread landmark parameters which may be used in the landmark detection method of Figure 4A according to a particular embodiment.

[0059] Figure 4D is a flow chart illustrating a method for determining tread landmark parameters which may be used in the landmark detection method of Figure 4A according to a particular embodiment.

[0060] Figure 4E is a flow chart illustrating a method for generating alignment-adjusted profiles which may be used in the landmark detection method of Figure 4A according to a particular embodiment.

[0061] Figure 5 is a representation of a combined profile point image, where a plurality of alignment-adjusted profiles have been overlaid to form a combined representation made up of a plurality of profile points.

[0062] Figure 5A is a schematic depiction of the Figure 5 profile point image overlaid with an axial by radial grid.

[0063] Figure 6A depicts an example portion of alignment-adjusted profile data that is generated and used in the method of Figure 1 .

[0064] Figure 6B illustrates an example portion of a reference curve which has been fit to the alignment-adjusted profile data that is generated and used in the method of Figure 1 .

[0065] Figure 6C is a schematic depiction of a technique for fitting a reference curve to the alignment-adjusted profile data that is generated and used in the method of Figure 1 according to a particular embodiment.

[0066] Figure 7 is a schematic depiction of a method for generating relative profile data based on the reference curve and the alignment-adjusted profile data which may be used in the Figure 1 method according to a particular embodiment.

[0067] Figure 8 is a visualization of example relative profile data according to a particular embodiment.

[0068] Figure 9 is a height map visualization of example relative profile data according to a particular embodiment.

[0069] Figure 10A depicts a height map visualization of example relative profile data wherein expected joins and splices in the tire are visible.

[0070] Figure 10B is an enlarged view of the area surrounding the irregularity identified in the bottom left corner of Figure 10A.

[0071] Figure 10C depicts a height map visualization of the same example relative profile data as Figure 10A after smooth regions identified in Figure 10B have been normalized.

[0072] Figure 10D is a flowchart illustrating a method of producing normalized relative profile data according to a particular embodiment.

[0073] Figure 10E depicts a height map visualization of example relative profile data wherein expected joins and splices in the tire are visible and a corresponding grey value plot of a portion of the height map.

[0074] Figure 10F is an enlarged view of the Figure 10E plot.

[0075] Figure 10G depicts a depicts a height map visualization of example relative profile data wherein expected joins and splices in the tire are visible and a corresponding grey value plot of a portion of the height map.

[0076] Figure 10H depicts a depicts a height map visualization of example normalized relative profile data wherein there are no visible joins or splices, and a corresponding grey value plot of a portion of the height map.

[0077] Figure 11 is a schematic elevation view of a profile data acquisition system for creating relative rotation between a tire and one or more sensors and for acquiring profile data according to a particular embodiment.

[0078] Figure 12 depicts a representation or rendering of the surface of an example point cloud model of a tire as viewed from near the tread surface.

[0079] Figures 13A and 13B show photographs of a laser system scanning a tire according to a particular embodiment, where the laser line visible on the surface of the tire corresponds to a single set of profile data.

[0080] Figure 14 is an example representation of a profile obtained using multiple sensors showing that there may be portions of the surface of the tire which are missing, and portions of the surface of the tire which may have been measured multiple times with inconsistent data.

[0081] Figure 15A depicts a binary image showing the tread bottom points of a portion of a tire with a circumferentially continuous tread pattern.

[0082] Figure 15B depicts a smoothed binary image showing the tread bottom points of a portion of a tire with a circumferentially continuous tread pattern.

[0083] Figure 16 depicts a tread profile template which may be used in some embodiments to detect landmark features and to generate landmark parameters.

[0084] Figure 17 depicts a binary image of a tread pattern for a tire without circumferentially continuous features.Description

[0085] Throughout the following description specific details are set forth in order to provide a more thorough understanding to persons skilled in the art. However, well known elements may not have been shown or described in detail to avoid unnecessarily obscuring the disclosure. Accordingly, the description and drawings are to be regarded in an illustrative, rather than a restrictive, sense.

[0086] Aspects of the invention provide methods for inspecting vehicular tires for irregularities. The method comprises: obtaining, during relative rotation between a tire and one or more sensors about a tire axis and at each of a plurality of relative angular orientations between the tire and the one or more sensors, a set of profile data, wherein each set of profile data comprises radial and axial spatial locations of a plurality of points on a surface of the tire at the corresponding relative angular orientation between the tire and the one or more sensors; obtaining a reference curve representative of at least a portion of the tire surface at an angular orientation about the tire axis; and comparing the representative curve to the measured data to assist with the identification of irregularities and / or possible defects in the tire. Comparing the representative curve to the measured data may comprise, for each set of profile data, processing the set of profile data and the reference curve to thereby obtain a corresponding set of relative profile data based on the set of profile data and the reference curve; and facilitating identification of one or more irregularities in the surface of the tire based at least in part on the sets of relative profile data.

[0087] Aspects of the invention provide systems and methods for inspecting vehicular tires for irregularities and / or possible defects. Such methods comprise: obtaining, during relative rotation between a tire and one or more sensors about a tire axis and at each of a plurality of relative angular orientations between the tire and the one or more sensors, a set of profiledata corresponding to spatial locations of a plurality of points on a surface of a tire; generating a reference curve based at least in part on the profile data over the relative rotation; for each set of profile data, processing the set of profile data and the reference curve to thereby obtain a corresponding set of relative profile data; and processing this relative profile data to generate and output height map images which may be used to assist with identifying one or more irregularities and / or possible defects in the tire.

[0088] Another aspect of the invention provides a system for inspecting vehicular tires for irregularities. The system comprises: one or more sensors for measuring locations of points on a surface; an apparatus for creating relative rotation between a tire and the one or more sensors; and a processor connected to receive signals from the one or more sensors, the processor configured to: obtain, during relative rotation between the tire and the one or more sensors about the tire axis and at each of a plurality of relative angular orientations between the tire and the one or more sensors, a set of profile data, wherein each set of profile data comprises radial and axial spatial locations of a plurality of points on a surface of the tire at the corresponding relative angular orientation between the tire and the one or more sensors; obtain a reference curve representative of at least a portion of the tire surface at an angular orientation about the tire axis; for each set of profile data, process the set of profile data and the reference curve to thereby obtain a corresponding set of relative profile data based on the set of profile data and the reference curve; and facilitate identification of one or more irregularities in the surface of the tire based at least in part on the sets of relative profile data.

[0089] Figure 1 schematically depicts a tire inspection method 100 according to an example embodiment. Method 100 comprises a measurement (or data acquisition) portion 110, a data processing portion 120 and an inspection portion 130. Measurement portion 110 of method 100 comprises the block 117 process of obtaining profile data 117A. The block 117 process of obtaining profile data 117A may occur during the optional step 113 of creating relative rotation between a tire 102 and one or more sensors 104 about a tire axis 106 that extends through a notional axial center of tire 102. The optional block 113 process of creating relative rotation between tire 102 and sensor(s) 104 is explained in more detail below.

[0090] Profile data 117A obtained in block 117 is obtained by sensor(s) 104 and comprises, for each of a plurality of n=1, 2, 3...N relative angular orientations about tire axis 106 between tire 102 and sensor(s) 104, a corresponding set of profile data 117A_n comprising spatial locations of a plurality of points on a surface 108 of tire 102. For brevity, each set ofprofile data 117A_n corresponding to one relative angular orientation about tire axis 106 between tire 102 and sensor(s) 104 may be referred to herein as a profile 117A_n. Profile data 117A therefore comprises a plurality of N profiles 117A_n, one such profile 117A_n for each of a plurality of n=1, 2, 3...N relative angular orientations about tire axis 106 between tire 102 and sensor(s) 104.

[0091] Spatial locations of points and / or features of tire 102 may be defined relative to the frame of reference of tire 102 using cylindrical coordinates comprising an axial coordinate (z) along tire axis 106, a radial component ( / ) extending orthogonally away from tire axis 106 and an azimuth coordinate (0) extending around axis 106. Each profile 117A_n may comprise the locations of a plurality of points on the surface 108 of tire 102 at a discrete azimuth coordinate (0). Each profile 117A_n may comprise a set of axial (z) and radial ( / ) coordinates of measured points at a particular azimuth coordinate (0).

[0092] In some embodiments, measurement portion 110 of method 100 is performed simultaneously, or in real time, with data processing portion 120 (e.g. data processing portion 120 acts on particular profiles 117A_n of profile data 117A while sensor(s) 104 are obtaining other profiles 117A_n of profile data 117A). In some embodiments, measurement portion 110 may occur before data processing portion 120 (e.g. profile data 117A may be obtained in block 117 for one or more tires before data processing portion 120 begins). In some embodiments measurement portion 110 may occur in the same location as data processing portion 120 (e.g. in the same manufacturing facility) while in other embodiments measurement portion 110 and data processing portion 120 may occur in separate locations.

[0093] Data processing portion 120 of method 100 begins in block 123, which comprises processing profile data 117A to mitigate variable alignment of tire 102 relative to sensor(s) 104 which may occur during the block 117 acquisition of profile data 117A. Variation in the relative alignment (which may also be referred to herein as alignment errors) between tire 102 and sensor(s) 104 during the block 117 acquisition of profile data 117A may be introduced by deformation, wobbles, bouncing, axial wandering, tilting or other motion or deformation of tire 102 (relative to sensor(s) 104) during block 117. Alignment errors may additionally or alternatively be introduced by motion of sensor(s) 104 relative to tire 102 during block 117. The output of the block 123 variable alignment mitigation process is alignment-adjusted profile data 123A. The block 123 process of mitigating the variable alignment of profile data 117A is described in more detail below.

[0094] Data processing portion 120 then proceeds to block 125, which comprises generating a reference curve 125A. The block 125 process of generating a reference curve 125A may be based, at least in part, on profile data 117A and / or alignment-adjusted profile data 123A. The block 125 process of generating reference curve 125A is described in more detail below.

[0095] Data processing portion 120 then proceeds to block 127, which determines a plurality of sets of relative profile data 127A. Relative profile data 127A may describe the difference between reference curve 125A and each of a plurality of profiles 117A_n among profile data 117A. Relative profile data 127A may comprise a plurality of sets of relative profile data 127A_n. Each set of relative profile data 127A_n may describe the difference between reference curve 125A and a profile 117A_n. The block 127 process for determining relative profile data 127A may be based at least in part on reference curve 125A and profile data 117A or alignment-adjusted profile data 123A. The block 127 process for determining relative profile data 127A is described in more detail below.

[0096] Inspection portion 130 of method 100 comprises block 133, which involves using relative profile data 127A to identify irregularities in tire 102.

[0097] Some aspects of the invention provide a system 200 (an example embodiment of which is shown in Figure 2) for performing one or more of (or any suitable portions of) the methods described herein (e.g. method 100 of Figure 1 or portions thereof). System 200 of the illustrated embodiment comprises a profile data acquisition system 204 for creating relative rotation between tire 102 and one or more sensor(s) 104 and for acquiring profile data 117A.

[0098] System 200 may comprise a processor 210, a memory module 235, an input module 220, and an output module 230. Memory module 235 may store any of the data, models, and / or representations described herein as well as any software used in the performance of the various methods described herein. Processor 210 may control the operation of profile data acquisition system 204. Processor 210 may receive (via input module 220 or otherwise) profile data 117A and may store these inputs in memory module 235. Processor 210 may perform data processing portion 120 of method 100 to generate alignment- adjusted profile data 123A, reference curve 125A, relative profile data 127A and may generate and / or receive other data (e.g. landmark parameters 311 , any of a variety of height maps 245 and / or the like) as described herein, and store such information in memory module 235.

[0099] Returning to Figure 1 , method 100 begins at optional block 113 which comprises creating relative rotation between tire 102 and sensor(s) 104. Such relative rotation may be effected in any suitable manner. For example relative rotation may be effected by rotating tire 102 relative to sensor(s) 104 about tire axis 106 while sensor(s) 104 remain stationary, rotating sensor(s) 104 about tire axis 106 while tire 102 remains stationary or rotating both tire 102 and sensor(s) 104 relative to one another about tire axis 106.

[0100] Figure 11 schematically depicts a profile data acquisition system 204 for creating relative rotation between a tire 102 and one or more sensor(s) 104 and for acquiring profile data 117A according to an example embodiment. In profile data acquisition system 204 of the Figure 11 embodiment, tire 102 is supported by a plurality of rollers 250 and one or more of rollers 250 may be rotated such that tire 102 is rotated about tire axis 106 while sensor(s) 104 remain stationary. In the Figure 1 1 embodiment of profile data acquisition system 204, the axes about which rollers rotate may be parallel to one another and horizontally oriented. In this manner, tire axis 106 about which tire 102 rotates may be similarly generally horizontally oriented, subject to alignment errors or variable alignment as described herein.

[0101] Block 117 (Figure 1 ) comprises obtaining a plurality of profiles 117A_n for each of a corresponding plurality of relative azimuth positions (0 coordinates) as between tire 102 and sensor(s) 104 about tire axis 106. Profile data 117A may be obtained during the relative rotation of tire 102 and sensor(s) 104 about tire axis 106. Each profile 117A_n comprises axial (z coordinate) and radial (r coordinate) position data for each of a plurality of points on the surface 108 of tire 102. In some embodiments, profile data 117A may have a resolution greater than 1 / 100thof an inch or 1 / 1000thof an inch. In some embodiments, profile data 117A may comprise more than 1000 profiles 117 A n, or more than 10,000 profiles 117 A n or more than 30,000 profiles 117A_n, or more than 100,000 profiles 117A_n (i.e. the number N of discrete azimuthal (0) coordinates as between tire 102 and sensor(s) 104 about tire axis 106 may be A / >1 ,000, A / >10,000, A / >30,000 or A / >100,000).

[0102] In some embodiments each profile 117A_n may comprise data from a single sensor 104; in other embodiments, each profile 117A_n may comprise data from a plurality of sensors 104. In some embodiments using multiple sensor(s) 104, a profile 117A_n may include overlapping data where multiple sensors 104 measure the same location (e.g. axial z location). Figure 14 depicts an example representation of a profile 117A_n obtained using multiple sensors 104. The Figure 14 representation of profile 117A_n shows the axial coordinates (z) and radial coordinates ( / ) of a number of points on the surface 108 of tire102. Since, for each profile 117A_n, the axial coordinates (z) and radial coordinates ( / ) are orthogonal distance coordinates in two dimensions, they may be considered to be, and / or referred to herein as, x and y Cartesian coordinates where the Cartesian x coordinate corresponds to the axial z coordinate and the Cartesian / coordinate corresponds to the radial r coordinate. It can be seen from the Figure 14 representation that there are portions 112 of the surface 108 of tire 102 which have been measured multiple times (e.g. by multiple sensors 104) with inconsistent data. Figure 14 also shows that there are portions 114 of the surface 108 of tire 102 where data is missing. It will be understood that some artefacts, such as the omission of portions 114 of the surface 108 of tire 102 or multiple inconsistent measurements of portions 112 of surface 108, may be smoothed out or otherwise removed from measured profile data 117A through averaging of a plurality of profiles 117A_n, as will be discussed in more detail herein. It will also be understood that some artefacts may not be capable of being smoothed out, for example in circumstances where deformation of tire 102 has made data acquisition for a particular regions of tire 102 difficult, impractical or impossible for a particular setup of sensors 104.

[0103] Figures 13A and 13B depict photographs of a laser-based profile data acquisition system 204 according to an example embodiment scanning a surface 108 of a tire 102. Figures 13A and 13B show the line 116 of laser-based profile data acquisition system 204 incident on the surface 108 of tire 102. Line 116 shown in Figures 13A and 13B corresponds to a single profile 117A_n.

[0104] In some embodiments, the surface 108 of tire 102 for which axial (z-coordinate or x- coordinate) position and radial (r-coordinate or y-coordinate) position coordinates captured in each profile 117A_n comprises: the exterior surface of tire 102, the interior surface of tire 102, the exterior sidewalls of tire 102, the interior sidewalls of tire 102, a combination of these surfaces and / or a subset of these surfaces. In some embodiments, obtaining profile data 117A for both the interior and exterior surfaces may allow a tire inspector to determine a gauge rating or thickness of the tire wall based on a difference in the coordinates between the interior and exterior surfaces.

[0105] Referring back to Figure 1 , upon completion of profile-data acquisition block 117, method 100 proceeds to block 123. As discussed above, a tire can be misaligned and / or otherwise have different alignments for different profiles 117A_n, which may be caused (by way of non-limiting example) by deformation, wobbles, bouncing, axial wandering, tilting or other motion or deformation of tire 102 (relative to sensor(s) 104) when tire 102 is rotated relative to sensor(s) 104 (e.g. during block 113) or otherwise. Block 123 comprisesmitigating variable alignment of profile data 117A. Mitigating variable alignment of profile data 117A may comprise adjusting or otherwise processing profile data 117A, such that individual profiles 117A_n better align with one another.

[0106] In some embodiments, the mitigation of variable alignment in block 123 comprises identifying features of tire 102, hereinafter referred to as landmarks, in profiles 117A_n and using positions of these landmarks to adjust individual profiles 117A_n. Landmarks may, in some embodiments, have discernable shapes or patterns that can be used to identify such landmarks from among other profile data 117A_n.

[0107] Figure 14 shows an example profile 117A_n. Referring to Figure 14, in some embodiments, landmarks used in block 123 may include, without limitation, the two beads 118A, 118B at the tire rim, a radial dimension extremity 122 of the tread surface, a known tread pattern, the tread ribs 124, combinations of these features and / or the like. The beads 118A, 118B are rigid circular features on each side of tire 102 by which tire 102 is mounted to a rim of a vehicle. In some embodiments, the landmarks may include the center(s) of one or more tread ribs 124, the centroid of tread ribs 124, the edges of tread ribs 124, the center and / or edge(s) of a known tread pattern, and / or any other suitably identifiable features.

[0108] In some embodiments, some landmarks (and / or corresponding landmark parameters) may be identified in individual profiles 117A_n while other landmarks (and / or corresponding landmark parameters) may be identified in averaged profiles 117A_n*. In some embodiments some landmarks (and / or corresponding landmark parameters) may be identified though the analysis of a plurality of profiles 117A_n. In some embodiments, landmarks (and / or corresponding landmark parameters) used to identify axial (x or z direction) motion of tire 102 between profiles 117A_n may be identified using averaged profiles 117 A n*, or a plurality of profiles 117A_n, while landmarks (and / or corresponding landmark parameters) used to identify radial (y or / -direction) motion of tire 102 and angular (e.g. ip - about an axis perpendicular to both axial (x or z) and radial (y or / ) directions) motion of tire 102 may be identified using individual profiles 117A_n. Averaged profiles 117A_n* may be created from a moving average of some number P of individual profiles surrounding and including a particular profile 117A_n. For example, where the width of the moving average filter is given by the number P of individual profiles, a moving average profile 117A_n* corresponding to a particular profile 117A_n may comprise the average of the particular profile 117A_n, the preceding profiles 117A_n-1 , 117A_n-2, ... 117A_n- (^■) and the succeeding profiles 117A_n+1 , 117A_n+2, ... 117A_n+(^-). Thisindexing formulation ignores edge effects, but because profiles 117A_n are taken about tire axis 106, the indexing may return to n=0 after reaching n=N.

[0109] In some embodiments, using moving average profiles 117A_n* may minimize the effect of data that may be missing from, and / or erroneously recorded in, any particular profile 117A_n, which may be particularly useful for tires 102 whose tread surface 108 includes circumferentially continuous features, such as a rib. The width P of the moving average filter may comprise any suitable number. In some embodiments, the width P of the moving average filter applied to profiles for the identification of landmarks may be P>10, P>100, P>1000 or P>1 ,500. Where landmark detection is performed using individual profiles 117A_n, it is clear that such landmarks are specific to such individual profiles. However, where landmark (and / or corresponding landmark parameter) detection is performed using average profiles 117 A n*, such landmarks (landmark parameters) may still have a one-to-one correspondence with the individual profile 117A_n at the center of (or at some other suitable location within) the moving average window. In either case, the landmarks (landmark parameters) corresponding to a particular individual profile 117A_n may be used to produce one or more axial (z, x), radial (r, y) and rotational (ip) transforms that may be applied to such individual profile 117A_n as explained in more detail below.

[0110] In some embodiments, where particular landmarks (and / or corresponding landmark parameters) are identified in individual profiles 117A_n, the set of landmarks (and / or corresponding landmark parameters) identified in a group of individual profiles 117A_n may be smoothed or averaged. This method of averaging landmarks or landmark parameters (rather than profiles) may be useful for tires 102 whose tread surface does not include circumferentially continuous features, such as tires with an irregular tread pattern. Smoothing or averaging of landmarks (and / or corresponding landmark parameters) identified in each of a group of individual profiles 117A_n may be performed over some number S of profiles, where the number S of such profiles may be any suitable number. In some embodiments, S>10, S>100, S>1000 or S> 1 ,500. It will be appreciated that there may still be a one-to-one correspondence between smoothed or averaged landmarks (and / or corresponding landmark parameters) and an individual profile 117A_n - e.g. the individual profile 117A_n at the center of (or at some other suitable location within) the moving average window. Consequently, the smoothed or averaged landmarks (and / or corresponding landmark parameters) corresponding to a particular individual profile 117A_n may be used to produce one or more axial (z, x), radial (r, y) and rotational (ip) transforms that may be applied to such individual profile 117A_n as explained in more detail below.

[0111] Figure 3 depicts a method 300 for mitigating variable alignment of profile data 117A to obtain alignment-adjusted profile data 123A (e.g. performing block 123 of the Figure 1 method 100) according to a particular embodiment. Method 300 may be performed in an automated manner by processor 210 of system 200 (Figure 2). Method 300 may be considered to have one branch 301 -1 , 301 -2, ... 301 -A / for each of the plurality of profiles 117A_n (n=1, 2... N) obtained in block 117 of method 100. Each branch 301 of method 300 receives as input, a corresponding profile 117A_n. Each branch 301 of method 300 produces, as output, a corresponding alignment-adjusted profile 123A_n (i.e.an alignment- adjusted profile 123A_n corresponding to its input profile 117A_n). Each branch 301 of method 300 may have access to other profiles 117A_n (n=1, 2...N), identified landmarks of other profiles 117A_n (n=1, 2...N) and / or landmark parameters of other profiles 117A_n (n=1, 2...N) for the purpose of performing moving average filtering, landmark detection, and / or landmark parameter smoothing, as discussed above. For brevity, branch 301 -1 of method 300 (corresponding to profile 117A_1 ) is described in detail, it being understood that the other branches 301 (302-2 to 302-A / corresponding to profiles 117A 2 to 117A A / and producing alignment-adjusted profiles 123A 2 to 123A A / ) of method 300 are analogous.

[0112] Branch 301 -1 comprises a profile alignment block 302-1 , which is performed on profile 117A 1 to produce alignment-adjusted profile 123A 1 . Profile alignment block 302-1 starts in optional block 306, which comprises applying a moving average to profile 117A 1 based on a number P of profiles 117 A n as discussed above to obtain a corresponding average profile 117A 1 *. In embodiments where profile averaging is unnecessary or otherwise not used, optional block 306 may be omitted.

[0113] Profile alignment block 302-1 then proceeds to block 308 which involves identifying landmarks in a group of profiles 117A_n, an average profile 117A 1 * (where optional profile averaging is performed in block 306), and / or in profile 117A 1 and generating (e.g. based on identified landmarks) one or more landmark parameters 311 corresponding to profile 117A 1 .

[0114] Figure 4A is a schematic depiction of a method 500 for determining landmark parameters 311 . Method 500 may be used, in some embodiments, to perform the block 308 landmark parameter determination of method 300 (Figure 3). Method 500 may receive as input, and operate on, an individual profile 117A_n, a plurality of individual profiles 117A_n, and / or an average profile 117 A n*. For brevity, method 500 and its component parts are explained as operating on a plurality of individual profiles 117A_n, it being understood thatanalogous techniques could be used for an average profile 117A_n* or an individual profile 117A_n. Method 500 of the illustrated embodiment may be divided into tread landmark portion 502 (which determines landmark parameters 311 corresponding to tread landmarks based on profiles 117A_n) and bead landmark portion 504 (which determines landmark parameters 311 corresponding to bead landmarks based on profiles 117 A n).

[0115] Tread landmark portion 502 comprises determining tread landmark parameters in block 506 and, in the illustrated embodiment, outputs a number of tread landmark parameters 311 comprising tread X parameter 508 corresponding to an axial (x or z) coordinate (see coordinate axes of Figures 5 and 14), a tread Y parameter 510 corresponding to a radial (yor / ) coordinate (see coordinate axes of Figures 5 and 14) and a tread parameter 512 corresponding to rotation about an axis 101 perpendicular to both the axial (z) direction and the radial (r) direction.

[0116] Tread landmark parameter determination block 506 may determine tread landmark parameters 508, 510, 512 differently depending on the tread style of tire 102. Figures 4B, 4D and 4C schematically depict three exemplary embodiments of methods which may be employed in tread landmark determination block 506 to determine tread landmark parameters 508, 510, 512.

[0117] Figure 4B schematically depicts a method 560 (which may be used in block 506 of Figure 4A) for determining tread landmark parameters 508, 510, 512 which may be used (or suitable) for tires which have a series of “parallel” (circumferential) tread ribs or other circumferentially contiguous features. An exemplary tread pattern having such circumferentially contiguous tread rib features is depicted in the binary mask of Figure 15A which includes three circumferentially contiguous tread ribs 1700. Method 560 takes as input some number S of profiles 117A_n, where the number S of such profiles 117A_n may be any suitable number. In some embodiments, S>10, S>100, S>1000 or S>1 ,500. Method 560 begins with block 562 which involves identifying tread bottom points 544 in each of the S profiles 117A_n. Tread bottom points 544, in each profile 117A_n, may be considered to be the points which make up the furthest radially exterior points of tire 102 (e.g. points that would ordinarily be in contact with the ground when tire 102 is in use). Exemplary tread bottom points 544 are shown on ribs 124 in Figures 5 and 14.

[0118] Referring to Figures 5 and 14, these tread bottom points 544 may be identified, in each profile 117A_n, as groups of axially (z, x direction) contiguous points 544 having radial (r, y-dimension) coordinates within some configurable range of the radial (r, y-dimension)extremum of the profile 117A_n and / or groups of axially (z, x direction) contiguous points 544 between rapid radial dimension (r, / direction) changes corresponding to edges of tread ribs 124. In some exemplary embodiments, block 562 comprises applying a standard deviation filter to profiles 117A_n. Applying such a standard deviation filter may comprise: partitioning candidate points in profile 117A_n (e.g. which may comprise a subset of points (e.g. subject to some to some radial (y or / -dimension) filtering or subject to some axial (x or z dimension) filtering) or which may comprise all of the points in profile 117_n) into a number of sections (which may or may not overlap); for each section: computing the standard deviation of the radial (y or / ) values (referred to hereinafter as y-values, for brevity) of the points in that section; and comparing the standard deviation of the y-values to a configurable threshold. If the standard deviation of the y-values of a section is less than the threshold, then the section of the profile may be considered to be part of the bottom (radially outward surface) of the tire 102 (i.e. to be made up of tread bottom points 544); if, on the other hand, the standard deviation of the y-values of a section is greater than the threshold, then the section of the profile may be concluded not to be part of the bottom (radially outward surface) of the tire 102 (i.e. not made up of tread bottom points 544).

[0119] The output of the block 562 filtering (e.g. standard deviation filtering) is a set of tread bottom points 544 (from among the points on profile 117A_n) that are considered to be part of the tread bottom (radially outer) surface of tire 102. It will be appreciated by those skilled in the art that there are other suitable techniques which may additionally or alternatively be used in block 562 for identifying tread bottom points 544.

[0120] Method 560 (Figure 4B) then proceeds to block 564 where a binary image of the tread bottoms (tread bottom binary image 547) is generated based on tread bottom points 544. In some embodiments, generating tread bottom binary image 547 may involve: identifying the axial position (z coordinate) and azimuth position (0 coordinate about tire axis 106) of each tread bottom point 544; assigning a particular binary value to these tread bottom points 544; and assigning the other binary value to other points in profiles 117A_n. Tread bottom binary image 547 may be represented as a binary image (or plot) of the tread bottom locations of the S profiles in the axial (z coordinate) and azimuthal (0 coordinate) directions. An example of such a tread bottom binary image 547 may be seen in Figure 15A, where white tread ribs 1700 represent tread bottom points 544 and dark points represent other points on the exterior surface 108 of tire 102.

[0121] Method 560 (Figure 4B) then proceeds to block 565, where tread bottom binary image 547 is smoothed, creating a smoothed tread binary image 548. In someembodiments the block 565 smoothing process involves removing gaps or other artefacts among tread rib features 1700, such that the tread rib features 1700 (Figure 15A) in tread bottom binary image 547 become continuous “rectangular” shaped features 1750 in smoothed tread bottom binary image 548 in a notional axial / azimuthal (z / 0) plane. Figure 15B shows the notional axial / azimuthal (z / 0) plane and the “rectangular” shapes of smoothed rib features 1750 in this notional plane in smoothed tread bottom binary image 548. In some embodiments, block 565 smoothing may comprise performing a closing morphology operation on profiles 117A_n and finding a number (e.g. three in the illustrated embodiment) of connected regions of profile 117A_n with the largest areas.

[0122] Method 560 (Figure 4B) then proceeds to block 566, where the centroids of the ribs (rib centroids) 567 are determined. Rib centroids 567 may comprise the axial and radial coordinates ((z,i coordinates or (x,y) coordinates - see Figures 5 and 14) of each of the rib features 1750 at the axial and azimuth (z / 0) center of each smoothed tread binary image 548 (see Figure 15B). A tread X parameter (tread X) 508 may be determined in block 568 as the axial (z or x coordinate) offset of the axial position of the centroid 567 of the middle rib feature 1750 from its expected position. The expected axial position of the middle rib may be known, for example, from a model or parameter specification of tire 102, or from some other measurements of tire 102. A tread ^PT parameter (tread ^PT) 512 may be determined in block 569 using the axial position (z or x coordinate) and radial position (ror y coordinate) of the centroids 567 of any two of the rib features 1750:^= arctanO‘It will be appreciated that numerous variations of this method may be possible which involve identifying a tread X parameter 508 and / or a tread ^PT parameter 512, which are based on tread profiles 117A_n of tire 102, but which may not involve using centroids of tread ribs.

[0123] Tread bottom points 544 may also be used to determine a tread Y parameter (tread Y) 510. In the illustrated embodiment of Figure 4B, tread Y 510 is determined in block 546 which comprises determining the mode the radial values (r, y-values) of tread bottom points 544. The mode of the y-values of tread bottom points 544 determined in block 546 may be output as tread Y parameter (tread Y) 510.

[0124] Figure 4C schematically depicts another method 600 which may be used to implement tread landmark parameter determination in block 506 (Figure 4A) according to another example embodiment. Method 600 performs a function similar to that of method 560 (Figure 4B) - i.e. determining tread X parameter 508, tread Y parameter 510 and tread14JT parameter 512, but differs from method 560 in that method 600 compares (e.g. attempts to match) profile data 117A_n to a tread pattern template 570 and determines the offset of profile data 117A_n from the template 570 to determine tread landmark parameters tread X and tread 4^ 508, 512.

[0125] Method 600 may be used for both tires which have circumferentially contiguous features and tires which do not have circumferentially contiguous features. A representation of a tire tread surface having a non-circumferentially contiguous tread pattern is depicted in Figure 17.

[0126] Similar to method 560, method 600 takes as input some number S of profiles 117A_n, where the number S of such profiles 117A_n may be any suitable number. In some embodiments, S>10, S>100, S>1000 or S>1 ,500. However, method 600 also takes as input a tread pattern template 570, which may model, parameterize or otherwise describe one or more template profiles comprising one or more tread features, which may be identifiable in corresponding measured profile(s) 117A_n or across several adjacent profiles 117A_n. Method 600 begins with block 562C, where tread bottom points 544 and tread Y parameter 510 are determined. Block 562C may comprise functionality similar to that of blocks 562 and 546 described above in connection with method 560 (Figure 4B). Method 600 then proceeds to block 564C, where a binary image 547 of the tread bottom is generated. Block 564C may be analogous to block 564 of method 560 (Figure 4B).

[0127] Method 600 then diverges from method 560 and proceeds to block 602 which comprises comparing (e.g. attempting to match) binary image 547 to tread pattern template 570. Tread pattern template 570 may comprise, for example, a model, parameterization (e.g. a curve or a set of points) and / or some other description of tire 102 which may comprise one or more template profiles comprising one or more tread features, which may be identifiable in corresponding measured profile(s) 117A_n or across several adjacent profiles 117A_n. Templates 570 may be configurable in the sense that there may be a template (or several templates) specific to a particular design of tire 102 or to a batch of tires 102 as identified by a tire identification code (“TIC”) or to each tire 102. In some embodiments, templates 570 corresponding to individual tires 102 will be similar to other templates 570 corresponding to other tires 102 with the same TIC. In some embodiments, one or more profiles 117A_n may be used to create a template 570. In some embodiments, templates 570 may be obtained from a CAD model, in some embodiments templates 570 may be produced manually. Tread pattern template 570 may be matched to binary image547 by processor 210 in block 602 using any suitable pattern matching technique. Block 5050 may additionally or alternatively comprise human-assisted pattern matching.

[0128] Method 600 then proceeds to block 604, which involves determining the offsets of profile data 117A_n relative to a ‘normal’ position (as dictated by template 570) in the axial (z, x-direction) and about axis 101 (in the '-(-’-direction) and outputting these offsets as the tread X and treadparameters 508, 512.

[0129] Figure 4D schematically depicts another method 610 which may be used to implement tread landmark parameter determination in block 506 (Figure 4A) according to another example embodiment. Method 610 performs a function similar to that of methods 560 (Figure 4B) and 600 (Figure 4C) - i.e. determining tread X parameter 508, tread Y parameter 510 and tread 4^ parameter 512, but differs from methods 560 and 600 in that method 610 compares (e.g. attempts to match) profile data 117A_n to a tread profile template 575 to determines tread landmark parameters tread X, tread Y and tread 4^ 508, 510, 512.

[0130] Method 610 begins with block 612, which comprises comparing (e.g. attempting to match) input profile data 117A_n to tread profile template 575. Tread profile template 575 may comprise, for example, a model, parameterization (e.g. a curve or a set of points) and / or some other description of tire 102 which may comprise one or more template profiles comprising one or more tread features, which may be identifiable in corresponding measured profile(s) 117A_n or across several adjacent profiles 117A_n. An example of a particular tread profile template is shown in Figure 16. Tread profile template 575 may be matched to input profile data 117 A n by processor 210 in block 612 using any suitable chamfer matching technique such as, but not limited to, least squares fitting, or hierarchical chamfer matching as described, for example, in “Hierarchical chamfer matching: a parametric edge matching algorithm”, IEEE Transactions on Pattern Analysis and Machine Intelligence. Borgefors, 1988 which is hereby incorporated herein by reference. Block 612 may additionally or alternatively comprise human-assisted chamfer matching. Tread profile templates 575 may be configurable in the sense that there may be a template specific to a particular design of tire 102 or to a batch of tires 102 as identified by a tire identification code (“TIC”) or to each tire 102. In some embodiments, one or more profiles 117A_n may be used to create a tread profile template 575.

[0131] Method 610 then proceeds to block 614, which involves determining the offsets of profile data 117A_n relative to a ‘normal’ position (as dictated by profile template 575) in theaxial (z, x-direction), radial (r, y-direction) and about axis 101 (in the MJ-direction) and outputting these offsets as the tread X, tread Y and tread ^PT parameters 508, 510, 512.

[0132] Returning to Figure 4A, it will be appreciated that whether tread landmark detection process 502 (block 506) is performed for an average profile 117 A n*, an individual profile 117 A n and / or a plurality of individual profiles 117 A n and / or by any one or more of the methods 560, 600, 610 of Figures 4B, 4C and 4D, one tread X parameter 508, one tread Y parameter 510 and one tread 4^ parameter 512 may be output for each individual profile 117A_n - i.e. there is a one to one relationship between each of tread landmark parameters 508, 510, 512 and a corresponding profile 117A_n.

[0133] Returning to Figure 4A, bead landmark determination process 504 comprises determining bead landmark parameters in block 514. Beads are rigid semi-circular features on each side of tire 102, typically reinforced with a steel core, by which tire 102 is mounted to a rim on a vehicle. Beads 118A, 118B are shown in Figure 5 and in example profile 117A_n of Figure 14.

[0134] The bead landmark determination process of block 514 is explained herein as being performed on average profile 117A_n*. However, it will be understood that analogous techniques could be performed for an individual profile 117A_n or a plurality of individual profile(s) 117A_n. Bead landmark detection block 514 receives as input average profile 117A_n* and outputs, for each individual profile 117A_n, bead_1 X, Y parameters 516 for first bead 118A and bead_2 X, Y parameters 518 for second bead 118B. Bead landmark detection block 514 may comprise application of a template matching technique such as, but not limited to, hierarchical chamfer matching as described in “Hierarchical chamfer matching: a parametric edge matching algorithm”, IEEE Transactions on Pattern Analysis and Machine Intelligence. Borgefors, 1988, and / or any other suitable template matching technique, which uses one or more configurable templates (similar to pattern templates 570 and / or profile templates 575 described above) to identify axial coordinates (x, z coordinates) and radial coordinates (y, r coordinates) of the two beads 118A, 118B and / or of features of the two beads 118A, 118B in average profile 117 A n*. As discussed above, such templates may comprise, for example, a model, parameterization and / or some other description of tire 102 and / or its beads 118A, 118B. While bead_1 X, Y coordinates 516 and bead_2 X,Y coordinates 518 may be determined for each average profile 117 A n*, it will be appreciated that each set of bead_1 X, Y coordinates 516 and bead_2 X,Y coordinates 518 obtained in block 514 relates to an individual profile 117A_n - that is, there is a one to one relationship between each of bead_1 X, Y coordinates 516 and bead_2 X,Y coordinates 518 and acorresponding profile 117A_n. The templates used in the block 514 determination of bead landmark parameters may be configurable in the sense that there may be a template specific to a particular design of tire 102 or to a batch of tires 102 as identified by a tire identification code (“TIC”) or to each tire 102. In some embodiments templates corresponding to individual tires 102 will be similar to other templates corresponding to tires 102 with the same TIC. In some embodiments one or more profiles 117A_n may be used to create a template.

[0135] In some embodiments, profiles 117A may encompass both the interior and the exterior surface 108 of tire 102, in others the interior and the exterior surfaces 108 may be represented by separate profiles, although there may be correlated or ordered pairs of interior and exterior profiles for each 0 coordinate about tire axis 106 - i.e.one exterior surface profile and one correlated interior surface profile for each 0. In embodiments where the interior and the exterior surfaces 108 are represented by the same profiles 117A, it will be understood that only one set landmark parameters would be necessary to adjust a profile 117A_n as has been discussed above. Similarly, in some embodiments the interior and exterior surfaces 108 of tire 102 may be represented by separate profiles which may be correlated by a shared 0 coordinate (e.g. in cases where they were measured simultaneously) and therefore the tread parameters for exterior profile may be used for adjusting the interior profile or vice versa. However, in some embodiments, profiles for the interior surface may not be correlated to profiles for the exterior surface. In such circumstances, it may be desirable to determine unique landmark parameters for the profiles of the interior surface. Such interior surface landmark parameters could be based on any identifiable features on the interior surface profiles or tire 102.

[0136] Returning to Figure 3, after determining landmark parameters 311 (e.g. tread X 508, tread Y 510, treadparameter 512, bead_1 X, Y 516, bead_2 X, Y 518 and / or any other landmark parameters, such as interior surface landmark parameters), branch 301 -1 (Figure 3) proceeds to optional block 309 which involves smoothing landmark parameters 311 identified in block 308. As was described above, this may involve smoothing or averaging of landmark parameters 311 identified in some number S of profiles, where the number S of such profiles may be any suitable number. In some embodiments, S>10, S>100, S>1000 or S>1 ,500. This smoothing or averaging of landmarks parameters 311 over S profiles may produce averaged or smoothed landmark parameters 311 * (e.g. tread X 508*, tread Y 510*, tread W 512, bead_1 X, Y 516*, bead_2 X, Y 518* and / or other landmark parameters). In some embodiments, branch 301 -1 skips block 309 and no landmark smoothing occurs.

[0137] Branch 301 -1 proceeds to block 310, which involves performing rotational and / or translational transformations to profile 117A 1 . Such rotational and / or translational transformations may be based on landmark parameters 311 and / or averaged / smoothed landmark parameters 311 * for profile 117A 1 determined in block 308 and / or block 309. The block 310 rotational and / or translational transformations may correct profile 117A 1 to mitigate variable alignment and to obtain alignment-adjusted profile 123A 1.

[0138] In some embodiments, some portions of branches 301 -1 , 301 -2 ... 301 -A / may be performed in parallel, for example landmark parameter determination block 308 may be performed for all branches before optional block 309 landmark smoothing occurs and profile adjustment block 310 may not occur until after landmark parameters have been determined (in block 308) and, optionally smoothed (in block 309), for all profiles.

[0139] Figure 4E depicts a method 580 for applying determined landmark parameters 311 (or smoothed landmark parameters 311 *) to a profile 117 A n to produce alignment adjusted profile 123A_n. Method 580 may be used to implement block 310 of branch 301 -1 (Figure 3) according to a particular embodiment. Method 580 is shown for a particular profile 117A_n and is described as using landmark parameters 311 , it being appreciated that method 580 may be repeated for each profile 117A_n and may additionally or alternatively use smoothed landmark parameters 311 *.

[0140] Method 580 begins with blocks 582 and 584 which involve respectively applying tread X parameter 508 to perform a X translation of profile 117A_n and applying tread Y parameter 510 to perform a Y translation of profile 117A_n. Profiles 117A_n may be considered to be represented as arrays of single points P(x,y), each with an axial (x) coordinate and a radial (y) coordinate - see axes depicted in Figures 5 and 14. Blocks 582 and 584 may involve comparing the block 308 landmark tread X and tread Y parameters 508, 510 to nominal tread X and tread Y values to derive AX and AY values for profile 117A_n. In some particular embodiments, the nominal tread X and tread Y values may be provided by a model or template (e.g. tread pattern template 570 described above), which may be different for each tire 102 and which may be based on a TIC for tire 102. The AX and AY values may then be applied to each of the points P(x,y) in a profile 117A_n to obtain a new array of points P(x+AX,y+AY).

[0141] Method 580 then proceeds to block 586 which involves determining a bead angle ( 'B). In some particular embodiments, block 586 may comprise determining bead angle ipBusing one or more of bead landmark parameters 516, 518.

[0142] In some embodiments, the block 586 bead angle iBmay be calculated as the angle between tire axis 106 and a line connecting bead 516 and bead 518 in profile 117A_n. This angle ipBmay be calculated as: ipB= arctan

[0143] Method 580 then proceeds to block 588, where a profile rotation iPis applied to profile 117A_n. In some embodiments, bead angle ipBmay be used as the block 588 profile rotation (ipP) for profile 117A_n. In some embodiments, tread angle ipTmay be used as profile rotation (ipP) for profile 117A_n. In some embodiments, tread angle ipTmay be used as the block 588 profile rotation (ipP) if bead landmark parameters 516, 518 (which may correspond to the positions of beads 118A, 118B) deviate from their expected values (or from one another) by a threshold amount (e.g. by 1 mm, or 10mm, or 50mm). In some embodiments tread angle ipTmay be used as the block 588 profile rotation (ipP) if bead landmark parameters 516, 518 (which may correspond to the positions of beads 118A, 118B) deviate from their expected values (or from one another) beyond a threshold distance in the axial (z or x) direction. In some embodiments, an average or weighted average of tread angle ipTand bead angle ipBmay be used as the block 588 profile rotation ipP.

[0144] In some embodiments, in block 588, a profile rotation ipPmay be applied to points P(x,y) of profile 117A_n to obtain a new array of points P(x’,y’) using an operation such as:where it is assumed that the block 582 and 584 translations have already been applied to points P(x,y). In some embodiments, other forms of rotation transformations may be used in block 588 to apply a rotation ipP. It will be understood that the block 588 rotation may be performed prior to the block 582, 584 translations or after the block 582, 584 translations. It will be further appreciated that other operations for performing translations and / or rotations of profiles 117A_n are known in the art and may be used to align profiles 117A_n into alignment adjusted profiles 123A_n.

[0145] The output of the block 310 (Figure 3) and method 580 (Figure 4E) process is alignment-adjusted profiles 123A_n for each measured profile 117A_n, n=1 , 2, ...A / . Referring back to Figure 1 , upon completion of block 123 and determination of alignment- adjusted profiles 123A_n, method 100 proceeds to block 125. Block 125 comprises generating reference curve 125A based on alignment-adjusted profiles 123A_n. As usedand explained in more detail herein, a “reference curve” is a representative curve corresponding to a surface 108 of tire 102 at a single azimuthal (0) coordinate, which, when rotated 360 degrees around tire axis 106 would form a representative surface of tire 102 excluding some non circumferentially continuous features such as embossed lettering or embossed design features. A reference curve may correspond to an exterior surface of tire 102, an interior surface of tire 102, or both the interior and exterior surfaces of tire 102.

[0146] In some embodiments reference curve 125A may be produced using a plurality of alignment-adjusted profiles 123A_n. Figure 5 illustrates an example representation of a combined profile point image 700, where a plurality of alignment adjusted profiles 123A_n have been overlaid on the same axial-radial plane to form a combined profile point image 700 comprising points from a plurality of alignment-adjusted profiles 123A_n. Such a combined profile point image 700 may be used to produce a reference curve 125A, as discussed in more detail below.

[0147] Block 125 may involve using alignment-adjusted profile data 123A to generate a reference curve 125A which is representative of a plurality of alignment-adjusted profiles 123A_n. A reference curve 125A may be obtained from alignment-adjusted profile data 123A by fitting a curve through alignment-adjusted profile data 123A using any known curve-fitting technique, such techniques may be performed manually (such as by an operator) or automatically (such as by processor 210). Figure 6A depicts an example portion of an alignment-adjusted profile 123A_n. Figure 6B illustrates an example portion of a reference curve 125A which has been fit through the portion of alignment-adjusted profile 123A_n shown in Figure 6A.

[0148] One suitable, but non-limiting technique for fitting a reference curve 125A to alignment-adjusted profile data 123A is depicted in Figure 6C. Figure 6C illustrates a technique of fitting a curve through alignment-adjust profile 123A_n by fitting a series of arcs and / or straight lines 650 (referred to hereinafter as arcs 650 for brevity) to alignmentadjust profile 123A_n wherein the tangents of the arcs 650 are equal at the point at which adjacent arcs 650 meet. The type of curve created by this technique is known as a g1 PolyArcSeg. The g1 continuity condition generally requires that the second derivative of two coincident curves (curves that meet at a point) be opposite; however, in the g1 PolyArcSeg technique, the curve used may include coincident arcs 650 with second derivatives of the same sign and may include arcs 650 that are straight lines. Other additional or alternative techniques may be used for fitting a curve to alignment-adjusted profile data 123A to obtain a reference curve 125A.

[0149] In some embodiments, the alignment adjusted profile(s) 123A_n used for fitting reference curve 125A may be a single representative alignment-adjusted profile 123A_n which has been selected for the purpose of producing reference curve 125A, a plurality of alignment-adjusted profiles 123A_n and / or an average alignment adjusted profile 123A* produced from a plurality of individual alignment-adjusted profiles 123A_n. In some embodiments, the block 125 curve fitting techniques are applied to a combined profile point image, such as combined profile point image 700 shown in Figure 5, where a plurality of alignment-adjusted profile 123A_n are combined to form a single set of points through which a reference curve 125A is fit.

[0150] In some embodiments, less than a hundred arcs 650 are used to generate a reference curve 125A that approximates an alignment-adjusted profile data 123A. In some embodiments more than a hundred arcs 650 are used to generate a reference curve 125A that approximates alignment adjusted profile data 123A. In some embodiments, the g1 PolyArcSeg curves may be generated using a computer processor, while in some embodiments the g1 PolyArcSeg curves may additionally or alternatively be produced manually by an operator. In some embodiments, an operator may use CAD software to produce the g1 PolyArcSeg curves. It will be understood that, in some embodiments, other types of curves other than G1 PolyArcSegs may be suitable for fitting a reference curve 125A to alignment-adjusted profile data 123A.

[0151] Another suitable non-limiting technique which may additionally or alternatively be used for producing reference curve 125A in block 125 involves the use of processor 210 to sort the data points contained in the alignment-adjusted profile data 123A into bins corresponding to defined axial (z or x coordinate) by radial (r or / coordinate) areas. Such bins can be understood visually with reference to Figure 5A, where the combined point image 700 of Figure 5 (which comprises points from a plurality of alignment-adjusted profiles 123A_n) is overlaid with an axial by radial mesh 750. Processor 210 may then fit a curve automatically through the axial (z, x) coordinates and the radial (r, y) coordinates of bins. In some embodiments, a set of bins through which reference curve 125A may be fit or which may be used to determine reference curve 125A includes bins have a threshold number of data points from alignment-adjusted profile data 123A.

[0152] Referring to Figure 5A, in some embodiments, selecting bins through which to fit the reference curve 125A comprises a method where a central point 725 is selected. By way of non-limiting example, such a central point 725, may be selected to be: a point 725 for which the difference between the distances from the point 725 to each bin containing alignment-adjusted profile data 123A (or a threshold number of points from alignment-adjusted profile data 123A) is minimized; a point for which an aggregate sum of the distances from the point 725 to each bin containing alignment-adjusted profile data 123A (or a threshold number of points from alignment-adjusted profile data 123A) is minimized; and / or any other suitable technique. In some such embodiments, distances between bins and point 725 may be weighted by the number of points in each bin.

[0153] As shown in Figure 5A, lines 740 may then be radiated from central point 725 in a plurality of angular directions (a subset of which lines 740 and angular directions are shown in Figure 5A). Histograms of the alignment-adjusted profile data 123A in bins along each line 740 may be created (e.g. based on the number of alignment-adjusted profile data 123A points in each bin intersected by a line 740), and centroids of each such histogram may be determined. These centroids may then be used as the points to which the reference curve 125A may be fit. It will be understood that a variety of additional and / or alternative methods of selecting appropriate points (based on alignment-adjusted profile data 123A) to which reference curve 125A may be fit may be used.

[0154] It will be understood that Figure 5A is merely for representative purposes and that the mesh used by processor 210 would generally be much finer than the mesh illustrated in Figure 5A. In some embodiments the mesh used may correspond to a resolution of approximately 0.25 mm2or finer.

[0155] In some embodiments, separate reference curves 125A may be generated for both the interior and exterior surfaces of tire 102. In some embodiments, a reference curve can be generated from a model (such as tread pattern template 570, for example). In some embodiments, a reference curve can be created using a CAD model and a TIC-specific tire specification.

[0156] Referring back to Figure 1 , upon completion of generating reference curve 125A in block 125, method 100 proceeds to block 127. Block 127 comprises determining a plurality of sets of relative profile data 127A. Each set of relative profile data 127A_n comprises an ordered set of scalar values representing the distance between each of a plurality of data points in an alignment-adjusted profile 123A_n and the nearest point on the reference curve 125A to each of the points in the alignment-adjusted profile 123A_n.

[0157] Each element of relative profile data 127A may be obtained from the distance between a point on an alignment adjusted profile 123A_n and the nearest point on reference curve 125A. One non-limiting example of a method 900 for determining anexample set of relative profile data 127A_n is schematically depicted in Figure 7. Method 900 of the illustrated embodiment depicted in Figure 7 comprises dividing reference curve 125A into a number of segments 930 (which may be of equal length and which are delineated in Figure 7 by cross-marks 930A) and then obtaining a distance 940 between the nearest point of the alignment adjusted profile 123A_n and each segment 930. Each distance 940 may then be recorded as the relative profile data 127A_n value for that segment 930, thereby obtaining a set of relative profile data values distributed along the length of reference curve 125A (one relative profile data value (a distance 940) for each segment 930) which together constitute relative profile data 127A_n. A person skilled in the art will recognize that many similar techniques may additionally or alternative be used to obtain relative profile data 127A_n, such as recording all of the distances between alignment-adjusted profile 123A_n points and reference curve 125A in each segment 930 and averaging the results or using the furthest distance rather than the nearest distance.

[0158] Upon completion of determining relative profile data 127A in block 127, method 100 (Figure 1 ) proceeds to block 133. Block 133 comprises identifying irregularities in tire 102, based at least in part on relative profile data 127A. Irregularities identified in block 133 may include, without limitation: excess material, insufficient material, contaminants which have affected the surface 108 of tire 102, blisters formed during the manufacturing process, incorrect splicing of fabric, bladder irregularities, and / or the like. Such irregularities may occur on any surface 108 of tire 102 including the sidewalls of tire 102, on the tread of tire 102 or on the interior surface of tire 102.

[0159] As a non limiting example of how relative profile data 127A may be used to identify irregularities in tire 102, relative profile data 127A may be used to produce visualizations and / or models of protrusions and depressions on the surface 108 of tire 102 which deviate from the expected surface of the tire 102. An example visualization is depicted in Figure 8. Figure 8 depicts a height map 800 (i.e. a type of visualization or model) for a portion of a surface 108 of tire 102 produced using relative profile data 127A. The luminosity (i.e. a grey level between black (zero luminosity) and white (maximal luminosity)) of each pixel of height map 800 indicates the magnitude of the relative profile data 127A (i.e. deviation from reference curve 125A) at that location of height map 800 which in turn corresponds to a location on surface 108 of tire 102.

[0160] The exemplary Figure 8 height map 800 relates to a portion of the surface 108 of a tire 102 that includes a portion of the sidewall. In the Figure 8 illustration, the vertical axis (ordinate) represents an extension along a reference curve 125A for a particular tire 102and the horizontal axis (abscissa) represents an extension about tire axis 106 (i.e. each abscissa coordinate corresponds to a particular azimuthal angle (0) about tire axis 106 or a particular set of relative profile data 127A_n corresponding to a particular profile 1 17 A n). It will be recognized that this Figure 8 style of visualization will result in distortion of the representation of tire 102 as the curvature of the surface 108 of tire 102 is represented with a flat image. The spacing between relative profile data 127A_n corresponding to particular profiles 117A_n is represented as a uniform distance or spacing in the Figure 8 height map 800 rather than being dependent upon the radial position of points on the profiles 117A_n. The inventors have determined that this distortion has minimal impact on the efficacy of identifying irregularities in height map 800. In the embodiment depicted in Figure 8, the luminosity of each pixel represents a deviation from the reference curve 125A at a particular location of tire surface 108, where lighter (more luminous) regions are higher (further to one side of reference curve 125A) than darker (less luminous) regions. It can be seen how lettering 820 and designs embossed and debossed on the surface 108 of the tire 102 are visible in height map 800, as they deviate from the smooth surface described by reference curve 125A.

[0161] Correlating irregularities identified on height map 800 to locations on surface 108 of tire 102 may be performed by determining: the position of an irregularity in the ordinate (vertical axis) of height map 800 which corresponds to a position along (e.g. a segment 930 (see Figure 7) of) a reference curve 125A and which may be correlated with one or more corresponding points on an alignment-adjusted profile 123A_n and / or a profile 117A_n (as discussed above); and the position of an irregularity in the abscissa (horizontal axis) of height map 800 which corresponds to a particular azimuthal angle (0) about tire axis 106 and a corresponding particular profile 117A_n.

[0162] In some embodiments, profile 117A 0 may be defined as the first profile taken at the beginning of the DOT code of tire 102 and each subsequent profile 117A counted in a particular angular direction about tire axis 106. It will be appreciated that the abscissa and ordinate of the Figure 8 height map 800 could be reversed. It will be further appreciated that a variety of techniques may be used to correlate relative profile data 127A to locations on tire 102. In some embodiments, it may be sufficient for an operator to know an approximate location of an irregularity relative to the DOT code to enable the operator to identify the irregularity on tire 102. In some embodiments an operator may be able to identify the approximate position based on knowing a component of relative profile data 127A_n on which the irregularity is present; in some embodiments a thumbnail image may be providedto the operator which identifies the region of tire 102 in which an irregularity is identified; in some embodiments a thumbnail image of height map 800 surrounding an irregularity may be sufficient for an operator to identify the location of the irregularity on surface 108 of tire 102.

[0163] In some embodiments, pixels of height maps 800 may be usefully encoded as 8-bit luminosity values, where each bit increment represents 25 pm or some other suitable discretization interval (e.g. between 10pm and 50pm). In some embodiments, regions of tire surface typically having features which exceed the 8-bit encoded depth may be encoded using larger bit increments or, in other embodiments, be offset to a second 8-bit region wherein the magnitude of relative profile data 127A represented by the second 8-bit region extends at least in part to a magnitude of relative profile data 127A beyond the range encodable by the first 8-bit region. In some embodiments, the images may be stored as Portable Network Graphics (“PNG”) file. It will be recognized that other encoding formats, bit ranges and bit increments may be used.

[0164] Another exemplary height map 950 is depicted in Figure 9. Height map 950 may be generated in a manner similar to the Figure 8 height map 800. The exemplary Figure 9 height map 950 corresponds to a portion of an interior surface of a tire 102. Height map 950 is a form height map which illustrates how visualization of relative profile data 127A allows the form of tire 102 to be easily identified. The vertical bands of lighter colour 960, and the horizontal bands of lighter colour 970, seen in height map 950 are indicative of locations of overlapping joins or splices in the various fabric components which are present in the tire surface due to the process of tire assembly. Height map 950 may be useful for identifying incorrect splices as well as anomalies, such as bladder irregularities. For example, irregularity 980 at the bottom left of the Figure 9 height map 950 is a blister, which has been rendered easily identifiable by the height map 950 visualization of relative profile data 127A.

[0165] In some embodiments, rule-based processing can be applied to relative profile data 127A or to height maps or other models or visualizations based on relative profile data 127A in block 133 to quickly identify irregularities in tire 102. In some embodiments, machine learning may be used to identify irregularities in relative profile data 127A, height maps and / or other models or visualizations based on relative profile data 127A in block 133. In some embodiments, human operators may additionally or alter natively review relative profile data 127A, height maps and / or other models or visualizations based on relative profile data 127A in block 133 to identify irregularities.

[0166] A non limiting example of a rule based processing method which may be used in block 133 to identify irregularities involves the use of a local standard deviation filter on regions expected to be nominally flat such as the surface of tread ribs 124 (see Figure 5 and / or Figure 14). With such a technique, any standard deviations beyond a threshold could indicate missing rubber known as a “tread light” anomaly. Another non limiting example of a rule-based processing method which may be used in block 133 to identify irregularities involves a rule which identifies features which exceed the average surface height by a configurable threshold distance. It will be appreciated that a number suitable rules based on thresholds, statistical information and / or the like may be used to identify irregularities in block 133 based on relative profile data 127A, height maps and / or other models or visualizations based on relative profile data 127A.

[0167] Figures 10A, 10B, 10C, 10D, 10E, 10F and 10G (collectively, Figure 10) depict a non limiting example of a technique which may be used in block 133 to identify irregularities. In the Figure 10 technique, a form height map 950 (Figure 10A) may be flattened to create a normalized height map 1000 (Figure 10C). Normalized height map 1000 may be based on normalized relative profile data 1240, which may be obtained from relative profile data 127A by: identifying smooth regions 1020 in relative profile data 127A (or original height map 950), fitting a curve to those smooth regions 1020 and normalizing the height values against the fitted curve to generate normalized relative profile data. This normalization process may remove large-scale features (such as joins or splices) from the heat map and allows an inspector and / or a suitably configured computer to quickly identify irregularities and / or defects by reducing distractions created by slight variations in the large-scale features of tire 102.

[0168] Figure 10A depicts a form height map (specifically, a portion of the Figure 9 form height map) 950 wherein expected joins and splices in tire 102 are visible as vertical bands 960 and horizontal bands 970. Irregularity 980 in area 1010 is visible in the lower left corner of the Figure 10A height map 950.

[0169] Figure 10B is an enlarged view of the region 1010 surrounding irregularity 980 identified in the bottom left corner of Figure 10A. In the enlarged scale of Figure 10B, the finer-scale texture of tire 102 may be seen clearly. In the Figure 10B example, the finer- scale texture of tire 102 is divided by lattice structures or lattices 1030 that are generally linear raised features on the surface of tire 102. Walls 1040 are also lighter (typically curved) features located between lattices 1030 and inset within wall 1040 are darker features referred to herein as ponds 1050. In some embodiments, ponds 1050 are smoothin comparison with walls 1040 or lattices 1030. In some such embodiments, smooth regions 1020 of surface 108 of tire 102 may be identified within and / or between ponds 1050.

[0170] In some embodiments, smooth regions 1020 may be identified by calculating variations in relative profile data 127A over a small region, for example an area between 0.1 mm2and 1 mm2or between 0.5mm2and 0.7mm2or an area of 0.6mm2or some other suitably sized region. Regions with a local standard deviation less than a threshold value may be considered smooth (e.g. to be smooth regions 1020). In some embodiments, such threshold values may be less than 1 mm, or less than 100pm or less than 50pm. It will be understood that the standard deviation used and / or the size of the area in which the standard deviation is calculated may be dependant upon the size of features present on a particular tire 102. For example, the standard deviation may be selected such that the roughness of walls 1040 is greater than the threshold selected, or the diameter of the area selected may be comparable to the width of lattices 1030, or half the width of lattices 1030.

[0171] Figure 10C depicts a normalized height map 1000 of the same region as shown in the Figure 10A height map 950, where the smooth regions 1020 identified in Figure 10B have been used to fit a surface between smooth regions 1020, where this fitted surface is in turn used to normalize relative profile data 127A. This normalization may be performed, for example, by fitting a surface to the identified smooth regions 1020 and normalized height map 1000 may be determined as a difference between relative profile data 127A and the fitted surface. The pattern visible in the Figure 10C height map 1000 is the fine-scale textured pattern created by a bladder that is inflated during a curing process during manufacturing to push the tire rubber assembly against a rigid metal mold which forms the external sidewall and tread pattern.

[0172] Figure 10D depicts a method 1100 of normalizing relative profile data 127A according to a particular embodiment. Method 1100 takes as input relative profile data 127A and begins with block 1120, where smooth regions 1020 of ponds 1050 are identified. In some embodiments, smooth regions 1020 may be identified as regions of the surface 108 of tire 102 where the variation (and / or standard deviation) in relative profile data 127A is less than a threshold value. In some embodiments block 1120 may identify smooth regions 1020 having areas between 0.1 mm2and 1 mm2or between 0.5mm2and 0.7mm2. In some embodiments, block 1120 may utilize a threshold standard deviation of less than 1 mm, or less than 100pm or less than 50pm to identify smooth regions 1020. Method 1100 then proceeds to block 1130, where a surface 1260 is interpolated (fit) between smooth regions 1020. Method 1100 then proceeds to block 1140, where normalized relative profile data1240 is determined. Block 1140 may determine normalized relative profile data 1240 by calculating the distance 1250 between relative profile data 127A and the block 1130 surface 1260.

[0173] Method 1100 may be understood graphically with reference to Figures 10E and 10F. Figure 10E depicts a portion of a height map 950 which includes a vertical splice 960. Figure 10E also depicts a plot 1080 of the grey scale values (or relative profile data 127A) along a section 1070 of the Figure 10E height map 950. The Figure 10E plot 1080 is shown enlarged in Figure 10F. Method 1100 may be employed to normalize relative profile data 127A (or a corresponding height map) such that the effect or appearance of a large-scale feature (such as vertical splice 960) may be mitigated.

[0174] Some of the steps of method 1100 can be described with reference to the Figure 10F plot 1080. Method 1100 begins in block 1120 by identifying smooth regions 1020 of ponds 1050. Ponds 1050 may be identified as the local minima of chart 1080. Method 1100 then proceeded to block 1130, in which surface 1260 is interpolated between smooth regions 1020. Surface 1260 is visually represented in Figure 10F by the dotted line fit along the local minima of plot 1080. Method 1100 then proceeds to block 1140 which involves determining normalized relative profile data 1240 to be the distance between surface 1260 and relative profile data 127A. Within the Figure 10F plot 1080, this block 1140 calculation is visually represented by distance 1250 between relative profile data 127A and surface 1260.

[0175] Figure 10G depicts an exemplary form height map 990 of a portion of a tire 102 created using relative profile data 127A that contains a splice 992. Figure 10H depicts an exemplary form height map 994 of a portion of a tire 102 where splice 992 has been removed from the normalized relative profile data 1240 used to create the Figure 10H height map 994. The height maps 990, 994 of Figures 10G and 10H are overlaid with plots of the greyscale values (relative profile data 127A in the case of the Figure 10G height map 990 and normalized relative profile data 1240 in the case of the Figure 10H height map 994) across similar regions of the height maps 990, 994. A person skilled in the art may understand from these plots the benefit provided by method 1100 in which the variation in height map 990 created by the large-scale feature (e.g. splice 992) is removed in height map 994. In particular, a person skilled in the art will appreciate that method 1100 to generate normalized relative profile data 1240 may allow for easier identification of irregularities and / or identification of irregularities within normalized relative profile data 1240 which would otherwise not be identifiable from relative profile data 127A.

[0176] In some embodiments, alignment-adjusted profile data 123A may be assembled into a point cloud model of the surface of tire 102. Figure 12 depicts an example rendering or representation of a point cloud model of the surface 108 of a tire 102 as viewed from near the tread surface.Interpretation of terms

[0177] While a number of exemplary aspects and embodiments have been discussed above, those of skill in the art will recognize certain modifications, permutations, additions and sub-combinations thereof. It is therefore intended that the following appended claims and claims hereafter introduced are interpreted to include all such modifications, permutations, additions and sub-combinations as are consistent with the broadest interpretation of the specification as a whole.

[0178] Where a component is referred to above, unless otherwise indicated, reference to that component (including a reference to a “means”) should be interpreted as including as equivalents of that component any component which performs the function of the described component (i.e. that is functionally equivalent), including components which are not structurally equivalent to the disclosed structure which performs the function in the illustrated exemplary embodiments of the invention.

[0179] Unless the context clearly requires otherwise, throughout the description and any accompanying claims (where present), the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, that is, in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof, means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, shall refer to this document as a whole and not to any particular portions. Where the context permits, words using the singular or plural number may also include the plural or singular number respectively. The word “or,” in reference to a list of two or more items, covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list.

[0180] Embodiments of the invention may be implemented using specifically designed hardware, configurable hardware, programmable data processors configured by the provision of software (which may optionally comprise “firmware”) capable of executing on the data processors, special purpose computers or data processors that are specificallyprogrammed, configured, or constructed to perform one or more steps in a method and / or to provide the functionality as explained in detail herein and / or combinations of two or more of these. Examples of specifically designed hardware are: logic circuits, application-specific integrated circuits (“ASICs”), large scale integrated circuits (“LSIs”), very large scale integrated circuits (“VLSIs”), and the like. Examples of configurable hardware are: one or more programmable logic devices such as programmable array logic (“PALs”), programmable logic arrays (“PLAs”), and field programmable gate arrays (“FPGAs”). Examples of programmable data processors are: microprocessors, digital signal processors (“DSPs”), embedded processors, graphics processors, math co-processors, general purpose computers, server computers, cloud computers, mainframe computers, computer workstations, and the like. For example, one or more data processors in a control circuit for a device may implement methods and / or provide functionality as described herein by executing software instructions in a program memory accessible to the processors.

[0181] Software and other modules may reside on servers, workstations, personal computers, tablet computers, image data encoders, image data decoders, PDAs, media players, PIDs and other devices suitable for the purposes described herein. Those skilled in the relevant art will appreciate that aspects of the system can be practiced with other communications, data processing, or computer system configurations, including: Internet appliances, hand-held devices (including personal digital assistants (PDAs)), wearable computers, all manner of cellular or mobile phones, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like.

[0182] While processes or blocks of some methods are presented herein in a given order, alternative examples may perform routines having steps, or employ systems having blocks, in a different order, and some processes or blocks may be deleted, moved, added, subdivided, combined, and / or modified to provide alternative or sub-combinations. Each of these processes or blocks may be implemented in a variety of different ways. Also, while processes or blocks are at times shown as being performed in series, these processes or blocks may instead be performed in parallel, or may be performed at different times. In addition, while elements are at times shown as being performed sequentially, they may instead be performed simultaneously or in different sequences. It is therefore intended that the following claims are interpreted to include all such variations as are within their intended scope.

[0183] Various features are described herein as being present in “some embodiments”. Such features are not mandatory and may not be present in all embodiments. Embodiments of the invention may include zero, any one or any combination of two or more of such features. This is limited only to the extent that certain ones of such features are incompatible with other ones of such features in the sense that it would be impossible for a person of ordinary skill in the art to construct a practical embodiment that combines such incompatible features. Consequently, the description that “some embodiments” possess feature A and “some embodiments” possess feature B should be interpreted as an express indication that the inventors also contemplate embodiments which combine features A and B (unless the description states otherwise or features A and B are fundamentally incompatible).

[0184] Specific examples of systems, methods and apparatus have been described herein for purposes of illustration. These are only examples. The technology provided herein can be applied to systems other than the example systems described above. Many alterations, modifications, additions, omissions, and permutations are possible within the practice of this invention. This invention includes variations on described embodiments that would be apparent to the skilled addressee, including variations obtained by: replacing features, elements and / or acts with equivalent features, elements and / or acts; mixing and matching of features, elements and / or acts from different embodiments; combining features, elements and / or acts from embodiments as described herein with features, elements and / or acts of other technology; and / or omitting combining features, elements and / or acts from described embodiments.

Claims

CLAIMS:1 . A method for inspecting vehicular tires for irregularities, the method comprising: obtaining, during relative rotation between a tire and one or more sensors about a tire axis and at each of a plurality of relative angular orientations between the tire and the one or more sensors, a set of profile data, wherein each set of profile data comprises radial and axial spatial locations of a plurality of points on a surface of the tire at the corresponding relative angular orientation between the tire and the one or more sensors; obtaining a reference curve representative of at least a portion of the tire surface at an angular orientation about the tire axis; for each set of profile data, processing the set of profile data and the reference curve to thereby obtain a corresponding set of relative profile data based on the set of profile data and the reference curve; and facilitating identification of one or more irregularities in the surface of the tire based at least in part on the sets of relative profile data.

2. The method of claim 1 or any other claim herein comprising: processing the profile data to mitigate variable alignment of the tire as between the plurality of relative angular orientations and to thereby obtain, for each set of profile data, a corresponding set of alignment-adjusted profile data.

3. The method of claim 2 or any other claim herein wherein obtaining the reference curve comprises generating the reference curve based at least in part on the alignment-adjusted profile data.

4. The method of claim 2 or any other claim herein wherein obtaining the reference curve representative of an angular profile of the tire comprises generating a reference curve based on a tire-specific model of the tire.

5. The method of any one of claims 2 to 4 or any other claim herein wherein processing the set of profile data and the reference curve to thereby obtain the corresponding set of relative profile data comprises comparing the corresponding set of alignment- adjusted profile data to the reference curve to thereby obtain the corresponding set of relative profile data.

6. The method of any one of claims 2 to 5 or any other claim herein wherein processing the set of profile data and the reference curve to thereby obtain the corresponding set of relative profile data comprises determining distances between the corresponding set of alignment-adjusted profile data and the reference curve at a number of locations along the reference curve, the corresponding set of relative profile data comprising the distances.

7. The method of 2 to 6 or any other claim herein wherein processing the profile data to mitigate variable alignment of the tire comprises, for each set of profile data, adjusting a position of the set of profile data.

8. The method of claim 7 or any other claim herein wherein adjusting the position of the set of profile data comprises: determining a location of a tire feature in one or more sets of profile data; and adjusting the position of the set of profile data based at least in past on the location of the tire feature.

9. The method of any one of claims 7 to 8 or any other claim herein wherein adjusting the position of the set of profile data comprises one or more of: adjusting an axial position of the set of profile data along the tire axis; adjusting a radial position of the set of profile data in a radial direction orthogonal to the tire axis; and adjusting an angular orientation of the set of profile data about an axis orthogonal to both the tire axis and the radial direction.

10. The method of claim 9 or any other claim herein wherein adjusting the axial position of the set of profile data along the tire axis comprises: determining an axial location of a tire feature in one or more sets of profile data; and adjusting the axial position of the set of profile data along the tire axis based at least in past on the axial location of the tire feature.11 . The method of any one of claims 9 to 10 or any other claim herein wherein adjusting the radial position of the set of profile data in the radial direction comprises: determining a radial location of a tire feature in one or more sets of profile data; and adjusting the radial position of the set of profile data along the tire axis based at least in past on the radial location of the tire feature.

12. The method of any one of claims 9 to 11 or any other claim herein wherein adjusting the angular orientation of the set of profile data around the axis orthogonal to both the tire axis and the radial direction comprises: determining an angular rotation of a tire feature in one or more sets of profile data; and adjusting the angular orientation of the set of profile data about the axis orthogonal to both the tire axis and the radial direction based at least in past on the angular orientation of the tire feature.

13. The method of any one of claims 9 to 11 or any other claim herein wherein the tire feature comprises any one or more of: a tread rib; a tread pattern; a tire bead; and a tread bottom.

14. The method of claim 8 or any other claim herein wherein determining the location of a tire feature comprises:identifying tread bottom points; and identifying a tread feature within the bottom points.

15. The method of claim 14 or any other claim herein wherein determining the location of a tire feature comprises generating a binary image of tread bottom points.

16. The method of claim 15 or any other claim herein, wherein generating the binary image of the tread bottom points comprises applying a standard deviation filter to the one or more sets of profile data.

17. The method of any one of claims 15 to 16 or any other claim herein wherein determining the location of a tire feature comprises smoothing the binary image to identify a circumferentially contiguous tire feature.

18. The method of any one of claims 15 to 17 or any other claim herein wherein determining the location of a tire feature comprises matching the binary image to a tread pattern template.

19. The method of claim 8 or any other claim herein wherein determining the location of a tire feature comprises matching one of the one or more sets of profile data to a tread profile template.

20. The method of claim 7 or any other claim herein wherein processing the profile data to mitigate variable alignment of the tire comprises: matching one or more sets of profile data to a tread pattern; identifying a tire feature in the one or more sets of profile data based at least in part on the tread pattern; determining an axial location of the tire feature in an axial direction parallel with the tire axis;determining radial locations of the tire feature in a radial direction orthogonal to the axial direction; and determining an angular orientation of the tire feature about an axis orthogonal to both the axial direction and the radial direction.21 . The method of any one of claims 8 to 20 or any other claim herein, wherein the one or more sets of profile data comprise averaged sets of profile data.

22. The method of any one of claims 3 to 21 or any other claim herein wherein generating the reference curve comprises fitting a series of arcs to data points of one or more of the plurality of sets of alignment-adjusted profile data.

23. The method of any one of claims 3 to 22 or any other claim herein wherein generating the reference curve comprises: grouping the profile data of each of the plurality of sets of alignment adjusted profile data into bins based on their axial and radial values; identifying bins containing a threshold amount of alignment adjusted data points; and fitting a curve through the identified bins.

24. The method of claim 23 or any other claim herein wherein each bin represents a rectangular area.

25. The method of any one of claims 22 to 24 or any other claim herein wherein the reference curve is a g1 PolySegArc.

26. The method of claim 5 or any other claim herein wherein comparing the set of alignment-adjusted profile data to the reference curve to thereby obtain the corresponding set of relative profile data comprises:dividing the reference curve into a plurality of segments; for each of the plurality of segments: determining a point on the set of alignment-adjusted profile data closest to the reference curve within the segment; assigning a distance between the determined point and the reference curve to be an element of the corresponding set of relative profile data.

27. The method of any one of claims 1 to 26 or any other claim herein wherein facilitating identification of one or more irregularities based at least in part on the sets of relative profile data comprises generating a height map of a surface of the tire based on the sets of relative profile data.

28. The method of claim 27 or any other claim herein wherein the height map is encoded as a two-dimensional image wherein a luminosity of each pixel represents an element of relative profile data.

29. The method of claim 28 or any other claim herein wherein the two-dimensional image comprises an 8-bit image, and the 8 bit image has a first bit increment which corresponds to a distance between 10 and 50 microns.

30. The method of claim 29 or any other claim herein wherein areas of the tire which exceed a range encodable using the first bit increment are encoded using a second bit increment, the second bit increment corresponding to a larger distance than the first bit increment.31 . The method of claim 29 or any other claim herein wherein areas of the tire which exceed a range encodable using the first bit increment are encoded using a second bit increment wherein a magnitude of relative profile data encoded by the second bit increment is offset from a magnitude of profile irregularity encoded by the first bit increment.

32. The method of any one of claims 1 to 31 or any other claim herein wherein facilitating identification of irregularities comprises: identifying features in the relative profile data; processing the relative profile data based on the identified features to obtain normalized relative profile data; and identifying irregularities based at least in part on the normalized relative profile data.

33. The method of claim 32 or any other claim herein wherein the identified features comprises one of more of: ponds, walls surrounding ponds, and lattice structures.

34. The method of any one of claims 32 to 33 or any other claim herein wherein processing the relative profile data based on the identified features to obtain normalized relative profile data comprises: identifying a tire structure located at a plurality of locations comprises identifying local smooth regions; fitting a curve between the identified local smooth regions; and determining distances between the relative profile data and the fitted curve to be the normalized relative profile data.

35. The method of claim 34 or any other claim herein wherein identifying local smooth regions comprise identifying regions with a standard deviation in relative profile data of less than an threshold distance.

36. The method of claim 19 or any other claim herein wherein the tread profile template is generated by fitting lines and arcs to one or more representative sets of profile data.

37. The method of any one of claims 1 to 36 or any other claim herein comprising creating the relative rotation between the tire and the one or more sensors about the tire axis.

38. The method of claim 37 or any other claim herein wherein creating the relative rotation between the tire and the one or more sensors about the tire axis comprises: providing one or more rollers, each roller having a roller axis about which the roller is rotatable and the roller axes of the one or more rollers oriented in parallel; bringing the surface of the tire into contact with the one or more rollers, such that the tire is at least approximately oriented with the tire axis parallel to the roller axes of the one or more rollers; and driving at least one of the one or more rollers to rotate the tire.

39. The method of any one of claims 1 to 38 or any other claim herein wherein the one or more sensors comprise one or more camera-laser pairs, and wherein obtaining the plurality of sets of profile data comprises measuring a distance from the cameralaser pairs to the surface of the tire.

40. A system for inspecting vehicular tires for irregularities, the system comprising: one or more sensors for measuring locations of points on a surface; an apparatus for creating relative rotation between a tire and the one or more sensors; and a processor connected to receive signals from the one or more sensors, the processor configured to: obtain, during relative rotation between the tire and the one or more sensors about the tire axis and at each of a plurality of relative angular orientations between the tire and the one or more sensors, a set of profile data, wherein each set of profile data comprises radial and axial spatial locations of a plurality of points on a surface of the tire at the corresponding relative angular orientation between the tire and the one or more sensors;obtain a reference curve representative of at least a portion of the tire surface at an angular orientation about the tire axis; for each set of profile data, process the set of profile data and the reference curve to thereby obtain a corresponding set of relative profile data based on the set of profile data and the reference curve; and facilitate identification of one or more irregularities in the surface of the tire based at least in part on the sets of relative profile data.41 . The system of claim 40 or any other claim herein wherein the processor is configured to perform any of the features, combinations of features and / or subcombinations of features of any of claims 1 to 39 or any other claims herein.

42. Systems comprising any combination or sub-combination of any of the features of the above-recited claims or otherwise described herein.

43. Methods comprising any combination or sub-combination of any of the features of the above-recited claims or otherwise described herein.

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