Patent Description:
Weaving is the most popular way of fabric manufacturing. It is primarily done by interlacing two orthogonal sets (warp and weft) of yarns in a regular and recurring pattern. Weaving involves repeating in sequence the operations of shedding, picking, and battening. All these processes are typically carried out by a loom. Shedding is the process by which warp yarns are raised or lowered to produce a space, known as the shed, through which a filler yarn may be passed. Picking is the process of inserting a filler yarn through the shed, such that it intersects the warp threads. Battening is the process of pressing the filler yarn against a fell, where the newly woven fabric is formed.

A number of faults occur in fabric during weaving process. Woven fabric faults include cut yarn, double yarn, hole, float, stain, etc. The quality of woven fabric depends upon the number of defects left in the fabric after the manufacturing process. Defects developing during any of the above-mentioned processes determine the quality of the finished fabric. Typically, the finished fabric is inspected for faults and graded by a quality index according to industry standards. For example, in the standard four-point system of fabric inspection, penalty points being given for detected defects. The size of the penalty depends also upon the length of the defect with <NUM> penalty point being given to defects of <NUM> inches or less, <NUM> penalty points being given to defects of between <NUM> to <NUM> inches, <NUM> penalty points being given to defects of between <NUM> to <NUM> inches and <NUM> penalty points being given to defects of above <NUM> inches. The quality of the batch of cloth is described by the number of penalty points per <NUM> yards of inspected cloth, with up to <NUM> points being generally considered an acceptable defect rate. Apart from the four-point system described above, other standard indices, such as the more complicated ten-point system or the Dallas System for knitted fabric, may be used to measure the quality of cloth.

Conventionally, manual inspection is done for the finished fabric. Through manual inspection, generally, a sample size of at least ten percent of a roll of finished fabric is inspected. Faults in uninspected rolls are typically left undetected until the cloth is sold on. Furthermore, although such defect inspections are standardized as far as possible, it is noted that they depend upon the subjective assessment of the inspector. What one inspector may consider being a defect, another inspector may consider being acceptable. Accordingly, the same roll of cloth may be assessed very differently by different inspectors regardless of its actual quality.

The use of technology has improved the way of fault detection during various stages of a fabric manufacture. The highly efficient techniques of image capturing and image analysis enable the inspection of a woven fabric.

By way of example, International Patent Publication Number <CIT>titled, "Apparatus and method for in-line reading and control of warp threads in a loom" describes an apparatus and method for reading and controlling warp threads, using a device to read images, and to compare between the acquired images and one or more predetermined samples in order to determine defects in the work cycle in order to instantly cut off the operation of the loom in response to the determined defect.

In another example, <CIT> titled, "Monitoring device for a weaving machine, weaving machine, and method for monitoring" describes a monitoring device including a camera and a weft-thread beat-up device. The weft-thread beat-up device includes a reed or batten extending along the weft-thread beat-up device. The camera is fastened to the weft-thread beat-up device and includes adjacent sensor elements arranged in a row that extends parallel to the longitudinal direction of the weft-thread beat-up device.

In still another example, <CIT>titled, "Detection of warp in reed dent before loom start up" describes a warp insertion monitoring method and apparatus for protecting woven cloth against defects due to warp insertion error or failure. A warp detector on a loom detects the presence or absence of the warp such that abnormalities in the positions at which warps are inserted through the reed are identified. Notably, in this system, timing for the warp detection is particularly selected to fall within a period during which the loom is stopped, so that the presence or absence of errors can be detected before the loom is restarted.

In still another example, <CIT> titled, "On-loom fabric inspection system and method" describes an on-loom fabric inspection system comprising at least one imaging device configured to collect images of at least one section of a weaving area of a loom including a shed region, a woven fabric region and a fell region. The system is operable to detect faults in the weaving area and to produce batches of woven fabric assigned with an objective quality index.

The article "<NPL>, discloses a machine vision system and a method for nondestructive onloom fabric inspection. It teaches imaging a section of fabric and processing the image to find node points, i.e., intersections between warp and weft where warps lie above wefts. The captured image is cross correlated with a defect-free reference image. The resulting node points are arranged into a lattice so that adjacent node points can be identified, and rows and columns of nodes can provide a guide to finding individual yarns.

The need remains for an improved technology to detect faults through the on-loom fabric inspection system in fast and cost-effective manner. The systems and methods described herein come to address this need.

It is one aspect of the invention to introduce an on-loom inspection system according to claim <NUM>.

In another aspect of the invention, a method according to claim <NUM> is taught for inspecting woven fabric.

Typically, the digital string comprises a sequence of binary or Boolean values. Additionally or alternatively, the digital string comprises a sequence of values further indicating color.

Where appropriate, the step of capturing the image of the pick further comprises: capturing an image of at least one section of a weaving area; transferring image data to at least one image processor; and identifying the pick within the image data. Optionally, the imaged section of weaving area comprises all of a shed region, a woven fabric region and a fell region.

Optionally, the method further comprises generating an accuracy metric based upon deviations of the digital string with the corresponding row of the reference matrix. Optionally, the accuracy metric indicates the presence of a weaving defect. Alternatively, or additionally, the method may include generating a standard quality index for the woven fabric.

Where required the method may further initiate an automatic correction process when the accuracy metric lies beyond a threshold value. For example, the automatic correction process may be selected from at least one of a group consisting of: stopping the loom, unweaving the cloth, adjusting battening force, producing an alert and the like as well as combinations thereof.

Variously, the step of obtaining a reference matrix comprises accessing a reference pattern stored in a memory component. Additionally or alternatively, the step of obtaining a reference matrix comprises: monitoring an ongoing weaving process; identifying a repeated cycle in the weaving process; generating the reference matrix according to the repeated cycle; and storing the reference matrix in a memory component.

It is a particular aspect of the disclosure to teach a method further comprising: providing at least one imaging device configured to collect images of at least one section of a weaving area of a loom; providing a frame grabber configured and operable to receive images from the imaging device; providing an image processor; and sending a compact image data package to the image processor.

Typically, the compact image data package comprises a sequence of Boolean values representing the characteristic sequence of warp-risers and warp-sinkers along the fell-pick. Additionally or alternatively, the compact image data package comprises a sequence of values representing a section of a captured image including only a reduced section of the shed region, the fell-pick and a section of the fell region.

For a better understanding of the embodiments and to show how it may be carried into effect, reference will now be made, purely by way of example, to the accompanying drawings.

With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of selected embodiments only, and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects. In this regard, no attempt is made to show structural details in more detail than is necessary for a fundamental understanding; the description taken with the drawings making apparent to those skilled in the art how the several selected embodiments may be put into practice.

Aspects of the present disclosure relate to systems and methods for on-loom fabric inspection.

As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely examples of the invention that may be embodied in various and alternative forms.

As appropriate, in various embodiments of the disclosure, one or more tasks as described herein may be performed by a data processor, such as a computing platform or distributed computing system for executing a plurality of instructions. Optionally, the data processor includes or accesses a volatile memory for storing instructions, data or the like. Additionally or alternatively, the data processor may access a non-volatile storage, for example, a magnetic hard disk, flash-drive, removable media or the like, for storing instructions and/or data.

It is particularly noted that the systems and methods of the disclosure herein may not be limited in its application to the details of construction and the arrangement of the components or methods set forth in the description or illustrated in the drawings and examples. The systems and methods of the disclosure may be capable of other embodiments, or of being practiced and carried out in various ways and technologies.

Alternative methods and materials similar or equivalent to those described herein may be used in the practice or testing of embodiments of the disclosure. Nevertheless, particular methods and materials are described herein for illustrative purposes only. The materials, methods, and examples are not intended to be necessarily limiting. Accordingly, various embodiments may omit, substitute, or add various procedures or components as appropriate. For instance, the method steps may be performed in an order different from described, and various steps may be added, omitted or combined. In addition, aspects and components described with respect to certain embodiments may be combined in various other embodiments.

<FIG> represents an exemplary configuration of an on-loom fabric inspection system <NUM>. A loom <NUM> includes a yarn roll <NUM>, a take-up roll <NUM>, a pair of heald frames 108A and 108B and a reed <NUM>. An array of warp yarns <NUM> are threaded through the heald frames 108A and 108B and the reed <NUM>. The heald frames 108A and 108B are made of wood or metal such as aluminium. They carry a number of heald wires (not shown) through which the ends of the warp yarns pass. The heald frames 108A and 108B are configured to raise and lower the warp yarns, thereby producing a shed <NUM> through which a filler yarn (not shown) may be inserted using some filling insertion mechanism (not shown) such as a shuttle, rapier, jet or the like. The reed <NUM> is a mettalic comb used to batten the filler yarn against newly woven fabric <NUM>. It also helps to maintain the position of the warp yarns <NUM>. The woven fabric <NUM> is collected by the take-up roll <NUM> as it is produced.

The on-loom fabric inspection system <NUM> is configured to monitor a weaving area <NUM> including the newly woven fabric <NUM>, the shed <NUM> and a fell region <NUM>. The fell region <NUM> is a section of the weaving area <NUM> where the reed <NUM> strikes a weft yarn along a fell line during the operation of the loom <NUM>. The fell line is the boundary beyond which the fabric <NUM> has been woven. The fabric inspection system <NUM> includes one or more image capturing devices <NUM> in communication with an image processor <NUM>. Exemplary image capturing device <NUM> includes an analog or digital still image camera, a video camera, an optical camera, a laser camera, a laser or 3D image scanner, or any other device capable of capturing high resolution images of the weaving area <NUM>. The image capturing device <NUM> can also be a high definition inbuilt camera of a communication device such as a computer, a laptop or a mobile phone. In an exemplary embodiment, to capture the images of high speed working loom, the camera required needs to be of very high speed, for example capturing more than <NUM> frames/second. The image processor <NUM> is operable to receive and process data collected by the image capturing devices <NUM>. The image processor <NUM> can be a server computer, a client user computer, a personal computer (PC), a tablet PC, a laptop computer, a desktop computer, a mobile phone, a control system and a network router, switch or bridge. Alternatively, the image processor <NUM> can be software application running on a virtual cloud environment. An output mechanism <NUM> such as a visual display unit associated with the image processor <NUM> may provide information to a user regarding the functioning of the loom <NUM> and upon detection of any fault. The information may be provided in form of images, graphical representations, numbers or text, and can relate to measurement data, statistical data, etc. The output mechanism <NUM> may also display an alert or a flag in case any deviation from the normal operation of the loom <NUM> is detected. It is noted that such a configuration of the on-loom fabric inspection system <NUM> may be operable to monitor the weaving area <NUM> during operation of the loom <NUM>. Accordingly, a computer may be connected to the loom <NUM> and operable to stop the loom <NUM> or otherwise adjust the loom <NUM> settings in response to data gathered from the monitored weaving area <NUM>.

Conventionally, the on-loom fabric inspection system <NUM> captures the images of the weaving area <NUM> when the heald frames 108A and 108B are separate. In such a state, since the warp yarns <NUM> and the fell region <NUM> are not coplanar with each other, it is not possible to focus on both the warp yarns <NUM> in the shed and the fell region <NUM>. Therefore, the object distance of image capturing device <NUM> needs to be adjusted to capture images of either warp yarns <NUM> or the fell region <NUM>. <FIG> illustrates an exemplary embodiment where the image capturing device <NUM> is focused to take images of the fell region <NUM> and the region <NUM> of the newly woven fabric. In this case the image capturing device <NUM> cannot capture the images of the warp yarns 202A, 202B in the shed region <NUM>. <FIG> illustrates another exemplary embodiment where the image capturing device <NUM> is focused to take images of the warp yarns 302A, 302B in the shed region <NUM>. In this case the image capturing device <NUM> cannot capture the images of the fell region <NUM> and the region <NUM> of the newly woven fabric.

As a remedial measure, multiple image capturing devices focused on different regions <NUM>, <NUM>, <NUM> (or <NUM>, <NUM>, <NUM>) may be used. However, this increases the cost and time for separate analysis of the images.

Reference is now made to the block diagram of <FIG>, which represents the main components of an on-loom fabric inspection system <NUM> according to the invention. The system <NUM> may identify faults during the process of fabric manufacture, thereby enabling early detection or prevention of fabric defects. On-loom systems <NUM> such as described herein may serve as a cost-effective tool for providing continuous monitoring of woven textiles during production and may provide an industry standard for quality control of such fabrics.

The on-loom fabric inspection system <NUM> includes an image-capture trigger-mechanism <NUM>, an image capturing device <NUM>, an image processor <NUM>, a controller <NUM> and an output mechanism <NUM>. The image-capture trigger-mechanism <NUM> is configured to trigger the image capturing device <NUM> based on a required condition. The image capturing device <NUM> is configured to collect image data from a weaving area <NUM> of a loom <NUM> and to transfer this data to the image processor <NUM>.

Various types of image capturing device <NUM> may be used which suits the requirement. Exemplary image capturing device <NUM> includes an analog or digital still image camera, a video camera, an optical camera, a laser camera, a laser or 3D image scanner, or any other device capable of capturing high resolution images of the weaving area <NUM>. The image capturing device <NUM> can also be a high definition inbuilt camera of a communication device such as a computer, a laptop or a mobile phone. In an exemplary embodiment, to capture the images of a high speed working loom, the camera required needs to be of very high speed, like capturing more than <NUM> frames/second. Further, an array camera or the like may be used having a resolution suitable to detect individual yarns within woven fabric. Resolution of the image capturing device <NUM> may be selected according to the cost and nature of the inspected fabric. The resolution may be less than <NUM> millimeter, e.g., around <NUM> millimeter as required.

The image-capture trigger-mechanism <NUM> may include a detector or sensor connected to the loom <NUM> and configured to detect the movement of heald frames 508A and 508B (shown in <FIG>) of the loom <NUM>. Accordingly, the image capturing device <NUM> may be triggered by the detector when the required condition is met for the heald frames. An exemplary detector may include a mechanical sensor, an electrical sensor, or an optical sensor. It should be noted that the scope of the invention should not be limited with the exemplary detectors described above and any other detector which can detect the motion of the heald frames can be used for the purpose.

In another embodiment, the image-capture trigger-mechanism <NUM> may additionally or alternatively include a timer such as a stroboscopic light or lamp which can be timed to produce a flash of light when the required condition is met for the heald frames.

In still other embodiments, the image-capture trigger-mechanism <NUM> may additionally or alternatively include a receiver in communication with the loom <NUM> and configured to receive output signals from an encoder of the loom engine. For example, a communication cable may be connected between an output terminal of the loom <NUM> and an input terminal of the image-capture trigger-mechanism <NUM>. Accordingly trigger signals may be sent when the required condition is met, for example the image-capture trigger-mechanism <NUM> may receive a pick signal indicating that the picking process has been initiated and the picking signal may serve as a trigger signal for the image capturing device <NUM>.

The image data collected by the image capturing device <NUM> is sent to the image processor <NUM> which may analyze the received image data and identify irregularities indicative of weaving faults. Various image processors <NUM> may be used with the system <NUM>. A processor, such as a computer, a field programmable gate array (FPGA), an application specific integrated circuit and a microprocessor may be selected to provide image processing at sufficiently fast rate. The processing rate may be fast enough to allow each frame imaged by the image capturing device <NUM> to be analyzed in real time. Optionally, the image processor <NUM> may be operable to segment each frame and to analyze each frame segment separately and possibly with individual sampling rates. Exemplary image processor <NUM> includes a server computer, a client user computer, a personal computer (PC), a tablet PC, a laptop computer, a desktop computer, a mobile phone, a control system and a network router, switch or bridge. Alternatively, the image processor <NUM> can be a software application running on a virtual cloud environment.

The controller <NUM> is provided to respond to the detection of weaving faults. The controller <NUM> may respond, for example, by outputting data to the output mechanism <NUM>. The output mechanism <NUM> such as a visual display unit associated with the image processor <NUM> may provide information to a user regarding the functioning of the loom <NUM> and upon detection of any fault. The information may be provided in form of images, graphical representations, numbers or text, and can relate to measurement data, statistical data, etc. The output mechanism <NUM> may also display an alert or a flag in case any deviation from the normal operation of the loom <NUM> is detected. The output mechanism <NUM> may also comprise a database to store the processed data of images. Where required, the controller <NUM> may be further operable to activate an override switch <NUM> to stop or otherwise adjust the loom <NUM> in response to the detection of defects. The override switch <NUM> may be an actuator or any other system which suits the requirement.

According to the present invention, the image-capture trigger-mechanism <NUM> is conditioned to trigger the image capturing device <NUM> when the heald frames 508A and 508B (shown in <FIG>) are aligned with each other and trigger the image capturing device <NUM> at that instance to capture images of the weaving area <NUM>. In such a situation, the warp yarns in the shed are coplanar with the fell region and the newly woven fabric.

Reference is now made to <FIG> which shows a schematic side view of an exemplary configuration of a fabric inspection system <NUM> according to the invention, integrated onto a loom <NUM>.

The configuration of the loom <NUM> of <FIG> is similar to <FIG> with the exception that the heald frames 508A and 508B are depicted at the same level and aligned with each other. Upper and lower warp yarns in a shed <NUM> are in the same plane as a fell region <NUM> of a cloth and a newly woven fabric <NUM>. The capturing of images of weaving area <NUM> enables a single object distance of image capturing device <NUM> to be used to image both regions, the shed region <NUM> and the fell region <NUM>, allowing irregularities to be detected in both. A detector <NUM> is included in the system <NUM> for the purpose. Preferably, in each movement cycle (up and down) of the heald frames 508A and 508B, the images of weaving area <NUM> are captured twice in order to capture both sets of warp yarns. The detector <NUM> may variously comprise a sensor such as a mechanical sensor, an electrical sensor, an optical sensor and the like, as well as combinations thereof.

<FIG> illustrates the schematic side view of a fabric inspection system <NUM> with an image capturing device <NUM> focused to take images of a weaving area <NUM>. Since upper and lower warp yarns in shed <NUM> are in same plane as fell region <NUM> and newly woven fabric <NUM>, the image capturing device <NUM> can use a single depth of focus over a wide angle <NUM> to capture the image of the complete weaving area <NUM>.

<FIG> illustrates another configuration of the fabric inspection system <NUM> in which the heald frames are separated so as to raise the upper warp yarns 602A and lower the lower warp yarns 604B, thereby creating the shed. It is particularly noted that where appropriate, images may be additionally captured in this configuration. Accordingly, the image capturing device <NUM> may image only the upper warp yarns 602A, thereby enabling the image processor <NUM> (shown in <FIG>) to distinguish more readily between warp-risers and warp-sinkers along the fell-pick yarn.

Referring back to <FIG>, in still another alternative embodiment, an image-capture trigger-mechanism <NUM> may optionally trigger the image capturing device <NUM> in other ways. For example, an image-capture trigger-mechanism <NUM> may include a timer <NUM> such that the shutter of the image capturing device <NUM> can be set for a fixed time to capture images of the weaving area <NUM>. The shutter timing can be set to the instance when the heald frames 508A and 508B are aligned with each other. The images of the weaving area <NUM> are captured at that instance without the need for being triggered by the detector <NUM>.

Additionally or alternatively, the image-capture trigger-mechanism <NUM> may further include a receiver <NUM> in communication with the loom <NUM> and configured to receive output signals from an encoder of the loom engine <NUM>.

Reference is now made to the flowchart of <FIG> which illustrates exemplary method steps of the present invention for detecting defects in a woven fabric using the on-loom fabric inspection system <NUM>.

The on-loom fabric inspection system <NUM> is provided at step <NUM>. During the operation of the loom <NUM>, optionally, at step <NUM>, an image-capture trigger-mechanism <NUM>, which may include a detector <NUM>, may monitor the position of the heald frames 508A and 508B. The image capturing device <NUM> is triggered at a required point in the cycle, when the heald frames 508A and 508B are aligned with each other, at step <NUM>. The image capturing device <NUM> then collects images of the weaving area <NUM>, including the shed <NUM>, the fell region <NUM> and the newly woven fabric <NUM>, at step <NUM>.

Image data is transferred to the image processor <NUM> at step <NUM>. The image processor <NUM> analyzes the received image data for irregularities and faults at step <NUM>. If an irregularity detected in the image data indicates at step <NUM> that a weaving fault has occurred, then this fault is recorded on the output mechanism <NUM> at step <NUM>. The process may continue by another image being collected and analyzed, such that the process may be repeated.

Optionally the on-loom fabric inspection system <NUM> may further include a frame grabber <NUM> configured and operable to receive images from the imaging device <NUM> and to send a compact image data package <NUM> to the image processor <NUM>.

It is noted that the recordation of the weaving fault may involve a simple fault count such as using a penalty point system such as the four-point for example. Alternatively more precise data relating to the types of faults detected and their statistical distribution for example may be recorded.

Referring to <FIG>, which shows the representation of one frame <NUM> of a weaving area <NUM> imaged by the image capturing device <NUM> of the on-loom fabric inspection system <NUM>. The frame <NUM> shows the shed <NUM>, the fell region <NUM> and the newly woven fabric <NUM>. An oil spot, caused by a soiled section <NUM> propagating along the newly woven fabric <NUM> is also shown. The image frame <NUM> is processed by the image processor <NUM> to detect the soiled section <NUM> and appropriate measure can be taken by a loom operator to resolve the issue.

Weaving faults may occur in any of these areas of the frame <NUM> and may be detected using the on-loom fabric inspection system <NUM>. For example, slubs, missing yarns, end outs and the like may be detected in the shed <NUM> and fell region <NUM> whereas oil spots, loom stop marks, start marks and the like may be detected in the newly woven fabric <NUM>.

Various faults occurring in the weaving area <NUM> during manufacture may cause defects in the finished fabric. These include slubs, holes, missing yarns, yarn variation, end out, soiled yarns, wrong yarn faults, oil spots, loom-stop marks, start marks, thin place, smash marks, open reed, mixed filling, kinky filling, mixed end, knots, jerk-in, dropped picks, broken picks, double picks, double ends, drawbacks, burl marks and the like. It should be noted that the listed faults are exemplary in nature and should not limit the scope of the invention.

In other embodiments of the fabric inspection, a novel, line by line method for identifying faults may be used. According to this method, a reference pattern representing the desired pattern of the fabric may be obtained. Such a reference pattern may be converted, for example, into a two dimensional matrix including an array of values arranged in rows and columns.

By way of illustration, in a reference pattern for weaving, each column of the array may correspond to a warp end and each row may correspond to a pick or an individual filling yarn to be inserted through the shed during picking so as to intersect with the warp threads.

Although, in the interest of brevity, only a weaving pattern is described herein, it is further noted that such an inspection system may be adapted for use with other fabric types such as tufted fabrics and the like.

It is particularly noted that where required the reference matrix may consist of Boolean values. Thus, for example, in a weaving pattern, a ZERO value may be used to indicate a warp-riser where the warp thread overlies the weft, and a ONE value may be used to indicate a warp-sinker, where the warp thread under lies the weft. Alternatively the ZERO value may be used to indicate a warp-sinker and the ONE value may be used to indicate a warp-riser.

Referring again to <FIG>, using a fabric inspection system such as described herein, images are collected of the shed <NUM>, fell region <NUM> and newly woven fabric <NUM>. Accordingly, during each cycle of a loom, the fell-pick <NUM> may be identified either before or after battening. As used herein the term fell-pick refers to the last pick yarn to be inserted into the shed and is therefore the furthest pick from the woven fabric.

With reference to <FIG> which schematically represents a section of the fell-pick yarn <NUM> interwoven between a set of warp threads <NUM>, it is a particular feature of the current method that the image of the fell-pick yarn <NUM> may be analyzed so as to identify its characteristic sequence of warp-risers <NUM> and warp-sinkers <NUM>. As shown in <FIG> this characteristic sequence of warp-risers <NUM> and warp-sinkers <NUM> may be represented by a one dimensional array or digital string <NUM> of Boolean values.

It is noted that when the shed is in the open configuration, the contrast between the warp-risers and the warp-sinkers may be enhanced; accordingly, an additional image collection may be usefully timed to coincide with the point in the weaving cycle when the shed is open. It is also noted that the contrast between the warp thread and the weft thread may be further enhanced by adjusting illumination between over-shed illumination and under-shed illumination as required.

The image of the fell-pick is captured at the point in the cycle when the warp yarns in the shed are coplanar with the fell region and the newly woven fabric such as described herein.

The characteristic sequence of the imaged fell-pick may be compared with the corresponding row of the reference matrix to generate an accuracy metric The accuracy metric may be used to indicate the presence of a weaving defect and may be used in a defect calculation function to generate a standard quality index for the woven fabric.

Where the accuracy metric lies beyond a threshold value, automatic processes may be initiated such as, in a non-limiting manner, stopping the loom, unweaving the cloth, adapting the force of the next battening cycle, producing an alert or the like.

By way of example, an accuracy metric may be determined by counting the number of errors occurring when either warp-risers or warp-sinkers do not match the corresponding values in the reference matrix. The Error Density may be determined, for example by counting the number of errors in a given length of fabric or within a given number of wefts. Thus fabric may be graded according to the Error Density, with fabric with fewer than one error in, say <NUM>,<NUM> wefts being a higher quality than fabric with fewer than one error in <NUM>,<NUM> wefts.

Additionally, or alternatively, an accuracy metric may be a weighted score, possibly assigning greater value to errors proximate to each other than errors more spaced apart. For example, the accuracy metric may be calculated by the calculation: <MAT> where AM represents accuracy metric, Ej represents a weighted error when a warp-riser or warp-sinker does not match the corresponding value in the reference matrix, Wj represents the weighting coefficient of Ej, which may vary according to the proximity of the detected errors.

The value of the accuracy metric itself may serve as an input parameter of a defect calculation function which may be combined with other quality indications, such as the weft spacing function, dropped pick count, missing yarn count, slub count, oil spot count, loom stop count such as described in <CIT>, which is incorporated herein by reference in its entirety, or other faults which will occur to those skilled in the art.

For example, a quality index may be determined by summing terms representing accuracy of weaving pattern, fault detection and weft spacing using a quality function such as: <MAT> where Q represents the calculated quality index value, KE represents a weighting factor for the accuracy metric, Fi represents a count of a particular fault, Wi represents the weighting coefficient of the particular fault type Fi, Nf represents the number of fault counts being recorded, Kf represents a weighting factor for the contribution of fault counts to the quality index, Sk represents the value of a weft-spacing value, Wk represents the weighting coefficient of each weft-spacing value Sk and Kf represents a weighting factor for the contribution of weft spacing deviation to the quality index.

It is noted that the above examples of quality function and accuracy matrix calculations are provided for illustrative purposes only and that other quality functions may be additionally or alternatively used as occur to those skilled in the art.

With reference now to <FIG>, an exemplary required weaving pattern is indicated including required warp-risers <NUM> and required warp-sinkers <NUM>. By way of example a basket weave is shown for illustrative purposes. The weaving pattern may be converted into a reference matrix <NUM> as shown in <FIG>. The reference matrix <NUM> is a two dimensional array of Boolean values in which each row <NUM>-<NUM> corresponds to a pick. Such a pattern will produce a woven fabric having the characteristic shown in <FIG>.

Referring now to <FIG> and <FIG>, a sequence of picks <NUM>-<NUM> is represented being added to a woven fabric. As each pick is added, it may be imaged such that the image may be compared to the corresponding row from the reference matrix <NUM> of <FIG>. Thus as the first pick <NUM> is added, it may be imaged and compared to the string <NUM> {<NUM>,<NUM>,<NUM>,<NUM>,<NUM>,<NUM>,<NUM>,<NUM>,<NUM>,<NUM>,<NUM>} of the first row of the corresponding first row <NUM> of the reference matrix <NUM>. Similarly the second pick <NUM> is compared to the string <NUM> and so on until the whole fabric is produced. In such a manner the cloth may be inspected and graded on-loom as it is produced.

Reference is now made to the flowchart of <FIG> representing a possible method for detecting defects using a one-dimensional inspection analysis such as described herein. The method includes the following steps:
A reference pattern is obtained <NUM>, for example by referring to a pattern stored in a memory component. Additionally or alternatively, a reference pattern may be generated by on-loom learning of a repeating cycle which may then be stored in a memory for reference by a processor.

The reference pattern is converted into a reference matrix <NUM>, typically comprising an array of Boolean values; however, other arrays may be preferred where more complex patterns are being inspected for example using colors or the like.

An image of the fell line is obtained <NUM>, preferably from a photograph including all three of the shed region, the fell region and a section of woven cloth.

The image of the fell line is used to identify where along the fell-pick there is a warp-riser and where there is a warp-sinker thereby generating a characteristic sequence <NUM>. A digital string is generated corresponding to the characteristic sequence <NUM>. The digital string for the characteristic sequence is compared with the corresponding row of the reference matrix <NUM>. If a difference is detected <NUM> then a defect may be recorded <NUM> as appropriate.

Technical and scientific terms used herein should have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosure pertains. Nevertheless, it is expected that during the life of a patent maturing from this application many relevant systems and methods will be developed. Accordingly, the scope of the terms such as computing unit, network, display, memory, server and the like intended to include all such new technologies a priori.

The terms "comprises", "comprising", "includes", "including", "having" and their conjugates as used herein mean "including but not limited to" and indicate that the components listed are included, but not generally to the exclusion of other components. Such terms encompass the terms "consisting of" and "consisting essentially of".

As used in this specification, the singular indefinite articles "a", "an", and the definite article "the" should be considered to include or otherwise cover both single and plural referents unless the content clearly dictates otherwise. In other words, these articles are applicable to one or more referents. As used in this specification, the term "or" is generally employed to include or otherwise cover "and/or" unless the content clearly dictates otherwise.

Any embodiment described as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or to exclude the incorporation of features from other embodiments.

Any particular embodiment of the disclosure may include a plurality of "optional" features unless such features conflict.

Conversely, various features of the disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination or as suitable in any other described embodiment of the disclosure.

Although the invention has been described in conjunction with specific embodiments thereof, it is evident that other alternatives, modifications, variations and equivalents will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications, variations and equivalents that fall within the appended claims. Additionally, the various embodiments set forth hereinabove are described in terms of exemplary block diagrams, flow charts and other illustrations. As will be apparent to those of ordinary skill in the art, the illustrated embodiments and their various alternatives may be implemented without confinement to the illustrated examples. For example, a block diagram and the accompanying description should not be construed as mandating a particular architecture, layout or configuration.

The presence of broadening words and phrases such as "one or more," "at least," "but not limited to" or other like phrases in some instances shall not be read to mean that the narrower case is intended or required in instances where such broadening phrases may be absent.

Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a computer-readable medium such as a storage medium. Processors may perform the necessary tasks.

Claim 1:
A computer-implemented of method for inspecting woven fabric, comprising:
providing an on-loom fabric inspection system (<NUM>) comprising at least one imaging device (<NUM>) configured to collect images of at least one section of a weaving area (<NUM>) of a loom (<NUM>) and at least one image-capture trigger-mechanism (<NUM>);
characterized by the steps of:
obtaining a reference matrix (<NUM>) representing a required weaving pattern, said reference matrix (<NUM>) comprising a two dimensional array of values arranged as a sequence of rows, each row corresponding to a series of required warp-risers (<NUM>) and required warp-sinkers (<NUM>) along a single pick;
selecting a required instant during the weaving cycle, said required instant coinciding with the point in the cycle when the warp yarns (<NUM>) in a shed (<NUM>) are coplanar with a fell region (<NUM>) and a newly woven fabric (<NUM>);
said at least one image-capture trigger-mechanism (<NUM>) triggering said imaging device (<NUM>) at said required instant during the weaving cycle to capture an image of a fell-pick (<NUM>) along a fell line of the weaving area (<NUM>);
identifying in said image a characteristic sequence of warp-risers (<NUM>) and warp-sinkers (<NUM>) along said fell-pick (<NUM>);
generating a digital string (<NUM>) corresponding to said characteristic sequence; and
comparing said digital string (<NUM>) with a corresponding row (<NUM>) of the reference matrix (<NUM>).