Method for testing the elongation at break of spun yarn products

Abstract

The present application provides a kind of spun yarn product elongation at break detection method, comprising the following steps: sample preparation and pretreatment step carries out sample form measurement and mathematical modeling step;Stretching test equipment calibration and parameter setting step;Stretching test execution and data acquisition step;Elongation at break calculation and preliminary analysis step;Stress, strain relationship analysis step;Data stability analysis step;Outlier detection and processing step;Result comprehensive analysis and report generation step;The present method realizes the accurate analysis of yarn form and stress, strain relationship, improves the accuracy of test result;Data stability and outlier detection analysis are carried out, ensure the reliability and stability of test data;For the performance evaluation and improvement of yarn provides strong support.The present method has feasibility and practicality, can significantly improve the efficiency and accuracy of spun yarn product elongation at break detection.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of spinning product elongation at break detection method. BACKGROUND

[0002] Elongation at break refers to the elongation of material when stretched to break and its initial length ratio, usually expressed in percentage. It is an important indicator to characterize the softness and elasticity of material. The greater the elongation at break, the better the softness and elasticity of material. In textile processing, the fiber with large elongation at break has soft hand feeling, can buffer stress, reduce hair and broken ends. The elongation at break of ordinary textile fiber is generally between 10% and 30%. For industrial strength silk, it requires high breaking strength and low elongation at break to prevent product deformation.

[0003] There is no accurate and reliable and stable spinning product elongation at break detection method in the field of spinning at present, so the present application provides a kind of spinning product elongation at break detection method to solve this problem. SUMMARY

[0004] The present application aims at the deficiencies of the prior art, and provides a kind of spinning product elongation at break detection method, which can well solve the above problems.

[0005] To achieve the above requirements, the technical scheme adopted by the present application is: a kind of spinning product elongation at break detection method is provided, and the spinning product elongation at break detection method comprises the following steps:

[0006] S1: the step of sample preparation and pretreatment is carried out;

[0007] At least 30 samples are randomly selected from the spinning product to be tested, to ensure that the samples cover different batches and different positions, to ensure the representativeness of the results, and the samples are placed at temperature 20±2 ℃, relative humidity 65±3%, for at least 48 hours, to eliminate the influence of environment on test results;

[0008] S2: the step of sample shape measurement and mathematical modeling;

[0009] The diameter and cross-sectional shape of each sample are measured using a high-precision optical microscope, and a mathematical model of the elliptical yarn shape is established based on the measured data using curve fitting technology in calculus, and the equation is expressed as:

[0010]

[0011] Wherein, a and b are the long semi-axis and short semi-axis of fractional order hyperellipse respectively;

[0012] α(x), β(y), γ(y), δ(x) are fractional order functions with respect to x and y;

[0013] x and y are arbitrary point coordinates on the cross section;

[0014] Through variational method combined with fractional calculus, the actual measured data is fitted to obtain the optimal values of a, b, α(x), β(y), γ(y), and δ(x);

[0015] S3: the step of performing tensile testing equipment calibration and parameter setting;

[0016] The tensile testing equipment is calibrated using standard samples to ensure the accuracy and stability of the equipment, the tensile speed is set to a constant value of 50 mm / min, and the sampling frequency of the data acquisition system is set to 100 Hz to ensure that the subtle changes of the yarn during the stretching process can be captured;

[0017] S4: the step of performing tensile testing and data acquisition;

[0018] The pretreated samples are installed on the tensile testing equipment and tensile testing is performed according to the set parameters, the data acquisition system records the tensile force, elongation, and time data in real time during the testing process, and each sample is tested at least five times to reduce the influence of random errors;

[0019] S5: the step of calculating the elongation at break and preliminary analysis;

[0020] According to the data obtained by tensile testing, the elongation at break of each sample is calculated, and the formula for calculating the elongation at break is:

[0021]

[0022] The calculated elongation at break is preliminarily analyzed to check the reasonableness and consistency of the data;

[0023] S6: the step of analyzing the stress-strain relationship;

[0024] Using the differential method in calculus, the stress-strain relationship of the yarn during the stretching process is analyzed, and the stress-strain relationship of the yarn conforms to the fractional order nonlinear elastic model, and the mathematical expression is:

[0025]

[0026] where σ is the stress, λ is the elongation ratio (λ = 1 + ∈, ∈ is the strain), C1 and C2 are material constants, α(λ), β(λ -1 ), γ(λ), δ(λ -1) is the fractional function of λ, the optimal values of C1, C2, α(λ), β(λ -1 ), γ(λ -1 ), δ(λ ijk ) are fitted by combining the data obtained by tensile test with fractional nonlinear least square method and variational principle;

[0027] S7: the step of performing data stability analysis;

[0028] The stability of test results between different batches and different positions is analyzed by ANOVA analysis method, the total variation is decomposed into inter-group variation and intra-group variation, whether the variation from different sources is significant is judged by comparing the proportion of inter-group variation and intra-group variation, Bayesian mixed effect variance analysis is adopted, and the mathematical model is:

[0029] Y ijk ~ Normal(μ+A i +B j +(AB) ij +S ijk , σ 2 );

[0030] Wherein, Y ijk is the observation value of the i th batch, the j th position and the k th repeated measurement;

[0031] μ is the overall mean;

[0032] A i is the batch effect;

[0033] B j is the position effect;

[0034] (AB) ij is the interaction effect of batch and position;

[0035] S ijk is the intra-subject effect, that is, the repeated measurement effect in the same batch and the same position;

[0036] σ 2 is the error variance;

[0037] S8: the step of performing outlier detection and processing;

[0038] The outliers in the test data are detected by using the box plot method in statistics, the quartiles Q1, Q2 and Q3 and the interquartile range IQR = Q3-Q1 of the data are calculated, the box plot is drawn, and the outliers are identified, and the necessary processing including deletion and replacement is performed on the detected outliers;

[0039] S9: the step of performing result comprehensive analysis and report generation;

[0040] The test data is comprehensively analyzed, including the calculation of the average value, standard deviation, maximum value, and minimum value of the elongation at break, the performance of the yarn is evaluated, and a test report is generated based on the analysis results.

[0041] The yarn product elongation at break detection method has the following advantages:

[0042] The method solves several key problems in the existing yarn product elongation at break detection: first, through the application of calculus formulas, the yarn morphology and stress-strain relationship are accurately analyzed, improving the accuracy of the test results; second, statistical formulas are used to analyze the stability and outliers of the data, ensuring the reliability and stability of the test data; finally, through detailed comprehensive analysis and report generation, strong support is provided for the performance evaluation and improvement of the yarn. The method is feasible and practical, and can significantly improve the efficiency and accuracy of the yarn product elongation at break detection. BRIEF DESCRIPTION OF DRAWINGS

[0043] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. In these drawings, the same reference numbers are used to represent the same or similar parts. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0044] Figure 1 A flowchart of a yarn product elongation at break detection method according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and specific embodiments.

[0046] In the following description, the use of the phrases "one embodiment", "an embodiment", "one example", "an example", and the like, indicates that the embodiment or example so described can include a particular feature, structure, characteristic, property, element, or limitation, but every embodiment or example can not necessarily include the particular feature, structure, characteristic, property, element, or limitation. In addition, repeated use of the phrase "according to an embodiment of the present application" does not necessarily refer to the same embodiment, although it can.

[0047] For simplicity, certain technical features known to those skilled in the art are omitted from the following description.

[0048] According to an embodiment of the present application, a yarn product elongation at break detection method is provided, as shown in Figure 1 including the following steps:

[0049] S1: Perform the steps of sample preparation and pretreatment;

[0050] Randomly select at least 30 samples from the spinning products to be tested, ensuring that the samples cover different batches and different locations to ensure the representativeness of the results, and place the samples at a temperature of 20±2℃ and a relative humidity of 65±3% for at least 48 hours to eliminate the influence of the environment on the test results; sample pretreatment is a key step to ensure test accuracy, and placing under standard temperature and humidity conditions can eliminate fluctuations in yarn performance caused by environmental changes. At the same time, the cleaning step removes impurities that may affect the test results.

[0051] S2: Perform the steps of sample shape measurement and mathematical modeling;

[0052] Use a high-precision optical microscope to measure the diameter and cross-sectional shape parameters of each sample, and based on the measurement data, use curve fitting techniques in calculus to establish a mathematical model of the cross-sectional shape of the elliptical yarn shape, whose equation is represented as:

[0053]

[0054] where a and b are the long and short semi-axes of the fractional order hyper-ellipse, respectively;

[0055] α(x), β(y), γ(y), and δ(x) are fractional order functions with respect to x and y;

[0056] x and y are the coordinates of any point on the cross-section;

[0057] By using the variational method combined with fractional calculus, the actual measurement data is fitted to obtain the optimal values of a, b, α(x), β(y), γ(y), and δ(x);

[0058] S3: Perform the steps of calibration and parameter setting of the tensile testing equipment;

[0059] Calibrate the tensile testing equipment using standard samples to ensure the accuracy and stability of the equipment, set the tensile speed to a constant value of 50mm / min, and set the sampling frequency of the data acquisition system to 100Hz to ensure that subtle changes in the yarn during the stretching process can be captured; equipment calibration is the basis for ensuring test accuracy, and through the test of standard samples, the accuracy and stability of the equipment can be verified. The setting of the tensile speed and the sampling frequency is determined according to the characteristics of the yarn material and the test requirements.

[0060] S4: Perform the steps of tensile test execution and data collection;

[0061] The pretreated sample is installed on the tensile testing equipment, and the tensile test is carried out according to the set parameters. During the test, the data acquisition system records the tensile force, elongation and time data in real time. Each sample is tested at least five times to reduce the influence of random errors. Tensile test is a key step to evaluate the elongation at break of yarn. By recording the key data in real time during the stretching process, the elongation at break of the yarn can be calculated. Repeated tests can improve the reliability of the test results.

[0062] S5: the step of calculating and preliminarily analyzing the elongation at break;

[0063] According to the data obtained by the tensile test, the elongation at break of each sample is calculated. The formula for calculating the elongation at break is:

[0064]

[0065] The calculated elongation at break is preliminarily analyzed to check the reasonableness and consistency of the data. Elongation at break is an important indicator for evaluating the performance of yarn. By calculating the ratio of elongation at break to initial length, the elongation at break of the yarn can be obtained. Preliminary analysis can help to find abnormal data or potential problems in the test process.

[0066] S6: the step of analyzing the stress-strain relationship;

[0067] The differential method in calculus is used to analyze the stress-strain relationship of the yarn during the stretching process. The stress-strain relationship of the yarn conforms to the fractional order nonlinear elastic model, and its mathematical expression is:

[0068]

[0069] where σ is the stress, λ is the elongation ratio (λ = 1 + ∈, ∈ is the strain), C1 and C2 are material constants, α(λ), β(λ -1 ), γ(λ), δ(λ -1 ) are fractional order functions of λ. The optimal values of C1, C2, α(λ), β(λ -1 ), γ(λ), δ(λ -1 ) are fitted by combining the fractional order nonlinear least squares method and the variational principle with the data obtained by the tensile test.

[0070] S7: the step of analyzing the stability of the data;

[0071] ANOVA analysis method is used to analyze the stability of the test results of different batches and different positions. The total variation is decomposed into inter-group variation and intra-group variation. By comparing the proportion of inter-group variation and intra-group variation, it is judged whether the variation from different sources is significant. Bayesian mixed effect variance analysis is used, and its mathematical model is:

[0072] Y ijk ~ Normal (μ + A i + B j + (AB) ij + S ijk , σ 2 );

[0073] where Y ijk is the observation of the i th batch, j th position, k th repeated measurement;

[0074] μ is the population mean;

[0075] A i is the batch effect;

[0076] B j is the position effect;

[0077] (AB) ij is the interaction effect of batch and position;

[0078] S ijk is the within-subject effect, i.e. the repeated measurement effect within the same batch, the same position;

[0079] σ 2 is the error variance;

[0080] S8: the step of performing outlier detection and processing;

[0081] Using the box plot method in statistics, detect the outliers in the test data, calculate the quartiles Q1, Q2, Q3 and interquartile range IQR = Q3-Q1, draw the box plot, and identify the outliers. For the detected outliers, perform necessary processing including deletion and replacement; outlier detection is an important step to ensure the accuracy of test data. Through the box plot method, outliers in the data can be identified intuitively and necessary processing can be performed.

[0082] S9: the step of performing result comprehensive analysis and report generation;

[0083] Comprehensively analyze the test data, including the calculation of average value, standard deviation, maximum value, minimum value indicators of elongation at break, evaluate the performance of the yarn, and generate the test report according to the analysis result. Result comprehensive analysis and report generation is the summary stage of the test work. Through comprehensive analysis of test data, the performance characteristics of the yarn can be fully understood, which provides basis for subsequent improvement and optimization.

[0084] According to one embodiment of the present application, the step S1 of preparing and pretreating the sample in the yarn product elongation at break detection method further comprises: cleaning the sample to remove surface impurities and ensure the accuracy of the test.

[0085] According to one embodiment of the present application, the mathematical model established in step S2 of the yarn product elongation at break detection method has the equation:

[0086]

[0087] wherein a and b are the long semi-axis and the short semi-axis of the fractional order hyper-ellipse, respectively;

[0088] α(x), β(y), γ(y), δ(x) are fractional order functions of x and y;

[0089] x and y are the coordinates of any point on the cross section;

[0090] The optimal values of a, b, α(x), β(y), γ(y), and δ(x) are obtained by fitting the actual measurement data through variational method combined with fractional calculus.

[0091] According to one embodiment of the present application, the mathematical model established in step S2 of the yarn product elongation at break detection method has the equation:

[0092]

[0093] wherein:

[0094] a and b: are the reference values of the long semi-axis and the short semi-axis of the fractional order generalized hyper-ellipse, respectively;

[0095] α(x,y), β(x,y), γ(x,y), δ(x,y): are fractional order functions of x and y, representing the complexity of the change of cross-sectional shape with position;

[0096] x and y: coordinates of any point on the cross section;

[0097] The optimal values of a, b, α(x,y), β(x,y), γ(x,y), δ(x,y) are obtained by fitting the actual measurement data through variational method combined with fractional calculus, and the periodic characteristics of the cross-sectional shape are analyzed using Fourier transform, and the nonlinear characteristics of the cross-sectional shape are extracted using Hilbert-Huang transform.

[0098] According to one embodiment of the present application, the mathematical expression of the stress-strain relationship of the yarn in step S6 of the yarn product elongation at break detection method conforms to the fractional order nonlinear elastic model:

[0099]

[0100] where σ is the stress, λ is the stretch ratio (λ = 1 + ∈, ∈ is the strain), C1 and C2 are material constants, α(λ), β(λ -1 ), γ(λ), δ(λ -1 ) are fractional order functions with respect to λ, and the optimal values of C1, C2, α(λ), β(λ -1 ), γ(λ), δ(λ -1 ) are fitted by combining the data obtained from the tensile test with the fractional order nonlinear least squares method and the variational principle.

[0101] According to an embodiment of the present application, the stress-strain relationship of the yarn in step S6 in the yarn product breaking elongation detection method conforms to the mathematical expression of the fractional order nonlinear elastic model:

[0102]

[0103] wherein:

[0104] σ(t): stress at time t;

[0105] K1(t-τ) and K2(t-τ): kernel functions related to time and strain history, respectively, representing the viscoelastic properties of the material;

[0106] λ(τ): stretch ratio at time τ;

[0107] α(λ(τ)) and β(λ(τ -1 )): fractional order functions with respect to the stretch ratio, representing the nonlinear characteristics of the stress-strain relationship;

[0108] γ(τ): fractional order function with respect to time, representing the non-integer order derivative of the strain rate;

[0109] d γ(τ) / dτ γ(τ) : represents the fractional order derivative, used to describe the non-integer order change of the strain rate.

[0110] According to an embodiment of the present application, in S7 of the yarn product breaking elongation detection method, whether the variation from different sources is significant is determined by using Bayesian mixed effect variance analysis, and the mathematical model is:

[0111] Y ijk ~ Normal(μ + A i +B j +(AB) ij +S ijk ,σ 2 );

[0112] wherein Y ijkYijk is the observation of the i th batch, j th location, k th replicate measurement;

[0113] μ is the overall mean;

[0114] A i is the batch effect;

[0115] B j is the location effect;

[0116] (AB) ij is the interaction effect of batch and location;

[0117] S ijk is the within-subject effect, i.e. the repeated measurement effect within the same batch, the same location;

[0118] σ 2 is the error variance.

[0119] According to one embodiment of the present application, whether the variations from different sources are significant in step S7 of the method for detecting the elongation at break of the spun yarn product is determined by using Bayesian mixed effect variance analysis, and the mathematical model thereof is:

[0120] g(μ ijk ) = η + A i + B j + (AB) ij + S ijk + Z l β l ;

[0121] wherein:

[0122] g(μ ijk ): a connection function for connecting the expected mean μ ijk with a linear predictor;

[0123] η: an intercept term;

[0124] A i : a batch effect, representing the influence of the i th batch on the result;

[0125] B j : a location effect, representing the influence of the j th location on the result;

[0126] (AB) ij : an interaction effect of batch and location;

[0127] S ijk : a within-subject effect, representing the repeated measurement effect within the same batch, the same location;

[0128] Z l : a design matrix, representing other possible covariates affecting the result;

[0129] β l : is the coefficient of the covariate.

[0130] The above-described embodiments are merely several embodiments of the present application, which are described in a more specific and detailed manner, but should not be understood as limiting the scope of the present application. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the claims.

Claims

1. A method for detecting the breaking elongation of spun yarn products, characterized in that, Includes the following steps: S1: Steps for sample preparation and pretreatment; At least 30 samples were randomly selected from the yarn products to be tested, ensuring that the samples covered different batches and different locations to ensure the representativeness of the results. The samples were placed at a temperature of 20±2℃ and a relative humidity of 65±3% for at least 48 hours to eliminate the influence of the environment on the test results. S2: Steps for sample morphology measurement and mathematical modeling; The diameter and cross-sectional shape parameters of each sample were measured using a high-precision optical microscope. Based on the measurement data, a mathematical model with an elliptical yarn shape was established using curve fitting techniques from calculus. S3: Steps for calibrating and setting parameters of tensile testing equipment; The tensile testing equipment was calibrated using standard samples to ensure its accuracy and stability. The tensile speed was set to a constant value of 50 mm / min, and the sampling frequency of the data acquisition system was set to 100 Hz to ensure that the subtle changes in the yarn during the tensile process could be captured. S4: Steps for performing tensile tests and collecting data; The pretreated sample is installed on the tensile testing equipment and subjected to tensile testing according to the set parameters. During the test, the data acquisition system records the tensile force, elongation and time data in real time. Each sample is tested at least five times to reduce the impact of random errors. S5: Steps for calculating and performing preliminary analysis of elongation at break; Based on the data obtained from the tensile test, the elongation at break of each sample was calculated. The formula for calculating the elongation at break is as follows: A preliminary analysis of the calculated elongation at break was conducted to check the rationality and consistency of the data. S6: Steps for performing stress-strain relationship analysis; Using the differential method in calculus, the stress-strain relationship of the yarn during the stretching process is analyzed. The stress-strain relationship of the yarn conforms to the fractional nonlinear elastic model. S7: Steps for performing data stability analysis; Using ANOVA analysis, the stability of test results across different batches and locations was analyzed. The total variation was decomposed into between-group variation and within-group variation. By comparing the proportions of between-group variation and within-group variation, the significance of variation from different sources was determined. S8: Steps for outlier detection and handling; Using the box plot method in statistics, outliers in the test data are detected. By calculating the quartiles Q1, Q2, Q3 and the interquartile range IQR = Q3 - Q1, a box plot is drawn and outliers are identified. For the detected outliers, necessary processing is carried out, including deletion and replacement. S9: Steps for comprehensive analysis of results and generation of reports; The test data is comprehensively analyzed, including the calculation of the average, standard deviation, maximum and minimum values ​​of the breaking elongation, to evaluate the performance of the yarn. Based on the analysis results, a test report is generated.

2. The method for detecting the breaking elongation of spun yarn products according to claim 1, characterized in that: The step S1, which involves sample preparation and pretreatment, further includes cleaning the sample to remove surface impurities and ensure the accuracy of the test.

3. The method for detecting the breaking elongation of spun yarn products according to claim 1, characterized in that, The mathematical model established in step S2 is expressed by the following equation: Where a and b are the major and minor axes of the fractional hyperellipse, respectively; α(x), β(y), γ(y), and δ(x) are fractional functions of x and y; x and y are the coordinates of any point on the cross section; By combining variational methods with fractional calculus, the optimal values ​​of a, b, α(x), β(y), γ(y), and δ(x) are obtained by fitting actual measurement data.

4. The method for detecting the breaking elongation of spun yarn products according to claim 1, characterized in that: The mathematical model established in step S2 is expressed by the following equation: in: a and b: These are the reference values ​​for the major and minor semi-axis of the fractional-order generalized hyperellipse, respectively. α(x,y), β(x,y), γ(x,y), δ(x,y): are fractional functions of x and y, representing the complexity of the cross-sectional shape as it changes with position; x and y: coordinates of any point on the cross section; By combining variational methods with fractional calculus, the optimal values ​​of a, b, α(x,y), β(x,y), γ(x,y), and δ(x,y) are obtained by fitting actual measurement data. The periodicity of the cross-sectional shape is analyzed using Fourier transform, and the nonlinearity of the cross-sectional shape is extracted by combining Hilbert-Huang transform.

5. The method for detecting the breaking elongation of spun yarn products according to claim 1, characterized in that, The stress-strain relationship of the yarn in step S6 conforms to the mathematical expression of a fractional-order nonlinear elastic model as follows: Where σ is stress, λ is elongation ratio (λ = 1 + ε, ε is strain), C1 and C2 are material constants, and α(λ), β(λ) are constants. -1 ), γ(λ), δ(λ) -1 ) is a fractional function of λ. Using data obtained from stretching tests, and combining fractional nonlinear least squares and variational principles, C1, C2, α(λ), and β(λ) are fitted to obtain... -1 ), γ(λ), δ(λ) -1 The optimal value of ).

6. The method for detecting the breaking elongation of spun yarn products according to claim 1, characterized in that: The stress-strain relationship of the yarn in step S6 conforms to the mathematical expression of a fractional-order nonlinear elastic model as follows: in: σ(t): Stress at time t; K1(t-τ) and K2(t-τ): These are kernel functions related to time and strain history, respectively, representing the viscoelastic properties of the material; λ(τ): Elongation ratio at time τ; α(λ(τ)) and β(λ(τ)) -1 : is a fractional function of elongation ratio, representing the nonlinear characteristics of the stress-strain relationship; γ(τ): is a fractional function of time, representing the non-integer derivative of the strain rate; d γ(τ) / dτ γ(τ) : Represents the fractional derivative, used to describe the non-integer order variation of strain rate.

7. The method for detecting the breaking elongation of spun yarn products according to claim 1, characterized in that... In step S7, to determine whether the variation from different sources is significant, a Bayesian mixed-effects analysis of variance is used. The mathematical model is as follows: Y ijk ~Normal(μ+A i +B j +(AB) ij +S ijk ,s 2 ); Among them, Y ijk It is the observation value of the i-th batch, the j-th location, and the k-th repeated measurement; μ is the population mean; A i It's a batch effect; B j It is a positional effect; (AB) ij It is the interaction effect between batch and location; S ijk It is an intra-subject effect, that is, the effect of repeated measurements within the same batch and the same location; σ 2 It is the error variance.

8. The method for detecting the breaking elongation of spun yarn products according to claim 1, characterized in that: In step S7, to determine whether the variation from different sources is significant, a Bayesian mixed-effects analysis of variance is used. The mathematical model is as follows: g(μ ijk )=η+A i +B j +(AB) ij +S ijk +Z l b l ; in: g(μ ijk ): A join function used to connect the expected mean μ ijk Associated with linear predictors; η: Intercept term; A i Batch effect: representing the impact of the i-th batch on the result; B j Position effect: representing the influence of the j-th position on the result; (AB ij ): The interaction effect between batch and location; S ijk Intra-subject effect: refers to the effect of repeated measurements within the same batch and at the same location; Z l Design a matrix to represent other covariates that may affect the outcome; β l : is the coefficient of the covariate.

Images