Dynamic compression shear testing machine adaptive control method and system based on machine learning

Abstract

The invention relates to the technical field of compression-shear testing machines, in particular to a dynamic compression-shear testing machine adaptive control method and system based on machine learning, and the method comprises the steps: building a material parameter prediction model, and predicting the detection parameters and mechanical properties of a material; according to the real-time response data of the material in the test process, correcting the control parameters of the pressure shearing machine through a self-adaptive control algorithm; the method comprises the following steps: acquiring a material surface image and an internal ultrasonic signal, extracting failure characteristic parameters such as a crack area proportion, a fracture number and a maximum fracture length, and carrying out weighted fusion to construct a material critical failure criterion; when the material failure characteristic parameter reaches a preset threshold value, safety protection is triggered, the test is automatically stopped, and related data are recorded; and performing incremental learning and continuous optimization on the material parameter prediction model by using the data recorded under the failure condition. By means of the method, intelligent self-adaptive control over the dynamic compression shear test process is achieved, and the test efficiency, precision and safety are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of a compression-shear testing machine, in particular to a dynamic compression-shear testing machine adaptive control method and system based on machine learning. BACKGROUND

[0002] Dynamic compression-shear testing is an important means for evaluating the mechanical properties of materials under high strain rate and large deformation conditions. Traditional dynamic compression-shear testing machines apply a load to the material by presetting control parameters, obtain the mechanical response of the material, and then calculate the mechanical performance indicators of the material according to the detection data. However, the optimal detection parameters for different materials differ, and using uniform control parameters cannot take into account various materials, resulting in reduced accuracy and reliability of the test results. In addition, the mechanical behavior of materials during dynamic compression-shear is complex and variable, and if the control parameters cannot be adjusted in a timely manner, the real-time response of the material cannot be adapted, which may cause premature failure or damage of the material, affecting the safety and effectiveness of the test.

[0003] In dynamic compression-shear testing, determining whether the material has reached the failure limit state is a key issue. Traditional methods mainly rely on the mutation point on the load-displacement curve or the macroscopic cracks on the material surface to determine material failure, but these indicators often lag behind the generation and evolution of internal damage in the material, making it difficult to accurately capture the starting time of material failure. The initiation and propagation of internal defects in the material are the fundamental cause of its ultimate failure, but since the internal damage process of the material is difficult to directly observe, it is impossible to monitor the integrity of the material in real time, and the early warning capability for material failure is insufficient.

[0004] Existing dynamic compression-shear testing machines lack effective safety protection mechanisms and cannot stop loading and record data in a timely manner when the material reaches the critical failure state. In addition, due to the lack of test data under the material failure state, it is difficult to conduct in-depth research on the failure mechanism of the material and summarize the failure rules, making it difficult to further improve the precision and reliability of the material mechanical property evaluation.

[0005] In view of this, the application proposes a dynamic compression-shear testing machine adaptive control method based on machine learning. SUMMARY

[0006] To achieve the above-mentioned purpose, the application provides a dynamic compression-shear testing machine adaptive control method and system based on machine learning, which comprises:

[0007] Obtain material data to be detected, and construct a material parameter prediction model based on machine learning technology;

[0008] Based on the real-time response data of the material during the dynamic compression-shear testing process, the control parameters of the dynamic compression-shear testing machine are corrected through a control algorithm to control the deformation and failure process of the material;

[0009] Collecting material surface image and internal ultrasonic signal, extracting crack area ratio, number of broken pieces and maximum fracture length, and constructing material critical failure criterion by weighted fusion, monitoring and warning material failure risk;

[0010] Setting safety protection mechanism of dynamic compression-shear testing machine, triggering safety protection mechanism when material failure characteristic parameter reaches preset threshold, automatically stopping test and recording material detection parameter and mechanical property;

[0011] According to material detection parameter and mechanical property data recorded under failure condition, incrementally learning and continuously optimizing material parameter prediction model.

[0012] Preferably, collecting detected material and standard material data, including material name, material type, geometric size parameter, detection parameter and mechanical property index, the detection parameter including loading rate, compression-shear angle and maximum load; the mechanical property index including shear strength, fracture toughness and impact toughness;

[0013] Based on collected detected material and standard material data, adopting support vector regression SVR algorithm to construct material parameter prediction model;

[0014] Outputting best detection parameter and expected mechanical property of material to be detected through material parameter prediction model.

[0015] Preferably, collecting real-time response data of material, including load, displacement and strain;

[0016] Correcting control parameter of dynamic compression-shear testing machine through control algorithm, including loading rate, compression-shear angle and maximum load; adopting proportional-integral-derivative PID control algorithm to dynamically adjust control parameter.

[0017] Preferably, constructing adaptive control strategy to online adjust parameter of PID controller, introducing adaptive law, dynamically adjusting parameter of PID controller according to deviation of material response and expected response and change rate of deviation.

[0018] Preferably, collecting material surface image sequence and internal ultrasonic signal;

[0019] Preprocessing material surface image sequence to extract crack area, calculating crack area ratio;

[0020] Preprocessing and imaging processing internal ultrasonic signal, extracting number of broken pieces and maximum fracture length;

[0021] Obtaining comprehensive criterion of material critical failure by weighted fusion of crack area ratio, number of broken pieces and maximum fracture length.

[0022] Preferably, the crack area ratio threshold, the number of broken pieces threshold, and the maximum broken length threshold, and the comprehensive criterion threshold are set;

[0023] When any one of the crack area ratio, the number of broken pieces, and the maximum broken length exceeds the corresponding crack area ratio threshold, the number of broken pieces threshold, and the maximum broken length threshold, it is determined whether the material outside and inside enters a critical failure state;

[0024] When the comprehensive criterion exceeds the comprehensive criterion threshold, it is determined that the material as a whole reaches a critical failure state, the detection data is recorded, and it is decided whether to continue detection.

[0025] Preferably, after the material as a whole reaches a critical failure state, the material surface image and internal ultrasonic signal data are continuously collected, and the growth rate of the crack area ratio, the number of broken pieces, and the maximum broken length in unit time is calculated;

[0026] The limits of the growth rates of the crack area ratio, the number of broken pieces, and the maximum broken length are set; the growth rates of the material failure characteristic parameters are continuously monitored, and when the growth rate of any one of the failure characteristic parameters continuously exceeds the corresponding limit value for multiple times, a safety protection mechanism is triggered, the dynamic compression-shear testing machine is stopped, and the detection data is automatically recorded.

[0027] Preferably, the detection data recorded automatically when the safety protection mechanism is triggered and the test is stopped includes: real-time response data of the material, including load, displacement, and strain; mechanical property indexes, including the shear strength, fracture toughness, and impact toughness corresponding to the stopping moment; and failure characteristic parameters, including the crack area ratio, the number of broken pieces, and the maximum broken length at the stopping moment.

[0028] Preferably, the material detection parameters and mechanical property data recorded when the safety protection mechanism is triggered during the dynamic compression-shear test are collected, a material failure data set is constructed, and the material failure data set is combined with the original training data set;

[0029] The material parameter prediction model is incrementally trained for the combined data set;

[0030] The material parameter prediction model after incremental learning is saved and deployed, and new material failure data is continuously collected, and the incremental learning process is triggered regularly to update and continuously optimize the material parameter prediction model.

[0031] The dynamic compression-shear testing machine adaptive control system based on machine learning is used to implement the dynamic compression-shear testing machine adaptive control method based on machine learning, and includes a material parameter prediction module, a dynamic compression-shear control optimization module, a material critical failure detection module, a test safety protection module, and a material parameter model optimization module.

[0032] The material parameter prediction module acquires material data to be detected and constructs a material parameter prediction model based on machine learning technology.

[0033] The dynamic compression and shear control optimization module corrects the control parameters of the dynamic compression and shear testing machine through a control algorithm based on real-time response data of the material during the dynamic compression and shear test.

[0034] The material critical failure detection module is used to collect material surface images and internal ultrasonic signals, extract crack area proportion, number of broken pieces, and maximum fracture length, and construct a material critical failure criterion through weighted fusion to monitor and warn the failure risk of the material.

[0035] The test safety protection module is used to set a safety protection mechanism of the dynamic compression and shear testing machine, and when the material failure characteristic parameter reaches a preset threshold, the safety protection mechanism is triggered to automatically stop the test and record the material detection parameters and mechanical properties.

[0036] The material parameter model optimization module performs incremental learning and continuous optimization on the material parameter prediction model according to the recorded material detection parameters and mechanical property data under the failure condition.

[0037] The application has the beneficial effects that the application can accurately predict the optimal detection parameters and mechanical properties of the material to be detected by constructing a material parameter prediction model through machine learning, thereby improving the test efficiency and result reliability.

[0038] The application can realize accurate control and optimization of the test process and improve the accuracy of the test results by adaptively adjusting the control parameters of the dynamic compression and shear testing machine according to the real-time response data of the material.

[0039] The application can accurately predict the failure risk by constructing a material critical failure criterion through machine learning and real-time monitoring of the material integrity through material surface images and internal ultrasonic signal data.

[0040] The application can ensure test safety and prevent material damage expansion by setting a safety protection mechanism for the dynamic compression and shear testing machine to automatically stop and record data when the material reaches a critical failure state.

[0041] The application can continuously optimize the prediction model by using the detection parameters and mechanical property data under the material failure condition, thereby continuously improving the accuracy and reliability of material performance evaluation.

[0042] The application realizes accurate evaluation of material mechanical properties by material performance prediction, adaptive control of the test process, intelligent detection of material failure, safety protection mechanism, and continuous optimization of the model, thereby improving the efficiency, accuracy, safety, and reliability of dynamic compression and shear testing and promoting the development and innovation of material dynamic mechanical property testing technology. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The flow chart of the adaptive control method of the dynamic compression shear testing machine based on machine learning provided in the present application is provided.

[0044] Figure 2 The flow chart of the material parameter prediction model construction provided in the present application is provided.

[0045] Figure 3 The adaptive control parameter correction flow chart provided in the present application is provided.

[0046] Figure 4 The material critical failure detection flow chart provided in the present application is provided.

[0047] Figure 5 The dynamic compression shear test safety protection flow chart provided in the present application is provided.

[0048] Figure 6 The adaptive control system structure diagram of the dynamic compression shear testing machine based on machine learning provided in the present application is provided. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0050] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited by the specific embodiments disclosed below.

[0051] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is separate or alternative to other embodiments.

[0052] Embodiment 1:

[0053] Reference Figures 1 to 5 The first embodiment of the present application provides an adaptive control method of a dynamic compression shear testing machine based on machine learning.

[0054] Step 1: Obtain the data of the material to be detected, and construct a material parameter prediction model based on machine learning technology, refer to Figure 2 The flow chart of the material parameter prediction model construction in this step is provided.

[0055] Collect basic information of the detected material and standard material, including material name M and material type C; obtain geometric size parameters G of the detected material and standard material, including length l, width w, thickness h, denoted as G = {l, w, h}; obtain detection parameters of the detected material and standard material in dynamic compression shear test, including loading rate v, compression shear angle θ, maximum load F max , and corresponding mechanical performance indicators, including shear strength τ, fracture toughness K IC , and impact toughness a k .

[0056] Based on the collected data of the detected material and standard material, a support vector regression (SVR) algorithm is used to construct a material parameter prediction model; the material name M, material type C, and geometric size parameter G are used as input features of the model, denoted as x = [M, C G] T ; the detection parameters v, θ, F max of the material in dynamic compression shear test and the mechanical performance indicators τ, K IC , a k are used as output parameters of the model, denoted as y = [v, θ, F max , τ, K IC , a k ] T .

[0057] A training data set D = {(x1, y1), (x2, y2),..., (x N , y N )} is constructed, where N is the number of samples, x N is the input feature of the Nth sample, and y N is the output parameter of the Nth sample; the SVR model is trained using the training data set D to obtain the material parameter prediction model for output detection parameters and mechanical performance.

[0058] The performance of the SVR model is evaluated on the test set, and the determination coefficient and root mean square error indicators are used to measure the accuracy of the prediction; the model hyperparameters, such as kernel function type and penalty coefficient, are optimized through cross-validation and grid search to improve the generalization ability of the model.

[0059] For the material to be detected, its material name M new , material type T new , and geometric size parameter G new are combined into a feature vector x new = [M new , T new , G new ] T , which is input into the trained material parameter prediction model, and the material parameter prediction model outputs the optimal detection parameters and expected mechanical properties

[0060] After the material parameter prediction model outputs the optimal detection parameters and expected mechanical properties of the material to be detected, the user can detect and verify the detection parameters and mechanical properties of the material through the dynamic compression shear testing machine: when the expected mechanical properties of the material are selected as the detection target, whether the material can complete the corresponding detection parameters is detected through the dynamic compression shear testing machine; when the optimal detection parameters of the material are selected as the detection target, whether the material reaches the expected mechanical properties is detected through the dynamic compression shear testing machine.

[0061] The material parameter prediction model constructed in this step can quickly and accurately predict the optimal detection parameters and expected mechanical properties of the material to be detected. Compared with the traditional trial-and-error method, the material parameter prediction model constructed in this step improves the efficiency and accuracy of material detection.

[0062] Step 2: Based on the real-time response data of the material during the dynamic compression shear test process, the control parameters of the dynamic compression shear testing machine are corrected through a control algorithm to control the deformation and failure process of the material, and refer to Figure 3 for the adaptive control parameter correction flowchart in this step.

[0063] During the dynamic compression shear test process, the real-time response data of the material is collected through sensors, including load F(t), displacement d(t) and strain ε(t); the load F(t) is measured by a force sensor, the displacement d(t) is measured by a displacement sensor, and the strain ε(t) is measured by a strain gauge, denoted as s(t) = [F(t), d(t), ε(t)] T , where t is time.

[0064] Based on the real-time response data s(t) of the material, the control parameters of the dynamic compression shear testing machine are corrected through a control algorithm, including the loading rate v(t), the compression shear angle θ(t) and the maximum load F max (t); a proportional-integral-derivative (PID) control algorithm is adopted to dynamically adjust the control parameters according to the deviation between the material response and the expected response; the input of the PID controller is the deviation between the material response and the expected response, and the output is the correction amount Δu(t) = [Δv(t), Δθ(t), ΔF max (t)] of the control parameters. T , where Δv(t) is the correction amount of the loading rate, Δθ(t) is the correction amount of the compression shear angle, and ΔF max (t) is the correction amount of the maximum load.

[0065] The mathematical expression of the PID controller is:

[0066]

[0067] , Kp K i K d These are the proportional, integral, and derivative coefficients, respectively, which are adjusted using an adaptive law. e(t) is the control error. This indicates that the error e(t) is integrated over time, reflecting the accumulation of the error over time. This represents the derivative of the error e(t) with respect to time t.

[0068] The control parameters of the dynamic compression-shear testing machine are corrected based on the output of the PID controller.

[0069] v(t)=v0+Δv(t), θ(t)=θ0+Δθ(t), F max (t)=F max，0 +ΔF max (t);

[0070] Where, v0, θ0, F max，0 The initial control parameters are the initial loading rate, initial compression-shear angle, and initial maximum load, which are given by the material parameter prediction model.

[0071] To adapt to changes in material properties and disturbances in experimental conditions, an adaptive control strategy is adopted to adjust the parameters of the PID controller online. An adaptive law is introduced, based on the deviation e(t) between the material response and the expected response and the rate of change of the deviation. Dynamically adjust the parameter K of the PID controller p K i and K d The mathematical expression for the adaptive law is:

[0072]

[0073] Where, γ p γ i γ d The learning rate for the adaptive law needs to be selected based on the material properties and experimental requirements.

[0074] For example, the learning rate γ of the adaptive law p γ i and γ d The selection steps are as follows:

[0075] Based on the nonlinear characteristics of the material, a smaller γ is selected. p Values, such as γ p =0.1; because the response of a nonlinear material is not linear with respect to the load, an excessively large γ p The value may cause the control parameters to change too quickly, resulting in fluctuations in the load curve;

[0076] Based on the anisotropy of the material, a larger γ is selected. i Values, such as γ i =0.5; because anisotropic materials have significantly different mechanical properties in different directions, a larger γ is required. i The values ​​are adapted to accommodate this difference, ensuring precise control in all directions;

[0077] Based on the strain rate sensitivity of the material, a larger γ is selected. d Values, such as γ d =0.3; because the mechanical properties of strain rate-sensitive materials change with strain rate, a larger γ d Values ​​can help control systems respond quickly to changes in strain rate and prevent fluctuations in the load curve.

[0078] Conduct dynamic compression-shear tests and observe whether the material response meets the test requirements; if the load curve shows significant fluctuations, the γ value can be appropriately reduced. p and γ d The value of γ; if the test is not stopped in time before the material fails, the value of γ can be appropriately increased. i The value of γ is determined; based on the experimental results, the learning rate of the adaptive law is fine-tuned until the optimal value suitable for the material and experimental conditions is found; the final learning rate γ is recorded. p γ i γ d , which is the optimal learning rate for the material under the experimental conditions.

[0079] Through adaptive control strategies, the parameters of the PID controller can be adjusted in real time to adapt to changes in material properties and disturbances in test conditions, thereby improving the control accuracy and robustness of dynamic compression-shear tests.

[0080] The PID control algorithm and adaptive law are integrated into the control system of the dynamic compression-shear testing machine to achieve real-time data acquisition, control parameter correction and adaptive control.

[0081] This step enables the dynamic compression-shear testing machine to adaptively adjust control parameters based on the real-time response data of the material, thereby achieving precise control over the material deformation and failure process. At the same time, the introduction of the adaptive control strategy allows the dynamic compression-shear testing machine to adapt to different materials and test conditions, exhibiting better robustness and versatility.

[0082] Step 3: Acquire surface images and internal ultrasonic signals of the material, extract crack area ratio, number of fractures, and maximum fracture length, and weightedly fuse these to construct a critical failure criterion for the material, monitoring and providing early warning of material failure risks. (See [link to relevant documentation]). Figure 4 This is a flowchart of the material critical failure detection process in this step.

[0083] During the dynamic compression-shear test, surface image data and internal ultrasonic signal data of the material were acquired simultaneously; a high-speed camera was used to capture the deformation and damage process of the material surface, and the recorded material surface image sequence was... Where N1 is the number of image frames. This represents the surface image of the material in frame N1; acoustic signals from inside the material were acquired using an ultrasonic probe array; the recorded internal ultrasonic signals are... Where N2 is the number of sampling points. This represents the internal ultrasonic signal at the N2th sampling point.

[0084] The acquired material surface image sequence I is preprocessed, including denoising, enhancement, and segmentation operations, to extract the image region of the crack on the material surface. An edge detection image segmentation algorithm is then used to extract the crack region, resulting in a binary mask image M of the crack. I Based on the binary mask image M of the crack I Calculate the pixel area A of the crack. I And compare it with the current cross-sectional area A0 of the material to obtain the crack area ratio B. I :

[0085] Set the crack area ratio threshold λ I When B I =λ I When the material reaches a critical failure state, the threshold λ is used to determine that the material's external structure has reached that state. I The parameters are preset based on factors such as the type and geometry of the material. For example, for a certain brittle material, λ can be set in advance. I =5%, while for tough materials, λ can be appropriately increased. I value.

[0086] The acquired ultrasonic signals R from within the material are preprocessed, including filtering, amplification, and calibration, to extract the ultrasonic echo signals from the internal fractures. Ultrasonic imaging algorithms are then used to process the echo signals, yielding ultrasonic images S of the material's interior. R ; for ultrasound images S R Image segmentation and feature extraction are performed to obtain the number N of fractures inside the material. R and maximum fracture length L R .

[0087] Set the fracture quantity threshold λ N and the maximum fracture length threshold λ L When N R ≥λ N or L R ≥λ L When the material reaches a critical failure state, the threshold λ is used to determine that the material has reached that state. N and λ LThe parameters are preset based on factors such as the type and geometry of the material. For example, for composite materials, λ can be set. N =10, λ L =5mm.

[0088] The proportion of surface crack area B of the material I and internal fracture characteristics N R L R A fusion method was used to construct a criterion for judging the overall critical failure of the material. A weighted fusion method was employed to sum the judgment results of surface cracks and internal fractures, resulting in a comprehensive criterion J for the overall critical failure of the material.

[0089]

[0090] in, This is an indicator function; it takes the value 1 if the condition is true, and 0 otherwise. I w N w L The weighting coefficients for the surface crack area ratio, the number of internal fractures, and the maximum fracture length, satisfying w I +w N +w L =1, which can be set according to the failure mechanism of the material and experimental experience; for example, for brittle materials, since the damage of brittle materials mainly occurs on the surface, w should be set. I ≥w N +w L For tough materials, since damage mainly occurs internally, a w value should be set. N ≥w L >w I .

[0091] Set the comprehensive criterion threshold λ J When J≥λ J When the material as a whole reaches a critical failure state, the test is automatically stopped and the test data is recorded; the comprehensive judgment threshold λ is used. J The value range of λ is (0, 1], and can be set according to the failure mechanism of the material and experimental experience. For example, λ can be chosen as the value of λ. J =0.6.

[0092] When it is determined that the material as a whole has reached a critical failure state, the test data is recorded and the test personnel decide whether to continue the test.

[0093] This step constructs a critical failure criterion for the material as a whole by using material surface images and internal ultrasonic signals, enabling real-time monitoring and early warning of the critical failure state during the dynamic compression and shearing process of the material.

[0094] This approach comprehensively considers two failure mechanisms: surface crack propagation and internal fracture evolution. It extracts key features of material failure through image segmentation and ultrasonic imaging algorithms, and uses a weighted fusion method to integrate and judge the collected multimodal information. This overcomes the limitations of a single failure criterion and improves the accuracy and reliability of material critical failure detection.

[0095] This step also sets reasonable thresholds and weighting coefficients, and combines the failure mechanism of materials and experimental experience to make the solution highly applicable and practical. It can provide effective failure monitoring and control methods for dynamic compression-shear tests, and improve the safety and efficiency of the test.

[0096] Step 4: Set the safety protection mechanism of the dynamic compression-shear testing machine. When the material failure characteristic parameters reach the preset threshold, the safety protection mechanism is triggered, automatically stopping the test and recording the material test parameters and mechanical properties. See [link / reference] Figure 5 This is a flowchart of the safety protection process for the dynamic compression-shear test in this step.

[0097] After the material reaches a critical failure state, surface images and internal ultrasonic signal data are continuously acquired, and the growth rate of each failure characteristic parameter per unit time is calculated:

[0098] Crack area growth rate v B (t i ): B c (t i ) indicates at time point t i Crack area at time B; c (t i-1 ) represents time point t i Crack area at time -1;

[0099] Fracture quantity growth rate v R (t i ): N R (t i ) indicates at time point t i The number of cracks at time N R (t i-1 ) represents time point t i The number of cracks at time -1;

[0100] Maximum fracture length growth rate v L (t i ): L max (t i ) indicates at time point t i The maximum crack length at time L max (ti-1 ) indicates at time point t i The maximum crack length at time -1; t i Let t be the time of the i-th data acquisition, and Δt be the time interval between two consecutive data acquisitions.

[0101] Limits are set for the crack area ratio, the number of fractures, and the maximum fracture length growth rate, respectively, V. B，lim v R，lim and V L，lim .

[0102] Continuously monitor the growth rate of material failure characteristic parameters to determine whether the corresponding limit has been exceeded multiple times consecutively: if the crack area ratio growth rate is continuously n B This exceeds the limit once, that is:

[0103] v B (t i )>V B，lim i = k, k+1, ..., k+n B -1;

[0104] This triggers the security protection mechanism, where k is the starting sequence number, indicating that starting from the k-th measurement time, the subsequent n measurements are continuously counted. B Second-rate.

[0105] If the rate of increase in the number of fractures is continuous n R This exceeds the limit once, that is:

[0106] v R (t i )>V R，lim i = k, k+1, ..., k+n R -1;

[0107] This will trigger the security protection mechanism.

[0108] If the maximum fracture length growth rate is continuously n L This exceeds the limit once, that is:

[0109] v L (t i )>V L，lim i = k, k+1, ..., k+n L -1;

[0110] This will trigger the security protection mechanism.

[0111] Number of consecutive exceedances n B n R and n L It can be set according to the material properties and test requirements, and is generally between 3 and 10.

[0112] When any safety protection mechanism is met, the shutdown procedure of the dynamic compression-shear testing machine is immediately initiated to cut off the load and stop the test, preventing damage to the test site and injury to the test personnel.

[0113] When the safety protection mechanism is triggered and the test is stopped, the following data are automatically recorded: real-time response data of the material, including load, displacement and strain; mechanical performance indicators, including shear strength, fracture toughness and impact toughness at the time of shutdown; failure characteristic parameters, including crack area ratio, number of fractures and maximum fracture length at the time of shutdown; and macroscopic failure mode of the material, which is captured by a high-definition camera at the state of the material at the time of shutdown.

[0114] The safety protection mechanism established in this step enables the dynamic compression-shear testing machine to promptly determine the integrity and load-bearing capacity of the material based on the fact that the material failure characteristic parameters exceed the limit multiple times in a row, and to initiate the shutdown procedure, effectively preventing catastrophic damage to the material and ensuring the safety and reliability of the testing process.

[0115] The technical solution in this step allows testers to explore the upper limit of material testing while ensuring safety. By recording in detail the various parameters and performance indicators when the material fails, it provides data support for subsequent analysis of the material's failure mechanism and improvement of the material's dynamic mechanical properties.

[0116] Step 5: Based on the material testing parameters and mechanical property data recorded under failure conditions, perform incremental learning and continuous optimization of the material parameter prediction model.

[0117] We collected and organized the material testing parameters and corresponding material mechanical property data recorded when the safety protection mechanism was triggered during the dynamic compression-shear test, and constructed a material failure dataset.

[0118] Material testing parameters include load, displacement, strain, crack area ratio, number of fractures, and maximum fracture length. Mechanical performance indicators include shear strength, fracture toughness, and impact toughness. The material failure dataset is preprocessed to remove outliers and ensure the accuracy and consistency of the data.

[0119] The established material failure dataset is merged with the original dataset used to train the material parameter prediction model. The merged dataset is randomly shuffled to ensure that the old and new data are fully mixed and to avoid the impact of abrupt changes in data distribution on model performance. Depending on the size of the dataset and the limitations of computing resources, a batch incremental learning strategy can be adopted, merging only a portion of the new failure data each time.

[0120] For the merged dataset, the hyperparameters of the material parameter prediction model are fine-tuned. For the support vector regression model, the type and parameters of the kernel function can be adjusted, such as the bandwidth of the Gaussian kernel and the degree of the polynomial kernel. The regularization parameters of the support vector regression model are optimized to balance the model's fitting ability and generalization ability. Grid search and random search methods are used to find the optimal parameter combination within the range of hyperparameter values.

[0121] Using the merged dataset, the material parameter prediction model is incrementally trained, and optimization algorithms such as mini-batch gradient descent are used to adjust the model parameters so that it achieves optimal fit on the new data distribution.

[0122] The optimal material parameter prediction model after incremental learning is saved as a new model version. This new model version is then deployed to the control system of the dynamic compression-shear testing machine, replacing the original model. In subsequent dynamic compression-shear tests, the updated material parameter prediction model is used to predict the material's test parameters and mechanical properties in real time. New material failure data is continuously collected, and the incremental learning process is triggered periodically to achieve adaptive updates and continuous optimization of the model.

[0123] This step utilizes newly collected material failure data from dynamic compression-shear tests to incrementally learn and optimize the material parameter prediction model. By merging the new data into the original training dataset, the model's hyperparameters are fine-tuned, and incremental training is performed on the merged dataset. This allows the model to adapt to constantly changing data distributions and improves prediction performance. This step also ensures that the material parameter prediction model can continuously learn and evolve through regular incremental learning and model updates, providing more accurate and reliable support for material performance evaluation and process optimization.

[0124] Example 2:

[0125] Reference Figure 6 The second embodiment of this application provides an adaptive control system for a dynamic compression-shear testing machine based on machine learning.

[0126] The system includes: a material parameter prediction module, a dynamic compression-shear control optimization module, a material critical failure detection module, an experimental safety protection module, and a material parameter model optimization module.

[0127] The material parameter prediction module acquires data on the material to be tested and constructs a material parameter prediction model based on machine learning technology.

[0128] The dynamic compression-shear control optimization module corrects the control parameters of the dynamic compression-shear testing machine through a control algorithm based on the real-time response data of the material during the dynamic compression-shear test.

[0129] The material critical failure detection module is used to acquire material surface images and internal ultrasonic signals, extract crack area ratio, fracture number and maximum fracture length, and weighted fuse them to construct material critical failure criteria, thereby monitoring and warning of material failure risks.

[0130] The test safety protection module is used to set the safety protection mechanism of the dynamic compression-shear tester. When the material failure characteristic parameters reach the preset threshold, the safety protection mechanism is triggered, the test is automatically stopped, and the material test parameters and mechanical properties are recorded.

[0131] The material parameter model optimization module performs incremental learning and continuous optimization of the material parameter prediction model based on the material testing parameters and mechanical property data recorded under failure conditions.

[0132] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0133] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments under the guidance of this application without departing from the spirit and scope of protection of the claims. All of these variations are within the protection scope of this application.

Claims

1. An adaptive control method for a dynamic compression-shear testing machine based on machine learning, characterized in that, include: Acquire data on the material to be tested and construct a material parameter prediction model based on machine learning technology; Based on the real-time response data of the material during the dynamic compression-shear test, the control parameters of the dynamic compression-shear testing machine are corrected by the control algorithm to control the material deformation and failure process; The system collects surface images and internal ultrasonic signals of materials, extracts the crack area ratio, number of fractures and maximum fracture length, and weights and fuses them to construct a critical failure criterion for materials, thereby monitoring and providing early warning of material failure risks. The safety protection mechanism of the dynamic compression-shear testing machine is set. When the material failure characteristic parameters reach the preset threshold, the safety protection mechanism is triggered, the test is automatically stopped, and the material test parameters and mechanical properties are recorded. Based on the material testing parameters and mechanical property data recorded under failure conditions, the material parameter prediction model is incrementally learned and continuously optimized.

2. The adaptive control method for a dynamic compression-shear testing machine based on machine learning according to claim 1, characterized in that, Collect data on tested materials and standard materials, including material name, material type, geometric parameters, test parameters, and mechanical property indicators. The test parameters include loading rate, compression-shear angle, and maximum load. The mechanical property indicators include shear strength, fracture toughness, and impact toughness. Based on the collected data of tested materials and standard materials, a material parameter prediction model is constructed using the support vector regression (SVR) algorithm. The material parameter prediction model outputs the optimal testing parameters and expected mechanical properties of the material to be tested.

3. The adaptive control method for a dynamic compression-shear testing machine based on machine learning according to claim 2, characterized in that, Collect real-time response data of the material, including load, displacement, and strain; The control parameters of the dynamic compression-shear testing machine, including loading rate, compression-shear angle, and maximum load, are corrected by a control algorithm; the proportional-integral-derivative (PID) control algorithm is used to dynamically adjust the control parameters.

4. The adaptive control method for a dynamic compression-shear testing machine based on machine learning according to claim 3, characterized in that, An adaptive control strategy is constructed to adjust the parameters of the PID controller online. An adaptive law is introduced to dynamically adjust the parameters of the PID controller based on the deviation between the material response and the expected response and the rate of change of the deviation.

5. The adaptive control method for a dynamic compression-shear testing machine based on machine learning according to claim 4, characterized in that, Acquire image sequences of the material surface and internal ultrasonic signals; Preprocess the image sequence of the material surface to extract the crack region and calculate the crack area ratio; The internal ultrasonic signals are preprocessed and imaged to extract the number of fractures and the maximum fracture length. By weighted fusion of crack area ratio, number of fractures and maximum fracture length, a comprehensive criterion for material critical failure is obtained.

6. The adaptive control method for a dynamic compression-shear testing machine based on machine learning according to claim 5, characterized in that, Set thresholds for crack area ratio, number of fractures, and maximum fracture length, as well as a comprehensive criterion threshold; When any one of the following data—crack area ratio, number of fractures, and maximum fracture length—exceeds the corresponding threshold for crack area ratio, number of fractures, and maximum fracture length, it is determined whether the material's exterior and interior have entered a critical failure state. When the comprehensive criterion exceeds the comprehensive criterion threshold, it is determined that the material as a whole has reached a critical failure state, the test data is recorded, and a decision is made on whether to continue testing.

7. The adaptive control method for a dynamic compression-shear testing machine based on machine learning according to claim 6, characterized in that, After the material as a whole reaches the critical failure state, the surface images and internal ultrasonic signal data of the material are continuously collected to calculate the growth rate of crack area ratio, number of fractures and maximum fracture length per unit time. Set limits for the crack area ratio, number of fractures, and maximum fracture length growth rate; continuously monitor the growth rate of material failure characteristic parameters; when the growth rate of any failure characteristic parameter exceeds the corresponding limit multiple times consecutively, trigger the safety protection mechanism, stop the dynamic compression-shear testing machine, and automatically record the test data.

8. The adaptive control method for a dynamic compression-shear testing machine based on machine learning according to claim 7, characterized in that, The detection data automatically recorded when the safety protection mechanism is triggered and the test is stopped includes: real-time response data of the material, including load, displacement and strain; mechanical performance indicators, including shear strength, fracture toughness and impact toughness at the time of shutdown; and failure characteristic parameters, including the crack area ratio, number of fractures and maximum fracture length at the time of shutdown.

9. The adaptive control method for a dynamic compression-shear testing machine based on machine learning according to claim 8, characterized in that, Collect material testing parameters and mechanical property data recorded when the safety protection mechanism is triggered during dynamic compression-shear test, construct a material failure dataset, and merge the material failure dataset with the original training dataset; For the merged dataset, the material parameter prediction model is incrementally trained; The incrementally learned material parameter prediction model is saved and deployed, and new material failure data is continuously collected. The incremental learning process is triggered periodically to update and continuously optimize the material parameter prediction model.

10. A machine learning-based adaptive control system for a dynamic compression-shear testing machine, used to implement the machine learning-based adaptive control method for a dynamic compression-shear testing machine as described in any one of claims 1 to 9, characterized in that, include: The module includes a material parameter prediction module, a dynamic compression-shear control optimization module, a material critical failure detection module, an experimental safety protection module, and a material parameter model optimization module. The material parameter prediction module acquires data of the material to be tested and constructs a material parameter prediction model based on machine learning technology. The dynamic compression-shear control optimization module corrects the control parameters of the dynamic compression-shear testing machine through a control algorithm based on the real-time response data of the material during the dynamic compression-shear test. The material critical failure detection module is used to acquire material surface images and internal ultrasonic signals, extract crack area ratio, fracture number and maximum fracture length, and weighted fuse them to construct material critical failure criteria, monitor and warn of material failure risks; The test safety protection module is used to set the safety protection mechanism of the dynamic compression-shear tester. When the material failure characteristic parameters reach the preset threshold, the safety protection mechanism is triggered, the test is automatically stopped, and the material test parameters and mechanical properties are recorded. The material parameter model optimization module performs incremental learning and continuous optimization of the material parameter prediction model based on the material testing parameters and mechanical property data recorded under failure conditions.

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