CN116309519A - Roller winding detection method, device and storage medium - Google Patents

Roller winding detection method, device and storage medium Download PDF

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Publication number
CN116309519A
CN116309519A CN202310343130.4A CN202310343130A CN116309519A CN 116309519 A CN116309519 A CN 116309519A CN 202310343130 A CN202310343130 A CN 202310343130A CN 116309519 A CN116309519 A CN 116309519A
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Prior art keywords
roller
area
winding
image data
gray
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Chinese (zh)
Inventor
谈昆伦
毛坤鹏
徐峰
史伟林
罗金
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Newtech Group Co Ltd
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Newtech Group Co Ltd
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Priority to CN202310343130.4A priority Critical patent/CN116309519A/en
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    • D—TEXTILES; PAPER
    • D03—WEAVING
    • D03D—WOVEN FABRICS; METHODS OF WEAVING; LOOMS
    • D03D47/00—Looms in which bulk supply of weft does not pass through shed, e.g. shuttleless looms, gripper shuttle looms, dummy shuttle looms
    • D03D47/34—Handling the weft between bulk storage and weft-inserting means
    • D03D47/36—Measuring and cutting the weft
    • D03D47/361—Drum-type weft feeding devices
    • D03D47/367—Monitoring yarn quantity on the drum
    • D—TEXTILES; PAPER
    • D03—WEAVING
    • D03D—WOVEN FABRICS; METHODS OF WEAVING; LOOMS
    • D03D47/00—Looms in which bulk supply of weft does not pass through shed, e.g. shuttleless looms, gripper shuttle looms, dummy shuttle looms
    • D03D47/34—Handling the weft between bulk storage and weft-inserting means
    • D03D47/36—Measuring and cutting the weft
    • D03D47/369—Communication systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30124—Fabrics; Textile; Paper
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Textile Engineering (AREA)
  • Multimedia (AREA)
  • Quality & Reliability (AREA)
  • Geometry (AREA)
  • Signal Processing (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)

Abstract

The invention relates to the technical field of textile, in particular to a roller winding detection method, which comprises the following steps: setting a monitoring area and acquiring image data of a roller surface; carrying out gray processing on the image data to obtain a first gray image; performing mean value filtering processing on the first gray level image to obtain a second gray level image; extracting a brighter region in the first gray level image to be used as a suspected region; and comparing the preset area threshold value with the characteristic parameters of the suspected area, and outputting a judging result. The roller is provided with a black roller surface corresponding to the white glass fiber bundles, so that the winding yarns in the acquired image data can be conveniently identified; the industrial camera is arranged at rest relative to the roller station, so that background parts except the roller surface area in the image data can be filtered conveniently, and the debugging time is saved; the production state of the roller is monitored in real time through the industrial camera, high-frequency labor of manual detection is avoided, the roller winding condition can be accurately identified at the first time, and the production efficiency is improved.

Description

Roller winding detection method, device and storage medium
Technical Field
The invention relates to the technical field of textile, in particular to a roller winding detection method, roller winding detection equipment and a storage medium.
Background
In the production process of glass fiber fabrics, a roller in a yarn storage device is in a reciprocating motion state and rotates rapidly in the motion process, when glass fiber yarns pass through the roller, yarn breakage occurs, and the yarn breakage is wound on a roller along with high-speed rotation of the roller.
When broken yarns are gradually wound on the roller surface along with the progress of the roller, the asymmetry of the partial roller surface of the roller can be caused, the normal running of the yarns near the broken yarns is affected, and the yarns are broken due to the instability of the tension of the surrounding yarns; and the yarns wound on the roller surface of the roller are increased, so that the yarns are lack in the production of the cloth surface, and the cloth surface is damaged.
Disclosure of Invention
The invention provides a roller winding detection method which can effectively solve the problems in the background technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a roller winding detection method comprises the following steps:
setting a monitoring area and acquiring image data of a roller surface;
carrying out gray processing on the image data to obtain a first gray image;
performing mean value filtering processing on the first gray level image to obtain a second gray level image;
extracting a brighter region in the first gray level image to be used as a suspected region;
and comparing the preset area threshold value with the characteristic parameters of the suspected area, and outputting a judging result.
Further, in the setting process of the monitoring area, the roller surface is set to be black, and the industrial camera corresponds to the roller and is fixedly arranged on the mounting frame.
Furthermore, the target position video is acquired in real time through the industrial camera and transmitted to the industrial personal computer, and communication is established between the industrial personal computer and the PLC;
the industrial personal computer extracts multi-frame images from the real-time video at intervals, screens the multi-frame images, and screens image data by controlling the industrial personal computer through the PLC.
Further, a whole target area is formed by collecting brighter pixel points in the image data;
all pixel points in the image data are put into a coordinate system, and brighter pixel points are extracted through a formula, wherein the specific formula is as follows:
Figure SMS_1
；
wherein,,
Figure SMS_2
representing the overall target area formed by the collection of brighter pixels in the image data,
Figure SMS_3
gray value representing each pixel point in the first gray image,/or->
Figure SMS_4
Gray value representing each pixel point in the second gray image, < >>
Figure SMS_5
Representing a preset gray level difference value for determining brighter pixel points.
Further, the connected region is divided into the whole target region, the region which is not connected is divided into independent regions, and a plurality of suspected regions are formed.
Further, in the judging process of the suspected area, firstly, the area of the maximum suspected area occupying the area is extracted, and whether the roller winding phenomenon occurs or not is judged by a formula, wherein the specific formula is as follows:
Figure SMS_6
；
if the conditions are met, judging that the roller winding phenomenon occurs on the roller;
wherein,,
Figure SMS_7
represents the area of the largest suspected region, +.>
Figure SMS_8
Representing a preset threshold.
Further, after the monitoring area is set, image data are acquired and a preset area threshold is set, specifically, the preset area threshold is set according to the early warning width of the winding roller yarn, and is calculated according to the following formula:
Figure SMS_9
；
wherein,,
Figure SMS_10
representing the area occupied by the roller surface in the image data, < >>
Figure SMS_11
Representing the early warning width dimension of the winding roller yarn, +.>
Figure SMS_12
The axial length dimension of the roller is shown.
Further, in the determination of the brighter pixel point,
Figure SMS_13
the value of (2) is set to 80, and the pixels of the whole target area are screened by the following formula:
Figure SMS_14
。
a computer device comprising an industrial camera, a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method described above when executing the computer program.
A storage medium having a computer program stored thereon, the computer program, when executed by a processor, performing the method.
The beneficial effects of the invention are as follows:
in the invention, the roller is provided with a black roller surface corresponding to the white glass fiber yarn bundles, so that the winding yarns in the acquired image data can be conveniently identified; the industrial camera is arranged stationary relative to the roller station, so that background parts except the roller surface area in the image data can be filtered conveniently, the camera is directly fixed on the yarn storage device, and can be applied to different factory building installation arrangements, the camera installation position is not required to be adjusted according to the platform arrangement condition, and the debugging time is saved; the production state of the roller is monitored in real time through the industrial camera, high-frequency labor of manual detection is avoided, the roller winding condition can be accurately identified at the first time, and the production efficiency is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
Fig. 1 is a schematic flow chart of a roller winding detection method in an embodiment of the invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
It will be understood that when an element is referred to as being "fixed to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only and are not meant to be the only embodiment.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
The roller winding detection method shown in fig. 1 comprises the following steps:
setting a monitoring area and acquiring image data of a roller surface;
carrying out gray processing on the image data to obtain a first gray image;
performing mean value filtering processing on the first gray level image to obtain a second gray level image;
extracting a brighter region in the first gray level image to be used as a suspected region;
and comparing the preset area threshold value with the characteristic parameters of the suspected area, and outputting a judging result.
In the setting process of the monitoring area, the roller surface is black, and the industrial camera corresponds to the roller and is fixedly arranged on the mounting frame.
In the implementation process, the industrial camera is directly fixed on the yarn storage device, the corresponding roller is arranged behind the roller, and the shooting angle of the industrial camera is set opposite to the roller surface, so that when the roller does not generate the winding condition, only the black roller surface exists in the collected image data, and when the winding condition occurs, a relatively obvious white glass fiber coverage area appears on the black roller surface in the collected image data.
The roller is provided with a black roller surface corresponding to the white glass fiber bundles, so that the winding yarns in the acquired image data can be conveniently identified; the industrial camera is arranged stationary relative to the roller station, so that background parts except the roller surface area in the image data can be filtered conveniently, the camera is directly fixed on the yarn storage device, and can be applied to different factory building installation arrangements, the camera installation position is not required to be adjusted according to the platform arrangement condition, and the debugging time is saved; the production state of the roller is monitored in real time through the industrial camera, high-frequency labor of manual detection is avoided, the roller winding condition can be accurately identified at the first time, and the production efficiency is improved.
Furthermore, the target position video is acquired in real time through the industrial camera and transmitted to the industrial personal computer, and communication is established between the industrial personal computer and the PLC; the industrial personal computer extracts multi-frame images from the real-time video at intervals, screens the multi-frame images, and screens image data by controlling the industrial personal computer through the PLC.
The yarn storage device can have a process that the speed tends to 0 at the starting point and the end point in the production process, the process is in a state with higher speed at the middle section, and the roller has larger jump in a high-speed running state, if the image data collected by the industrial camera is put into a detection algorithm for processing at the moment, false alarm can be generated with high probability.
In the implementation process, the industrial personal computer establishes communication with the PLC through the Modbus-RTU, and when the industrial personal computer receives data sent by the PLC, the roller winding detection software on the industrial personal computer puts the image data acquired by the current camera into a detection algorithm for processing. The timing of sending data by the PLC can be set according to the running condition of a high-speed locomotive platform, namely, the image data collected by a camera can be screened according to the production state of the locomotive platform, so that a detection algorithm processes the screened image data, and the false alarm rate is reduced.
Forming an integral target area by collecting brighter pixel points in the image data;
all pixel points in the image data are put into a coordinate system, and brighter pixel points are extracted through a formula, wherein the specific formula is as follows:
Figure SMS_15
；
wherein,,
Figure SMS_16
representing the overall target area formed by the collection of brighter pixels in the image data,
Figure SMS_17
gray value representing each pixel point in the first gray image,/or->
Figure SMS_18
Gray value representing each pixel point in the second gray image, < >>
Figure SMS_19
Representing a preset gray level difference value for determining brighter pixel points.
And carrying out gray scale processing on the acquired image data, putting the image data into a coordinate system, carrying out position marking on each pixel point, and facilitating the aggregation of the pixel points meeting the screening conditions and carrying out next condition judgment.
And carrying out mean value filtering processing on the first gray level image, adopting a preset filter window, replacing each pixel point with the average value of pixels in a neighborhood window, removing noise points, enabling the edge contour of a wire winding area possibly existing in the image data to be clearer, and improving the image quality.
The wire wrap region and its edge profile are further identified from the filtered gray scale deviation by comparing the second gray scale image with the first gray scale image.
In this embodiment, in the determination of the brighter pixel point,
Figure SMS_20
the value of (2) is set to 80, and the pixels of the whole target area are screened by the following formula:
Figure SMS_21
。
in the process of simulating the working environment of the roller through experiments, selecting and judging the optimal gray level deviation value of the winding area, and realizing the accurate identification of the winding area of the roller surface in the specific production process.
Further, after the brighter pixel points are collected to obtain the whole target area, the whole target area is divided into connected areas, the area which is not connected is divided into independent areas, and a plurality of suspected areas are formed.
In the judging process of the suspected area, firstly extracting the area of the largest suspected area of the occupied area, and judging whether the roller winding phenomenon occurs or not through a formula, wherein the specific formula is as follows:
Figure SMS_22
；
if the conditions are met, judging that the roller winding phenomenon occurs on the roller;
wherein,,
Figure SMS_23
represents the area of the largest suspected region, +.>
Figure SMS_24
Representing a preset threshold.
In an experimental environment, analyzing an ROI (region of interest) intercepted in camera software, wherein when the axial length of a roller surface of a roller occupies about 1700 pixels, if the width of a wire winding appearing on the roller surface of the roller occupies 80 pixels, the standard of alarming the wire winding is achieved; in summary, when the winding width is about 1/20 of the length of the roller, it is determined that the roller is winding.
As a preferred detection method of the present application, when the yarn winding occurs in the roller, the yarn covers the width direction of the roller surface, and therefore the yarn width ratio determined as the winding roller phenomenon can be converted into the winding area ratio, that is, the winding width/roller surface length is equal to the winding area/roller surface area.
In the implementation process, after the monitoring area is set, image data are acquired and a preset area threshold value is set, specifically, the preset area threshold value is set according to the early warning width of the winding roller yarn, and is calculated through the following formula:
Figure SMS_25
；
wherein,,
Figure SMS_26
representing the area occupied by the roller surface in the image data, < >>
Figure SMS_27
Representing the early warning width dimension of the winding roller yarn, +.>
Figure SMS_28
The axial length dimension of the roller is shown.
In the processing process of the image data, the roller surface area and the area of the maximum suspected area can be directly obtained by counting the number of occupied pixel points.
The invention further discloses a computer device, which comprises an industrial camera, a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the oxidation furnace flame identification and detection method is realized when the processor executes the computer program.
A storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described oxidation oven flame identification detection method.
Any process or method description in a process frame diagram or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
Portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.

Claims (10)

1. The roller winding detection method is characterized by comprising the following steps of:
setting a monitoring area and acquiring image data of a roller surface;
carrying out gray processing on the image data to obtain a first gray image;
performing mean value filtering processing on the first gray level image to obtain a second gray level image;
extracting a brighter region in the first gray level image to be used as a suspected region;
and comparing the preset area threshold value with the characteristic parameters of the suspected area, and outputting a judging result.
2. The method for detecting the winding of the roller according to claim 1, wherein the roller surface is black during the setting of the monitoring area, and the industrial camera is fixedly arranged on the installation frame corresponding to the roller.
3. The method for detecting the winding yarn of the roller according to claim 1, wherein the industrial camera is used for collecting the target position video in real time and transmitting the target position video to the industrial personal computer, and the industrial personal computer and the PLC are communicated;
the industrial personal computer extracts multi-frame images from the real-time video at intervals, screens the multi-frame images, and screens image data by controlling the industrial personal computer through the PLC.
4. The roller yarn winding detection method as claimed in claim 2, wherein the integral target area is formed by collecting brighter pixel points in the image data;
all pixel points in the image data are put into a coordinate system, and brighter pixel points are extracted through a formula, wherein the specific formula is as follows:
Figure QLYQS_1
；
wherein,,
Figure QLYQS_2
representing the overall target area formed by the collection of brighter pixels in the image data,
Figure QLYQS_3
gray value representing each pixel point in the first gray image,/or->
Figure QLYQS_4
Gray value representing each pixel point in the second gray image, < >>
Figure QLYQS_5
Representing a preset gray level difference value for determining brighter pixel points.
5. The method of detecting a winding yarn around a roller according to claim 4, wherein the whole target area is divided into connected areas, the area without connection is divided into independent areas, and a plurality of suspected areas are formed.
6. The method for detecting the winding yarn of the roller according to claim 5, wherein in the process of judging the suspected area, the area of the largest suspected area of the occupied area is firstly extracted, and whether the winding roller phenomenon occurs is judged by a formula, wherein the specific formula is as follows:
Figure QLYQS_6
；
if the conditions are met, judging that the roller winding phenomenon occurs on the roller;
wherein,,
Figure QLYQS_7
represents the area of the largest suspected region, +.>
Figure QLYQS_8
Representing a preset threshold.
7. The method for detecting the winding of the roller according to claim 6, wherein the image data is collected and a preset area threshold is set after the completion of the setting of the monitoring area, and specifically, the preset area threshold is calculated according to the winding yarn early warning width setting by the following formula:
Figure QLYQS_9
；
wherein,,
Figure QLYQS_10
indicating that the roller surface is onOccupied area in image data->
Figure QLYQS_11
Representing the early warning width dimension of the winding roller yarn, +.>
Figure QLYQS_12
The axial length dimension of the roller is shown.
8. The method for detecting yarn winding of roller as claimed in claim 4, wherein, in the process of determining the brighter pixel point,
Figure QLYQS_13
the value of (2) is set to 80, and the pixels of the whole target area are screened by the following formula:
Figure QLYQS_14
。
9. a computer device comprising an industrial camera, a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the roller winding detection method according to any one of claims 1-8 when executing the computer program.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements the roller wrap detection method according to any one of claims 1-8.
CN202310343130.4A 2023-04-03 2023-04-03 Roller winding detection method, device and storage medium Pending CN116309519A (en)

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Application Number Priority Date Filing Date Title
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103759662A (en) * 2013-12-31 2014-04-30 东华大学 Dynamic textile yarn diameter rapid-measuring device and method
CN112680872A (en) * 2020-12-17 2021-04-20 常州市新创智能科技有限公司 Warp yarn winding roller broken yarn detection method
CN113724241A (en) * 2021-09-09 2021-11-30 常州市宏发纵横新材料科技股份有限公司 Broken filament detection method and device for carbon fiber warp-knitted fabric and storage medium

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103759662A (en) * 2013-12-31 2014-04-30 东华大学 Dynamic textile yarn diameter rapid-measuring device and method
CN112680872A (en) * 2020-12-17 2021-04-20 常州市新创智能科技有限公司 Warp yarn winding roller broken yarn detection method
CN113724241A (en) * 2021-09-09 2021-11-30 常州市宏发纵横新材料科技股份有限公司 Broken filament detection method and device for carbon fiber warp-knitted fabric and storage medium

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