A batch processing method for fiber static tensile test data

Abstract

The present application provides a batch processing method for fiber static tensile test data, comprising: data reading and data processing, stress-strain data extraction, Young's modulus related calculation fitting point screening, linear fitting, data point marking and point graph output, data cleaning and origin processing, breaking strength, elongation and breaking work calculation, unit calibration and single group data output, cyclic processing and multi-group data operation, single group data Excel output, single Excel statistics and total Excel output. The present application solves the problem of lack of batch and accurate automatic processing method for fiber tensile machine data, significantly improves the efficiency and accuracy of processing batch data, is simple to operate, can be applied to various fiber tensile stress-strain data processing, and through the output of tensile stress-strain point graph, the data accuracy can be observed intuitively, effectively helps people to reduce the time cost consumption in the process of processing data, and provides a more efficient, accurate and user-friendly solution for fiber tensile test data processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fiber tensile test data processing, in particular to a batch processing method for fiber static tensile test data. BACKGROUND

[0002] At present, the tensile mechanical property data of fiber materials under room temperature conditions will change with the increase of displacement during the static tensile test process. The change rates of Young's modulus are different in the tensile process of different fibers. In order to improve the accuracy of data, manual correction is usually used, but it is obvious that this method will consume a lot of time cost when facing a large amount of tensile data processing. In addition, during the static tensile process of fibers, sometimes the data curve fluctuates, which will also interfere with the accuracy of the data, so it is necessary to correct the tensile data. Therefore, in the process of batch data processing, the judgment of Young's modulus and the reduction of data fluctuation determine the accuracy of the static tensile mechanical property data of fibers.

[0003] In the traditional tensile test process, the tensile testing machine test recording system will draw a "stress-strain" curve according to the sensor. Then the experimenter needs to import the data into the Origin software, rely on the naked eye to observe the Young's modulus straight line segment part on the tensile curve, determine the end value of the straight line segment, and then use these straight line segment values to fit the straight line to obtain the Young's modulus value. In addition, it is also necessary to manually remove the abnormal values of the "stress-strain" curve with data fluctuations, and finally the software can obtain the data of breaking strength, elongation and breaking work.

[0004] Therefore, it is necessary to design a batch processing method for fiber static tensile test data to solve the problems existing in the current fiber tensile test data processing. The current data processing method mainly corrects the error through formula, and often adjusts the tensile machine device to process the problem of unclear starting point of data. For the problem of curve fluctuation, the existing method cannot effectively solve it; the processing time of complex mechanical property indexes such as Young's modulus is long and there is a certain error, the fitting effect of stress-strain curve is dependent on artificial judgment, and the fitting parameters are corrected, but the correction process lacks visualization and is complicated. In addition, due to the limitation of tensile test objects, the existing method lacks universality and is difficult to be widely used. Moreover, the source of the code is unclear, which leads to the inability to verify the accuracy of the data, and for the fiber materials with small stress, there is a lack of convenient and fast data processing method. SUMMARY

[0005] In view of this, the present application provides a batch processing method for fiber static tensile test data, aiming to solve the problems existing in the current fiber tensile test data processing. The current data processing method mainly corrects errors through formulas, and often adjusts the tensile machine device to process the unclear starting point of data. For the problem of curve fluctuation, the existing method cannot effectively solve it; the processing time of complex mechanical property indexes such as Young's modulus is long and there is a certain error, which depends on manual judgment of the fitting effect of stress-strain curve and correction of fitting parameters, but the correction process lacks visualization and is cumbersome. In addition, due to the limitation of the tensile test object, the method lacks universality and is difficult to be widely used. Moreover, the source of the code is unclear, which leads to the inability to verify the accuracy of the data, and for fiber materials with small stress, there is a lack of convenient and fast data processing method.

[0006] In one aspect, the present application provides a batch processing method for fiber static tensile test data, comprising:

[0007] Read the tensile test data, batch select the Excel file of the tensile mechanical property data of the input and output fiber sample, and group the data, read the data, find the column name of all four columns, and grab the stress and strain data of each group of data;

[0008] Record each group of 5 adjacent points as a group, find the maximum stress difference Vmax and all points greater than Vmax*0.75, add the maximum and minimum points and divide by 2 to get the middle point of the straight line part of Young's modulus, start with the middle point, gradually select the values away from the point at both ends, and set a threshold value to judge. When the selected point Young's modulus is greater than the threshold value, the remaining point values away from the middle point are not used for fitting the straight line segment;

[0009] The points used for fitting are fitted with a straight line by the least square method to obtain the Young's modulus, and the points used for fitting are marked with red, and the other points are marked with blue. Output the point graph of this group of stress-strain curve data, judge the minimum stress value of the red point and delete the data smaller than the minimum stress value of the red point, judge the maximum stress value in the remaining data after processing and delete the subsequent data smaller than the maximum stress. The processed data is subjected to data originization processing and output as "calibrated raw data";

[0010] Judge the maximum stress value in the "calibrated raw data", output as the breaking strength, and the strain value corresponding to the maximum stress point is output as the elongation. The area is calculated by the method of integration, and the breaking strength, elongation, Young's modulus and breaking work are calibrated to international units. Complete the output of one group of data, and loop process each group of data;

[0011] The breaking strength, elongation, Young's modulus and breaking work of each set of data of each Excel file are output as Excel files with corresponding file names. The average value and standard deviation of each set of breaking strength, elongation, Young's modulus and breaking work of a single Excel file are calculated, and the average value and standard deviation of all Excel files are output as a total Excel file.

[0012] Further, the reading of the tensile machine test data, batch selection of input and output fiber sample tensile mechanical property data Excel file, and grouping of data, including:

[0013] Select the original data folder, use the read_excel function in the pandas library of Python according to the folder path where the Excel file containing the tensile machine test data is located, traverse all Excel files under the selected folder, and read the data into the DataFrame data structure;

[0014] Data filtering and grouping, identifying column information in the file, filtering out data columns related to tensile mechanical properties according to preset rules, grouping data according to fiber sample number, determining the format of output tensile mechanical property data, and creating a new DataFrame to store subsequent calculation results.

[0015] Further, the reading of data, finding the column names of all four columns of each group, and extracting the stress and strain data of each group of data, including:

[0016] Obtain column name information. For each Excel file data read into memory, access the columns attribute of DataFrame to obtain a list of all column names;

[0017] Filtering stress and strain column names, according to pre-set rules and user-specified conditions, find the column names of the four columns related to stress and strain data in the column name list;

[0018] Extract and group data storage. Using the found stress and strain column names, accurately extract the corresponding stress and strain data columns of each group of data from the original data with the help of DataFrame indexing method, store the extracted data according to fiber sample number, form a dictionary data structure, the key is the sample number, and the value is the list containing the stress and strain data of the sample.

[0019] Further, the 5 adjacent points in each group are recorded as a group, and the maximum stress difference Vmax and all points greater than Vmax*0.75 are found, the maximum and minimum points are added and divided by 2 to obtain the intermediate point of the Young's modulus straight line portion, and the values of the two ends away from the intermediate point are gradually selected, and a threshold is set to judge, when the Young's modulus of the selected point is greater than the threshold, the remaining point values away from the intermediate point are not used to fit the straight line segment, including:

[0020] Group stress difference calculation: for each group of stress data, divide every 5 adjacent data points into a group, for each group, calculate the stress difference between adjacent points in the group to obtain a set of stress difference data;

[0021] Determine the maximum stress difference Vmax: in all stress difference data of the groups, use the max function to find the maximum value, which is defined as Vmax;

[0022] Screening related points to determine the intermediate point: reiterate the stress difference data to find all points greater than Vmax*0.75, in these related points, determine the maximum and minimum stress points, add their stress values and divide by 2 to obtain the reference stress value in the middle of the Young's modulus straight line, and record the corresponding strain value, take the average of the stress and strain as the intermediate reference point coordinates;

[0023] Screening fitting points from the intermediate point: starting from the intermediate reference point, gradually select other points to the two ends of the stress-strain curve, for each selected point, calculate its Young's modulus according to the stress-strain data, set a threshold, when the Young's modulus of the selected point is greater than the threshold, stop selecting points, thereby determining the range of effective data points for fitting the straight line.

[0024] Further, the points used for fitting are fitted with a straight line to obtain the Young's modulus, and the points used for fitting are marked in red and the other points are marked in blue, and the point graph of this group of stress-strain curve data is output, including:

[0025] Linear fitting and Young's modulus calculation: the stress and strain data of the selected points for fitting the straight line are extracted and arranged into a format suitable for least squares fitting, a linear function form is defined for least squares fitting, the prepared data and fitting function are used as input to perform linear fitting using the optimize.curve_fit function in the scipy library, the parameters of the fitted straight line are obtained, and the slope obtained by fitting is defined as the value of the Young's modulus;

[0026] Data point marking and plot preparation, create a figure object and an axis object using the matplotlib library, mark the points to be fitted on the graph with red markers, and mark the other points not selected for fitting with blue markers, use the scatter function of the figure object, respectively pass in the strain, stress data of the fitting points and non-fitting points and the corresponding color parameters, add labels to the axis to represent the strain axis label, set the figure title, add a legend to explain the meaning of red and blue points, and use the savefig function of the matplotlib library to save the stress-strain curve point graph as a picture file.

[0027] Further, the minimum stress value of the red point is judged and the data smaller than the minimum stress value of the red point is deleted, the maximum stress value in the remaining data after judgment processing is determined, and the subsequent data smaller than the maximum stress is deleted, and the processed data is subjected to data origin processing and output as "calibrated raw data", including:

[0028] Determination of minimum stress red point and data deletion, in the fitting points marked as red, traverse the stress data to find the minimum stress red point, and then traverse the entire stress-strain data set again to delete all data points with stress values less than the minimum red stress value;

[0029] Determination of maximum stress value and subsequent data deletion, find the maximum stress value in the remaining data after processing, and then delete all data points with stress values less than the maximum stress value;

[0030] Data origin processing and output, the stress-strain data after two times of data deletion processing is subjected to origin processing, the stress-strain value corresponding to the initial unstretched state is set to (0, 0), and other data point coordinates are adjusted accordingly by subtracting the initial stress-strain value, the processed data is stored as a new DataFrame data structure, and the file named "calibrated raw data" is output according to the set output format and path.

[0031] Further, the maximum stress value in the "calibrated raw data" is determined and output as the breaking strength, and the strain value corresponding to the maximum stress point is output as the elongation, and the area is calculated by integration method and output as the breaking work, including:

[0032] Determination of breaking strength and elongation, read the "calibrated raw data" file, directly obtain the maximum value from the stress data column as the breaking strength, and find the strain value corresponding to the breaking strength in the data as the elongation, which reflects the elongation of the fiber before breaking;

[0033] The fracture work is calculated, the calibrated stress-strain curve is integrated by using the integrate. quad function in the scipy library, the area under the stress-strain curve is calculated, the area represents the energy absorbed by the fiber during the stretching process (fracture work), and the calculated fracture work value is recorded as an important parameter for evaluating the tensile properties of the fiber.

[0034] Further, the fracture strength, elongation, Young's modulus and fracture work are calibrated to international units, a set of data is output, and each set of data is processed in a loop, including:

[0035] Unit calibration: according to the requirements of the International System of Units, the calculated fracture strength, elongation, Young's modulus and fracture work and other parameters are converted and calibrated, the fracture strength unit is converted to megapascal (MPa), the elongation output unit is converted to 1, the Young's modulus output unit is converted to gigapascal (GPa), and the fracture work unit is converted to megajoule per cubic meter (MJ / m 3 );

[0036] Single data output: the calibrated data of each group is arranged according to the set format and output to the specified result file and data storage structure;

[0037] Loop processing each set of data: through the loop structure, the data of the next group of fiber samples is continuously processed until all groups of data are processed.

[0038] Further, the fracture strength, elongation, Young's modulus and fracture work of each set of data of each Excel file are output as an Excel file with a corresponding file name, including:

[0039] Extract and organize data: for each set of data in each processed Excel file, extract its corresponding fracture strength, elongation, Young's modulus and fracture work parameters, and organize them according to the DataFrame structure of pandas;

[0040] Output as a separate Excel file: output the organized data as a separate Excel file with the fiber sample number of each set of data or as a file name, and store it in the specified output folder.

[0041] Further, the average value and standard deviation of each set of fracture strength, elongation, Young's modulus and fracture work of a single Excel file are calculated, and the average value and standard deviation of all Excel files are output as a total Excel file, including:

[0042] Calculate single Excel internal statistical value, respectively calculate the average value and standard deviation of the four parameters of breaking strength, elongation, Young's modulus and breaking work for all group data in a single Excel file, use the mean function provided by the pandas library to calculate the average value and the std function to calculate the standard deviation;

[0043] Summary to the total Excel file, traverse all processed Excel files, collect the average value and standard deviation data of the four parameters calculated in each Excel file, organize these data into a new DataFrame structure, each row represents the summary information of an Excel file, each column is the average value of breaking strength, elongation, Young's modulus, breaking work, breaking strength standard deviation, elongation standard deviation, Young's modulus standard deviation, breaking work standard deviation, and finally output the summarized DataFrame as a total Excel file and store it in a specified location.

[0044] Compared with the prior art, the beneficial effects of the present application are that the batch processing method for fiber static tensile test data of the present application accurately determines the position of the straight line segment by a unique Young's modulus straight line segment determination method combined with accurate calculation and screening, providing a reliable basis for subsequent analysis; marking the straight line segment in red not only enables intuitive judgment of the accuracy of Young's modulus, but also realizes the visualization of data accuracy, facilitating users to quickly evaluate data quality; and the use of the straight line segment effectively reduces the volatility of the stress-strain curve, further improves the accuracy of the data, and overall makes the method more accurate and efficient in processing fiber tensile test data, greatly improving the quality and efficiency of data processing, and providing a more advantageous data processing means for fiber material research and other related fields. BRIEF DESCRIPTION OF DRAWINGS

[0045] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for purposes of illustration only and are not considered a limitation of the present application. Moreover, like reference numerals are used to designate identical components throughout the specification. In the drawings:

[0046] Figure 1 Flowchart of the batch processing method for fiber static tensile test data of the embodiments of the present application;

[0047] Figure 2 Example point graph of the tensile stress-strain curve of the embodiments of the present application;

[0048] Figure 3 Example code diagram for modifying the Young's modulus threshold of the embodiments of the present application.

[0049] Figure 4Figure 1 is a schematic diagram of a software interface according to an embodiment of the present application DETAILED DESCRIPTION

[0050] Exemplary embodiments of the present application will be described in detail with reference to the drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be embodied in various forms without being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail with reference to the drawings and in conjunction with the embodiments.

[0051] Reference Signs List Figure 1 As shown in the drawings, in some embodiments of the present application, a batch processing method for fiber static tensile test data includes:

[0052] Reading the tensile tester data, batch selecting the Excel file of the tensile mechanical property data of the input and output fiber samples, grouping the data, reading the data, finding the column names of all four columns of each group, and grabbing the stress and strain data of each group of data;

[0053] Each group of 5 adjacent points is recorded as a group, and the maximum stress difference Vmax and all points greater than Vmax*0.75 are found. The maximum and minimum points are added and divided by 2 to obtain the middle point of the Young's modulus straight line portion. Starting from the middle point, gradually select the values away from the point at both ends, and set a threshold value to determine when the selected point's Young's modulus is greater than the threshold value, then from this point, the remaining point values away from the middle point are not used for fitting the straight line segment;

[0054] The points used for fitting are fitted with a straight line by the least squares method to obtain the Young's modulus, and the points used for fitting are marked in red and the other points are marked in blue. The point graph of this group of stress-strain curve data is output. The minimum stress value of the red points is determined and the data smaller than the minimum stress value of the red points is deleted. The maximum stress in the remaining data after processing is determined and the data smaller than the maximum stress is deleted. The processed data is subjected to data originization processing and output as "calibrated raw data";

[0055] The maximum stress value in the "calibrated raw data" is determined and output as the breaking strength. The strain value corresponding to the maximum stress point is output as the elongation. The area is calculated by the integral method and output as the breaking work. The breaking strength, elongation, Young's modulus and breaking work are calibrated in international units. The output of one group of data is completed and each group of data is processed in a loop;

[0056] The breaking strength, elongation, Young's modulus and breaking work of each set of data of each Excel file are output as an Excel file with the corresponding file name. The average value and standard deviation of each set of breaking strength, elongation, Young's modulus and breaking work of a single Excel file are calculated, and the average value and standard deviation of all Excel files are output as a total Excel file.

[0057] Specifically, the above series of steps are realized by a software for analyzing the static tensile stress-strain curve of composite fibers. It can be understood that the software is simple to operate and can be applied to the processing of various fiber tensile stress-strain data. It can also understand the accuracy of the data by observing the tensile stress-strain point graph, which greatly improves the efficiency and accuracy of processing batch data.

[0058] It can be understood that through the unique Young's modulus straight line segment judgment method, the straight line segment is determined through accurate calculation and screening, which provides a reliable basis for subsequent analysis; marking the straight line segment in red not only enables intuitive judgment of the accuracy of Young's modulus, but also realizes the visualization of data accuracy, which is convenient for users to quickly evaluate data quality; and the use of straight line segments effectively reduces the volatility of the stress-strain curve, further improving the accuracy of the data, making the method more accurate and efficient in processing fiber tensile test data, greatly improving the quality and efficiency of data processing, and providing a more advantageous data processing method for related fields such as fiber material research.

[0059] Specifically, through the corresponding button in the software interface, the user specifies the folder path where the Excel file containing the tensile machine test data is located. The software uses the read_excel function in the pandas library of Python to traverse all Excel files under the selected folder, reads the data into the DataFrame data structure, identifies the column information in the file, and filters out the data columns related to tensile mechanical properties, such as stress, strain, etc. according to the preset rules or user-defined settings (such as column name containing specific keywords, data type, etc.). Then, according to the fiber sample number or other grouping basis, the data is grouped for subsequent processing of each group of data. At the same time, the format of outputting the tensile mechanical property data is determined, for example, a new DataFrame is created to store the subsequent calculation results, or the corresponding data structure is prepared.

[0060] Specifically, for each Excel file data that has been read into memory (in the form of a DataFrame), the software obtains a list of all column names by accessing the columns attribute of the DataFrame. According to pre-set rules or user-specified conditions (such as keyword search, data type judgment, and other related information), the software searches for the column names of the four columns related to stress and strain data in the list of column names. Using the found stress and strain column names, the software accurately extracts the stress and strain data columns corresponding to each group of data from the original data by using the indexing methods of the DataFrame (such as loc or iloc). The extracted data is stored in groups according to the previously determined grouping basis (such as the fiber sample number), forming a data structure that is convenient for subsequent processing, such as using a dictionary with the sample number as the key and the list or DataFrame slice containing the stress and strain data of the sample as the value.

[0061] Referring to Figure 2 and Figure 3 Specifically, for each group of stress data, the software divides each group into a set of every 5 adjacent data points by looping. For each group, the software calculates the stress difference between adjacent points in the group to obtain a set of stress difference data. Among all the stress difference data of the groups, the software finds the maximum value using the max function or other methods, and defines it as Vmax. The software then iterates through the stress difference data again to find all values greater than Vmax*0.75. Among these related points, the software determines the points with the maximum and minimum stress values, and adds their stress values and divides by 2 to obtain a reference stress value that is approximately in the middle of the linear portion of the Young's modulus. The software also records the corresponding strain value, takes the average of the stress and strain as the intermediate reference point coordinates, and starts from the intermediate reference point to gradually select other points towards both ends of the stress-strain curve. For each selected point, the software calculates its Young's modulus (Young's modulus = stress / strain). The software sets a threshold value (which can be modified by the user through the software interface according to the material properties, experience, or other factors, such as adjusting the values of point1 and point2 in the config file in the example to adjust the threshold value), and stops selecting points in that direction when the Young's modulus of the selected point is greater than the threshold value, thereby determining the range of effective data points for fitting the straight line.

[0062] It can be understood that the unique calculation and screening method in the data processing of fiber static tensile test can accurately locate the point in the middle of the linear portion of the Young's modulus, and by setting a threshold value, the software can reasonably select the values for fitting the straight line, effectively excluding the interference of the nonlinear part of the curve, making the subsequent calculation of parameters such as Young's modulus more accurate and reliable, and helping to improve the scientificity and stability of the entire data processing process, thereby improving the accuracy of the analysis of fiber tensile properties and providing an efficient and high-quality data processing strategy for batch processing of fiber tensile test data.

[0063] Specifically, the software will extract the stress and strain data of the selected points for fitting a straight line, and organize them into a format suitable for least squares fitting, such as storing the stress data as the dependent variable and the strain data as the independent variable in two arrays (e.g., numpy arrays), defining a linear function form (e.g., y = ax + b, where y is stress, x is strain, a is the slope, and b is the intercept), and using the optimize.curve_fit function in the scipy library or similar tools to perform linear fitting using the prepared data and fitting function as input to obtain the parameters (slope a and intercept b) of the fitted straight line. According to the definition of Young's modulus, the slope a obtained by fitting is taken as the value of Young's modulus.

[0064] Specifically, a figure object (Figure) and one or more coordinate axis objects (Axes) are created using the matplotlib library to draw a stress-strain curve point graph, and the points used for fitting are marked in red on the graph, while the other points not selected for fitting are marked in blue. Use the scatter function or similar plotting function of the Axes object to pass in the strain and stress data of the fitting points and non-fitting points, respectively, and the corresponding color parameters (c = 'r' for red, c = 'b' for blue). Add labels to the coordinate axes (e.g., xlabel('Strain') for the strain axis label and ylabel('Stress') for the stress axis label), set the graph title (e.g., title('Stress-Strain Curve with Fitting')), add a legend (legend) to explain the meaning of red and blue points to make the graph more readable and professional, and use the savefig function of the matplotlib library to save the drawn stress-strain curve point graph as a picture file (such as PNG, PDF, etc.). If you need to view the graphical effect immediately in the script running environment, you can use the show function to display the graph, but ultimately the software application relies more on saving the graph file for subsequent viewing and analysis.

[0065] It can be understood that the least squares fitting of the straight line to obtain the Young's modulus can obtain accurate modulus values based on scientific mathematical principles, providing key data for evaluating the stiffness characteristics of fiber materials; marking the points used for fitting in red and the other points in blue and outputting the stress-strain curve point graph realizes the visualization of the data processing process, not only intuitively presenting the fitting effect, but also facilitating the judgment of the rationality and accuracy of the data, and providing intuitive basis for subsequent analysis of data quality and adjustment of processing strategies, effectively enhancing the reliability, interpretability and practicality of the entire fiber static tensile test data batch processing method.

[0066] Specifically, among the fitted points marked in red, the software traverses the stress data to find the red point with the minimum stress. Then the entire stress-strain data set is traversed again to delete all data points with stress values less than the minimum red stress value, to remove data that may be unstable or abnormally low in the initial stage. Among the remaining data after the above processing, the maximum stress value is found. Then all subsequent data points with stress values less than the maximum stress value are deleted, to exclude possible abnormal fluctuations or invalid data in the later stage of stretching, to ensure the effectiveness and consistency of the data before the maximum stress point. The stress-strain data after two data deletion processes are origin processed. The stress-strain values corresponding to the initial unstretched state are usually set to (0, 0), and other data point coordinates are adjusted accordingly, for example, subtracting the initial stress-strain values. The processed data are stored as a new data structure (such as DataFrame), and a file named "calibrated raw data" is output in the output format and path set by the software, as the basis data for the next step of calculation.

[0067] It can be understood that in the data processing of the fiber static tensile test, this method can effectively remove abnormal fluctuations or non-effective data in the initial and final stages of the data. Through the judgment of the minimum stress red point and the maximum stress value and the corresponding data deletion, the data is more pure and conforms to the actual stretching rule. The data origin processing provides a unified and reasonable benchmark for subsequent calculation. The "calibrated raw data" finally output greatly improves the data quality, lays a solid foundation for accurate analysis of the tensile properties of the fiber, calculation of the breaking strength, elongation and other key parameters, and thus improves the accuracy and reliability of the entire batch processing method.

[0068] Specifically, the software reads the "calibrated raw data" file (Excel file output in the previous step or processed data structure in memory), directly obtains the maximum value from the stress data column as the breaking strength, and finds the strain value corresponding to the maximum stress value in the data as the elongation. The strain value reflects the elongation of the fiber before breaking. The calibrated stress-strain curve is calculated using numerical integration methods (such as the integrate.quad function in the scipy library or other suitable integration tools). The physical meaning of integration is to calculate the area under the stress-strain curve, which represents the energy absorbed by the fiber during stretching, i.e. the breaking work. The calculated breaking work value is recorded as one of the important parameters for evaluating the tensile properties of the fiber.

[0069] Specifically, according to the requirements of the International System of Units (SI), the software converts and calibrates the parameters such as breaking strength, elongation, Young's modulus, and breaking work calculated in step 6. For example, the breaking strength unit may be converted from the original output unit of the tensile machine to megapascal (MPa), the elongation output unit to 1, the Young's modulus output unit to gigapascal (GPa), the breaking work unit to megajoule per cubic meter (MJ / m 3 ) and the like, to ensure that the output parameter units conform to international standards, facilitate data universality and comparison, and arrange the calibrated data set (including fiber sample number, breaking strength, elongation, Young's modulus, and breaking work parameters) in the format set by the software, and output to the specified result file or data storage structure.

[0070] Specifically, through the loop structure, the software continues to process the data of the next group of fiber samples until all groups of data have completed the above processing flow, realizing the automatic processing of the entire batch of data, ensuring that each group of data obtains accurate analysis results. For each group of data in the Excel file processed, the software extracts its corresponding breaking strength, elongation, Young's modulus, and breaking work parameters, and arranges them in a specific format (such as the DataFrame structure of pandas, with each row representing a group of data and each column corresponding to a parameter). With the specific identifier of each group of data (such as the fiber sample number or other meaningful name) as the file name, the software outputs the arranged data as a separate Excel file and stores it in the specified output folder, making it easy for users to view and analyze the specific performance parameters of each group of data and facilitating in-depth research or data tracing for individual tests.

[0071] Specifically, for all group data in a single Excel file, the software calculates the average and standard deviation of the four parameters of breaking strength, elongation, Young's modulus and breaking work, respectively, using the statistical functions provided by numpy or pandas library (such as mean function to calculate the average, std function to calculate the standard deviation). These statistical values are used to describe the overall performance characteristics of the relevant fiber samples in the Excel file. The software traverses all processed Excel files and collects the average and standard deviation data of the four parameters calculated in each Excel file. These data are arranged into a new DataFrame structure, with each row representing the summary information of an Excel file and each column representing the average values of breaking strength, elongation, Young's modulus and breaking work, as well as the standard deviations of breaking strength, elongation, Young's modulus and breaking work. Finally, this summarized DataFrame is output as a total Excel file and stored in a designated location (usually in a folder related to other result data), providing macro statistical analysis results of all processed data, helping users to grasp the overall performance of fiber tensile test and data dispersion, which is helpful for evaluating the quality and tensile performance consistency of fiber materials.

[0072] Referring to Figure 4 As shown, the software interface includes folder selection buttons and processing buttons, which can complete the data analysis of fiber samples without complex operations.

[0073] Specifically, buttons ①②③④ are folder selection buttons, ① is to select the data folder of fiber samples that need to be processed, and the data Excel files needed for processing can be placed in the folder. Buttons ②③④ are used to select the output file data folder, and the output file data results are the calibrated raw Excel data, the average and standard deviation data of the four parameters calculated in each Excel file, and the original four parameter data of each sample, and the stress-strain curve point graph of each Excel file. Buttons ⑤⑥⑦ are processing buttons, ⑤ is to read and process raw data to generate calibrated raw data, which needs to wait for data loading to be completed before clicking button ⑥. ⑥ is to generate the average and standard deviation data of the four parameters calculated in each Excel file and the original four parameter data of each sample, which needs to wait for data loading to be completed before clicking button ⑦. ⑦ is to generate the stress-strain curve point graph of each Excel file, and each Excel will generate a corresponding folder. This operation method is simple and fast, which can quickly help users learn to use the software.

[0074] It should be noted that:

[0075] In the description provided herein, a large number of specific details are explained. However, it is understood that embodiments of the application can be practiced without these specific details. In some instances, well-known structures and techniques have not been shown in detail in order not to obscure the understanding of this description.

[0076] Furthermore, those skilled in the art will recognize that embodiments described herein, while including certain features, can embody combinations of features from different embodiments. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

[0077] The above description is only preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for batch processing of fiber static tensile test data, characterized by, The method comprises the following steps: reading tensile tester test data, batch selecting input and output fiber sample tensile mechanical property data Excel files, grouping data, reading data, finding all four column names of each group, and grabbing stress and strain data of each group of data; regarding every five adjacent points as a group, finding the maximum stress difference Vmax, and finding all points greater than Vmax*0.75, adding the maximum and minimum points and dividing by 2 to obtain the middle point of the Young's modulus straight line portion, starting from the middle point, gradually selecting values away from the middle point, and setting a threshold value to determine whether the Young's modulus of the selected point is greater than the threshold value, and if so, the remaining point values away from the middle point are not used for fitting the straight line segment; fitting the points for fitting with a straight line to obtain the Young's modulus, marking the points for fitting with red, marking the other points with blue, outputting the point graph of the stress-strain curve data of this group, determining the minimum stress value of the red points and deleting data smaller than the minimum stress value of the red points, determining the maximum stress in the remaining data after processing, and deleting subsequent data smaller than the maximum stress, performing data originization processing on the processed data, and outputting "calibrated original data"; determining the maximum stress value in the "calibrated original data", outputting the breaking strength, and outputting the elongation corresponding to the maximum stress point, calculating the area by integration, and outputting the breaking work, calibrating the breaking strength, elongation, Young's modulus and breaking work to international units, completing the output of one group of data, and processing each group of data in a loop; outputting the breaking strength, elongation, Young's modulus and breaking work of each group of data of each Excel file as an Excel file with the corresponding file name, calculating the average value and standard deviation of each group of breaking strength, elongation, Young's modulus and breaking work of a single Excel file, and outputting the average value and standard deviation of all Excel files as a total Excel file.

2. The method of claim 1, wherein the fiber static tensile test data is processed in batches. The method comprises the following steps: selecting an original data folder, using the read_excel function in the pandas library of Python to read data into a DataFrame data structure according to the folder path of the Excel file containing the tensile tester test data, and traversing all Excel files under the selected folder; data filtering and grouping, identifying column information in the file, filtering out data columns related to tensile mechanical properties according to a preset rule, grouping data according to fiber sample numbers, determining the format of output tensile mechanical property data, and creating a new DataFrame to store subsequent calculation results.

3. The method of claim 1, wherein the method is characterized by: The method comprises the following steps: obtaining column name information, obtaining all column name lists by accessing the columns attribute of the DataFrame for each Excel file data read into memory, and Screening stress-strain column name, according to the pre-set rules and user-specified conditions in the column name list to find four columns of column names related to stress and strain data; Extract and group storage data, use the found stress and strain column name, accurately extract the stress and strain data column of each group of data from the original data by means of the index method of DataFrame, store the extracted data according to the fiber sample number, form a dictionary data structure, the key is the sample number, and the value is the list containing the stress and strain data of the sample.

4. The method of claim 1, wherein the fiber static tensile test data is processed in batches. The maximum stress difference Vmax and all points greater than Vmax*0.75 are found, the maximum and minimum points are added and divided by 2 to obtain the intermediate point of the Young's modulus straight line portion, and the threshold value is set to judge when the selected point Young's modulus is greater than the threshold value, the remaining point values away from the intermediate point are not used to fit the straight line segment, including: Grouping and calculating stress difference, for each stress data, divide every 5 adjacent data points into a group, for each group, calculate the stress difference between adjacent points to obtain a set of stress difference data; Determine the maximum stress difference Vmax, find the maximum value in all stress difference data using the max function, and define it as Vmax; Screening related points to determine the intermediate point, reiterate the stress difference data to find all points greater than Vmax*0.75, and determine the maximum and minimum stress points among these related points, add their stress values and divide by 2 to obtain the reference stress value in the middle of the Young's modulus straight line, and record the corresponding strain value, take the average stress-strain value as the intermediate reference point coordinate; Screening fitting points from the intermediate point, starting from the intermediate reference point, gradually select other points to the ends of the stress-strain curve, for each selected point, calculate its Young's modulus according to the stress-strain data, and set a threshold value, when the selected point Young's modulus is greater than the threshold value, stop selecting points, thereby determining the effective data point range for fitting the straight line.

5. The method of claim 1, wherein the fiber static tensile test data is processed in batches. The points used for fitting are fitted with a straight line to obtain the Young's modulus, and the points used for fitting are marked in red and the other points are marked in blue, and the point graph of this group of stress-strain curve data is output, including: Linear fitting and Young's modulus calculation, first extract the stress and strain data of the points screened for fitting the straight line, arrange them into a format suitable for least squares fitting, define a linear function form for least squares fitting, use the optimize.curve_fit function in the scipy library, input the prepared data and fitting function, perform linear fitting, get the parameters of the fitted straight line, and according to the definition of Young's modulus, the slope obtained by fitting is taken as the value of Young's modulus; Data point marking and plot preparation, using the matplotlib library to create a figure object and an axis object, the points used for fitting are marked and plotted in red on the graph, and other points not selected for fitting are marked and plotted in blue, using the scatter function of the figure object, respectively, the strain and stress data of the fitting points and non-fitting points and the corresponding color parameters are passed in, the axis is labeled to represent the strain axis label, the figure title is set, the legend is added to explain the meaning of red and blue points, and the savefig function of the matplotlib library is used to save the stress-strain curve point graph as a picture file.

6. The method of claim 1, wherein the fiber static tensile test data is processed in batches. The minimum stress value of the red point is judged and the data smaller than the minimum stress value of the red point is deleted, the maximum stress value in the remaining data after judgment processing is judged, and the data smaller than the maximum stress value is deleted, and the processed data is processed to the original point, and the output is "calibrated original data", including: Determination of minimum stress red point and data deletion, in the fitting points marked as red, traverse the stress data to find the minimum red point, and then traverse the entire stress-strain data set again, delete all data points with stress value less than the minimum red stress value; Determination of maximum stress value and subsequent data deletion, find the maximum stress value in the remaining data after processing, and then delete all data points with stress value less than the maximum stress value; Data original point processing and output, after two times of data deletion processing, the stress-strain data is processed to the original point, the stress-strain value corresponding to the initial unstretched state is set to (0, 0), and other data point coordinates are adjusted accordingly, subtract the initial stress-strain value, store the processed data as a new DataFrame data structure, and output the file named "calibrated original data" according to the set output format and path.

7. The method of claim 1, wherein the fiber static tensile test data is processed in batches. The maximum stress value in the "calibrated original data" is judged, and the output is the breaking strength, and the strain value corresponding to the maximum stress point is output as the elongation, and the area is calculated by the method of integral, and the output is the breaking work, including: Determination of breaking strength and elongation, reading the "calibrated original data" file, directly obtaining the maximum value from the stress data column as the breaking strength, and finding the strain value corresponding to the breaking strength in the data as the elongation, which reflects the elongation of the fiber before breaking; Calculate the breaking work, use the integrate.quad function in the scipy library to integrate the calibrated stress-strain curve, calculate the area under the stress-strain curve, which represents the energy absorbed by the fiber during stretching, and record the calculated breaking work value as an important parameter for evaluating the tensile properties of the fiber.

8. The method of claim 1, wherein the method is a batch processing method for fiber static tensile test data. The breaking strength, elongation, Young's modulus and breaking work are calibrated to international units, the output of a group of data is completed, and each group of data is processed in a loop, including: Unit calibration, according to the requirements of the International System of Units, the calculated fracture strength, elongation, Young's modulus and fracture work parameters are unit conversion and calibration, fracture strength unit from the original output unit of the tensile machine is converted to megapascal, elongation unit is converted to 1, Young's modulus unit is converted to gigapascal, fracture work unit is converted to megajoule per cubic meter; Single group data output, the calibrated data is arranged according to the set format, and output to the specified result file and data storage structure; Cycle processing each group of data, through the loop structure, continue to process the next group of fiber sample data until all groups of data are completed.

9. The method of claim 1, wherein the method further comprises: The fracture strength, elongation, Young's modulus and fracture work of each group of data of each Excel file are output as corresponding file name Excel files, including: Extract and arrange data, for each group of data in each processed Excel file, extract its corresponding fracture strength, elongation, Young's modulus and fracture work parameters, and arrange them according to the DataFrame structure of pandas; Output as a separate Excel file: take the fiber sample number of each group of data as the file name, output the arranged data as a separate Excel file, and store it in the specified output folder.

10. The method of claim 1, wherein the method is a batch processing method for fiber static tensile test data. The average value and standard deviation of each group of fracture strength, elongation, Young's modulus and fracture work of a single Excel file are calculated, and the average value and standard deviation of all Excel files are output as a total Excel file, including: Calculate statistical values in a single Excel, calculate the average value and standard deviation of the four parameters of fracture strength, elongation, Young's modulus and fracture work for all groups of data in a single Excel file, using the mean function provided by pandas library to calculate the average value and the std function to calculate the standard deviation; Summarize to a total Excel file, traverse all processed Excel files, collect the average value and standard deviation data of the four parameters calculated in each Excel file, arrange these data into a new DataFrame structure, each row represents the summary information of an Excel file, and each column is respectively the average value of fracture strength, elongation, Young's modulus, fracture work, fracture strength standard deviation, elongation standard deviation, Young's modulus standard deviation, fracture work standard deviation, and finally output this summarized DataFrame as a total Excel file and store it in the specified location.

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