CN118886791A - A simulation analysis method for yarn durability quality detection - Google Patents

Abstract

The invention belongs to the technical field of yarn quality analysis, and particularly discloses a yarn durability quality detection simulation analysis method, which comprises the following steps: performing strength analysis, wear resistance analysis and weather analysis on the yarn through strength detection, wear resistance detection and weather detection of the yarn to obtain a strength qualification index, a wear resistance qualification index and a weather resistance qualification index of the yarn, further analyzing a quality durability qualification index of the target yarn, and generating a yarn durability quality analysis report according to the quality durability qualification index; according to the invention, the yarn quality is comprehensively analyzed in multiple dimensions through the strength detection, the wear resistance detection and the climate detection of the yarn, so that the comprehensiveness of the yarn quality analysis result is improved, the reliability of the yarn in use is ensured, and meanwhile, the weather resistance qualification index of the yarn is analyzed according to the climate simulation data of the yarn under each simulated climate condition, the accuracy of the yarn outdoor performance evaluation is improved, and the stability of the yarn quality is further ensured.

Description

A simulation analysis method for yarn durability quality detection

Technical Field

The invention belongs to the technical field of yarn quality analysis and relates to a yarn durability quality detection simulation analysis method.

Background Art

The durable quality of yarn refers to the comprehensive characteristics of yarn that can maintain its stable performance and long-term use under various conditions of use. It includes the strength, wear resistance and weather resistance of the yarn. Yarn with high durable quality can produce better quality and more reliable textiles, so it is necessary to test and simulate the durable quality of yarn.

For example, the Chinese patent with authorization announcement number CN109765229B discloses a CCD-based online yarn quality detection method and detection device. The detection method mainly includes: online acquisition of images of yarns in an instantaneous stable state and processing them to obtain yarn quality information. The appearance quality of the yarn is detected and analyzed, and the quality of the yarn is analyzed from the apparent characteristics of the yarn. The detection device includes a light source, an analog/digital converter, an image processor, a display element, and an image collector, a signal processor, a driving device, and a yarn instantaneous stabilization device connected in sequence. The image collector, the analog/digital converter, the image processor, and the display element are connected in sequence, and the yarn instantaneous stabilization device contains a curved surface for contacting the yarn to make it in an instantaneous stable state.

The above existing technologies have the following deficiencies: 1. The current yarn quality inspection mainly focuses on the appearance quality inspection of the yarn quality, that is, only the single dimension of the yarn appearance quality is inspected and analyzed, which cannot guarantee the comprehensiveness of the yarn quality analysis, and thus cannot guarantee the reliability of the yarn use, and further cannot guarantee the timeliness of the discovery of potential quality problems, and at the same time leads to a waste of resources.

2. Currently, the quality analysis of yarn is only carried out based on the apparent characteristics of the yarn. The impact of climate on the yarn has not been analyzed by simulating the climate. It is impossible to accurately evaluate the outdoor performance of the yarn, and thus it is impossible to guarantee the quality stability of the yarn, which affects the subsequent service life and performance of the yarn.

Summary of the invention

In view of this, in order to solve the problems raised in the above background technology, a yarn durability quality detection simulation analysis method is proposed.

The objective of the present invention can be achieved through the following technical scheme: The present invention provides a yarn durability quality detection simulation analysis method, comprising: step one, yarn data recording: randomly selecting a set number of yarns from the yarns to be detected as target yarns, and recording the initial data of the target yarns at the same time.

Step 2: Yarn strength test: Detect the strength data of the target yarn under the tensile test at a set speed. The strength data includes breaking strength data and rebound strength data.

Step 3: Yarn strength analysis: Analyze the strength qualification index of the target yarn according to the strength data of the tensile test of the target yarn at the set speed.

Step 4: Yarn wear resistance test: Detect the friction data of the target yarn under the set friction times and friction pressure. The friction data includes appearance data and quality data.

Step 5: Yarn wear resistance analysis: Analyze the wear resistance qualified index of the target yarn according to the friction data of the friction test of the target yarn under the set friction times and friction pressure.

Step 6: Yarn climate detection: Detect the simulated data of the target yarn under various simulated climate conditions, the simulated data including yarn color data, yarn strength data and yarn appearance data.

Step 7: Yarn climate analysis: Analyze the target yarn's climate resistance qualification index based on the simulated data of the target yarn under various simulated climate conditions.

Step 8. Yarn quality analysis: Analyze the quality durability index of the target yarn according to the strength qualified index, wear resistance qualified index and weather resistance qualified index of the target yarn.

Step 9: Generating a yarn quality report: Generating a yarn durability quality analysis report based on the quality durability qualified index of the target yarn.

Preferably, the strength qualification index of the target yarn is analyzed, including: A1, extracting the maximum tensile force of each broken yarn at the time of breaking and the length of the yarn before breaking from the breaking strength data of the target yarn under the set speed tensile test, and recording them as and , Number the broken yarns, .

A2. Extract the initial yarn length of the target yarn from the initial data of the target yarn, denoted as .

A3. Statistical breaking strength qualified index of target yarn ,

, and are the elongation and tension of the reference respectively. is the number of broken yarns.

A4. According to the rebound strength data of the target yarn under the set speed, the rebound strength qualified index of the target yarn is calculated. .

A5. Statistical strength index of target yarn , , and are the weights of the set fracture strength qualified index and rebound strength qualified index, , .

Preferably, the statistical target yarn rebound strength qualified index comprises: extracting the maximum stretching length and the length after rebound of each rebound yarn from the rebound strength data of the target yarn tensile test at a set speed, and recording them as and , Number the rebound yarn, .

Statistics of the rebound rate of each rebound yarn , .

Statistics of the rebound strength qualified index of each rebound yarn , , To set the reference spring rate.

Will The mean value is calculated and the result is used as the qualified index of the target yarn's rebound strength and recorded as .

Preferably, the analysis of the wear resistance qualified index of the target yarn includes: according to the appearance data of the friction test of the target yarn under the set friction times and friction pressure, calculating the friction appearance qualified index of the target yarn .

The weight of each friction yarn is extracted from the mass data of the friction test of the target yarn under the set friction number and friction pressure, and recorded as , Number the friction yarns, .

The initial weight of the target yarn is extracted from the initial data of the target yarn and recorded as .

Statistical analysis of the actual weight loss ratio of each friction yarn , .

Statistical target yarn friction weight qualified index , , is the set allowable loss weight ratio, is the number of friction yarns.

The diameter of each friction yarn is extracted from the mass data of the friction test of the target yarn under the set friction number and friction pressure, according to The friction diameter qualified index of the target yarn is obtained by the same statistical method and recorded as .

Statistical friction quality index of target yarn , .

Statistical wear resistance qualified index of target yarn , , and are the weights of the friction appearance qualified index and friction quality qualified index, respectively. , .

Preferably, the statistical target yarn friction appearance qualified index includes: extracting the friction fuzzing data of the target yarn from the appearance data of the friction test of the target yarn under the set friction number and friction pressure, and then extracting the fuzzing length, the area of each fuzzing region and the fuzzing density of each friction yarn.

The average of each fuzz length of a friction yarn is calculated, and the result is taken as the average fuzz length of the friction yarn, which is recorded as .

The total area of the fuzzy area of the friction yarn is obtained by summing up the areas of each fuzzy area, which is recorded as , and the fuzz density of the friction yarn is recorded as .

The initial area of the target yarn is extracted from the initial data of the target yarn and recorded as .

Calculate the qualified index of the friction yarn , , , and The reference fuzz length, fuzz area ratio and fuzz density are set respectively, and then the qualified fuzz index of each friction yarn is obtained by statistics, and the average is calculated to obtain the qualified fuzz index of the target yarn, which is recorded as .

The friction pilling data of the target yarn is extracted from the appearance data of the friction test under the set friction times and friction pressure, and then the pilling diameter, pilling area and pilling density of each friction yarn are extracted, and then the pilling diameter, pilling area and pilling density of each friction yarn are extracted according to the The pilling qualified index of the target yarn is obtained by the same analysis method and recorded as .

from and The minimum value is selected as the friction appearance qualified index of the target yarn, which is recorded as .

Preferably, the analysis of the target yarn's weather resistance qualified index includes: G1, according to the yarn color data of the target yarn after each simulated climate condition, statistically calculating the target yarn's color change qualified index .

G2. According to the yarn strength data of the target yarn under each simulated climate condition, the strength change qualified index of the target yarn is calculated. .

G3. According to the yarn appearance data of the target yarn under each simulated climate condition, the qualified index of the appearance change of the target yarn is calculated. .

G4. Statistical weather resistance index of target yarn ,

, , and The reference color change qualified index, intensity change qualified index and appearance change qualified index are set respectively. is a natural constant.

Preferably, the statistical target yarn color change qualified index comprises: extracting the lightness, red-greenness and yellow-blueness of each simulated climate yarn from the yarn color data of the target yarn after each simulated climate condition, and recording them as , and , Number the simulated climate yarns. .

The initial lightness, initial red-greenness, and initial yellow-blueness of the target yarn are extracted from the initial data of the target yarn and recorded as , and .

Statistical analysis of the total chromaticity difference of yarns in each simulated climate ,

.

The total chromaticity difference of each simulated climate yarn is compared with the total chromaticity difference of the set reference. If the chromaticity difference of a simulated climate yarn is less than the total chromaticity difference of the set reference, the simulated climate yarn will be recorded as a yarn with qualified color, and the number of yarns with qualified color will be counted.

The number of simulated climate yarns was counted, and the ratio of the number of color qualified yarns to the number of simulated climate yarns was taken as the color change qualified index of the target yarn, which was recorded as .

Preferably, the statistical determination of the strength variation qualification index of the target yarn comprises: extracting the breaking strength data and the rebound strength data of each simulated climate yarn from the yarn strength data of the target yarn under each simulated climate condition.

Extract the strength qualification index of the target yarn , and then according to The strength qualification index of the target yarn after simulated climate is obtained by the same analysis method and recorded as .

Statistical target yarn strength loss rate , , It is used to set the qualified index of simulated climate intensity for reference.

Statistical target yarn strength change qualified index , , To set the reference strength loss rate.

Preferably, the statistical target yarn appearance change qualified index comprises: extracting the simulated climate fuzzing data and simulated climate pilling data of the target yarn from the yarn appearance data of the target yarn under each simulated climate condition.

Extract the target yarn's qualified index and the target yarn pilling qualified index , and then according to and The same analysis method is used to analyze the target yarn after the simulated climate to obtain the qualified fuzzing index and pilling index, which are recorded as and .

Each simulated climate yarn shape image is extracted from the yarn appearance data of the target yarn under each simulated climate condition.

An initial shape image of the target yarn is extracted from the initial data of the target yarn.

The simulated climate yarn shape images are overlapped and compared with the initial shape images to obtain the overlapped shape image area of each simulated climate yarn, which is recorded as .

The initial shape image area of the target yarn is recorded as .

Will and The ratio of is taken as the deformation qualification index of each simulated climate yarn, the average is calculated, and the calculation result is taken as the shape qualification index of the target yarn after simulated climate, recorded as .

from , and The minimum value is selected as the qualified index of appearance change of the target yarn and recorded as .

Preferably, the analysis of the target yarn's quality durability qualified index includes: extracting the target yarn's strength qualified index , target yarn wear resistance qualified index and target yarn weatherability qualification index .

Statistical quality and durability index of target yarn , , , and are the weights of the strength qualified index, wear resistance qualified index and weather resistance qualified index respectively. , .

Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention conducts a multi-dimensional comprehensive analysis of yarn quality through yarn strength testing, wear resistance testing and climate testing, avoiding the shortcomings of the current testing and analysis of only a single dimension of yarn appearance quality, improving the comprehensiveness of yarn quality analysis results, thereby ensuring the reliability of yarn use, and further ensuring the timeliness of the discovery of potential quality problems, while improving the effective use of resources.

(2) The present invention analyzes the yarn's qualified weather resistance index based on the simulated data of the yarn under various simulated climate conditions, thereby breaking the current deficiency of analyzing the climate impact of the yarn without combining it with the simulated climate, improving the accuracy of the yarn's outdoor performance evaluation, and further ensuring the stability of the yarn quality, thereby ensuring the subsequent service life and performance of the yarn.

(3) The present invention analyzes the strength qualification index of the yarn by using the two aspects of breaking strength and rebound strength, thus avoiding the limitation of single indicator evaluation and improving the accuracy of the analysis results of the strength qualification index of the yarn, thereby facilitating a more comprehensive understanding of the mechanical properties of the yarn in different usage scenarios.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

FIG. 1 is a schematic diagram showing the connection of various steps of the method of the present invention.

FIG. 2 is a schematic diagram showing the connection of the strength qualification index analysis steps of the present invention.

FIG. 3 is a schematic diagram showing the connection steps of the weather resistance qualified index analysis step of the present invention.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Please refer to Figure 1, the present invention provides a yarn durability quality detection simulation analysis method, the method includes: step one, yarn data recording: randomly extracting a set number of yarns from the yarns to be tested as target yarns, and recording the initial data of the target yarns.

It should be added that the initial data of the target yarn includes: the initial yarn length, initial weight, initial area, initial brightness, initial red-green saturation, initial yellow-blue saturation and initial shape image of the target yarn.

It should be added that the process of obtaining the target yarn is as follows: the yarns to be tested are first numbered, and then samples are taken at certain intervals, and then one target yarn is taken every certain number of yarns. For example, if a batch of yarns has 1,000 yarns and 100 target yarns are to be taken, one can be taken every 10 yarns.

It should also be added that the target yarns include: broken yarns, rebound yarns, friction yarns and simulated climate yarns.

Step 2: Yarn strength test: Detect the strength data of the target yarn under the tensile test at a set speed. The strength data includes breaking strength data and rebound strength data.

It should be added that the breaking strength data are the maximum tensile force of each broken yarn when it breaks and the length of the yarn before breaking, and the rebound strength data are the maximum tensile length of each rebound yarn and the length after rebound.

Step 3: Yarn strength analysis: Analyze the strength qualification index of the target yarn according to the strength data of the tensile test of the target yarn at the set speed.

As shown in FIG. 2 , for example, the strength qualification index of the target yarn is analyzed, including: A1, extracting the maximum tensile force of each broken yarn at the time of breaking and the length of the yarn before breaking from the breaking strength data of the target yarn under the tensile test at the set speed, and recording them as and , Number the broken yarns, .

It should be added that the maximum tension of each broken yarn when it breaks is monitored by a strain gauge tension sensor installed on the yarn stretching device, and the yarn length of each broken yarn before it breaks is monitored by a laser rangefinder installed on one side of the yarn.

A2. Extract the initial yarn length of the target yarn from the initial data of the target yarn, denoted as .

A3. Statistical breaking strength qualified index of target yarn ,

, and are the elongation and tension of the reference respectively. is the number of broken yarns.

It should be added that and All are extracted from the yarn durability quality test technical specification table.

A4. According to the rebound strength data of the target yarn under the set speed, the rebound strength qualified index of the target yarn is calculated. .

Further, the statistical target yarn rebound strength qualified index includes: A4-1, extracting the maximum stretching length and the length after rebound of each rebound yarn from the rebound strength data of the target yarn tensile test at a set speed, and recording them as and , Number the rebound yarn, .

It should be added that the maximum tensile length of each rebound yarn is obtained by monitoring the laser displacement sensor, and the length of each rebound yarn after rebound is obtained by extracting it from the image collected by the camera.

A4-2. Statistical analysis of the rebound rate of each rebound yarn , .

In a specific embodiment, the initial length of the rebound yarn is 100 mm, the maximum stretching length is 150 mm, and the length after rebound is 120 mm, then the rebound rate = (120-100)/(150-100) = 0.4.

A4-3. Calculate the rebound strength qualified index of each rebound yarn , , To set the reference spring rate.

It should be added that It is extracted from the technical specification table of yarn durability quality testing.

A4-4. The mean value is calculated and the result is used as the qualified index of the target yarn's rebound strength and recorded as .

A5. Statistical strength index of target yarn , , and are the weights of the set fracture strength qualified index and rebound strength qualified index, , .

It should be added that the breaking strength is directly related to whether the yarn can withstand a certain tensile force without breaking in actual use. For example, in the production of ropes, safety belts and other products with extremely high strength requirements, the breaking strength of the yarn is a crucial indicator. If the breaking strength does not meet the standard, the product may have serious safety hazards during use, and the tensile rebound performance is not the key factor in determining whether the product can be used normally. Even if the tensile rebound performance is slightly worse, the product may still meet the basic use requirements, so setting , in order to facilitate analysis, The specific value can be 0.6. The specific value can be 0.4.

The embodiment of the present invention analyzes the strength qualification index of the yarn by means of breaking strength and rebound strength, thereby avoiding the limitation of single indicator evaluation, improving the accuracy of the analysis results of the strength qualification index of the yarn, and facilitating a more comprehensive understanding of the mechanical properties of the yarn in different usage scenarios.

Step 4: Yarn wear resistance test: Detect the friction data of the target yarn under the set friction times and friction pressure. The friction data includes appearance data and quality data.

Step 5: Yarn wear resistance analysis: Analyze the wear resistance qualified index of the target yarn according to the friction data of the friction test of the target yarn under the set friction times and friction pressure.

Exemplarily, the analysis of the wear resistance qualified index of the target yarn includes: according to the appearance data of the friction test of the target yarn under the set friction times and friction pressure, calculating the friction appearance qualified index of the target yarn .

Furthermore, the statistical target yarn friction appearance qualified index includes: extracting the friction fuzzing data of the target yarn from the appearance data of the friction test of the target yarn under the set friction number and friction pressure, and then extracting the fuzzing length, the area of each fuzzing region and the fuzzing density of each friction yarn.

It should be added that the fuzz lengths of the friction yarns are obtained by extracting from the pictures collected by the camera, the fuzz area is obtained by extracting from the pictures collected by the camera, and the fuzz density is obtained by monitoring with an optical fiber sensor.

The average of each fuzz length of a friction yarn is calculated, and the result is taken as the average fuzz length of the friction yarn, which is recorded as .

The total area of the fuzzy area of the friction yarn is obtained by summing up the areas of each fuzzy area, which is recorded as , and the fuzz density of the friction yarn is recorded as .

The initial area of the target yarn is extracted from the initial data of the target yarn and recorded as .

Calculate the qualified index of the friction yarn , , , and The reference fuzz length, fuzz area ratio and fuzz density are set respectively, and then the qualified fuzz index of each friction yarn is obtained by statistics, and the average is calculated to obtain the qualified fuzz index of the target yarn, which is recorded as .

It should be added that , and All are extracted from the yarn durability quality test technical specification table.

The friction pilling data of the target yarn is extracted from the appearance data of the friction test under the set friction times and friction pressure, and then the pilling diameter, pilling area and pilling density of each friction yarn are extracted, and then the pilling diameter, pilling area and pilling density of each friction yarn are extracted according to the The pilling qualified index of the target yarn is obtained by the same analysis method and recorded as .

It should be added that the pilling diameter of each friction yarn is obtained by monitoring with a laser profile scanner, the area of each pilling region is obtained by extracting from the image collected by the camera, and the pilling density is obtained by monitoring with an optical fiber sensor.

from and The minimum value is selected as the friction appearance qualified index of the target yarn, which is recorded as .

The weight of each friction yarn is extracted from the mass data of the friction test of the target yarn under the set friction number and friction pressure, and recorded as , Number the friction yarns, .

The initial weight of the target yarn is extracted from the initial data of the target yarn and recorded as .

Statistical analysis of the actual weight loss ratio of each friction yarn , .

Statistical target yarn friction weight qualified index , , is the set allowable loss weight ratio, is the number of friction yarns.

It should be added that It is extracted from the technical specification table of yarn durability quality testing.

The diameter of each friction yarn is extracted from the mass data of the friction test of the target yarn under the set friction number and friction pressure, according to The friction diameter qualified index of the target yarn is obtained by the same statistical method and recorded as .

In a specific embodiment, the formula for the diameter reduction is (initial diameter-diameter after friction)/initial diameter. For example, if the initial diameter is 1 mm and the diameter after friction is 0.98 mm, then the diameter reduction is (1-0.98)/1=0.02.

The formula for the diameter change qualified index is (1-diameter reduction range/allowable diameter reduction range). Assuming the allowable diameter reduction range is 0.05, the diameter change qualified index is (1-0.02/0.05) = 0.6.

Statistical friction quality index of target yarn , .

Statistical wear resistance qualified index of target yarn , , and are the weights of the friction appearance qualified index and friction quality qualified index, respectively. , .

It should be added that the weight change and diameter change in the quality index are directly related to the strength and dimensional stability of the yarn. If the yarn loses too much mass or the diameter decreases too much after friction, it will seriously affect its ability to withstand tension, which will lead to easy breakage during use, thereby greatly shortening the service life of the yarn and the products made from it. The pilling, pilling and color changes in the appearance index will affect the appearance of the product, but in most cases, these appearance problems will not directly affect the performance of the product, so it is set , in order to facilitate analysis, The specific value can be 0.4. The specific value can be 0.6.

Step 6: Yarn climate detection: Detect the simulated data of the target yarn under various simulated climate conditions, the simulated data including yarn color data, yarn strength data and yarn appearance data.

Step 7: Yarn climate analysis: Analyze the target yarn's climate resistance qualification index based on the simulated data of the target yarn under various simulated climate conditions.

Please refer to FIG. 3 , for example, the analysis of the target yarn's weather resistance qualified index includes: G1, according to the yarn color data of the target yarn after each simulated climate condition, statistically calculating the target yarn's color change qualified index; .

Furthermore, the statistical target yarn color change qualified index includes: G1-1, extracting the lightness, red-green color and yellow-blue color of each simulated climate yarn from the yarn color data of the target yarn after each simulated climate condition, and recording them as , and , Number the simulated climate yarns. .

It should be added that the lightness, red-green shade, and yellow-blue shade of each simulated climate yarn were monitored by a spectrophotometer.

G1-2, extract the initial lightness, initial red-green color and initial yellow-blue color of the target yarn from the initial data of the target yarn, and record them as , and .

G1-3. Statistical analysis of the total chromaticity difference of yarns in each simulated climate ,

.

It should be added that brightness refers to the brightness of the color, ranging from the darkest black to the brightest white. The value range of brightness is from 0 to 100, where 0 represents pure black, which means no light reflection at all, and is the darkest state. 100 represents pure white, which means all light is fully reflected and is the brightest state. The values in between represent different degrees of gray or color brightness. For example, Indicates a medium-light gray.

Red-green refers to the direction of red and green. A positive value indicates a color shift towards red. When the value is negative, the color tends to be green, for example, Indicates that the color has a certain degree of red tint. Indicates that the color has a slight green tint.

Yellow-blue refers to the direction of yellow and blue. A positive value indicates a color shift towards yellow. When the value is negative, the color tends to be blue, for example, Indicates that the color has a certain degree of yellow tint. Indicates that the color has a slight blue tint.

In a specific embodiment, the initial lightness, initial red-green shade, and initial yellow-blue shade of the target yarn are , and The lightness, red-green and yellow-blue of the simulated climate yarn are , and .

Statistical simulation of the total color difference of climate yarn ,

.

G1-4. Compare the total chromaticity difference of each simulated climate yarn with the total chromaticity difference of the set reference. If the chromaticity difference of a simulated climate yarn is less than the total chromaticity difference of the set reference, the simulated climate yarn will be recorded as a yarn with qualified color, and the number of yarns with qualified color will be counted.

G1-5. Count the number of simulated climate yarns, and take the ratio of the number of color qualified yarns to the number of simulated climate yarns as the color change qualified index of the target yarn, recorded as .

G2. According to the yarn strength data of the target yarn under each simulated climate condition, the strength change qualified index of the target yarn is calculated. .

Furthermore, the statistical target yarn strength change qualified index includes: G2-1, extracting the breaking strength data and rebound strength data of each simulated climate yarn from the yarn strength data of the target yarn under each simulated climate condition.

G2-2. Extract the strength qualification index of the target yarn , and then according to The strength qualification index of the target yarn after simulated climate is obtained by the same analysis method and recorded as .

G2-3. Statistical strength loss rate of target yarn , , It is used to set the qualified index of simulated climate intensity for reference.

It should be added that It is extracted from the technical specification table of yarn durability quality testing.

G2-4. Statistical target yarn strength change qualified index , , To set the reference strength loss rate.

It should be added that It is extracted from the technical specification table of yarn durability quality testing.

G3. According to the yarn appearance data of the target yarn under each simulated climate condition, the qualified index of the appearance change of the target yarn is calculated. .

Furthermore, the statistical target yarn appearance change qualified index includes: G3-1, extracting the simulated climate fuzzing data and simulated climate pilling data of the target yarn from the yarn appearance data of the target yarn under each simulated climate condition.

G3-2. Extract the target yarn’s qualified fuzz index and the target yarn pilling qualified index , and then according to and The same analysis method is used to analyze the target yarn after the simulated climate to obtain the qualified fuzzing index and pilling index, which are recorded as and .

G3-3. Extract each simulated climate yarn shape image from the yarn appearance data of the target yarn under each simulated climate condition.

It should be added that each simulated climate yarn shape image is obtained by extracting the image collected by the camera.

G3-4. Extracting the initial shape image of the target yarn from the initial data of the target yarn.

G3-5. Overlap and compare the shape images of each simulated climate yarn with the initial shape image to obtain the overlapping shape image area of each simulated climate yarn, recorded as .

G3-6. Record the initial shape image area of the target yarn as .

G3-7, and The ratio of is taken as the deformation qualification index of each simulated climate yarn, the average is calculated, and the calculation result is taken as the shape qualification index of the target yarn after simulated climate, recorded as .

G3-8, from , and The minimum value is selected as the qualified index of appearance change of the target yarn and recorded as .

G4. Statistical weather resistance index of target yarn ,

, , and The reference color change qualified index, intensity change qualified index and appearance change qualified index are set respectively. is a natural constant.

It should be added that , and All are extracted from the yarn durability quality test technical specification table.

The embodiment of the present invention analyzes the yarn's weather resistance qualification index based on the simulation data of the yarn under various simulated climate conditions, thereby breaking the current deficiency of analyzing the climate impact of the yarn without combining simulated climate, improving the accuracy of the yarn's outdoor performance evaluation, and further ensuring the stability of the yarn quality, thereby ensuring the subsequent service life and performance of the yarn.

Step 8. Yarn quality analysis: Analyze the quality durability index of the target yarn according to the strength qualified index, wear resistance qualified index and weather resistance qualified index of the target yarn.

Exemplarily, the analysis of the quality durability index of the target yarn includes: extracting the strength index of the target yarn , target yarn wear resistance qualified index and target yarn weatherability qualification index .

Statistical quality and durability index of target yarn , , , and are the weights of the strength qualified index, wear resistance qualified index and weather resistance qualified index respectively. , .

It should be added that the strength of the yarn is directly related to whether it can withstand the required tension, pressure and other external forces during use. If the strength is insufficient, the yarn is easy to break during use, which will lead to product failure and even cause safety problems. The wear resistance of the yarn determines the ability of the product to resist friction and wear during use. If the yarn has poor wear resistance, the product is prone to pilling, pilling, thinning and other phenomena, which affect the appearance quality of the product and indirectly reduce the strength of the yarn. Therefore, the weight of the strength qualification index is set to be greater than the weight of the wear resistance qualification index. The weather resistance of the yarn mainly affects its performance stability under different climatic conditions. Poor weather resistance may cause problems such as color change, reduced strength and deformation of the yarn, but in most cases, these problems have relatively little impact on the overall performance and safety of use of the product, so it is set , in order to facilitate analysis, The specific value can be 0.5. The specific value can be 0.3. The specific value can be 0.2.

Step 9: Generating a yarn quality report: Generating a yarn durability quality analysis report based on the quality durability qualified index of the target yarn.

The embodiment of the present invention performs a multi-dimensional comprehensive analysis of yarn quality through yarn strength testing, wear resistance testing and climate testing, avoiding the deficiency of currently only testing and analyzing the single dimension of yarn appearance quality, improving the comprehensiveness of the yarn quality analysis results, thereby ensuring the reliability of yarn use, and further ensuring the timeliness of the discovery of potential quality problems, while improving the effective use of resources.

The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims (10)

1. A yarn durability quality detection simulation analysis method, characterized in that the method comprises: Step 1, yarn data recording: randomly selecting a set number of yarns from the yarns to be tested as target yarns, and recording the initial data of the target yarns; Step 2: Yarn strength test: test the strength data of the target yarn under the set speed, the strength data includes breaking strength data and rebound strength data; Step 3: Yarn strength analysis: Analyze the strength qualification index of the target yarn according to the strength data of the tensile test of the target yarn at the set speed; Step 4: Yarn wear resistance test: Detect the friction data of the target yarn under the set friction times and friction pressure, and the friction data includes appearance data and quality data; Step 5, yarn wear resistance analysis: according to the friction data of the friction test of the target yarn under the set friction times and friction pressure, the wear resistance qualified index of the target yarn is analyzed; Step 6, yarn climate detection: detecting the simulated data of the target yarn under various simulated climate conditions, the simulated data including yarn color data, yarn strength data and yarn appearance data; Step 7, Yarn climate analysis: Analyze the qualified index of weather resistance of the target yarn according to the simulation data of the target yarn under various simulated climate conditions; Step 8, yarn quality analysis: according to the strength qualified index, wear resistance qualified index and weather resistance qualified index of the target yarn, analyze the quality durability qualified index of the target yarn; Step 9: Generating a yarn quality report: Generating a yarn durability quality analysis report based on the quality durability qualified index of the target yarn. 2. A yarn durability quality detection simulation analysis method according to claim 1, characterized in that: the strength qualification index of the target yarn analyzed includes: A1. Extract the maximum tensile force of each broken yarn at break and the yarn length before break from the breaking strength data of the target yarn under the set speed tensile test, and record them as and , Number the broken yarns, ; A2. Extract the initial yarn length of the target yarn from the initial data of the target yarn, denoted as ; A3. Statistical breaking strength qualified index of target yarn , , and are the elongation and tension of the reference respectively. is the number of broken yarns; A4. According to the rebound strength data of the target yarn under the set speed, the rebound strength qualified index of the target yarn is calculated. ; A5. Statistical strength index of target yarn , , and are the weights of the set fracture strength qualified index and rebound strength qualified index, , . 3. A yarn durability quality detection simulation analysis method according to claim 2, characterized in that: the statistical target yarn rebound strength qualified index includes: The maximum tensile length and the length after rebound of each rebound yarn were extracted from the rebound strength data of the target yarn under the set speed tensile test and recorded as and , Number the rebound yarn, ; Statistics of the rebound rate of each rebound yarn , ; Statistics of the rebound strength qualified index of each rebound yarn , , To set the reference rebound rate; Will The mean value is calculated and the result is used as the qualified index of the target yarn's rebound strength and recorded as . 4. A yarn durability quality detection simulation analysis method according to claim 1, characterized in that: the wear resistance qualified index of the target yarn analyzed includes: According to the appearance data of the friction test of the target yarn under the set friction times and friction pressure, the friction appearance qualified index of the target yarn is calculated ; The weight of each friction yarn is extracted from the mass data of the friction test of the target yarn under the set friction number and friction pressure, and recorded as , Number the friction yarns, ; The initial weight of the target yarn is extracted from the initial data of the target yarn and recorded as ; Statistical analysis of the actual weight loss ratio of each friction yarn , ; Statistical target yarn friction weight qualified index , , is the set allowable loss weight ratio, is the number of friction yarns; The diameter of each friction yarn is extracted from the mass data of the friction test of the target yarn under the set friction number and friction pressure, according to The friction diameter qualified index of the target yarn is obtained by the same statistical method and recorded as ; Statistical friction quality index of target yarn , ; Statistical wear resistance qualified index of target yarn , , and are the weights of the friction appearance qualified index and friction quality qualified index, respectively. , . 5. A yarn durability quality detection simulation analysis method according to claim 4, characterized in that: the statistical target yarn friction appearance qualified index includes: Extracting friction fuzzing data of the target yarn from the appearance data of the friction test of the target yarn under the set friction times and friction pressure, and then extracting each fuzzing length, each fuzzing area and fuzzing density of each friction yarn; The average of each fuzz length of a friction yarn is calculated, and the result is taken as the average fuzz length of the friction yarn, which is recorded as ; The total area of the fuzzy area of the friction yarn is obtained by summing up the areas of each fuzzy area, which is recorded as , and the fuzz density of the friction yarn is recorded as ; The initial area of the target yarn is extracted from the initial data of the target yarn and recorded as ; Calculate the qualified index of the friction yarn , , , and The reference fuzz length, fuzz area ratio and fuzz density are set respectively, and then the qualified fuzz index of each friction yarn is obtained by statistics, and the average is calculated to obtain the qualified fuzz index of the target yarn, which is recorded as ; The friction pilling data of the target yarn is extracted from the appearance data of the friction test under the set friction times and friction pressure, and then the pilling diameter, pilling area and pilling density of each friction yarn are extracted, and then the pilling diameter, pilling area and pilling density of each friction yarn are extracted according to the The pilling qualified index of the target yarn is obtained by the same analysis method and recorded as ; from and The minimum value is selected as the friction appearance qualified index of the target yarn, which is recorded as . 6. A yarn durability quality detection simulation analysis method according to claim 1, characterized in that: the weather resistance qualified index of the analyzed target yarn includes: G1. According to the yarn color data of the target yarn under each simulated climate condition, the color change qualified index of the target yarn is calculated. ; G2. According to the yarn strength data of the target yarn under each simulated climate condition, the strength change qualified index of the target yarn is calculated. ; G3. According to the yarn appearance data of the target yarn under each simulated climate condition, the qualified index of the appearance change of the target yarn is calculated. ; G4. Statistical weather resistance index of target yarn , , , and The reference color change qualified index, intensity change qualified index and appearance change qualified index are set respectively. is a natural constant. 7. A yarn durability quality detection simulation analysis method according to claim 4, characterized in that: the statistical target yarn color change qualified index includes: The lightness, red-greenness, and yellow-blueness of each simulated climate yarn are extracted from the yarn color data of the target yarn under each simulated climate condition and recorded as , and , Number the simulated climate yarns. ; The initial lightness, initial red-greenness, and initial yellow-blueness of the target yarn are extracted from the initial data of the target yarn and recorded as , and ; Statistical analysis of the total chromaticity difference of yarns in each simulated climate , ; The total chromaticity difference of each simulated climate yarn is compared with the total chromaticity difference of the set reference. If the chromaticity difference of a simulated climate yarn is less than the total chromaticity difference of the set reference, the simulated climate yarn is recorded as a color-qualified yarn, and the number of color-qualified yarns is counted; The number of simulated climate yarns was counted, and the ratio of the number of color qualified yarns to the number of simulated climate yarns was taken as the color change qualified index of the target yarn, which was recorded as . 8. A yarn durability quality detection simulation analysis method according to claim 6, characterized in that: the statistical target yarn strength change qualified index includes: Extracting the breaking strength data and the rebound strength data of each simulated climate yarn from the yarn strength data of the target yarn under each simulated climate condition; Extract the strength qualification index of the target yarn , and then according to The strength qualification index of the target yarn after simulated climate is obtained by the same analysis method and recorded as ; Statistical target yarn strength loss rate , , To set the reference simulated climate intensity qualification index; Statistical target yarn strength change qualified index , , To set the reference strength loss rate. 9. A yarn durability quality detection simulation analysis method according to claim 6, characterized in that: the statistical target yarn appearance change qualified index includes: Extracting simulated climate pilling data and simulated climate pilling data of the target yarn from the yarn appearance data of the target yarn under each simulated climate condition; Extract the target yarn's qualified index and the target yarn pilling qualified index , and then according to and The same analysis method is used to analyze the target yarn after the simulated climate to obtain the qualified fuzzing index and pilling index, which are recorded as and ; Extracting each simulated climate yarn shape image from the yarn appearance data of the target yarn under each simulated climate condition; extracting an initial shape image of the target yarn from initial data of the target yarn; The simulated climate yarn shape images are overlapped and compared with the initial shape images to obtain the overlapped shape image area of each simulated climate yarn, which is recorded as ; The initial shape image area of the target yarn is recorded as ; Will and The ratio of is taken as the deformation qualification index of each simulated climate yarn, the average is calculated, and the calculation result is taken as the shape qualification index of the target yarn after simulated climate, recorded as ; from , and The minimum value is selected as the qualified index of appearance change of the target yarn and recorded as . 10. A yarn durability quality detection simulation analysis method according to claim 9, characterized in that: the quality durability qualified index of the target yarn analyzed comprises: Extract the strength qualification index of the target yarn , target yarn wear resistance qualified index and target yarn weatherability qualification index ; Statistical quality and durability index of target yarn , , , and are the weights of the strength qualified index, wear resistance qualified index and weather resistance qualified index respectively. , .