Cryogenic Tensile Testing Temperature Drift Compensation Method and System

Abstract

The application provides a cryogenic low-temperature tensile test temperature drift compensation method and system, comprising: applying a standard load at a preset temperature point, collecting the output value of a load sensor to obtain a sample group; establishing an initial temperature drift model according to the sample group; collecting the output value of a temperature and a load sensor during a tensile test, and inputting the temperature and the output value into the initial temperature drift model to calculate a temperature drift compensation value; calculating a compensated net load according to the output value and the temperature drift compensation value; calculating a residual according to the net load and a theoretical stress-strain curve to obtain a residual group; updating the initial temperature drift model according to the residual group to obtain a final temperature drift model, and the final temperature drift model compensates according to real-time temperature and output value.

Description

Technical Field

[0001] This application relates to the field of cryogenic mechanical property testing technology, and more specifically to a method and system for compensating for temperature drift in cryogenic tensile testing. Background Technology

[0002] In cryogenic tensile testing, load cells are affected by temperature changes, leading to temperature drift and nonlinear shifts in the output signal. Especially in extremely low temperatures ranging from 4K to 77K, the strain coefficient and temperature coefficient of resistance of the sensor's sensitive elements change significantly. Existing compensation methods often use fixed calibration values, which cannot adapt to real-time temperature changes and drift behavior under complex stress conditions, affecting the accuracy and stability of tensile test data. Summary of the Invention

[0003] The purpose of this application is to provide a method and system for compensating for temperature drift in cryogenic tensile testing, so as to correct the temperature drift error of the load sensor, improve the accuracy of load measurement, and ensure the stability and reliability of test data.

[0004] To achieve the above objectives, this application provides a method for temperature drift compensation in cryogenic tensile testing, comprising: applying a standard load at a preset temperature point and collecting the output values ​​of a load sensor to obtain a sample group; establishing an initial temperature drift model based on the sample group; collecting the temperature and the output values ​​of the load sensor during the tensile test, and inputting the temperature and the output values ​​into the initial temperature drift model to calculate a temperature drift compensation value; calculating the compensated net load based on the output values ​​and the temperature drift compensation value; calculating the residuals based on the net load and the theoretical stress-strain curve to obtain a residual group; updating the initial temperature drift model based on the residual group to obtain a final temperature drift model, wherein the final temperature drift model is compensated based on the real-time temperature and the output values.

[0005] Preferably, the sample set includes zero-load output values ​​collected at multiple different preset temperature points, and at least two standard load output values ​​with different amplitudes; wherein: the zero-load output values ​​are used to establish a temperature drift offset function; and the standard load output values ​​are used to establish a sensitivity change function.

[0006] Preferably, the initial temperature drift model is a second-order polynomial function with temperature as the variable, and the formula for calculating the temperature drift compensation value is: ΔF(T)=a0+a1T+a2T 2 In the formula, a0, a1, and a2 are the error correction coefficients obtained from the fitting, and T is the temperature.

[0007] Preferably, the formula for calculating the net load is: f(t)=S(t)-ΔF(T); where S(t) is the output value of the load sensor and ΔF(T) is the temperature drift compensation value.

[0008] Preferably, the formula for calculating the residual is: R(t)=σ(t)-σ′(t); where σ(t) is the measured stress currently obtained, and σ′(t) is the theoretical stress obtained by strain mapping.

[0009] Preferably, the theoretical stress-strain curve is obtained by regression fitting based on the linear segment data of the initial stage of the tensile test.

[0010] Preferably, the method for updating the initial temperature drift model includes: using the residual set within a sliding window to fit the error correction coefficient using the weighted least squares method.

[0011] Preferably, a preset sampling period is set, and the error correction coefficient is updated using the weighted least squares method every preset sampling period.

[0012] Preferably, the preset temperature point covers at least three temperature measurement zones, namely, a low temperature zone, an intermediate temperature zone, and a high temperature zone.

[0013] This application, in another aspect, provides a cryogenic tensile testing temperature drift compensation system, comprising: a sample acquisition module configured to apply a standard load at a preset temperature point and acquire the output value of a load sensor to obtain a sample set; an initial modeling module configured to establish an initial temperature drift model based on the sample set; a compensation calculation module configured to input the temperature value and the sensor output value into the initial temperature drift model to calculate a temperature drift compensation value, and calculate the compensated net load based on the output value and the temperature drift compensation value; a residual analysis module configured to calculate the residual based on the net load and the theoretical stress-strain curve to obtain a residual set; a model update module configured to update the initial temperature drift model based on the residual set to obtain a final temperature drift model; and a dynamic compensation module configured to perform temperature drift compensation on the real-time acquired temperature value and sensor output value based on the final temperature drift model.

[0014] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0015] The accompanying drawings, which are incorporated herein and form part of this specification, illustrate one or more embodiments of the present application and, together with the description, serve to explain the principles of the present application and to enable those skilled in the art to make and use the present application.

[0016] Figure 1 This is a flowchart of a load error compensation method for cryogenic tensile testing provided in an embodiment of this application;

[0017] Figure 2 This is a flowchart of the sample group construction method provided in the embodiments of this application;

[0018] Figure 3 This is a flowchart of the method for constructing and applying a second-order polynomial temperature drift compensation model with temperature as the variable, provided in an embodiment of this application.

[0019] Figure 4 This is a flowchart of the residual calculation method provided in the embodiments of this application;

[0020] Figure 5 This is a flowchart of the method for constructing theoretical stress-strain curves provided in the embodiments of this application;

[0021] Figure 6 This is a flowchart of the temperature drift model update method based on sliding window residual group provided in the embodiments of this application;

[0022] Figure 7 This is a flowchart of the temperature drift model update method based on a fixed sampling period provided in the embodiments of this application;

[0023] Figure 8 This is a flowchart of the temperature drift model initialization method provided in the embodiments of this application;

[0024] Figure 9 This is a flowchart of the stress-strain model self-identification method provided in the embodiments of this application;

[0025] Figure 10 This is a schematic diagram of a load error compensation system for cryogenic tensile testing provided in an embodiment of this application. Detailed Implementation

[0026] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments may be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, the description of these embodiments is intended to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to provide a deeper understanding of embodiments of this application.

[0027] In one embodiment of this application, a load error compensation method for cryogenic tensile testing is provided, which is suitable for material mechanical property testing in the range of 4K to 77K.

[0028] Specifically, such as Figure 1 As shown, the method includes the following steps:

[0029] S10: Apply a standard load to the load sensor at multiple preset temperature points and collect its output value to form a data sample set containing temperature information, the relationship between load input and load sensor response;

[0030] S20: Establish an initial temperature drift model based on the sample group to reflect the drift characteristics of the load sensor under different temperature conditions.

[0031] S30: During the tensile test, real-time temperature data and load sensor output values ​​are continuously collected and input into the initial temperature drift model to calculate the temperature drift compensation value at the current moment.

[0032] S40: Combine the compensation value with the original output value of the load sensor to obtain the current net load value, which is then used for subsequent stress analysis.

[0033] S50: The stress value calculated based on the net load and the strain data of the specimen is compared with the theoretical stress-strain curve to form a residual set, which is used to reflect the difference between the current model prediction value and the actual test behavior.

[0034] S60: Update the initial temperature drift model based on the residual set to obtain a final temperature drift model that is more adapted to the current test conditions, and use the final model in real time to perform temperature drift compensation in subsequent tests.

[0035] In one embodiment of this application, a sample set construction method is provided to support the training of a temperature drift compensation model. The sample set includes zero-load output values ​​collected at multiple different preset temperature points and at least two standard load output values ​​with different amplitudes, which are used to establish a temperature drift offset function and a sensitivity change function, respectively.

[0036] like Figure 2 As shown, S11: Before the tensile test, select multiple preset temperature points covering the test temperature range, such as 77K, 50K, 30K, and 10K.

[0037] S12: At each preset temperature point, the load is kept at 0, i.e., no sample is loaded, and the output value S0(t) of the load sensor is collected. This output value should theoretically be 0, but due to temperature drift, there is an offset. The zero-load output values ​​at each temperature are recorded to form a zero-load sample set: {(T i S0(T) i The temperature drift offset function is fitted using this zero-load sample set, and its formula is as follows:

[0038] Δ0(T)=f0(T);

[0039] In the formula, Δ0(T) is the zero-point offset value of the load sensor at temperature T, and the function can be a first-order or second-order polynomial.

[0040] S13: At each preset temperature point, apply at least two standard loads of different amplitudes, such as 50N and 100N, and record the output value S1(T,F) of the load sensor respectively.

[0041] After subtracting the zero-point offset value corresponding to the preset temperature point, the effective response is obtained, and its calculation formula is as follows:

[0042] Δ1(T,F)=S1(T,F)-S0(t);

[0043] Next, calculate the unit response value under different load amplitudes to fit the sensitivity change function. The formula is as follows:

[0044]

[0045] In the formula, k(T) is the temperature-dependent equivalent sensitivity, which represents the degree of response of the load sensor to a unit force and varies with temperature.

[0046] The sensitivity of multiple preset temperature points is fitted to a sensitivity variation function, and the formula is as follows:

[0047] k(T) = f1(T);

[0048] S14: Finally, a complete temperature drift compensation model is constructed, combining the offset function and the sensitivity function to represent the relationship between the load sensor output and the actual load F at any temperature T. The formula is as follows:

[0049] S1(T,F)=Δ0(T)+k(T)·F;

[0050] This model is used for subsequent real-time compensation calculations, either by inferring the load value from the load sensor output or by calculating the current temperature drift compensation deviation. This example uses a zero-load point and two standard points to independently identify additive offset and proportional response errors.

[0051] In one embodiment of this application, a method for constructing a second-order polynomial temperature drift model based on temperature is provided, which is used to correct the temperature drift error in the load sensor output in real time during cryogenic tensile testing.

[0052] like Figure 3As shown, S21: Before the experiment begins, experimental data are collected at multiple preset temperature points, such as 77K, 50K, 30K, and 10K, to obtain the load response deviation of the load sensor under different temperature conditions, forming a training sample group. Based on the temperature T and the corresponding error deviation value ΔF recorded in the sample group, a temperature drift compensation model of the following form is established using a polynomial fitting method:

[0053] ΔF(T)=a0+a1T+a2T 2 ;

[0054] In the formula, T is the temperature of the current test environment, ΔF(T) is the estimated temperature drift error value at that temperature, and a0, a1, and a2 are the error correction coefficients obtained by fitting using the least squares method.

[0055] S22: In one example, the fitting results are: a0 = 1.12, a1 = -0.045, a2 = 0.00038. Then, at the current temperature of 42.0K, the compensation value can be calculated according to the temperature drift compensation model:

[0056] ΔF(42.0)=1.12-0.045·42.0+0.00038·42.02=-0.42N;

[0057] This indicates that the load sensor output contains a negative offset of approximately 0.42N at this time.

[0058] In one embodiment of this application, a net load calculation method is provided, which is applicable to the load error compensation system in cryogenic tensile testing. The method calculates the actual net load based on the real-time output value of the load sensor and the temperature drift compensation model.

[0059] During the tensile test, the load sensor outputs a signal value related to the applied load in real time, denoted as S(t), where t represents the current sampling time. Simultaneously, based on the current temperature T(t), the temperature drift compensation value ΔF(T) at that moment is calculated using a pre-built temperature drift compensation model.

[0060] The formula for calculating the net load is as follows:

[0061] F(t) = S(t) - ΔF(T);

[0062] In the formula, F(t) is the net load at time t, that is, the actual load after eliminating the temperature drift error, S(t) is the original output value of the load sensor at time t, and ΔF(T) is the temperature drift compensation value calculated based on the current temperature T, which is used to reflect the offset effect of temperature on the sensor output.

[0063] In one example, the current load sensor output value S(t) = 28.4 N is collected, and the current temperature is T = 45.0 K. Substituting into the temperature drift compensation model, we get ΔF(45.0) = 1.2 N. The net load calculation result is: F(t) = 28.4 - 1.2 = 27.2 N, which means that the effective load actually acting on the sample at this time is 27.2 N.

[0064] This example demonstrates how the system can eliminate temperature errors in the load sensor output throughout the experiment, ensuring the accuracy and stability of the load data, which can then serve as the basis for subsequent stress-strain analysis.

[0065] In one embodiment of this application, a residual calculation method is provided to evaluate the accuracy of load measurement after temperature drift compensation and to provide feedback for updating the temperature drift model.

[0066] like Figure 4 As shown in Figure S51: During the tensile test, at the current time point, the true stress value is calculated from the compensated net load value and the cross-sectional area of ​​the specimen. The calculation formula is as follows:

[0067]

[0068] In the formula, F(t) is the net load at the current moment, that is, the load value after temperature drift compensation, A is the cross-sectional area of ​​the specimen, which is a known fixed parameter, and σ(t) is the calculated measured stress, which reflects the actual stress level currently applied to the specimen and can be used to compare with the theoretical stress.

[0069] S52: Further, the theoretical stress is the predicted stress at the current strain in the theoretical stress-strain curve, and its formula is as follows:

[0070] σ′(t)=f(ε(t));

[0071] In the formula, ε(t) is the strain value of the current sample, f() is the theoretical model function, and the difference between the measured stress and the theoretical stress is the residual. The residual can be used to infer whether the current temperature drift model is accurate.

[0072] In one example, the theoretical model function is fitted with a linear segment, and its formula is as follows:

[0073] σ′(t)=E·ε(t);

[0074] In the formula, E is the elastic modulus obtained by fitting the measured stress-strain relationship based on the initial elastic stage of the tensile test, which is used to construct the theoretical stress prediction function.

[0075] S53: Calculate the residual at the current time step using the following formula:

[0076] R(t) = σ(t) - σ′(t);

[0077] The residual R(t) represents the degree of deviation between the measured stress and the theoretical stress, and is used to evaluate the accuracy of temperature drift compensation. The smaller the residual, the more accurate the temperature drift model compensation effect. If the residual shows a continuous shift trend within a certain time window, the model update mechanism is triggered to further optimize the compensation coefficient.

[0078] In one example, the net load after compensation at a given moment is F(t) = 30.0 N, and the cross-sectional area of ​​the specimen is A = 10.0 mm². 2 Then the measured stress σ(t) = 30.0 / 1.0 × 10 -5 =3.0MPa. If the strain at that moment is 0.00010 and the elastic modulus E = 28GPa, then the theoretical stress σ′(t) = 28GPa × 0.00010 = 2.8MPa. The residual is obtained as: R(t) = 3.0 - 2.8 = 0.2MPa. This residual is used as one of the bases for updating the temperature drift model and enters the sliding residual window for error trend analysis and compensation model parameter optimization.

[0079] In one example, to obtain the theoretical stress-strain relationship function for residual calculation, the stress and strain data of the specimen are linearly fitted in the early stages of the tensile test to determine the elastic modulus E.

[0080] like Figure 5 As shown, S521: Select the first few sampling points after the start of the tensile test, such as the first 10 to 20 sets of data. These data are located in the elastic deformation range before the material enters the yield stage, which can ensure that there is a linear relationship between stress and strain.

[0081] S522: For each set of data, calculate the measured stress: The strain value ε(t) was measured simultaneously.

[0082] S523: The least squares method is used to perform a linear regression fitting on the selected stress-strain data, with strain as the independent variable and stress as the dependent variable. The fitting equation is: σ=E·ε. The slope in the fitting result is the equivalent elastic modulus E of the specimen under the current test conditions.

[0083] S524: Furthermore, the determination coefficient R of the linear fit can also be set. 2 The sample size should not be lower than a preset threshold, which can be set to 0.98 to ensure that the selected data segment has high linearity. If the requirement is not met, the sampling window should be expanded or outliers should be excluded before refitting.

[0084] In one embodiment of this application, a temperature drift model update method for optimizing temperature drift compensation accuracy is provided. This method achieves adaptive correction of the initial temperature drift model parameters by performing weighted least squares fitting on the residual set during the tensile test.

[0085] Based on the examples above, it has been determined that the temperature drift compensation model is in the form of a second-order polynomial:

[0086] ΔF(T)=a0+a1T+a2T 2 ;

[0087] like Figure 6 As shown in S61: During the actual tensile process, the system calculates the measured stress σ(t) based on the net load F(t) and strain ε(t) at each time point t, and compares it with the theoretical stress σ′(t) to obtain the residual:

[0088] R(t) = σ(t) - σ′(t);

[0089] The residual values ​​calculated for the latest N time points, for example, N=10, and their corresponding temperatures T(t) are combined to form a sliding window residual set: {(T i R i |i = t - N + 1, ..., t};

[0090] S62: To enhance the sensitivity of recent residuals to temperature drift model updates, a weighting factor w is introduced. i For each set of data within the sliding window, perform weighted least squares fitting and update the temperature drift model coefficients:

[0091]

[0092] Among them, R i For the i-th residual, T i This corresponds to the temperature.

[0093] Through the above fitting, new temperature drift error correction coefficients (a′0, a′1, a′2) are obtained, and the updated temperature drift model is constructed:

[0094] ΔF′(T)=a′0+a′1T+a′2T 2 ;

[0095] S63: The updated temperature drift model ΔF′(T) replaces the original model and continues to be used for calculating the temperature drift compensation value in subsequent tensile processes. By limiting the number of residual samples through a sliding window and controlling the accumulation of historical biases, the response rate of the temperature drift model can be improved. At the same time, a weighting strategy is introduced to enhance the influence of recent data, making the temperature drift model adaptable to changes in load sensor status or environmental interference. In addition, least squares fitting ensures update convergence and numerical stability, facilitating efficient calculation in embedded devices.

[0096] In one embodiment of this application, a temperature drift model update mechanism based on a fixed sampling period is provided to periodically optimize the error correction coefficient of the temperature drift compensation model during cryogenic tensile testing, so as to ensure that the sensor compensation accuracy continuously adapts to changes in the environment and equipment status as the testing progresses.

[0097] like Figure 7 As shown, S631: During the initialization phase, a preset sampling period is set, denoted as T. s , indicating that every time T passes s A temperature drift model update operation is performed after each data sampling point. This periodicity value can be set according to the test duration, material type, and system stability; for example, setting T... s =500, meaning an update is performed after every 500 sets of data are collected, set according to the time interval, such as updating every 60 seconds.

[0098] S632: Within each sampling period, the continuously collected data includes the current temperature T(t), the compensated net load F(T), the measured strain ε(t), and the calculated residual R(t). The T(t) and R(t) collected within that period are combined to form a residual sample set.

[0099] S633: After the cycle ends, the system performs weighted least squares fitting on the residual sample set to update the error correction coefficients (a0, a1, a2) in the temperature drift model. The fitting objective function is:

[0100]

[0101] S634: After fitting is complete, the temperature drift compensation model will be automatically replaced with the new model.

[0102] ΔF′(T)=a′0+a′1T+a′2T 2 ;

[0103] The new model will be used for compensation calculations in the next sampling period, thus forming a cyclical mechanism of sampling-residual accumulation-periodic update. Periodic updates can avoid system oscillations caused by high-frequency adjustments, ensuring that the temperature drift model can gradually adapt to environmental changes and sensor drift during the experiment. The weighted least squares algorithm takes into account both historical trends and recent errors, resulting in more stable update results.

[0104] In one embodiment of this application, a sample collection method is provided, which selects at least three temperature points covering the temperature zone to be measured for data sampling before constructing the temperature drift compensation model, so as to enhance the adaptability and fitting accuracy of the temperature drift model throughout the cryogenic test range.

[0105] In this embodiment, the entire test temperature range is divided into the following three typical temperature ranges: low temperature range: 0K–30K, intermediate temperature range: 30K–60K, and high temperature range: 60K–90K. This zoning method covers the areas of drastic sensor error changes, transition areas, and relatively stable areas that are common in cryogenic testing.

[0106] When selecting preset temperature points, at least one representative preset temperature point should be selected for sample collection within each temperature zone. For example, select 10K for the low temperature zone, 45K for the middle temperature zone, and 77K for the high temperature zone.

[0107] It is conceivable that if the accuracy of the temperature drift model needs to be improved, the sampling point density in each temperature zone can be increased, for example, one point can be set every 10K.

[0108] At the preset temperature point selected above, a sample collection operation is performed, including collecting the output value of the load sensor and constructing a data sample group containing temperature and output value. The obtained sample group will be used to fit the temperature drift model. By covering different temperature zones, the model can be guaranteed to have good prediction and compensation capabilities throughout the entire operating temperature range, avoiding model distortion or excessive extrapolation error due to too narrow sample distribution.

[0109] The three temperature ranges covered in this example can adapt to different thermal response characteristics, improve the breadth of the temperature drift model, ensure that the temperature drift model has training support in the low-temperature nonlinear region, the intermediate transition region and the high-temperature stable region, and improve the robustness of the temperature drift model under complex temperature paths.

[0110] In one embodiment of this application, an initial temperature drift model initialization method is provided, which combines experimental sampling data with historical or factory calibration data to construct an initial temperature drift model in a multi-source fusion manner, suitable for occasions with high compensation accuracy requirements or significant changes in the test environment.

[0111] like Figure 8 As shown in S101: The training sample data used for initialization includes two types of sources: local field sampling data and historical / calibration reference data. The first type of source is obtained by applying standard loads to the load sensor at multiple preset temperature points during the test preparation stage and collecting the output values ​​of the load sensor. The second type of source includes the load sensor factory calibration curve, the residual correction model of the previous test, or the statistical model of multiple samples.

[0112] S102: The weighted least squares fitting method is used to fuse the two types of data to obtain the initial temperature drift model after fusion fitting.

[0113] S103: The model quality judgment index can also be set, such as the root mean square error of residuals, etc. Only when the goodness of fit of the fused model is better than that of the single-source fitting model, the fused model is adopted; otherwise, it reverts to the pure measured sample modeling method to ensure the stability of the initial temperature drift model.

[0114] In an embodiment of the present application, there is provided a model self-identification method for stress-strain relationships, which is used to automatically construct a theoretical stress-strain curve applicable to residual analysis.

[0115] In actual tensile tests, the stress-strain behaviors of different materials (such as metals, polymers, ceramics, composite materials) in the initial loading stage are significantly different. Some materials have a longer elastic section, and the stress-strain is strictly linear. Some materials exhibit yield or non-linear responses at an early stage, making it difficult to model with simple linear fitting.

[0116] As Figure 9 shown, S501: In the initial stage of the tensile test, a continuous segment of stress-strain raw data samples is collected, and the number of sampling points is 10 - 30 points to ensure coverage of the elastic stage.

[0117] S502: First, try to use first-order linear regression:

[0118] σ = E·ε + b;

[0119] In the formula, b is the intercept term.

[0120] In an ideal situation, b = 0, and the first-order linear regression expression is: σ = E·ε. However, in actual tests, due to factors such as the instrument not being zero-calibrated, the sensor having a temperature drift, and initial prestress, all stress values may be shifted by a constant as a whole.

[0121] After fitting, calculate the coefficient of determination R 2 , set the coefficient of determination threshold R0 to 0.98. If R 2 0 ≤ R

[0122] S503: If R 2 < R0, it indicates that the data has a significant non-linear deviation, and alternative methods of multi-segment linear fitting or polynomial fitting can be performed. Among them, in the method of multi-segment linear fitting, the initial interval is divided into two or three segments, and different slopes are fitted respectively; in the method of polynomial fitting, a second-order or third-order polynomial model can be used.

[0123] The above identification results will be used as the basis for generating the theoretical stress-strain curve for residual calculation. The identification results are recorded as the model type identifier, and subsequent residual calculations are uniformly performed according to the following formula:

[0124] R(t) = σ(t) - σ′(ε(t));

[0125] In the formula, σ′() is the output of the automatically selected theoretical model. This residual is input into the temperature drift model update module to determine whether to update the correction coefficient.

[0126] This example avoids the residual deviation caused by forcibly using a linear model, which could lead to frequent updates of the compensation model.

[0127] In one embodiment of this application, a dynamic residual threshold triggering method is provided to determine whether a temperature drift model correction operation needs to be performed, so as to avoid frequent updates of compensation parameters by the system due to slight fluctuations or occasional errors.

[0128] This example extracts statistical characteristic parameters (such as mean, standard deviation, variance, maximum value, etc.) from the residual values ​​within the statistical sliding window, and compares them with dynamically calculated threshold values ​​to determine whether to trigger the temperature drift model update operation.

[0129] Record the residual R(t) in each sampling period and maintain a sliding window of length N: R N ={R(t-N+1),...,R(t)}; N is the window length, which can be selected from 10 to 30, depending on the system response cycle.

[0130] Within the current window, the calculated statistical indicators include the mean, standard deviation, and maximum residual magnitude; these are then combined with model fitting error, historical residual stability, and self-learning error to dynamically generate residual thresholds.

[0131] Y = λ·σ R +δ;

[0132] In the formula, λ is an adjustment factor, taking a value of 2-3, representing the tolerance for deviation, and σ R The threshold is the mean of the historical residual standard deviations, which can be initialized to the value used during the training phase. δ is a constant offset used to counteract small amounts of noise. It is conceivable that this threshold can be automatically adjusted as the system runs.

[0133] Furthermore, the temperature drift model update is triggered if any of the following conditions occur within the sliding window:

[0134] When the mean is greater than the threshold, it indicates a significant mean shift.

[0135] When standard deviation > σ R When α is greater than 1.5, it indicates that the residual fluctuation is too large; the value of α is between 1.5 and 2.

[0136] When the maximum residual amplitude > R max When this occurs, it indicates that some residuals are too large, R maxSet the maximum safety deviation (e.g., 5 MPa).

[0137] Once any of the above conditions are met, an update is triggered, and the weighted least squares method is started to refit the temperature drift error correction coefficient. The new model parameters replace the current temperature drift model, and the triggering behavior is recorded in the log for subsequent anomaly tracking and training data accumulation. If no update is triggered, the current temperature drift model continues to be used to keep the parameters stable.

[0138] Using this example, in typical high-sensitivity sensor tests, the model update frequency can be reduced, the temperature difference compensation value is stable, the system response curve is smooth, and there are no frequent jumps.

[0139] In one embodiment of this application, a cryogenic tensile test temperature drift compensation system is provided, which is suitable for material mechanics testing scenarios in low-temperature environments such as 4K to 77K, and is used to improve the stability and accuracy of load measurement by load sensors.

[0140] like Figure 10 As shown, the sample acquisition module 01 is used to apply standard loads and acquire signals to the load sensor at multiple preset temperature points before the experiment. At least two standard loads can be applied at each preset temperature point, and the output signal of the load sensor can be recorded simultaneously to form a temperature-load-signal sample set. The acquired data is used to establish a training dataset for the temperature drift model.

[0141] Initial modeling module 02 is used to establish an initial temperature drift model based on the sample group. It is based on the load sensor output and temperature data, and completes the modeling through a second-order function polynomial fitting, resulting in the following initial temperature drift model:

[0142] ΔF(T)=a0+a1T+a2T 2 ;

[0143] The initial temperature drift model outputs the error compensation value corresponding to the temperature.

[0144] During the tensile test, the compensation calculation module 03 collects the current temperature value T(t) and the load sensor output value S(t) in real time, inputs both into the initial temperature drift model, calculates the temperature drift compensation value ΔF(T), and calculates the net load according to the following formula:

[0145] F(t) = S(t) - ΔF(T);

[0146] The net load obtained is the actual load value after eliminating temperature drift error.

[0147] The residual analysis module 04 performs stress transformation on the net load and compares it with the theoretical model. The measured stress σ(t) is obtained from the net load and the cross-sectional area A. Based on the real-time strain ε(t), the theoretical stress value σ′(t) is constructed, and the residual is calculated.

[0148] R(t) = σ(t) - σ′(t);

[0149] The residuals at multiple preset time points are combined into a residual group, which is used to provide feedback on the modeling quality.

[0150] The model update module 05 is activated periodically or according to triggering conditions, such as when the cumulative residuals deviate from the threshold. It uses the residual group within the sliding window to refit the error correction coefficients using the weighted least squares method, updates the temperature drift model parameters a0, a1, and a2, and generates a new temperature drift model.

[0151] The dynamic compensation module 06 replaces the old model with the updated temperature drift model. In subsequent tensile tests, the final temperature drift model is continuously used to compensate for temperature and output in real time, forming a closed-loop adaptive system to ensure the accuracy of measurement data.

[0152] In one embodiment of this application, a graphical interface and a manual calibration module are provided to enhance the system's visual operation capabilities and human intervention capabilities, making it suitable for low-temperature load measurement scenarios.

[0153] The graphical interface and manual calibration module allow operators to view the load compensation status in real time, adjust model parameters, and manually intervene in system operation when necessary. The graphical interface and manual calibration module includes a real-time data monitoring interface, a model parameter display and adjustment panel, a residual trend graph and error threshold warning, a manual correction input and overwrite mechanism, and log export and experimental recording functions.

[0154] Specifically, the real-time data monitoring interface displays the following information: current sampling time, real-time temperature, original output value of the load sensor, temperature drift compensation value, compensated net load, measured stress and theoretical stress, and residual. The display can be a combination of tables and line graphs to allow users to intuitively identify abnormal fluctuations.

[0155] The model parameter display and adjustment panel shows the current fitting parameters of the temperature drift model in read-only or edit mode. In the default mode, it displays a0, a1, and a2 that are automatically fitted by the system. In debug mode, users can switch to manual mode and modify the parameter values ​​through the input boxes. After the user confirms, the system replaces the current temperature drift model and records the operation.

[0156] The residual trend chart shows the distribution of temperature drift model residuals over a specific time period. If the residuals exceed the preset threshold, the system will display a warning message and suggest whether the user should allow the system to update the model or intervene manually.

[0157] The manual correction input and overwrite mechanism can prevent the automatic model from being unsuitable for certain extreme test conditions. For example, if the user identifies a constant deviation in the system, they can directly input the correction amount for rapid offset calibration. All operations are recorded in the background log with details of the operator, time, and parameter changes.

[0158] The system provides a log export function, including: parameter change records for each temperature drift model update, thresholds and response behaviors for each residual abnormality trigger, each manual intervention or correction operation, and experimental data snapshots (temperature, load, residuals, etc.).

[0159] Several embodiments of the present invention have been described, but these embodiments are provided as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in a wide variety of other ways, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the scope of the invention and its equivalents as described in the claims.

Claims

1. A method for compensating for temperature drift in a deep cryogenic tensile test, characterized by, The method comprises: applying a standard load at a preset temperature point, collecting the output value of the load sensor to obtain a sample group; establishing an initial temperature drift model according to the sample group; collecting the output value of the temperature and the load sensor in the tensile test, and inputting the temperature and the output value into the initial temperature drift model to calculate a temperature drift compensation value; calculating a compensated net load according to the output value and the temperature drift compensation value; According to the net load and a theoretical stress-strain curve, a residual error is calculated to obtain a residual error group; wherein, a calculation formula of the residual error is: , wherein, is a current measured stress, determined by a ratio of the compensated net load and a cross-sectional area of the sample, is a theoretical stress obtained by strain mapping, the theoretical stress-strain curve is obtained by regression fitting according to linear segment data in an initial stage of the tensile test, and the theoretical stress is a predicted stress at a current strain in the theoretical stress-strain curve. updating the initial temperature drift model according to the residual group to obtain a final temperature drift model, which compensates according to the real-time temperature and the output value.

2. The method of claim 1, wherein, The sample group comprises zero load output values collected at a plurality of different preset temperature points and standard load output values of at least two different amplitudes; wherein: The zero load output value is used to establish a temperature drift offset function; The standard load output value is used to establish a sensitivity change function.

3. The method of claim 1, wherein, The calculation formula of the net load is: ； In the formula, is the output value of the load sensor, is the temperature drift compensation value.

4. The method of claim 1, wherein, The preset temperature points cover at least three to-be-measured temperature zones, which are a low temperature section, an intermediate temperature, and a high temperature section, respectively.

5. A cryogenic low temperature tensile test temperature drift compensation system implementing the method of any one of claims 1 to 4, characterized by, The system comprises: a sample collection module configured to apply a standard load at a preset temperature point and collect the output value of the load sensor to obtain a sample group; an initial modeling module configured to establish an initial temperature drift model according to the sample group; a compensation calculation module configured to input the temperature and the sensor output value collected in the tensile test into the initial temperature drift model to calculate a temperature drift compensation value, and calculate a compensated net load according to the output value and the temperature drift compensation value; a residual analysis module configured to calculate a residual according to the net load and a theoretical stress-strain curve to obtain a residual group; a model updating module configured to update the initial temperature drift model according to the residual group to obtain a final temperature drift model; and a dynamic compensation module configured to compensate the sensor output value based on the final temperature drift model and the real-time collected temperature value.

Images