CN117517045A - Intelligent assessment method for textile color fastness based on machine vision - Google Patents
Intelligent assessment method for textile color fastness based on machine vision Download PDFInfo
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- 239000004753 textile Substances 0.000 title claims abstract description 113
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000005286 illumination Methods 0.000 claims abstract description 7
- 239000004744 fabric Substances 0.000 claims description 26
- 229920000742 Cotton Polymers 0.000 claims description 18
- 238000011156 evaluation Methods 0.000 claims description 9
- 230000007246 mechanism Effects 0.000 claims description 7
- 239000000835 fiber Substances 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 238000009941 weaving Methods 0.000 claims description 6
- 238000010521 absorption reaction Methods 0.000 claims description 4
- 238000002791 soaking Methods 0.000 claims description 3
- 238000003756 stirring Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 25
- 230000008569 process Effects 0.000 abstract description 19
- 238000012360 testing method Methods 0.000 abstract description 8
- 238000005070 sampling Methods 0.000 abstract description 7
- 230000000694 effects Effects 0.000 abstract description 4
- 230000008859 change Effects 0.000 abstract description 3
- 238000012545 processing Methods 0.000 description 5
- 241000282414 Homo sapiens Species 0.000 description 4
- 239000000049 pigment Substances 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000001125 extrusion Methods 0.000 description 2
- 238000007654 immersion Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 241000692783 Chylismia claviformis Species 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 210000003128 head Anatomy 0.000 description 1
- 230000008595 infiltration Effects 0.000 description 1
- 238000001764 infiltration Methods 0.000 description 1
- 238000009533 lab test Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 210000003296 saliva Anatomy 0.000 description 1
- 239000013535 sea water Substances 0.000 description 1
- 230000035900 sweating Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/02—Details
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/02—Details
- G01N3/06—Special adaptations of indicating or recording means
- G01N3/068—Special adaptations of indicating or recording means with optical indicating or recording means
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/56—Investigating resistance to wear or abrasion
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Abstract
The invention discloses a textile color fastness intelligent assessment method based on machine vision, which is characterized in that the warp and weft densities of textiles are adjusted after the textiles are wetted and before the textiles are detected, so that the density of the wetted textiles is consistent with that of the textiles before the textiles are not wetted, the chromaticity of the textiles can be ensured not to deviate due to the change of the warp and weft densities in the testing process, and the accuracy of the textiles in color fastness detection is improved; the color fastness detection is carried out on the textile by adopting a machine vision mode, and the illumination condition is adjusted before the image sampling, so that the image sampling is in the same sampling condition in the detection process, the rapid detection in the textile color fastness detection process can be ensured, and the efficiency and the accuracy of the textile color fastness are improved; the friction direction of the textile is parallel to the surface of the textile, and the friction direction is consistent with the arrangement direction of yarns, so that the influence on the warp and weft density of the textile can be reduced, and the color fastness detection effect of the textile is enhanced.
Description
Technical Field
The invention relates to the technical field of textile detection, in particular to an intelligent evaluation method for textile color fastness based on machine vision.
Background
Machine vision is a technique that utilizes computer and image processing techniques to simulate and implement human vision. The image or video data is collected, processed and analyzed to simulate the visual ability of human beings, so that the understanding and recognition of objects, scenes and images are realized.
The color fastness (color fastness for short) refers to the degree of fading of dyed fabrics under the action of external factors (extrusion, friction, water washing, rain, solarization, illumination, seawater immersion, saliva immersion, water stains, perspiration and the like) in the use or processing process, and is an important index of the fabrics. Because the conditions to which the fabric is subjected during processing and use vary widely, the requirements vary.
A method for evaluating the color fastness of textiles by utilizing computer vision and image processing technology when evaluating the color fastness of textiles by utilizing machine vision. The color fastness grade of the textile is judged by analyzing and processing the image of the textile. The advantage of machine vision assessment of textile color fastness is its rapid, automated and non-destructive nature. It can greatly raise evaluation efficiency and reduce error of manual operation. However, it should be noted that the machine vision evaluation method still needs to be combined with the conventional laboratory test to ensure the accuracy and reliability of the evaluation result. Therefore, a dry and wet rubbing color fastness detection method with high detection capability is selected to carry out basic detection on the color fastness of the textile.
In the prior art CN113804619a, a dry and wet rubbing color fastness detection method for textile testing is disclosed, in the test method, two placed product samples are rubbed by a rubbing head after being subjected to dry and wet treatment respectively, and are colored by a gray sample card, so that the color fastness of the textile samples can be detected, but in the wet rubbing degree detection, the textile may deform after being immersed in warm water, so that the warp and weft density of the textile changes, and the color fastness of the textile deviates in the detection process.
Accordingly, there is a need for improvements in the prior art methods of detecting textile color fastness to solve the above-described problems.
Disclosure of Invention
The invention overcomes the defects of the prior art, provides an intelligent evaluation method for the color fastness of textiles based on machine vision, and aims to solve the defect that the chromaticity deviation of the textiles is caused in the detection process in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme: an intelligent evaluation method for color fastness of textiles based on machine vision comprises the following steps:
s1: inputting color corresponding to the color fastness level in advance in a processor, and adjusting the illumination condition of the image input equipment;
s2: cutting two sections of textile samples with the same size, recording the warp width, weft width and warp and weft density of the samples, and placing the samples in an incubator for at least 18 hours;
s3: soaking a sample in the S2 in warm water and stirring;
s4: rubbing the stirred sample in the step S3 with standard cotton cloth, and adjusting the longitude and latitude density of the rubbed sample according to the fiber weaving sequence to ensure that the longitude and latitude density of the sample is consistent with that of the sample in the step S2;
s5: placing the sample obtained in the step S4 and standard cotton cloth at the position of the image input equipment in the step S1 for color fastness comparison;
s6: rubbing the other sample in the S2 with standard cotton cloth, and adjusting the longitude and latitude density of the rubbed sample according to the fiber weaving sequence to ensure that the longitude and latitude density of the sample is consistent with that of the sample in the S2;
s7: and (3) placing the sample obtained in the step (S6) and standard cotton cloth at the image input equipment in the step (S1) for color fastness comparison.
In a preferred embodiment of the present invention, the temperature in the incubator in S2 is 25±2 ℃, and the humidity is 60±5%rh.
In a preferred embodiment of the present invention, the textile in S2 is a low moisture absorption textile.
In a preferred embodiment of the present invention, the warp and weft widths of the textile in S2 are uniform.
In a preferred embodiment of the present invention, the working direction of the image input device in S4 and S6 is perpendicular to the surface of the textile sample.
In a preferred embodiment of the present invention, the rubbing directions of the samples in S4 and S6 and the standard cotton cloth are both horizontal directions.
In a preferred embodiment of the present invention, the samples in S4 and S6 are rubbed with the standard cotton cloth along the yarn direction.
An intelligent assessment device for textile color fastness based on machine vision, comprising: a frame, and an image entry device and a clamping structure respectively disposed on the frame;
the clamping structure comprises: a plurality of grippers, a gripping unit provided on each of the grippers, and a plurality of stretching mechanisms provided between adjacent grippers;
each of the clamping units includes: the clamping support is provided with a clamping rod arranged on the clamping support and a plurality of heddles arranged on one side of the clamping support; the clamping support is fixedly connected with the clamping device, a plurality of heddles are in sliding connection with the clamping device, each heddle is vertically arranged, each heddle is provided with a heddle eye, and the heddle eyes are used for wires to pass through;
each of the stretching mechanisms includes: the air cylinder is fixedly connected with the supporting rod; the cylinder with the binding clasp fixed connection, branch is used for connecting adjacently the binding clasp, a plurality of branch all parallel arrangement just is located same horizontal plane.
In a preferred embodiment of the invention, the healds are arranged on opposite sides of the adjacent clamps, and the axes of the healds on the adjacent clamps are parallel and positioned on the same horizontal plane.
In a preferred embodiment of the invention, the clamping rods are slidably connected with the clamping support, the lower end of each clamping rod is fixedly connected with a clamping block, and the clamping blocks are horizontally arranged.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
(1) According to the method, the warp and weft densities of the textile are adjusted after the textile is wetted and before the textile is detected, so that the density of the wetted textile is consistent with that of the textile before the textile is not wetted.
(2) According to the invention, the color fastness of the textile is detected in a machine vision mode, and the illumination condition is adjusted before the image is sampled, so that the image is sampled under the same sampling condition in the detection process.
(3) When the color fastness is detected by friction on the textile sample, the friction direction is parallel to the surface of the textile and is consistent with the arrangement direction of the yarns, compared with the prior art, the color fastness friction method has the advantages that the friction process of the textile is more uniform, the influence on the warp and weft density of the textile is reduced, and the color fastness detection effect of the textile is enhanced.
Drawings
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 can be obtained according to these drawings without inventive effort for a person skilled in the art;
FIG. 1 is a process flow diagram of a preferred embodiment of the present invention;
FIG. 2 is a perspective view of a clamping structure of a preferred embodiment of the present invention;
FIG. 3 is a side view of a clamping unit of a preferred embodiment of the present invention;
in the figure: 100. a clamp; 200. a clamping unit; 210. clamping a bracket; 220. a clamping bar; 221. a clamping block; 230. heddles; 240. brown eyes; 300. a stretching mechanism; 310. a cylinder; 320. a supporting rod.
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. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
In the description of the present application, it should be understood that the terms "center," "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, merely to facilitate description of the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the scope of protection of the present application. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", etc. may include one or more of the feature, either explicitly or implicitly. In the description of the invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application can be understood by those of ordinary skill in the art in a specific context.
Example 1
As shown in fig. 1, a machine vision-based intelligent evaluation method for color fastness of textiles comprises the following steps:
s1: inputting color corresponding to the color fastness level in advance in a processor, and adjusting the illumination condition of the image input equipment;
s2: cutting two sections of textile samples with the same size, recording the warp width, weft width and warp and weft density of the samples, and placing the samples in an incubator for at least 18 hours;
s3: soaking a sample in the S2 in warm water and stirring;
s4: rubbing the stirred sample in the step S3 with standard cotton cloth, and adjusting the longitude and latitude density of the rubbed sample according to the fiber weaving sequence to ensure that the longitude and latitude density of the sample is consistent with that of the sample in the step S2;
s5: placing the sample obtained in the step S4 and standard cotton cloth at the position of the image input equipment in the step S1 for color fastness comparison;
s6: rubbing another sample in the S2 with the standard cotton cloth, and adjusting the longitude and latitude density of the rubbed sample according to the fiber weaving sequence to ensure that the longitude and latitude density of the sample is consistent with that of the sample in the S2;
s7: and (3) comparing the color fastness of the sample obtained in the step (S6) with that of the standard cotton cloth at the image input equipment in the step (S1).
After the textile is wetted, the warp and weft densities of the textile are adjusted before the textile is detected in S4 and S6, so that the density of the wetted textile is consistent with that of the textile before being wetted, the chromaticity of the textile can be ensured not to deviate due to the change of the warp and weft densities in the test process, and the accuracy of the textile in color fastness detection is improved.
And S2, the temperature in the incubator is 25+/-2 ℃, and the humidity is 60+/-5% Rh. The temperature and the humidity of the incubator keep consistent, so that the textile sample can be ensured to be stably attached to the surface pigment in the incubator, and the accuracy of the experiment can be ensured in the subsequent friction test.
The textile in S2 is a low moisture absorption textile. If the moisture absorption rate of the textile is too high, the yarn density is increased in the textile infiltration process, the porosity of the textile is reduced, and the textile fabric density is increased as a whole, so that the color fastness in the test is influenced.
The warp and weft widths of the textile in S2 are identical. The textile with the consistent warp width and weft width is selected, so that the yarns are prevented from being excessively deviated due to friction in a friction test, and the warp and weft density of the textile can be effectively maintained.
As shown in fig. 2 and 3, an intelligent evaluation device for color fastness of textile based on machine vision includes: a frame, and an image entry device and a clamping structure respectively arranged on the frame.
The clamping structure comprises: a plurality of grippers 100, a gripping unit 200 provided on each of the grippers 100, and a plurality of stretching mechanisms 300 provided between adjacent grippers 100.
Each clamping unit 200 includes: a clamping bracket 210, a clamping bar 220 provided on the clamping bracket 210, and a plurality of heddles 230 provided at one side of the clamping bracket 210; the clamping bracket 210 is fixedly connected with the clamping device, a plurality of heddles 230 are slidably connected with the clamping device, each heddle 230 is vertically arranged, each heddle 230 is provided with a heddle eye 240, and each heddle eye 240 is used for a thread to pass through.
Each stretching mechanism 300 includes: the cylinder 310, the strut 320 fixedly connected with the cylinder 310; the air cylinder 310 is fixedly connected with the clamping device, the supporting rods 320 are used for connecting adjacent clamping devices 100, and a plurality of supporting rods 320 are arranged in parallel and located on the same horizontal plane. The air cylinder 310 is used for stretching the adjacent clamp 100, and after the yarn is clamped, the stretching of the air cylinder 310 drives the yarn to straighten.
The heddles 230 are all arranged on opposite sides of the adjacent clamps, and the axes of the heddle eyes 240 on the adjacent clamps are parallel and positioned on the same horizontal plane. The heald eyes 240 are located on the same horizontal plane, so that all threads of the textile fabric can be located on the same horizontal plane, and the threads can be effectively stretched in the adjustment process, and the fabric structure is guaranteed.
The clamping bars 220 are slidably connected with the clamping support 210, the lower end of each clamping bar 220 is fixedly connected with a clamping block 221, and the clamping blocks 221 are horizontally arranged. The clamping block 221 is used for pressing and clamping the wires, and when the wires are placed, the wires are all positioned below the clamping block 221, and finally, a plurality of wires between the clamping block 221 and the clamping device 100 are clamped.
Example two
The working direction of the image entry device in S4 and S6 is perpendicular to the textile sample surface. The detection process is most visual, the influence of the change of the warp and weft densities of the textile fabric is minimal, and when the repeated test is carried out to replace the sample, the redundant alignment procedure can be avoided, the sample replacement speed is accelerated to a certain extent, and the efficiency of the whole test is improved.
The color fastness detection is carried out on the textile in a machine vision mode, and the illumination condition is adjusted before the image sampling, so that the image sampling is in the same sampling condition in the detection process, the rapid detection in the textile color fastness detection process can be ensured, and the efficiency and the accuracy of the textile color fastness are improved.
The friction directions of the samples in S4 and S6 and the standard cotton cloth are all horizontal directions. The friction mode related to the friction is vertical friction, the friction force of vertical extrusion is smaller, compared with horizontal friction, the friction degree of the friction on the surface pigment of the textile is insufficient, and the horizontal friction is more similar to the friction mode of people and clothes in real life.
The samples in S4 and S6 were rubbed with standard cotton in the yarn direction. When the yarn is rubbed along the yarn direction, the swing amplitude of the yarn is weakened, the main effect of friction force is to pigment on the surface of the yarn, and when the yarn is at a certain angle with the yarn direction, the friction force can enable the yarn to swing, the friction force does idle work on the yarn, the consumption of power is improved in yarn friction, and the friction force received by the pigment of the textile fabric can be reduced.
The friction direction is parallel to the surface of the textile and is consistent with the arrangement direction of yarns, compared with the prior art, the friction process of the color fastness of the textile is more uniform, the influence on the warp and weft density of the textile is reduced, and the color fastness detection effect of the textile is enhanced.
In the process of adjusting the warp and weft densities of the textile samples, the warp and weft densities obtained by recording in the step S1 are adjusted, so that the arrangement densities of the healds 230 on the adjacent clamps 100 are consistent with the warp and weft densities obtained by recording.
After the density of the healds 230 is adjusted to be consistent with the warp density of the textile, the warp yarns of the textile are respectively inserted into the heald eyes 240 on the healds 230, and the clamping bars 220 are used for downwards moving, the warp yarns are extruded and pressed under the clamping blocks 221 in the process that the clamping blocks 221 gradually descend, and finally the warp yarns are pressed between the clamping blocks 221 and the clamping device 100.
When the warp yarns on adjacent grippers 100 are all pressed, cylinder 310 is activated and struts 320 are extended. During the extension of the struts 320, the warp yarns are stretched and gradually arranged at a preset warp yarn density, and the warp yarn density is finally adjusted to the state in S1 on the premise that the warp yarns are not stretch broken.
After the density of the healds 230 is adjusted to be consistent with the weft density of the textile, the weft yarns of the textile are respectively inserted into the heald eyes 240 on the healds 230, and the clamping bars 220 are used for downwards moving, the weft yarns are extruded and pressed under the clamping blocks 221 in the process that the clamping blocks 221 gradually descend, and finally the weft yarns are pressed between the clamping blocks 221 and the clamping device 100.
When all weft yarns on adjacent grippers 100 have been squeezed, cylinder 310 is activated and struts 320 are extended. During extension of the bar 320, the weft yarns are stretched and gradually arranged at a preset weft yarn density, which is finally adjusted to the state in S1, under the premise of ensuring that the weft yarns are not stretch broken.
And after the warp and weft densities are regulated, sequentially performing color fastness detection on the two textile fabrics. The reason for simultaneously carrying out the dry and wet color fastness detection is that textiles under different environments are respectively simulated. The wet textile sample corresponds to the clothing of the human body after sweating, the dry textile sample corresponds to the clothing of the human body in a dry state, and the adaptability of the textile color fastness can be detected by detecting under the condition that various textiles are positioned, so that the textile color fastness can be more comprehensively detected.
The above-described preferred embodiments according to the present invention are intended to suggest that, from the above description, various changes and modifications can be made by the person skilled in the art without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1. The intelligent evaluation method for the color fastness of the textile based on the machine vision is characterized by comprising the following steps of:
s1: inputting color corresponding to the color fastness level in advance in a processor, and adjusting the illumination condition of the image input equipment;
s2: cutting two sections of textile samples with the same size, recording the warp width, weft width and warp and weft density of the samples, and placing the samples in an incubator for at least 18 hours;
s3: soaking a sample in the S2 in warm water and stirring;
s4: rubbing the stirred sample in the step S3 with standard cotton cloth, and adjusting the longitude and latitude density of the rubbed sample according to the fiber weaving sequence to ensure that the longitude and latitude density of the sample is consistent with that of the sample in the step S2;
s5: placing the sample obtained in the step S4 and standard cotton cloth at the position of the image input equipment in the step S1 for color fastness comparison;
s6: rubbing the other sample in the S2 with standard cotton cloth, and adjusting the longitude and latitude density of the rubbed sample according to the fiber weaving sequence to ensure that the longitude and latitude density of the sample is consistent with that of the sample in the S2;
s7: and (3) placing the sample obtained in the step (S6) and standard cotton cloth at the image input equipment in the step (S1) for color fastness comparison.
2. The intelligent assessment method for color fastness of textiles based on machine vision according to claim 1, wherein: the temperature in the incubator in S2 is 25+ -2deg.C, and the humidity is 60+ -5% Rh.
3. The intelligent assessment method for color fastness of textiles based on machine vision according to claim 1, wherein: the textile in S2 is a low moisture absorption textile.
4. The intelligent assessment method for color fastness of textiles based on machine vision according to claim 1, wherein: the warp and weft widths of the textile in S2 are identical.
5. The intelligent assessment method for color fastness of textiles based on machine vision according to claim 1, wherein: the working direction of the image input device in the S4 and the S6 is perpendicular to the surface of the textile sample.
6. The intelligent assessment method for color fastness of textiles based on machine vision according to claim 1, wherein: and the friction directions of the samples in the S4 and the S6 and the standard cotton cloth are horizontal directions.
7. The intelligent assessment method for color fastness of textiles based on machine vision according to claim 1, wherein: the samples in S4 and S6 are rubbed with the standard cotton cloth along the yarn direction.
8. A machine vision-based intelligent assessment device for color fastness of textiles, based on the machine vision-based intelligent assessment method for color fastness of textiles as claimed in any one of claims 1 to 7, comprising: a frame, and an image input device and a clamping structure which are respectively and fixedly arranged on the frame;
the clamping structure comprises: a plurality of grippers, a gripping unit provided on each of the grippers, and a plurality of stretching mechanisms provided between adjacent grippers;
each of the clamping units includes: the clamping support is provided with a clamping rod arranged on the clamping support and a plurality of heddles arranged on one side of the clamping support; the clamping support is fixedly connected with the clamping device, a plurality of heddles are in sliding connection with the clamping device, each heddle is vertically arranged, each heddle is provided with a heddle eye, and the heddle eyes are used for wires to pass through;
each of the stretching mechanisms includes: the air cylinder is fixedly connected with the supporting rod; the cylinder with the binding clasp fixed connection, branch is used for connecting adjacently the binding clasp, a plurality of branch all parallel arrangement just is located same horizontal plane.
9. A textile color fastness warp and weft density adjustment device according to claim 8, characterized in that: the healds are arranged on opposite sides of the adjacent clamps, and the axes of the heald eyes on the adjacent clamps are parallel and positioned on the same horizontal plane.
10. A textile color fastness warp and weft density adjustment device according to claim 8, characterized in that: the clamping rods are connected with the clamping supports in a sliding mode, the lower ends of the clamping rods are fixedly connected with clamping blocks, and the clamping blocks are horizontally arranged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311487214.1A CN117517045A (en) | 2023-11-09 | 2023-11-09 | Intelligent assessment method for textile color fastness based on machine vision |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311487214.1A CN117517045A (en) | 2023-11-09 | 2023-11-09 | Intelligent assessment method for textile color fastness based on machine vision |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117808901A (en) * | 2024-03-01 | 2024-04-02 | 深圳市富安娜家居用品股份有限公司 | Textile color fastness prediction method and system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117808901A (en) * | 2024-03-01 | 2024-04-02 | 深圳市富安娜家居用品股份有限公司 | Textile color fastness prediction method and system |
| CN117808901B (en) * | 2024-03-01 | 2024-04-26 | 深圳市富安娜家居用品股份有限公司 | Textile color fastness prediction method and system |
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