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WO2025054744A1 - Procédé et dispositif de classification d'un événement dans une éprouvette textile allongée - Google Patents

Procédé et dispositif de classification d'un événement dans une éprouvette textile allongée Download PDF

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Publication number
WO2025054744A1
WO2025054744A1 PCT/CH2024/050041 CH2024050041W WO2025054744A1 WO 2025054744 A1 WO2025054744 A1 WO 2025054744A1 CH 2024050041 W CH2024050041 W CH 2024050041W WO 2025054744 A1 WO2025054744 A1 WO 2025054744A1
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WO
WIPO (PCT)
Prior art keywords
event
yarn
value
events
classification
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PCT/CH2024/050041
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German (de)
English (en)
Inventor
Parameswaran GANAPATHY
Rolf Joss
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Uster Technologies AG
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Uster Technologies AG
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Publication date
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Publication of WO2025054744A1 publication Critical patent/WO2025054744A1/fr
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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65H—HANDLING THIN OR FILAMENTARY MATERIAL, e.g. SHEETS, WEBS, CABLES
    • B65H63/00—Warning or safety devices, e.g. automatic fault detectors, stop-motions ; Quality control of the package
    • B65H63/06—Warning or safety devices, e.g. automatic fault detectors, stop-motions ; Quality control of the package responsive to presence of irregularities in running material, e.g. for severing the material at irregularities ; Control of the correct working of the yarn cleaner
    • D—TEXTILES; PAPER
    • D01—NATURAL OR MAN-MADE THREADS OR FIBRES; SPINNING
    • D01H—SPINNING OR TWISTING
    • D01H13/00—Other common constructional features, details or accessories
    • D01H13/14—Warning or safety devices, e.g. automatic fault detectors, stop motions ; Monitoring the entanglement of slivers in drafting arrangements
    • D01H13/22—Warning or safety devices, e.g. automatic fault detectors, stop motions ; Monitoring the entanglement of slivers in drafting arrangements responsive to presence of irregularities in running material
    • D—TEXTILES; PAPER
    • D01—NATURAL OR MAN-MADE THREADS OR FIBRES; SPINNING
    • D01H—SPINNING OR TWISTING
    • D01H13/00—Other common constructional features, details or accessories
    • D01H13/32—Counting, measuring, recording or registering devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/36—Textiles
    • G01N33/365—Filiform textiles, e.g. yarns

Definitions

  • the present invention lies in the field of textile quality control. It relates to a computer-implemented method and a device for classifying an event in an elongated textile test sample.
  • the invention also relates to a yarn clearing system and a yarn processing machine. It is preferably, but not exclusively, used in yarn clearers on spinning or dishwashing machines.
  • the invention relates to a computer-implemented method for providing at least one reference data set for the method or device according to the invention for classifying an event.
  • the invention also relates to a computer-readable medium on which at least one reference data set provided according to the method is stored.
  • a yarn clearer contains a measuring head with at least one sensor that scans the moving yarn.
  • Sensor principles are capacitive and optical, both of which are described in WO-2012/051730 A1.
  • the purpose of scanning is to detect events in the yarn.
  • ⁇ events in the elongated textile test piece are defined as points in the longitudinal direction (i.e., having a finite length of usually less than 1 m) at which at least one specific measured value deviates from a corresponding target value. If the elongated textile test piece is moved along its longitudinal direction, an event occurs in a finite time interval (of (usually less than 1 s) a fixed point, e.g., a sensor. Examples of events are thick spots, thin spots, or foreign matter in a yarn.
  • Measurement results obtained from the sensor signal are continuously evaluated against predefined criteria, such as a cleaning limit. If a yarn defect is below the cleaning limit, it is tolerable; if it is above the cleaning limit, it is an intolerable yarn defect that must be removed from the yarn or at least recorded.
  • predefined criteria such as a cleaning limit. If a yarn defect is below the cleaning limit, it is tolerable; if it is above the cleaning limit, it is an intolerable yarn defect that must be removed from the yarn or at least recorded.
  • the basis of yarn clearing is therefore a classification of yarn defects into a classification system with two classes: tolerable and intolerable yarn defects.
  • a method for defining a cleaning limit is disclosed in WO-2011/038524 A1.
  • a statistical representation of the yarn is determined by measurements on the yarn. Based on the statistical representation, the cleaning limit is calculated and suggested for application.
  • a length-related number of inadmissible events expected with this cleaning limit is calculated and output. An operator can comment on the expected number of inadmissible events, after which the cleaning limit is automatically determined according to the comment.
  • the yarn events can be plotted as points in a two-dimensional classification scheme, in which the defect length is typically plotted along the abscissa and the defect size (deviation of the mass per yarn length, the yarn cross-section, the yarn reflectivity, etc. from a target value) is plotted along the ordinate. Examples of a cleaning limit and a classification scheme are also given in WO-2011/038524 A1.
  • yarn defects are recorded, entered into the classification scheme, and counted in each class. This creates a defect pattern in the classification scheme.
  • This defect pattern is compared with predefined model reference patterns that allow conclusions to be drawn about the causes of the detected defects.
  • the predefined reference patterns can be determined through prior testing or from experience.
  • event densities in the classification scheme are determined from the defect size and length.
  • a yarn body is represented as a surface. The surface is bounded by the abscissa, the ordinate, and a line in the event field that follows a constant event density.
  • the events in the yarn body can be considered statistical "noise" belonging to the actual yarn and should not be removed from the yarn.
  • WO-2013/185246 A1 also teaches the representation of a first density line in the classification scheme, which refers to a first test sample and follows a constant event density.
  • a reference density line is represented, which follows the same event density as the first density line but refers at least partially to a reference test sample that is different from the first test sample. This enables a comparison of the courses of the first density line and the reference density line. From the comparison of the courses of the density lines, a comparison of the qualities of the first test sample and the reference test sample can be made.
  • WO-00/73189 A1 aims to enable improved, simplified, and rapid adjustment of the cleaning limit so that its effect on the final product can be more accurately predicted.
  • representations of defects in the final product e.g., in the yarn, are to be generated based on the cleaning limit, which visualize the effect of the defects in the final product.
  • WO-97/31262 A1 deals with the electronic simulation of fabric patterns. It proposes imaging yarn sections with a video camera, digitally storing the images, and interweaving them to obtain a realistic image of the resulting fabric. The simulated image can be output via a screen or printer.
  • a method and a device for classifying an event in an elongated textile test piece moved along its longitudinal direction which reduce or eliminate the disadvantages of the prior art.
  • as many disruptive yarn defects as possible are to be classified as such without unnecessarily classifying permissible yarn events as also disruptive, and as many permissible yarn events as possible are to be classified as such without classifying disruptive yarn defects as permissible. This is intended to achieve high quality and, at the same time, high productivity.
  • a further object of the invention is to provide a method and a yarn clearing system for yarn clearing that reduce or eliminate the disadvantages of the prior art. In particular, the productivity of the yarn clearing process and the quality of the cleared yarn are to be increased.
  • a method and a yarn clearing system for yarn clearing, as well as advantageous embodiments, are specified in the dependent claims.
  • the invention is based on the consideration that the metrological assessment of a yarn event does not always correspond to that perceived by the human eye.
  • the invention solves these problems by having the yarn event assessed and classified by an operator. Furthermore, the yarn event is measured using a sensor, as is known from the prior art. The classification is assigned to the measurement results associated with the yarn event and stored together with them as a reference data set. This process is preferably repeated for several different yarn events, thus creating and providing a database of reference data sets.
  • the computer-implemented method according to the invention serves to classify an event in an elongated textile test specimen. It comprises the following steps: a) measuring at least one measured value of at least one event parameter for the event; b) providing at least one reference data set, which contains, for each reference event, at least one reference value of the at least one event parameter and a classification of the reference event; c) comparing the at least one measured value with the at least one reference value of all reference data sets; and d) classifying the event according to the classification of the reference event whose at least one reference value is most similar to the at least one measured value according to a predetermined similarity criterion.
  • the at least one event parameter is an event variable, e.g., a deviation of a length-related mass density, a cross-section, or a reflectivity of the test material from a target value
  • the at least one reference value and the at least one measured value are each a signal consisting of a plurality of values of the event variable as a function of a length position or time.
  • the similarity criterion can include a cross-correlation of the two signals.
  • the at least one event parameter comprises an event size, e.g., a deviation of a length-related mass density, a cross-section, or a reflectivity of the test material from a target value, and an event length.
  • the similarity criterion can include a distance between a point representing the event and a point representing the reference event in a coordinate system spanned by the event length and the event size.
  • At least one first event parameter can be measured capacitively, for example, and at least one second event parameter can be measured optically, for example.
  • the measured value of the at least one event parameter is measured while the elongated textile test material is moved along its longitudinal direction.
  • the at least one reference data set can be provided by measuring the at least one reference value for each reference event, visually assessing and classifying the reference event by an operator, and assigning the classification to the at least one reference value and storing it together with the reference value as a reference data set.
  • the reference event is preferably assessed visually by taking an image of the reference event and presenting the image to the operator for visual assessment.
  • the classification is carried out in a classification system with exactly two classes, namely a first class with permissible events and a second class with impermissible events.
  • events in the yarn are classified into exactly the two classes described above, and events classified in the second class are removed from the yarn.
  • the device according to the invention for classifying an event in an elongated textile test material comprises a) a measuring device for measuring at least one measured value of at least one event parameter for the event; and a computer system with b) a memory for storing at least one reference data set, which contains at least one reference value of the at least one event parameter and a classification of the reference event for each reference event; c) a processor configured to compare the at least one measured value with the at least one reference value of all reference data sets; d) a processor configured to classify the event according to the classification of the reference event whose at least one reference value is most similar to the at least one measured value according to a predetermined similarity criterion.
  • the at least one event parameter is an event variable, e.g., a deviation of a length-related mass density, a cross-section, or a reflectivity of the test material from a target value.
  • the at least one reference value and the at least one measured value are each a signal consisting of a plurality of values of the event variable as a function of a length position or time.
  • the measuring device is configured to record the signal.
  • the at least one event parameter can comprise an event variable, e.g., a deviation of a length-related mass density, a cross-section, or a reflectivity of the test material from a target value, and an event length.
  • the measuring device is configured to measure a plurality of different event parameters.
  • the measuring device can, for example, be configured to capacitively measure at least one first event parameter and to optically measure at least one second event parameter.
  • the processor is configured for classifying in a classifying system with exactly two classes, namely a first class with permissible events and a second class with impermissible events.
  • the yarn processing machine for example a dishwasher or spinning machine, includes a plurality of yarn processing stations, at each of which yarn is wound onto a bobbin, and the yarn clearing system according to the invention described above.
  • the yarn processing machine includes at least one camera which is set up at one of the yarn processing stations to record images of events in the yarn, an output unit connected to the at least one camera for outputting an image recorded by the camera, and an input unit connected to the processor for inputting a classification of the output image.
  • At least one reference value of at least one event parameter for the reference event is measured for each reference event in an elongated textile test material, the reference event is The operator visually assesses and classifies the values, and the classification is assigned to at least one reference value and stored together with the at least one reference value as a reference data set.
  • the reference event is visually assessed by capturing an image of the reference event and presenting the image to the operator for visual assessment.
  • the invention also relates to a computer-readable medium on which at least one reference data set provided according to the invention is stored.
  • One advantage of the invention is that events in the test material are classified according to human perception rather than abstract parameters. This helps ensure that the final textile product produced from the test material is free of defects that would be perceived as disturbing by the human eye. During yarn cleaning, all defects perceived as disturbing are removed from the yarn, ensuring high yarn quality. At the same time, the removal of non-disturbing yarn events is avoided, increasing productivity.
  • FIG. 1 shows a schematic of a yarn winding machine with a yarn cleaner system.
  • Figure 2 shows (a) a fictitious measurement signal and (b) a fictitious reference signal in
  • Figure 3 shows (a) a fictitious mass signal and (b) a fictitious diameter signal as a function of the length position.
  • Figure 4 shows a diagram illustrating a similarity criterion for the two signals of Figure 3.
  • Figures 5-7 show (a) four different measurement signals for one and the same yarn event and (b) a photograph of the yarn event.
  • Figure 8 shows a schematic representation of an event field with a yarn body, a cleaning curve and various reference events.
  • Figure 9 shows a flowchart of an embodiment of the inventive method for classifying an event.
  • Figure 10 shows a flowchart of an embodiment of the method according to the invention for providing at least one reference data set.
  • Figure 11 shows a schematic table of a database with reference data records.
  • Figure 1 shows a very schematic illustration of a yarn winding machine 100 according to the invention with several winding stations 101. At each winding station 101, yarn 110 is rewound from a bobbin 111 onto a cheese 112 during the rewinding process. In this example, the yarn 110 moves along its longitudinal direction from bottom to top, which is indicated by arrows 113.
  • a device 120 according to the invention is installed in the dishwasher 100.
  • the yarn 110 is monitored by a measuring head 121 of the device 120 according to the invention.
  • the measuring head 121 measures at least one measured value of at least one event parameter for events in the yarn 110.
  • it can be equipped with a capacitive, optical, and/or other sensor as well as an evaluation unit for evaluating the sensor signals.
  • Such measuring heads 121 are known per se and need not be explained further here.
  • a cutting device is also attached to each winding station 101. This is used to cut out yarn sections with unacceptable defects from the yarn 110.
  • the cutting device receives the corresponding cutting commands from the evaluation unit of the measuring head.
  • the cutting device is preferably integrated into the measuring head 121.
  • All measuring heads 121 are connected via a data line 122 to a central control unit 123 of the device 120 according to the invention.
  • the measuring heads 121 are adjusted and controlled by the control unit 123, and they transmit data such as the measured values to the control unit 123.
  • the data line 122 can be designed, for example, as a serial bus.
  • the control unit 123 is equipped with a central computer 124, which serves, on the one hand, to collect and further process the measurement data originating from the measuring heads 121 and, on the other hand, to adjust the measuring heads 121.
  • the central computer 124 is connected to or contains a database 125.
  • the control unit 123 has an output unit 126 and an input unit 127 for an operator.
  • the output unit 126 can be designed, for example, as a screen and/or printer.
  • the input unit 127 can, for example, contain a keyboard or a computer mouse.
  • the output unit 126 and the input unit 127 can be configured together as a touchscreen.
  • the output unit 126 and the input unit 127 can be configured as a mobile terminal that is wirelessly connected to the central computer 124.
  • Figure 2(a) shows a fictitious measurement signal 210 for a yarn event, as it could be measured by a measuring head 121.
  • the measurement signal 210 is composed of many measured values of an event parameter AM (vertical axis 202) recorded along a length position L (horizontal axis 201) of the yarn 110.
  • the event parameter AM can, for example, be a capacitively measured deviation of a length-related mass density M of the yarn 110 from a target value.
  • Another example of an event parameter is an optically measured deviation of a yarn diameter from a target value.
  • the target value is preferably determined by continuously calculating the average value over a large number of measurements.
  • the measured values can, for example, be recorded every 2 mm and combined to form the measurement signal 210 according to Figure 2(a).
  • Figure 2(b) shows a fictitious reference signal 220 for a reference event.
  • the reference signal 220 is again composed of many Reference values of the same event parameter AM recorded along the length position L of a reference yarn.
  • the reference yarn may be different or the same as the one for which the measurement signal 210 of Figure 2(a) was recorded. If it is a different yarn, it should nevertheless have similar properties to enable meaningful classification of the yarn events; in particular, it should be made of the same material, have the same yarn count, and be produced using the same spinning process.
  • the signals 210, 220 could be represented as a function of time instead of the length position L.
  • Each of the two signals 210, 220 can easily be converted from length dependence to time dependence and vice versa if the instantaneous speed of the yarn is known.
  • the measurement signal 210 is compared with the reference signal 220.
  • the two signals 210, 220 can be compared, for example, using the cross-correlation function, which is known per se to those skilled in the art.
  • the maximum of the cross-correlation function is a measure of the similarity of the two signals 210, 220: the larger the value, the more similar.
  • those skilled in the art will be aware of other methods for comparing two signals 210, 220.
  • a first event parameter is an average event magnitude AMM or AMR, e.g., an average of all signal values that reach at least 80% of the signal maximum.
  • a second event parameter is an event length LM or LR, e.g., the length of the event at 50% of the average event height. This allows each event to be represented by a two-dimensional vector (LM, AMM) or (LR, AMR). The length of the difference vector is a measure of the similarity of the two events: the shorter, the more similar.
  • Figure 3 shows two different fictitious measurement signals 310, 320 for one and the same yarn event as a function of their length position L (horizontal axis 301).
  • a first measurement signal 310 according to Figure 3(a) can consist of measured values of the deviation AM (vertical axis 302) of a length-related mass density M of the yarn from a target value, as in Figure 2(a).
  • a second measurement signal according to Figure 3(b) can consist of measured values of the deviation AD (vertical axis 303) of a yarn cross-section D from a target value.
  • Each of the two measurement signals 310, 320 can be individually compared with a corresponding (not shown) reference signal, e.g., using the cross-correlation function as discussed above.
  • the two maxima R max (AM) (horizontal axis 401) and R max (AD) (vertical axis 402) of the cross-correlation function (e.g., normalized to the value 1) can then be entered as coordinates of a point 410 in a two-dimensional coordinate system.
  • a distance 411 of point 410 from the ideal corner point P(l, 1) is a measure of the similarity of the two measurement signals 310, 320 of Figure 3 with the corresponding reference signals: the shorter, the more similar.
  • each of the two measurement signals 310, 320 of Figure 3 can be reduced to a few measured values of a few event parameters, as discussed above with reference to Figure 2.
  • Each yarn event can then be represented, for example, by a four-dimensional vector whose components are the event size and event length determined from the two measurement signals 310, 320.
  • the length of the difference vector between the measurement vector and the reference vector is a measure of the similarity of the two events: the shorter, the more similar.
  • Figures 5-7 refer to real game events, which can be events to be classified or reference events.
  • Sub- Figures 5(a), 6(a), and 7(a) each show real measurement signals of four event variables (vertical axes 502, 602, 702) as a function of their longitudinal position or time (horizontal axes 501, 601, 701).
  • the four measurement signals are as follows: • solid line: measurement signal 511, 611, 711 of a capacitive sensor for mass deviations;
  • any of the four measurement signals, a combination of any two or three measurement signals or all four measurement signals can be taken into account.
  • Sub- Figures 5(b), 6(b), and 7(b) each show an image 520, 620, 720, namely a photograph, of the corresponding yarn event 540, 640, 740 in a yarn 530, 630, 730, but not necessarily at the same scale as in sub- Figures 5(a), 6(a), 7(a) and possibly shifted in the length axis 501, 601, 701 relative to the measurement signals.
  • the vertical lines in the background merely represent a scale (10 mm) on a yarn chart used.
  • Table 1 gives an overview of the three game events 540, 640, 740 and some of their measured values.
  • the mass signal 511, 611 and the diameter signal 512, 612 show significant deviations , while the two impurity signals 513, 514, 613, 614 exhibit rather insignificant deflections.
  • the yarn events can be represented in a known event field 800, as shown schematically in Figure 8.
  • the event field 800 is a two-dimensional Cartesian coordinate system. Values of an event variable, e.g., the deviation AM of a length-related mass density M of the yarn from a target value, are plotted along a vertical axis 802, and values of an event length L are plotted along a horizontal axis 801 (see Figure 2).
  • yarn events i.e., the measured values and/or reference values, can be entered as points.
  • the points 811, 812 entered in the example of Figure 8 are reference values.
  • a cleaning limit is entered in the form of a cleaning curve 830.
  • the cleaning curve 830 divides the event field 800 into two areas 831, 832: a first area 831 in which deviations are tolerated, and a second area 832, which is complementary to the first area 831 and in which deviations are cut out of the yarn or at least registered as unacceptable defects.
  • the cleaning curve 830 thus indicates how far a yarn event of a given length may deviate from the target value, or how long a yarn event with a given deviation may be in order to just be tolerated.
  • a yarn body 840 as known from WO-2010/078665 A1, is shown in event field 800 of Figure 8.
  • a second type 812 of reference events lies above the cleaning curve 830. This is where the disturbing yarn defects should be located, which should generally be cut out. However, there may be certain yarn events 812 located above the cleaning curve 830 that, upon closer inspection, are not disturbing at all. Shifting the cleaning curve 830 upwards would result in other, disturbing yarn defects not being cut out, thus reducing the quality. In this case too, the present invention provides exceptions to the general rule, namely certain reference events 812 above the cleaning curve 830, which are marked with circles in Figure 8. Yarn events that are similar to these reference events 812 of the second type should be spared from being cut out as exceptions to the general rule.
  • At least one measured value of at least one event parameter for the event to be classified is measured 901.
  • a first example of an event parameter is the mass deviation AM discussed in connection with Figure 2, of which a plurality of measured values are combined to form a measurement signal 210.
  • a second example are the two event parameters event length L and mass deviation AM, also discussed in connection with Figure 2.
  • Further examples are the capacitive and optical measurements discussed in connection with Figure 3, of which the length-dependent signals 310, 320 and/or the deviation AM or AD and the length L can be used as event parameters.
  • At least one reference data set and preferably a plurality of reference data sets are provided 902, preferably in a reference database.
  • Each reference data set relates to a reference event and includes at least one reference value of the at least one event parameter and a classification of the reference event.
  • Classification here refers to the assignment of exactly one class to the reference event. The provision of the reference data sets will be discussed in more detail below with reference to Figure 10.
  • classification 908 preferably takes place in a classification system with exactly two classes, namely a first class with permissible events and a second class with impermissible events. Events classified in the first class are left in the yarn, while events classified in the second class are removed from the yarn.
  • the classification system may include more than two classes.
  • a first example is a classification system with the following classes for yarn events:
  • the flowchart of Figure 10 shows an example of a method according to the invention for providing at least one reference data set for the method according to the invention or the device according to the invention for classifying an event.
  • the method can be carried out in a dedicated learning phase and/or during production, e.g., during yarn cleaning.
  • the reference event is present in the same test material in which the other events are later to be classified.
  • this can be achieved by examining a sufficiently long yarn section for reference events in a learning phase before yarn clearing and classifying them as described below, or by continuously using and classifying the yarn events found as reference events during yarn clearing.
  • reference events below the cleaning curve 830 can also be easily taken into account, which is difficult when the yarn clearing function is switched on.
  • the second alternative has the advantage that the yarn is cleaned from the first meter onwards and the reference database is continuously completed; however, no reference data sets are available at the start of the cleaning process. Both alternatives are not mutually exclusive and can be combined.
  • the reference event is present in a different test material than the events to be classified.
  • this test material should have similar properties to the test material in which the events to be classified are located.
  • the provision of the at least one reference data set can be carried out, for example, on a specially configured winding station 101 of a yarn winding machine 100 (see Figure 1), and the reference data sets thus provided can be used to classify the yarn events on all other winding stations 101.
  • the reference data sets can be provided by a supplier and made available to spinning mills.
  • a reference parameter is selected for the present reference event 1002. At least one reference value of the event parameter is measured 1003. This is repeated if necessary for further reference values and reference parameters 1004, 1005. The same reference parameters are used for each reference event.
  • An image of the reference event is captured 1006. This can be done with a camera.
  • a camera 130 is schematically shown on a specific winding unit 102, which can be referred to as the "pilot winding unit.”
  • the camera 130 can be a digital camera with a planar image sensor. Alternatively, for a moving yarn 110, it can only have a line sensor, whose successive images can be combined to form a two-dimensional image.
  • the camera 130 can be connected to the measuring head 121 of the pilot winding unit 120 or directly to the control unit 123 in order to transmit the images it captures to the control unit 123.
  • the remaining winding units 101 do not require a camera. Those winding units 101 that process yarn 110 of the same type as the pilot winding unit 102 can use the reference data sets from the database 125 provided for this yarn type to classify yarn events.
  • Classification 1007 is performed by an operator.
  • the image is output to the operator on a suitable output unit 126 (see Figure 1) so that the operator can visually perceive it.
  • the operator assesses the reference event depicted in the image and classifies it according to their assessment. They enter the classification into the inventive device 120 via a suitable input unit 127. Appropriate instructions, e.g., "Please enter your classification,” can be output to the operator on the output unit.
  • classification is performed by means of guided human-machine interaction.
  • An alternative embodiment does not require an image to be taken. Instead of an image, the operator assesses the event directly, e.g., by viewing it with the naked eye or through a microscope. For this purpose, the relevant winding unit must be stopped until the event classification is complete.
  • the classification is assigned to at least one reference value 1008 and stored together with the reference value as a reference data set 1009.
  • FIG 11 schematically shows a table 1100 of a database 125 (see Figure 1) with reference data records for the method and device according to the invention.
  • Each row 1111, 1112, ... of table 1100 contains a data tuple that refers to a specific reference data record.
  • a first column 1121 of table 1100 can contain a sequence number as a primary key for table 1100.
  • a second column 1122 contains the at least one reference value, e.g., the reference signal (see Figure 2(b)).
  • a third column 1123 contains the image of the respective reference event.
  • a fourth column 1124 contains the classification of the respective reference event.
  • the method according to the invention can, on the one hand, be implemented as an extension of the known yarn clearing method.
  • a clearing limit is specified, for example, in the form of a clearing curve 830 according to Figure 8.
  • the reference events 811, 812 define exceptions to the clearing according to the clearing limit and cancel the clearing rule specified by the clearing limit.
  • the relative similarity criterion was discussed in detail above.
  • the absolute similarity criterion defines how similar a reference value must at least be to the present measured value in order to be considered at all in the relative similarity criterion.
  • a threshold value of the maximum of the cross-correlation function can be specified for the absolute similarity criterion.
  • a reference signal 220 is only considered for the classification of an event if the maximum of its cross-correlation function with the measurement signal 210 of the event is greater than or equal to the threshold value.
  • a first graphical representation of an absolute similarity criterion s is shown in Figure 4 as a circular sector 420 around the ideal corner point P(l, 1), a second graphical representation in Figure 8 as a circular disk 820 around a reference point 811.
  • the absolute similarity criterion is met if the measured values of an event lie in one of the hatched areas 420, 820, i.e., close enough to a reference event, which applies, for example, to point 410 in Figure 4. If no reference value fulfills the absolute similarity criterion for the present measured value, i.e., the event is too far away from all reference events, the event is assessed as usual according to the cleaning rule given by the cleaning boundary. Only when at least one reference event is close enough to the event can it cancel the cleaning rule given by the cleaning boundary.
  • This embodiment has the advantage that it also works with only a few reference data sets, or even with a single reference data set.
  • the method according to the invention can be carried out autonomously, without a conventional cleaning limit. Yarn cleaning is then carried out exclusively according to the available reference data sets.
  • the advantage of this embodiment is that the yarn is cleaned exclusively according to a manually performed classification rather than an abstract cleaning limit.
  • the disadvantage is that a larger number of reference data sets are required to cover all possible yarn events as well as possible.

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  • Engineering & Computer Science (AREA)
  • Textile Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Mechanical Engineering (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Quality & Reliability (AREA)
  • Filamentary Materials, Packages, And Safety Devices Therefor (AREA)
  • Treatment Of Fiber Materials (AREA)

Abstract

L'invention concerne un procédé mis en œuvre par ordinateur pour la classification d'un événement dans une éprouvette textile allongée, dans lequel au moins une valeur mesurée d'au moins un paramètre d'événement pour l'événement est mesurée (901). Au moins un jeu de données de référence, contenant, pour un événement de référence respectif, au moins une valeur de référence de l'au moins un paramètre d'événement et une classification de l'événement de référence, est fourni (902). L'au moins une valeur mesurée est comparée (904) avec l'au moins une valeur de référence de tous les jeux de données de référence. L'événement est classé (908) conformément à la classification de l'événement de référence dont au moins une valeur de référence est la plus similaire à l'au moins une valeur mesurée conformément à un critère de similarité prédéfini. Le procédé parvient à une qualité élevée et, en même temps, une productivité élevée.
PCT/CH2024/050041 2023-09-11 2024-08-20 Procédé et dispositif de classification d'un événement dans une éprouvette textile allongée Pending WO2025054744A1 (fr)

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CHCH000988/2023 2023-09-11
CH000988/2023A CH721119A1 (de) 2023-09-11 2023-09-11 Verfahren und Vorrichtung zum Klassieren eines Ereignisses in einem länglichen textilen Prüfgut

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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5537811A (en) * 1991-09-11 1996-07-23 Roospark Ag Method for categorizing yarn defects and cleansing yarn
WO1997031262A1 (fr) 1996-02-21 1997-08-28 Lawson-Hemphill, Incorporated Systeme et procede d'evaluation electronique des qualites escomptees d'un tissu
EP0685580B1 (fr) * 1994-06-02 2000-03-15 Zellweger Luwa Ag Procédé et dispositif pour déterminer les causes de défauts des fils, mèches et rubans de fibres
DE69420972T2 (de) * 1993-11-10 2000-05-11 Lawson-Hemphill, Inc. System zur elektrischen anzeige von garnqualitäten
WO2000073189A1 (fr) 1999-05-29 2000-12-07 Zellweger Luwa Ag Procede et dispositif permettant le nettoyage de fils
WO2010078665A1 (fr) 2009-01-07 2010-07-15 Uster Technologies Ag Procédé et dispositif de caractérisation d'un échantillon textile allongé
WO2011038524A1 (fr) 2009-10-02 2011-04-07 Uster Technologies Ag Procédé d'établissement d'une limite de nettoyage sur une installation de nettoyage de fil
WO2012051730A1 (fr) 2010-10-19 2012-04-26 Uster Technologies Ag Purgeur de fil et procédé de purge du fil
WO2013185246A1 (fr) 2012-06-11 2013-12-19 Uster Technologies Ag Comparaison des qualités d'échantillons textiles allongés
EP2686261B1 (fr) * 2011-03-16 2016-05-18 Uster Technologies AG Caractérisation d'un échantillon textile de forme allongée

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5537811A (en) * 1991-09-11 1996-07-23 Roospark Ag Method for categorizing yarn defects and cleansing yarn
DE69420972T2 (de) * 1993-11-10 2000-05-11 Lawson-Hemphill, Inc. System zur elektrischen anzeige von garnqualitäten
EP0685580B1 (fr) * 1994-06-02 2000-03-15 Zellweger Luwa Ag Procédé et dispositif pour déterminer les causes de défauts des fils, mèches et rubans de fibres
WO1997031262A1 (fr) 1996-02-21 1997-08-28 Lawson-Hemphill, Incorporated Systeme et procede d'evaluation electronique des qualites escomptees d'un tissu
WO2000073189A1 (fr) 1999-05-29 2000-12-07 Zellweger Luwa Ag Procede et dispositif permettant le nettoyage de fils
WO2010078665A1 (fr) 2009-01-07 2010-07-15 Uster Technologies Ag Procédé et dispositif de caractérisation d'un échantillon textile allongé
WO2011038524A1 (fr) 2009-10-02 2011-04-07 Uster Technologies Ag Procédé d'établissement d'une limite de nettoyage sur une installation de nettoyage de fil
WO2012051730A1 (fr) 2010-10-19 2012-04-26 Uster Technologies Ag Purgeur de fil et procédé de purge du fil
EP2686261B1 (fr) * 2011-03-16 2016-05-18 Uster Technologies AG Caractérisation d'un échantillon textile de forme allongée
WO2013185246A1 (fr) 2012-06-11 2013-12-19 Uster Technologies Ag Comparaison des qualités d'échantillons textiles allongés
EP2859341B1 (fr) * 2012-06-11 2016-11-23 Uster Technologies AG Comparaison des qualités d'échantillons textiles allongés

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