Abstract:
A process is disclosed for evaluating data obtained from textile fabrics. In order to devise a process which allows data obtained from textile fabrics to be easily compared, assessed in a differentiated manner as to their significance and evaluated, the data are determined in a section ( 3   a   ,3   b ) of the surface of the fabric, sorted according to at least two parameters ( 13,14 ) and represented in an image ( 12, 30 ) as a function of the parameters.

Description:
FIELD OF THE INVENTION 
   The invention relates to a method for evaluating data determined on textile fabrics. 
   BACKGROUND OF THE INVENTION 
   When producing textile fabrics such as woven fabrics, knitted fabrics, etc., faults which cause the ideally regular and precisely structured surface to exhibit irregularities or faults are a frequent occurrence. In terms of extent, faults of this kind may range from being very small and inconspicuous to very large or, for other reasons, conspicuous and may reduce the value and the function, e.g. the strength or the appearance of the fabric. The finished fabrics are therefore subjected to an examination for the purpose of indicating faults in the structure. This may be a visual or a machine examination and often takes place both before dyeing or dressing and also before making up. An increase in the quantity of detected faults is to be expected in particular when carrying out a machine or automated examination, so that a correspondingly greater data flow may result. 
   One disadvantage in this case lies in the fact that, although a considerable amount of data is available, these data are likely to cause confusion and may not just serve to improve the quality of the products. It should also be borne in mind that there are a great many producers of textile fabrics of all kinds and that each producer and also many customers are inclined to define and implement their own quality criteria. This means that textile fabrics which are assessed by different individuals or institutions result in assessments which cannot easily be compared with one another. 
   SUMMARY OF THE INVENTION 
   As characterized in the claims, the invention therefore achieves the object of providing a method by which faults which are determined in textile fabrics can easily be compared with one another and assessed and evaluated as to their significance in a differentiated manner. 
   This is achieved by determining the data on a swatch of the surface of the fabric and sorting this data according to at least two parameters. A swatch can be understood to be the entire surface under consideration of a fabric or a section from the surface. A section of this kind may be moved or changed after a period required for acquiring the data, so that new data on other zones or swatches of the fabric are periodically obtained. The intensity of a pixel or surface element, a longitudinal coordinate, a latitudinal coordinate, etc. may be considered as data and therefore also as parameters, for example. The acquired data on the faults are then represented in an image as a function of selected parameters, which in turn may be divided into zones which in themselves are conceived as homogeneous. If two parameters are selected, the result is a one-dimensional representation. If three parameters are selected, the resulting image is a two-dimensional representation. The image then represents, for example, a classifying field consisting of individual fields which define a class. The class is characterized by the extent of the field, which lies in a plane which is regarded as the location for values of two parameters. A further parameter may be displayed by symbols entered in the field. 
   The advantages achieved by means of the invention lie in particular in the fact that it enables a structured and standardized assessment of faults in textile fabrics to be carried out. Thus on the one hand values of predetermined parameters for the most varied faults can be indicated, while on the other criteria can be created which help to identify the significance or value of the faults and to compare this with the value of other faults. A large data flow on faults in the fabrics can thus also be processed to provide accurate information on the faults occurring. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The invention is illustrated in detail in the following on the basis of an example and with reference to the accompanying figures, in which: 
       FIG. 1  shows a respective swatch of the surface of a textile fabric, 
       FIG. 2  shows a respective swatch according to  FIG. 1  with different faults, and 
       FIGS. 3 to 11  in each case show a classifying field. 
   

   DETAILED DESCRIPTION 
     FIG. 1  shows the same run  1  of a textile fabric three times with a fault  2 . Information on the position of this fault  2  can be obtained, for example, via coordinates x and y, on its size via values of the extent in two directions s and k, and on its intensity or deviation, for example in terms of color, from the surrounding area via a value delta i. 
     FIG. 2  shows a respective swatch  3   a ,  3   b  of a textile fabric with a grid  4  and four different faults  5 ,  6 ,  7  and  8 . The swatch  3   a  shows a first possibility for evaluating the size of the faults  5 ,  6 ,  7  and  8  and the swatch  3   b  a second possibility. For this purpose the grid  4  divides the swatches  3   a ,  3   b  into individual small fields  9 , and the occupancy of these fields by the faults  5 – 8  is interpreted differently in the two swatches  3   a  and  3   b , as will be discussed further in the following. However in both cases this means that the extent of the faults through the number of occupied fields is selected as a parameter. Although—should this be a woven fabric—the faults  5 ,  6 ,  7 ,  8  extend in two directions, weftwise  10  and warpwise  11 , the values of the parameters only indicate that the intensity of the faults  5 – 8  has exceeded a threshold value and one of the number of occupied fields  9  has a proportional extent. The swatches  3   a ,  3   b  preferably form at least one rectangle whose sides extend parallel and perpendicularly to boundaries of the fabric or run  1 . 
     FIG. 3  shows an image  12  with two axes  13 ,  14 , along which values of parameters are plotted. Here the values along the axis  13  are values for the length of a fault, for example viewed weftwise in a woven fabric, and those along the axis  14  values for the width of a fault, for example viewed warpwise in a woven fabric. Lines  15 ,  17 ,  19  and  21  divide the width of the faults into five classes, while lines  16 ,  18 ,  20  and  22  divide the length of the faults into five classes. This results overall in twenty five classes for classifying the faults according to size. Symbols  23 – 29  are drawn in at a plurality of class boundaries, which are indicated by the lines  15 – 22 , these symbols representing the form of a fault as is to be expected on the basis of dimensions according to the said lines. Numerical values are also entered in the fields defined by the lines  15  to  22 , these values indicating the number of detected faults which fall within the class concerned. For this purpose it is assumed that a class represents a homogeneous zone, i.e. no distinction is made as to whether or not the values of the parameters lie near upper or lower class boundaries or lines  15 – 22 . 
     FIG. 4  shows an image  30  with axes and lines defining classes as is already known from  FIG. 3 . The axes, lines and symbols have therefore been given the same reference numbers. Dots  31 ,  32 ,  33 , etc. are entered in the fields, the position of which dots in relation to the axes  13  and  14  indicates the size of the fault accurately or in a differentiated manner. Each dot therefore corresponds to a fault, and the distribution of the faults or the dots thereof is also an indication of the predominant type of fault in the fabric. Characters A to E are also entered along the axis  13  between the lines  14  to  22  and integral numbers 1 to 5 along the axis  14  between the lines  13  to  21 . Each field and therefore each class can therefore be clearly designated by the combination of a number and a character.  FIG. 5  shows an image  34  with axes and lines defining classes as is already known from  FIG. 3 . The axes, lines and symbols have therefore been given the same reference numbers. Diagonally ascending numerical values, which indicate the intensity of a fault, are provided in the individual fields, which correspond to fault classes. Here the position of a figure indicates the intensity, while the value of the figure indicates the number of faults with this intensity. Thus numerical values located in the bottom left-hand side of a field indicate high intensities and numerical values located in the top right-hand side indicate low intensities.  FIG. 6  shows an image  35  with axes  36  and  37 . Values for the area of a fault, for example in CM 2 , are plotted along the axis  36  and values for the intensity of a fault in percentages along the axis  37 . This image  35  is also divided into fields or classes by lines  38  to  43 . Symbols which indicate the intensity of the fault through the strength of the color are drawn in at the intersections of the lines  38 – 43 . Numerical values in the fields indicate the number of faults occurring in the class concerned. 
     FIG. 7  shows an image  44  with axes  45  and  46 . Values for the length of a fault, for example in cm, are plotted along the axis  45  and values for the intensity of a fault, for example in percentages, along the axis  46 . This image  44  is also divided into fields or classes by lines  47  to  52 . The number of detected faults is indicated by the figures in the fields, as already known from  FIG. 3 .  FIG. 8  shows an image  53  with axes  54  and  55 . Values for the number of occupied fields  9  according to  FIG. 2  are plotted along the axis  54  and values for the intensity of a fault along the axis  55 . This image  53  is also divided into fields or classes by lines  56  to  61 . The number of detected faults is indicated by the figures in the fields, as already known for  FIG. 3 . 
     FIG. 9  shows an image  62  with axes  63  and  64 . Values for the length of faults in cm are plotted along the axis  63 . The axis  64  is divided into a plurality of zones  64   a  to  e , and values for the intensity are given in percentages in each zone. Each of the zones  64   a  to  64   e  relates to a certain type of fault, for example the zone  64   a  relates to weft faults, the zone  64   b  to warp faults, the zone  64   c  to surface faults, the zone  64   d  to edge faults and the zone  64   e  to holes. Lines  65  to  76  again divide the image  62  into fields or classes in which numerical values indicate the number of detected faults in the class concerned. The position of the numerical value in relation to the zone on the axis  64  indicates the intensity of the fault. Several numerical values may thus also occur in one class. The image  62  thereby illustrates a classification which is based on different types of fault. Different known types of fault may be grouped together as desired. So, for example, the term “weft faults” is here generally understood to mean faults which predominantly extend weftwise in a woven fabric. Such faults are known under the following terms: join, fell, straightening point, shed, weft bar, lashing-in, slubber, fly, thread breakage, mispick. 
     FIG. 10  shows an image  80  with an axis  81  which is divided into zones  81   a  to  d . Values for intensities in percentages are given along another axis  82 . Lines  83  to  93  divide the image  80  into fields or classes. Values for the number of detected faults can again be entered in the fields or classes. For example, the intensity of weft faults can be entered in zone  81   a , the intensity and size of wrap faults in zone  81   b , the intensity or size of holes in zone  81   c , the intensity of edge faults, etc. in zone  81   d , and the numbers thereof. 
     FIG. 11  shows an image  94  with axes and lines as already found in images  12  and  30  ( FIGS. 3 and 4 ). Here the fields or classes are divided by a boundary  97  into two groups  95  and  96 , with the boundary extending along lines  15 ,  17 ,  19  and  16 ,  18 ,  20 . However it is also possible to define a boundary  98  which also divides the individual fields or classes. 
   The method according to the invention is carried out as follows: The textile fabric is scanned in a manner known per se, for example by a camera, and images for swatches of the surface of the fabric are made and signals derived therefrom are processed. Using algorithms, which do not constitute the subject matter of this invention, for image processing, faults or unusual features in the images of the surface are determined from the derived signals by comparison with predetermined limit values, patterns, etc. Thus data on faults in a swatch of the fabric are produced. A swatch of this kind is shown, for example, in  FIG. 1  and called a run  1 . A fault  2 , which is distinguished by various parameters, can be recognized in this. These parameters are its position, which is given by coordinates x and y, its size, which is given by the values s and k, and its intensity, which causes the fault to actually stand out from the area surrounding it and which is quantified by a qualitative datum, here called delta i. 
   Different parameters are significant, according to how the fault is subsequently dealt with. For example, if every fault is to be removed, all that is of interest is its position, possibly also its size. If the fabric is then to be assessed as to where the faults are most numerous, such as at the edge, for example, it is again just the position which is of interest. The data are then sorted according to parameters such as length and width and accordingly represented in an image. 
   Should there be a requirement for assessing how the fault appears to the eye or how it influences subsequent processing of the fabric, such as dyeing or dressing, its size is of interest and possibly also its intensity. Then the parameters according to which the data are sorted are the length s and the width k of the fault, as well as its intensity delta i. 
   Just one dimension may be determined from the signals obtained from image processing in order to detect the size of a fault, or an evaluation according to  FIG. 2  may be undertaken. In this case an investigation is carried out to establish how many fields  9  are affected or at least partly covered by a fault. These fields, as marked in swatch  3   a , are counted for each fault and the number is plotted, for example, along the axis  54  in  FIG. 8 . However it is also possible, as shown for swatch  3   b , to take the fields  9  occupied for each fault and to complete them to an extent such that together they form a rectangle which encompasses the fault. The fields  9  which are comprised in this rectangle then have to be counted and plotted. 
   In order to detect the intensity of a fault, the color or brightness of the area surrounding the fault is taken as a starting point and an attempt is made to quantify deviations of the color or brightness more or less accurately or in a graduated manner, this being expressed by a value delta i. The devices used for image processing determine the degree to which this is successful. 
   In order to represent the size of the fault in an image, its length can be detected in the swatch in a manner known per se and represented in an image  12 ,  30  by a value on the axis  13 . The width of the fault can be represented in the same way by a value on the axis  14 . Together these two values produce, for example, a dot  33  ( FIG. 4 ). This can be left as a dot or simply treated as a fault in class C 2 , which would mean that just one counting value would then be increased by one for this class. For this purpose it is possible to specify certain fields or classes as acceptable and others as unacceptable beforehand. The position of the fault in image  13 ,  30  then immediately reveals how the fault is to be assessed. Should values for faults accumulate in individual classes, this will equally provide an indication for assessing the fabric. 
   The intensity of a fault can be represented according to the possibilities already presented on the basis of the images  34 ,  35 ,  44  and  53  ( FIGS. 5–8 ). 
   As shown in  FIG. 1 , swatches of the surface from which the data are acquired which form a rectangle are particularly suitable, for the fabrics in question are also already in the form of rectangles, this being a result of the manufacturing process. Then sides of the swatches should also lie parallel and perpendicularly to the boundaries of the fabric. However the swatch concerned does not conventionally constitute the entire surface of the fabric. This applies to swatches  3   a ,  3   b  according to  FIG. 2 , which is an enlarged view of a part of the run  1  according to  FIG. 1 . 
   The form of a fault, as represented by the symbols  23  to  29  in  FIG. 3 , may also be directly considered as a parameter. In fact a parameter of this kind ultimately consists of two parameters (length and width). However it would also be possible to combine the parameter “form” with the parameter “intensity”, as known from  FIG. 6 , and in this way obtain another combination and therefore another image representation. It thus becomes obvious that only a few possibilities are indicated here, although these can also be developed according to the invention in an obvious manner by combination, for example by interchanging the axes. 
   Data can be evaluated and, optionally, the textile fabric processed in a differentiated manner, according to whether the determined data belong to groups  95  or  96  ( FIG. 11 ), which are separated by a boundary  97 ,  98 . For example, the weighting of the faults in group  96  may be reduced with respect to the faults in group  95 . Or faults of group  96  are only marked, for example, at the edge of a cloth run, while faults of group  95  are removed, for example by unraveling the woven fabric in the area around these faults. Generally speaking, boundaries  97 ,  98 , etc. can form groups of classes or categories of faults which initiate different actions.