Metal stretching control system and method

Abstract

The present invention belongs to the technical field of metal tensile testing, and specifically relates to a metal tensile control system and method, comprising a dual-end synchronous tensile module for applying an axial tensile load to a specimen; a multi-frequency vibration excitation module integrating a mechanical vibration unit and an ultrasonic vibration unit, superimposing vibration energy through swept-frequency excitation technology and mechanically coupling with the central region of the specimen; a composite environment simulation module comprising a noise generator and a temperature adjustment unit for simulating mechanical noise and temperature control compensation; a multi-source data fusion module, which uses a laser vibrometer and an infrared camera to synchronously collect stress-strain curves, morphological changes, and thermal field distribution, and eliminates signal leakage interference through wavelet transform; and an intelligent control terminal. The present invention can further improve data accuracy, ensure data reliability for subsequent resonance frequency analysis and temperature compensation, and actively control material properties.

Description

Technical Field

[0001] The present invention belongs to the technical field of metal tensile testing, and in particular relates to a metal tensile control system and method. Background Art

[0002] As an indispensable cornerstone of modern industrial development, the accurate assessment of the mechanical properties of metal materials is crucial for product design, quality control, and the research and development of new materials. Tensile testing is the most basic and commonly used test method for characterizing the mechanical properties of metal materials. However, during the actual tensile process, especially under high strain rates, complex loading paths, or specific environmental conditions, the metal specimen itself and the test system often produce associated vibrations. These associated vibrations may cause deviations in stress-strain measurement data, induce local stress concentration, and even affect the microstructural evolution of the material, thereby adversely affecting the reliability and accuracy of the test results and the true reflection of material properties. Therefore, how to effectively suppress or precisely control the associated vibrations during metal tensile testing to ensure the authenticity of test data and the stability of material properties has become one of the important challenges facing the current field of materials science and engineering.

[0003] Problems with existing technologies:

[0004] Currently, in the field of tensile testing of metal materials, traditional tensile testing machines usually focus on mechanical property testing under static or quasi-static loading conditions. There is a lack of systematic control and suppression measures for the occurrence and propagation of associated vibrations during the tensile process and their impact on test results. Some existing technologies use simple vibration isolation measures or single-frequency vibration excitation devices to reduce the interference of the external environment on the test or simulate specific vibration conditions. However, these methods are often difficult to adapt to the dynamic changes in vibration characteristics during the tensile process, and are unable to effectively and finely regulate multi-band and multi-mode associated vibrations. In addition, when faced with high-frequency vibration interference, existing data acquisition and processing systems may have problems such as incomplete signal denoising, data distortion, or insufficient multi-source data fusion, resulting in limited accuracy of key data such as stress-strain curves. Summary of the Invention

[0005] The purpose of the present invention is to provide a metal stretching control system and method, which can further improve data accuracy, ensure the data reliability of subsequent resonance frequency analysis and temperature compensation, and actively regulate material properties.

[0006] The technical solutions adopted by the present invention are as follows:

[0007] A metal stretching control method comprises the following steps:

[0008] S1. Apply axial tensile load to the specimen in a vibration-free environment and record the original stress-strain curve as a reference.

[0009] S2. Start the multi-frequency vibration module and the noise simulation module, superimpose vibration energy through the frequency sweep excitation technology, generate mechanical noise at the same time, and synchronously collect the dynamic response data of the sample;

[0010] S3, based on the multi-source data collected in step S2, using wavelet transform to separate the vibration noise components, and combining with cubic spline interpolation algorithm to reconstruct the real material response curve;

[0011] S4, performing frequency domain superposition analysis on the real material response curve data reconstructed in step S3, locating the resonant frequency range of the vibration signal and the noise signal, and optimizing the frequency combination strategy in the vibration parameter database;

[0012] S5. Based on the infrared temperature measurement data, the local temperature rise caused by the vibration energy conversion in step S2 is corrected, and a temperature-vibration coupling compensation model is established to eliminate thermal effect interference;

[0013] S6. Input the reconstructed data of step S3, the resonance analysis results of step S4, and the temperature compensation model of step S5 into the AI ​​analysis model, dynamically adjust the stretching rate and vibration excitation parameters, and realize material performance regulation.

[0014] In step S3, a dynamic interpolation algorithm is used to smooth the stress-strain curve fluctuations caused by vibration interference, including:

[0015] The vibration noise components are separated by wavelet transform and the real material response curve is reconstructed based on cubic spline interpolation.

[0016] In step S4, the resonant frequency range is positioned based on the distribution characteristics of vibration energy in the mechanical vibration and ultrasonic vibration frequency bands, and the vibration parameter combination is optimized in combination with the change trend of the yield strength of the sample material.

[0017] In step S6, the AI ​​analysis model uses a neural network algorithm to generate a dynamically adjusted stretching rate and vibration excitation parameter control model based on vibration, noise and temperature training in multi-source data.

[0018] A metal stretching control system, comprising:

[0019] Double-end synchronous tensile module, used to apply axial tensile load to the specimen;

[0020] The multi-frequency vibration excitation module integrates a mechanical vibration unit and an ultrasonic vibration unit, superimposes vibration energy through swept frequency excitation technology, and mechanically couples with the central area of ​​the specimen;

[0021] Composite environment simulation module, including noise generator and temperature control unit, used to simulate mechanical noise and temperature control compensation;

[0022] Multi-source data fusion module, which uses a laser vibrometer and an infrared camera to synchronously collect stress-strain curves, morphological changes, and thermal field distribution, and eliminates signal leakage interference through wavelet transform;

[0023] The intelligent control terminal is equipped with a vibration parameter database and AI analysis model, and dynamically optimizes the associated vibration control strategy based on comparative experimental results.

[0024] The mechanical vibration unit adopts an electromagnetic exciter or an eccentric wheel mechanism, and the vibration frequency range is 5-200Hz;

[0025] The ultrasonic vibration unit includes a piezoelectric ceramic transducer with an operating frequency of 20-100 kHz.

[0026] The multi-source data fusion module integrates a high-speed strain gauge and a laser vibrometer to synchronously capture dynamic deformation and vibration response data during the stretching process.

[0027] The intelligent control terminal includes:

[0028] The control group setting module is used to establish a non-vibration stretching control group and experimental groups with different vibration parameters;

[0029] The curve correction module cleans and reconstructs the abnormal stress-strain curve based on wavelet transform and cubic spline interpolation algorithm.

[0030] The surface of the sample is coated with a conductive coating, and temperature field compensation is achieved through current self-heating, thereby eliminating the local temperature rise effect caused by vibration.

[0031] A computer-readable storage medium stores a computer program, wherein the computer program implements any of the aforementioned methods when executed by a processor.

[0032] and a computer program product for running the computer program, wherein the computer program implements any of the aforementioned methods when executed by a processor.

[0033] The technical effects achieved by the present invention are:

[0034] The present invention simulates composite interference through the synergistic effect of the multi-frequency vibration excitation module and the noise simulation module through the swept frequency excitation technology; the multi-source data fusion module uses wavelet transform and cubic spline interpolation to reconstruct the real material response curve; the AI ​​analysis model dynamically optimizes the stretching rate and vibration parameters based on multi-source data, and combines the temperature-vibration coupling compensation model to eliminate thermal effects, thereby achieving high precision and stability in material performance testing.

[0035] The present invention is based on a benchmark test of a control group. In the control group, external vibration interference is eliminated, and the intrinsic mechanical properties of the sample under ideal conditions are obtained as a reference standard for subsequent composite excitation experiments. Through the synchronous superposition of multi-frequency vibration and mechanical noise, it is used to comprehensively evaluate the dynamic response characteristics of the sample under complex loads. Vibration noise components are extracted through multi-scale decomposition, background noise interference is suppressed, the signal-to-noise ratio is improved and the mean square error is reduced. The denoised data points are smoothed and fitted through a cubic spline interpolation algorithm, local fluctuations caused by high-frequency vibration are eliminated, and a continuous and physically clear stress-strain curve is reconstructed, which can further improve data accuracy and ensure the data reliability of subsequent resonance frequency analysis and temperature compensation.

[0036] The present invention uses a neural network algorithm to train multi-source data sets (vibration frequency, noise intensity, temperature compensation value), establishes an "input parameter-output performance" mapping model, outputs the optimal control parameter combination, dynamically adjusts the stretching rate and vibration excitation intensity, and realizes active regulation of material properties.

[0037] The present invention eliminates material softening or performance abnormalities caused by local temperature rise through infrared temperature measurement and a thermal-vibration coupling model to ensure the authenticity of the test data. Based on the temperature field data collected by the infrared camera, the local temperature rise caused by the vibration energy conversion in step S2 is corrected, and a temperature-vibration coupling compensation model is established to eliminate thermal effect interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the method of the present invention;

[0039] Figure 2 It is a structural diagram of the system in the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose and advantages of the present invention more clearly understood, the present invention is described in detail below with reference to the following examples. It should be understood that the following text is only used to describe one or more specific embodiments of the present invention and does not strictly limit the scope of protection of the present invention.

[0041] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] According to an embodiment of the present invention, a method embodiment of a metal stretching control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] like Figure 1 As shown, a metal stretching control method includes the following steps:

[0044] S1. Apply axial tensile load to the specimen in a vibration-free environment and record the original stress-strain curve as a reference.

[0045] S2. Start the multi-frequency vibration module and the noise simulation module, superimpose vibration energy through the frequency sweep excitation technology, generate mechanical noise at the same time, and synchronously collect the dynamic response data of the sample;

[0046] S3, based on the multi-source data collected in step S2, using wavelet transform to separate the vibration noise components, and combining with cubic spline interpolation algorithm to reconstruct the real material response curve;

[0047] S4, performing frequency domain superposition analysis on the real material response curve data reconstructed in step S3, locating the resonant frequency range of the vibration signal and the noise signal, and optimizing the frequency combination strategy in the vibration parameter database;

[0048] S5. Based on the infrared temperature measurement data, the local temperature rise caused by the vibration energy conversion in step S2 is corrected, and a temperature-vibration coupling compensation model is established to eliminate thermal effect interference;

[0049] S6. Input the reconstructed data of step S3, the resonance analysis results of step S4, and the temperature compensation model of step S5 into the AI ​​analysis model, dynamically adjust the stretching rate and vibration excitation parameters, and realize material performance regulation.

[0050] According to the above steps, in step S1, the benchmark test environment is in a vibration-free environment for sample stretching. The operating basis is that in an experimental environment without vibration interference, an axial tensile load is applied to the sample based on the GB / T228.1-2010 standard. The axial direction referred to here includes but is not limited to the x-axis, y-axis, z-axis or an axis having an angle with the above axes. Stress-strain data are collected synchronously by a high-speed strain gauge and a laser vibrometer, the raw data is recorded, and a corresponding curve is generated based on the data.

[0051] Furthermore, the stress-strain curve is used to calculate the stress value using the following formula:

[0052] ;

[0053] in, is the elastic modulus, is the strain measurement value, and They are the cross-sectional areas of the non-standard moment section and the gauge length section respectively. The data based on the above-mentioned benchmark measurement test are the benchmark measurement values, which provide a basis for subsequent sample experiments as a control group, and are used to achieve the repeatability of the test data. Based on the benchmark test of the control group, in the control group, the external vibration interference is eliminated, and the intrinsic mechanical properties of the sample under ideal conditions are obtained, which serve as the reference standard for subsequent composite excitation experiments.

[0054] In step S2, the dynamic response characteristics of the sample under complex loads are comprehensively evaluated by synchronous superposition of multi-frequency vibration and mechanical noise.

[0055] Furthermore, the multi-frequency vibration module and the noise simulation module are started to generate dynamic vibration signals through the frequency sweep excitation technology, and the dynamic response data of the sample are collected at the same time.

[0056] Among them, the multi-frequency vibration module includes a mechanical vibration unit, whose vibration frequency range is 5-200Hz, and an ultrasonic vibration unit, whose vibration frequency range is 20-100kHz, and the sound pressure level of the noise simulation module is greater than or equal to 85dB.

[0057] Furthermore, the swept-frequency excitation technology uses an improved method of generating fast narrowband swept-frequency signals, covering the 5-200Hz and 20-100kHz frequency bands, to simulate multi-band vibration interference in actual working conditions. The mechanical noise simulation uses an electromagnetic exciter or airborne sound source to simulate mechanical noise greater than or equal to 85dB, reproducing the acoustic interference in the actual operating environment.

[0058] In step S3, the multi-source data includes stress-strain curves, vibration responses, and temperature fields (heat generated by vibration, collected using an infrared camera). Signal processing is performed on the multi-source data, and the vibration noise component is separated using wavelet transform. The true material response curve is reconstructed in combination with the cubic spline interpolation algorithm.

[0059] Furthermore, the wavelet transform extracts vibration noise components through multi-scale decomposition, suppresses background noise interference, improves the signal-to-noise ratio and reduces the mean square error. The denoised data points are smoothed and fitted through the cubic spline interpolation algorithm to eliminate local fluctuations caused by high-frequency vibration and reconstruct a continuous and physically clear stress-strain curve. Through this step, the data accuracy can be further improved, ensuring the data reliability of subsequent resonance frequency analysis and temperature compensation.

[0060] In step S4, frequency domain superposition analysis and parameter optimization are performed to accurately locate the resonant frequency of the specimen, avoid local stress concentration or structural failure caused by resonance, and improve test safety.

[0061] Furthermore, the stress-strain curve reconstructed in step S3 is Fourier transformed to be converted into a frequency domain signal, the energy distribution of the vibration excitation and the noise signal is superimposed, the resonant frequency range is located, and the frequency combination strategy in the vibration parameter database is optimized.

[0062] Furthermore, frequency domain superposition analysis uses energy distribution diagrams to identify the resonance peaks of vibration energy in the 5-200Hz mechanical vibration and 20-100kHz ultrasonic vibration frequency bands. The location of the resonance frequency range is based on the distribution characteristics of vibration energy in the mechanical vibration and ultrasonic vibration frequency bands. The vibration parameter combination is optimized in combination with the change trend of the material yield strength. The parameter database update feeds back the resonance frequency range to the vibration parameter database to dynamically adjust the excitation frequency range of subsequent experiments.

[0063] Furthermore, the fast narrowband sweep excitation technology is used to optimize the vibration parameter combination, dynamically scanning the sample response in the 5-200Hz and 20-100kHz frequency bands, converting the time domain signal into a frequency domain energy distribution diagram through Fourier transform, accurately locating the resonant frequency range of the material, and combining the change trend of the material's yield strength to screen out sensitive frequency bands that are prone to cause local stress concentration, providing a basis for subsequent parameter optimization.

[0064] The reconstructed stress-strain curve is subjected to frequency domain superposition analysis to extract the energy coupling characteristics of the vibration signal and the noise signal. Based on the resonant frequency range, the frequency combination strategy in the vibration parameter database is dynamically adjusted.

[0065] By training multi-source data sets (vibration frequency, noise intensity, temperature compensation value) through neural network algorithms, an "input parameter-output performance" mapping model is established, the optimal control parameter combination is output, the stretching rate and vibration excitation intensity are dynamically adjusted, and active regulation of material properties is achieved. For example, when a resonance peak shift is detected, the AI ​​model automatically reduces the excitation power of the current frequency band and switches to an adjacent safe frequency band.

[0066] The local temperature rise caused by vibration energy conversion is corrected based on infrared temperature measurement data, and the conductive coating self-heating technology is used to balance the thermal effect. This compensation mechanism avoids material softening or resonant frequency drift caused by temperature rise, ensuring the stability of parameter optimization results.

[0067] In step S5, infrared temperature measurement and thermal-vibration coupling model are used to eliminate material softening or performance abnormalities caused by local temperature rise to ensure the authenticity of the test data. Based on the temperature field data collected by the infrared camera, the local temperature rise caused by vibration energy conversion in step S2 is corrected, and a temperature-vibration coupling compensation model is established to eliminate thermal effect interference.

[0068] Furthermore, infrared temperature measurement uses a non-contact infrared camera to monitor the surface temperature distribution of the sample and capture the local thermal field of vibration energy conversion. The thermal-vibration coupling model uses the self-heating technology of the conductive coating to balance the temperature rise effect. The coating heating methods include but are not limited to heating by heating coils, circular irradiation of the sample by a heating device, and direct contact between the heating device and the coating on the sample surface.

[0069] In step S6, the reconstructed data in step S3, the resonance analysis results in step S4, and the temperature compensation model in step S5 are input into the AI ​​analysis model to dynamically adjust the stretching rate and vibration excitation parameters to achieve regulation of the sample performance. The AI ​​analysis model preferably uses a neural network, and the neural network algorithm is based on a multi-layer perceptron training data set to output the optimal control parameter combination. The data set includes vibration frequency, noise intensity, and temperature compensation value. Through a closed-loop feedback control mechanism, the stretching rate and vibration frequency are adjusted to adapt to the dynamic response characteristics of the sample.

[0070] According to this step, by dynamically adjusting the stretching rate and vibration parameters, intelligent dynamic control can be achieved, and the test efficiency and data consistency can be improved. It is suitable for the universal testing needs of different metal materials. In particular, by coating the surface of the sample with a conductive coating, the test application range can be applied to the testing of non-metallic materials, wherein the conductive coating has thermal conductivity.

[0071] like Figure 2 As shown, a metal stretching control system includes:

[0072] Double-end synchronous tensile module, used to apply axial tensile load to the specimen;

[0073] The multi-frequency vibration excitation module integrates a mechanical vibration unit and an ultrasonic vibration unit, superimposes vibration energy through swept frequency excitation technology, and mechanically couples with the central area of ​​the specimen;

[0074] Composite environment simulation module, including noise generator and temperature control unit, used to simulate mechanical noise and temperature control compensation;

[0075] Multi-source data fusion module, which uses a laser vibrometer and an infrared camera to synchronously collect stress-strain curves, morphological changes, and thermal field distribution, and eliminates signal leakage interference through wavelet transform;

[0076] The intelligent control terminal is equipped with a vibration parameter database and AI analysis model, and dynamically optimizes the associated vibration control strategy based on comparative experimental results.

[0077] As an optional embodiment, according to the above-mentioned system module, the double-end synchronous stretching module is a stretching sample machine, and the stretching methods used at both ends include but are not limited to hydraulic stretching, mechanical stretching, etc., and the clamping mechanisms set at both ends are used to clamp the two ends of the sample.

[0078] As an optional embodiment, the mechanical vibration unit adopts an electromagnetic exciter or an eccentric wheel mechanism with a vibration frequency range of 5-200Hz, and the ultrasonic vibration unit includes a piezoelectric ceramic transducer with an operating frequency of 20-100kHz. The piezoelectric ceramic transducer uses but is not limited to piezoelectric ceramics such as model PZT3904.

[0079] Furthermore, the mechanical coupling methods with the central area of ​​the sample include but are not limited to clamping, semi-enclosed abutment, adhesive connection, elastic clamping, etc.

[0080] As an optional embodiment, the noise generator in the composite environment simulation module is used to generate a noise signal, and the temperature adjustment unit is used to achieve temperature compensation during the sample experiment.

[0081] As an optional embodiment, the multi-source data fusion module integrates a high-speed strain gauge and a laser vibrometer to synchronously capture dynamic deformation and vibration response data during the stretching process, wherein the sampling frequency of the high-speed strain gauge is greater than or equal to 10 kHz.

[0082] As an optional embodiment, the intelligent control terminal includes:

[0083] The control group setting module is used to establish a non-vibration stretching control group and experimental groups with different vibration parameters;

[0084] The curve correction module cleans and reconstructs the abnormal stress-strain curve based on wavelet transform and cubic spline interpolation algorithm.

[0085] As an optional embodiment, a conductive coating is coated on the surface of the sample to achieve temperature field compensation through current self-heating, thereby eliminating the local temperature rise effect caused by vibration.

[0086] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0087] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any one of the aforementioned methods is implemented.

[0088] And a computer program product for running a computer program, which implements any of the above methods when executed by a processor.

[0089] The foregoing is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained herein shall, unless otherwise specified or limited, be implemented in accordance with conventional means in the art.

Claims

1. A metal stretching control method, characterized in that: The steps include: S1. Apply axial tensile load to the specimen in a vibration-free environment and record the original stress-strain curve as a reference. S2. Start the multi-frequency vibration module and the noise simulation module, superimpose vibration energy through the frequency sweep excitation technology, generate mechanical noise at the same time, and synchronously collect the dynamic response data of the sample; S3, based on the multi-source data collected in step S2, using wavelet transform to separate the vibration noise components, and combining with cubic spline interpolation algorithm to reconstruct the real material response curve; S4, performing frequency domain superposition analysis on the real material response curve data reconstructed in step S3, locating the resonant frequency range of the vibration signal and the noise signal, and optimizing the frequency combination strategy in the vibration parameter database; S5. Based on the infrared temperature measurement data, the local temperature rise caused by the vibration energy conversion in step S2 is corrected, and a temperature-vibration coupling compensation model is established to eliminate thermal effect interference; S6. Input the reconstructed data of step S3, the resonance analysis results of step S4, and the temperature compensation model of step S5 into the AI ​​analysis model, dynamically adjust the stretching rate and vibration excitation parameters, and realize material performance regulation.

2. A metal stretching control method according to claim 1, characterized in that: In step S3, a dynamic interpolation algorithm is used to smooth the stress-strain curve fluctuations caused by vibration interference, including: The vibration noise components are separated by wavelet transform and the real material response curve is reconstructed based on cubic spline interpolation.

3. The metal stretching control method according to claim 1, characterized in that: In step S4, the resonant frequency range is positioned based on the distribution characteristics of vibration energy in the mechanical vibration and ultrasonic vibration frequency bands, and the vibration parameter combination is optimized in combination with the change trend of the yield strength of the sample material.

4. The metal stretching control method according to claim 1, characterized in that: In step S6, the AI ​​analysis model uses a neural network algorithm to generate a dynamically adjusted stretching rate and vibration excitation parameter control model based on vibration, noise and temperature training in multi-source data.

5. A metal stretching control system, operating the method according to any one of claims 1 to 4, characterized in that: include: Double-end synchronous tensile module, used to apply axial tensile load to the specimen; The multi-frequency vibration excitation module integrates a mechanical vibration unit and an ultrasonic vibration unit, superimposes vibration energy through swept frequency excitation technology, and mechanically couples with the central area of ​​the specimen; Composite environment simulation module, including noise generator and temperature control unit, used to simulate mechanical noise and temperature control compensation; Multi-source data fusion module, which uses a laser vibrometer and an infrared camera to synchronously collect stress-strain curves, morphological changes, and thermal field distribution, and eliminates signal leakage interference through wavelet transform; The intelligent control terminal is equipped with a vibration parameter database and AI analysis model, and dynamically optimizes the associated vibration control strategy based on comparative experimental results.

6. The metal stretching control system according to claim 5, characterized in that: The mechanical vibration unit adopts an electromagnetic exciter or an eccentric wheel mechanism, and the vibration frequency range is 5-200Hz; The ultrasonic vibration unit includes a piezoelectric ceramic transducer with an operating frequency of 20-100 kHz.

7. The metal stretching control system according to claim 5, characterized in that: The multi-source data fusion module integrates a high-speed strain gauge and a laser vibrometer to synchronously capture dynamic deformation and vibration response data during the stretching process.

8. The metal stretching control system according to claim 5, characterized in that: The intelligent control terminal includes: The control group setting module is used to establish a non-vibration stretching control group and experimental groups with different vibration parameters; The curve correction module cleans and reconstructs the abnormal stress-strain curve based on wavelet transform and cubic spline interpolation algorithm.

9. The metal stretching control system according to claim 5, characterized in that: The surface of the sample is coated with a conductive coating, and temperature field compensation is achieved through current self-heating, thereby eliminating the local temperature rise effect caused by vibration.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented; and a computer program product for running the computer program, wherein the computer program implements the method according to any one of claims 1 to 4 when executed by a processor.

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