Vickers hardness test control method and system based on multi-parameter optimization

Abstract

The invention relates to the technical field of material mechanical property testing, and discloses a Vickers hardness test control method and system based on multi-parameter optimization. The method comprises the following steps: acquiring load force value distribution data through a sensor, calculating a global load uniformity coefficient and dynamically adjusting an application speed; pressure head inclination angle control parameters are generated in combination with inclination angle sensor data, and an adjusting mechanism is driven to achieve high-precision calibration; geometric features are extracted from the standardized indentation shape, vibration influence coefficients are calculated through fusion of vibration data, and high-frequency vibration is suppressed through a self-adaptive damping algorithm; finally, based on material surface characteristics and mechanical property parameters, the dynamic parameter adjustment model is used for outputting collaborative optimization test control parameters, and through collaborative optimization of multi-sensor closed-loop control and an intelligent algorithm, the precision, stability and data accuracy of the test process are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of material mechanical property testing technology, and in particular to a Vickers hardness test control method and system based on multi-parameter optimization. Background Technology

[0002] Vickers hardness testing, a widely used method for testing the mechanical properties of materials, calculates the hardness value of a material by measuring the diagonal length of the indentation formed when an indenter is pressed into the material surface under a specific load. Since its inception, the Vickers hardness testing method has evolved from manual operation to automated control. Early Vickers hardness testing relied heavily on manual operation, and its accuracy and repeatability were significantly affected by human factors. With technological advancements, automated Vickers hardness testing equipment has gradually emerged, improving testing efficiency and accuracy through mechanical and electronic control technologies.

[0003] In recent years, with the rapid development of materials science, higher requirements have been placed on the accuracy and reliability of materials mechanical property testing. Especially in the testing of complex materials such as high-strength alloys, composite materials, and micro / nano-scale thin film materials, traditional Vickers hardness testing methods face numerous challenges. These challenges mainly include: uneven load distribution: During load application, uneven load distribution may occur due to equipment inaccuracies, uneven material surfaces, or operational errors. This unevenness can cause the indentation shape to deviate from the standard, thus affecting the accuracy of hardness calculations; indenter tilt angle deviation: The perpendicularity of the indenter to the material surface is one of the key factors affecting the standardization of indentation shape. Tilt angle deviation may lead to asymmetry in the indentation shape, thereby interfering with the accurate measurement of the indentation diagonal length; vibration interference: External environmental vibration or equipment vibration can interfere with the load application and indentation measurement process, leading to unstable test data. Vibration interference is particularly significant in high-frequency or long-term tests, and may accumulate and amplify, severely affecting test accuracy. Multi-parameter coupling problem: There are complex coupling relationships between load distribution, tilt angle deviation and vibration disturbance. For example, adjusting the load may cause new tilt angle deviation, while tilt angle correction may introduce additional vibration disturbance. This dynamic change of multi-factor coupling makes it difficult for traditional test systems to maintain stability and consistency under complex working conditions.

[0004] To address the aforementioned challenges, recent research has focused on the application of multi-parameter optimization techniques in Vickers hardness testing. By introducing sensor technology, automated control technology, and advanced algorithm models, researchers are attempting to achieve synergistic optimization of load uniformity, indentation accuracy, and vibration suppression. For example, load distribution is monitored in real time using a force sensor array, and the load application speed is dynamically adjusted using a servo control system; precise calibration of the indenter indentation angle is achieved using an indentation sensor and a stepper motor; and vibration interference is suppressed using a vibration sensor and an adaptive damping algorithm. These technological advancements provide new ideas and methods for improving the accuracy and stability of Vickers hardness testing.

[0005] However, existing multi-parameter optimization techniques still have certain limitations in practical applications. For example, the synergistic optimization among parameters has not yet reached an ideal level, and the intelligence level of the testing system needs to be improved, especially in scenarios involving complex materials and high-precision testing requirements. Therefore, this invention aims to propose a Vickers hardness testing control method and system based on multi-parameter optimization. By comprehensively considering factors such as load uniformity, tilt angle accuracy, and vibration suppression, it achieves high-precision, high-stability, and high-repeatability Vickers hardness testing to meet the stringent requirements of modern materials science for testing the mechanical properties of materials. Summary of the Invention

[0006] This invention relates to a Vickers hardness test control method and system based on multi-parameter optimization, aiming to improve the accuracy and stability of Vickers hardness testing. By real-time acquisition and analysis of force distribution data during load application, this invention can effectively evaluate the uniformity of load application and optimize load application parameters accordingly, thereby ensuring the reliability of the testing process.

[0007] In a first aspect, this application provides a Vickers hardness test control method based on multi-parameter optimization, the method comprising: Step 1: Collect force distribution data during the load application process using sensors, calculate the global load uniformity coefficient, and obtain a load application uniformity assessment report; Step 2: Adjust the load application parameters according to the load application uniformity assessment report to determine the optimized load application parameters; Step 3: Extract velocity and force data from the optimized load application parameters, and calculate the tilt angle adjustment amount by combining the tilt angle data to obtain the indenter tilt angle control parameters; Step 4: Drive the adjustment mechanism to perform tilt angle calibration based on the indenter tilt angle control parameters to obtain standardized indentation shape data; Step 5: Extract geometric features from the standardized indentation shape data and combine them with vibration data to calculate the vibration influence coefficient and obtain the vibration suppression requirement parameters; Step 6: Adjust the response characteristics of the damping mechanism based on the vibration suppression requirement parameters to determine the real-time control parameters for vibration suppression; Step 7: Drive the damping device to perform high-frequency vibration smoothing operation according to the real-time control parameters to obtain stable test process data; Step 8: Extract material surface characteristics and mechanical property parameters from the test process data, and calculate the co-optimization parameters through dynamic parameter adjustment model to obtain the final test control parameters.

[0008] Secondly, this application provides a Vickers hardness test control system based on multi-parameter optimization, the system comprising: The load assessment module is used to collect force distribution data during the load application process through sensors, calculate the global load uniformity coefficient, and obtain a load application uniformity assessment report. The parameter optimization module is used to adjust the load application parameters based on the load application uniformity assessment report and determine the optimized load application parameters. The tilt angle calculation module is used to extract velocity and force data from the optimized load application parameters, and combine the tilt angle data to calculate the tilt angle adjustment amount and obtain the indenter tilt angle control parameters; The calibration execution module is used to drive the adjustment mechanism to perform tilt angle calibration according to the indenter tilt angle control parameters, so as to obtain standardized indentation shape data; The vibration analysis module is used to extract geometric features from standardized indentation shape data and, in combination with vibration data, calculate the vibration influence coefficient to obtain vibration suppression requirement parameters. The damping control module is used to adjust the response characteristics of the damping mechanism according to the vibration suppression requirement parameters and determine the real-time control parameters for vibration suppression. The vibration suppression module is used to drive the damping device to perform high-frequency vibration smoothing operation according to real-time control parameters, so as to obtain stable test process data. The collaborative optimization module is used to extract material surface characteristics and mechanical property parameters from the test process data, and calculate collaborative optimization parameters through dynamic parameter adjustment model to obtain the final test control parameters.

[0009] Compared with the prior art, the beneficial effects of the technical solution of this application are at least as follows: 1. By collecting force distribution data in real time and dynamically adjusting the load application speed, the problem of uneven load distribution can be effectively solved, ensuring that the load uniformity coefficient reaches the preset optimization conditions, thereby improving the accuracy of test results.

[0010] 2. By combining load parameters and tilt angle data, the tilt angle adjustment can be accurately calculated and dynamic calibration can be achieved. The tilt angle of the indenter can be accurately calibrated to ensure the perpendicularity of the indenter to the test surface, which significantly improves the standardization of the indentation shape and thus improves the test accuracy.

[0011] 3. By analyzing vibration data and indentation geometry, the impact of vibration on the test can be quantified. An adaptive damping algorithm is used to dynamically adjust the damping mechanism, effectively suppressing vibration interference, stabilizing the test process, and ensuring the reliability of the test data.

[0012] 4. Taking into account load, tilt angle and vibration factors, the model achieves multi-parameter collaborative optimization through dynamic parameter adjustment, further improving test accuracy and stability, adapting to the testing needs of different materials, and providing efficient and reliable technical support for the mechanical property testing of complex materials.

[0013] 5. The automated control and intelligent optimization functions of this invention reduce human error, improve testing efficiency and repeatability, and can meet the stringent requirements of modern materials science for testing the mechanical properties of materials. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of the Vickers hardness test control method based on multi-parameter optimization according to this application; Figure 2 This is a schematic diagram of the Vickers hardness test control system based on multi-parameter optimization according to this application. Detailed Implementation

[0016] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms “comprising” or “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] For ease of understanding, the specific process of the embodiments of this application is described below. Figure 1 The diagram shows a flowchart of an embodiment of the Vickers hardness test control method based on multi-parameter optimization provided by the present invention. The flowchart specifically includes the following steps: Step 1: Collect force distribution data during the load application process using sensors, calculate the global load uniformity coefficient, and obtain a load application uniformity assessment report.

[0018] In one specific embodiment, the process of performing step 1 may specifically include the following steps: (1) Calculate the root mean square error and average value of force at each sensor location based on the force distribution data; (2) Calculate the standard deviation of the root mean square of all force values ​​and the average of the average of all force values, and define the ratio of the average to the standard deviation as the global load uniformity coefficient; (3) Determine whether the global load uniformity coefficient is less than the first preset threshold. If yes, generate a first evaluation result that characterizes the uneven load distribution. If no, generate a second evaluation result that characterizes the uniform load distribution. (4) Output a load application uniformity assessment report based on the assessment results.

[0019] Specifically, multiple force sensors are arranged at different locations on the load application device to form a sensor array, which collects force data in real time. The sensors synchronously collect data at a preset sampling frequency to ensure the capture of dynamic changes during load application. The analog signals collected by these sensors are converted into digital signals to form time-series force data. Each sensor records the real-time force change at its location, providing a comprehensive reflection of the load application state within the test area. The collected force data undergoes preprocessing such as data smoothing filtering and outlier detection to remove outliers and noise interference, ensuring the accuracy of the force data.

[0020] The root mean square deviation (RMSD) and average force value are calculated for each sensor location. The MSD reflects the dispersion of force value fluctuations at that sensor location and is a key indicator for measuring load application uniformity. By calculating the MSD, it can be determined whether there is uneven force distribution during the application process. After calculating the MSD, the average force value for all sensor locations is calculated. The average force value reflects the overall level of force values ​​collected by each sensor. Then, based on these force MSDs and average force values, a global load uniformity coefficient is further calculated. Specifically, the global load uniformity coefficient quantifies the uniformity of load distribution by the ratio of the overall average value to the standard deviation, assessing the load distribution fluctuations (macroscopic uniformity) between different sensors. If the calculated uniformity coefficient is lower than a preset threshold, it indicates that the load application is uneven and further adjustments to the load application process are needed; if the uniformity coefficient meets the requirements, it indicates that the load application is uniform.

[0021] During this process, the calculated load uniformity coefficient is compared with a preset uniformity threshold (i.e., the first preset threshold), and a corresponding evaluation report is generated based on the comparison result. If the uniformity coefficient is less than the first preset threshold, an evaluation result characterizing uneven load distribution is generated, indicating the need for further improvement in the load application process; if the uniformity coefficient is greater than or equal to the first preset threshold, it indicates that the load application meets the requirements and the load distribution is uniform, generating an evaluation result characterizing uniform load distribution. The evaluation results (first evaluation result or second evaluation result) provide important reference for subsequent testing processes and output a load application uniformity evaluation report. The report includes sensor layout information, force distribution data, root mean square error calculation results, load uniformity coefficient and its comparison results with the threshold, etc. Presented through a combination of charts and numerical values, it facilitates technicians to evaluate and judge the uniformity of load application in real time during testing and take timely optimization measures.

[0022] The technical solution of this invention can effectively solve the problem of uneven load distribution during load application, improving the accuracy and reliability of test results. It employs a sensor array to acquire comprehensive load data, and through the calculation of the root mean square error and uniformity coefficient, assesses the uniformity of load application in real time, providing a precise basis for subsequent control and adjustment. This ensures coordinated control of factors such as load, tilt angle, and vibration during testing, thereby improving the accuracy and stability of material testing.

[0023] Step 2: Adjust the load application parameters according to the load application uniformity assessment report to determine the optimized load application parameters.

[0024] In one specific embodiment, the process of performing step 2 may specifically include the following steps: (1) Extract the load uniformity coefficient from the load application uniformity assessment report; (2) Based on the load uniformity coefficient, the load application speed is adjusted by a servo control system, the adjusted load application speed is obtained, and new force distribution data is collected. (3) Based on the new force distribution data, calculate the coefficient of variation at each sensor location, and use the reciprocal of the coefficient of variation as the single-point load stability coefficient. (4) Determine whether the single-point load stability coefficient meets the preset optimization conditions. If so, use the adjusted load application speed and the new force distribution data as the optimized load application parameters.

[0025] Specifically, the global load uniformity coefficient reflects an important quantitative indicator of the uniformity of the current load application process. Its value determines whether the load application speed needs to be adjusted to optimize the load distribution. Based on the extracted uniformity coefficient, the servo control system adjusts the load application speed according to the control logic. The uniformity coefficient is input into the feedback control loop of the servo control system. The servo control system processes the input uniformity coefficient through a proportional-integral-derivative (PID) controller. Specifically, the servo control system adopts a proportional-integral-derivative (PID) control strategy, calculating a speed adjustment amount based on the deviation between the input uniformity coefficient and the target uniformity coefficient. The PID controller uses the proportional element to respond to the current deviation, the integral element to eliminate steady-state error, and the derivative element to predict future deviation trends. Through the synergistic effect of these three elements, the load application speed is accurately adjusted. The servo control system drives the actuator of the load application mechanism according to the speed adjustment amount. The actuator adjusts the load application speed by changing the speed of the drive motor. Specifically, when the uniformity coefficient is detected to be lower than a preset threshold, the servo control system reduces the load application speed to improve the uniformity of the load distribution; when the uniformity coefficient is higher than the preset threshold, the application speed is appropriately increased to improve testing efficiency.

[0026] After adjusting the load application speed, the system continues to monitor the new force distribution data in real time. New force data is collected from multiple sensors to form a force distribution dataset. Real-time monitoring of the force distribution data helps determine whether the adjusted load application speed meets the uniformity requirements. After data preprocessing, a single-point load stability coefficient is calculated at each sensor location based on the updated force distribution data. This single-point load stability coefficient is the ratio of the average force value of a single sensor to its standard deviation, i.e., the reciprocal of the coefficient of variation, and can be used to evaluate the load stability (microscopic uniformity) at a single sensor location.

[0027] Next, the single-point load stability coefficient is compared with preset optimization conditions to determine whether the optimization requirements are met. Optimization conditions include a minimum threshold for the stability coefficient and a required duration of stability. When the stability coefficient is greater than or equal to the minimum threshold and remains stable within a set time window, the optimization conditions are considered met. At this point, the adjusted load application speed and corresponding force distribution data are determined as the optimized load application parameters. If the optimization conditions are not met, a new global load uniformity coefficient is calculated. This updated uniformity coefficient is used as a new input, and iterative adjustments are continued through the servo control system to form a closed-loop optimization control. After multiple adjustments, the uniformity of load application gradually improves until the optimal state is reached.

[0028] Finally, the system outputs optimized load application parameters, including the optimal load application speed, corresponding force distribution characteristics, and uniformity coefficient, to ensure that the applied load remains uniform during further indenter tilt angle control and other tests, thereby improving the reliability and accuracy of the test results.

[0029] Through the dynamic adjustment and closed-loop optimization process of the servo control system, the load application speed can be precisely adjusted to ensure the uniformity of the load application process, thereby improving the stability and accuracy of the test. Through repeated iterative optimization, it can adapt to different testing environments and accuracy requirements, resolving test errors caused by uneven load distribution.

[0030] Step 3: Extract velocity and force data from the optimized load application parameters, and calculate the tilt angle adjustment amount by combining the tilt angle data to obtain the indenter tilt angle control parameters.

[0031] In one specific embodiment, the process of performing step 3 may specifically include the following steps: (1) Obtain the tilt angle data collected in real time by the indenter tilt angle sensor; (2) Obtain the theoretical tilt angle value based on the velocity and force data, and use the difference between the theoretical tilt angle value and the tilt angle data as the tilt angle deviation value of the pressure head; (3) Calculate the tilt adjustment amount using a preset tilt adjustment algorithm based on the tilt deviation value; (4) Generate head tilt angle control parameters that include adjustment direction and adjustment range based on the tilt angle adjustment amount.

[0032] Specifically, the velocity and corresponding force values ​​of the current load application are extracted from the optimized load application parameters. The velocity value reflects the dynamic changes in the applied load, while the force data reveals the characteristics of the load distribution. Furthermore, a high-precision tilt sensor mounted on the indenter collects real-time tilt data in the X and Y axes, reflecting the current tilt state of the indenter.

[0033] By combining velocity and force data, the ideal standard tilt angle is calculated, which represents the zero-deviation state the indenter should maintain under ideal load conditions. This standard tilt angle is dynamically calculated based on the actual applied load velocity, force data, and standard test conditions, and is used for comparison with the actually measured tilt angle. Specifically, a mathematical model is established to determine the relationship between load application parameters and tilt angle, which is used to generate the standard tilt angle. This mathematical model needs to consider the influence coefficient α of the rate of change of velocity on the tilt angle, the influence coefficient β of the non-uniformity of force distribution on the tilt angle, and the stability coefficient γ of the current tilt angle state. For example, assuming the model is linearly weighted, the corresponding formula is: θ_standard = α × V + β × F + γ × θ_offset, where θ_standard is the standard tilt angle value, V is the load velocity, F is the force data, and θ_offset is the reference tilt angle correction value of the material or equipment itself.

[0034] The calculated standard tilt angle value is compared with the actual tilt angle value acquired by the sensor to obtain the tilt angle deviation value. This deviation value reflects the difference between the actual position and the ideal position of the pressure head, and can accurately guide subsequent tilt angle adjustments. Based on this deviation, a preset tilt angle adjustment algorithm can be implemented. Specifically, a proportional-integral-derivative (PID) control algorithm is used to process the tilt angle deviation value and generate the corresponding adjustment amount. The PID control algorithm takes the current tilt angle deviation value as input, processes the current deviation through a proportional term, eliminates accumulated errors through an integral term, and predicts the trend of deviation changes through a derivative term, thereby improving the flexibility and accuracy of the adjustment. Specifically, the proportional coefficient Kp determines the response strength to the current deviation, the integral coefficient Ki is used to eliminate steady-state errors, and the derivative coefficient Kd provides predictive adjustment for deviation changes. During processing, the PID algorithm is optimized in conjunction with the dynamic characteristics of the applied load. For example, when the load application speed is high, the system will appropriately increase the proportional coefficient in the PID control algorithm to accelerate the adjustment response; when the force value changes drastically, the weight of the derivative term may be increased to improve the stability of the adjustment process. By dynamically adjusting control parameters, the system can ensure smooth adjustment while responding quickly, thus avoiding vibration or errors caused by over-adjustment. For example, when the speed suddenly increases from 5 mm / min to 15 mm / min, the proportional coefficient is adjusted from the standard value of 1.2 to 1.8, ensuring the indenter can quickly adapt to changes in the applied load. In one possible implementation, the tilt angle adjustment algorithm employs an adaptive parameter adjustment mechanism to meet the testing requirements of different materials. For harder metals, the weight of the integral term is increased to eliminate the influence of minor vibrations during testing; for softer materials, the weight of the derivative term is increased to provide a smoother adjustment process.

[0035] Based on the calculated final tilt angle adjustment, precise control parameters including the adjustment direction and magnitude are generated. These generated tilt angle accuracy control parameters are output and transmitted to the stepper motor drive for dynamic tilt angle calibration. The adjustment direction is determined by the sign of the tilt angle adjustment; a positive value indicates adjustment in the positive direction, and a negative value indicates adjustment in the negative direction. The adjustment magnitude is calculated based on the stepper motor's step angle and mechanical transmission ratio, ensuring both speed and precision in the adjustment process. This control parameter includes two stages: coarse adjustment and fine adjustment. Coarse adjustment is used to quickly approach the target position, while fine adjustment is used for fine-tuning to achieve high-precision positioning. For example, when the applied load force is 500N and the application speed is 10mm / min, the tilt sensor detects a 0.15-degree deviation in the X-axis direction. Through tilt angle deviation calculation, the theoretical standard tilt angle is determined to be 0.02 degrees, while the actual deviation is 0.13 degrees. Using a proportional-integral-derivative (PID) control algorithm, with a proportional coefficient of 1.5, an integral coefficient of 0.3, and a derivative coefficient of 0.1, the calculated adjustment is 0.12 degrees. Considering the current load condition is stable, the adjustment correction factor is 0.95, and the final tilt angle adjustment is determined to be 0.114 degrees. Based on the stepper motor's step angle of 0.018 degrees, it is calculated that 6.33 steps are needed. This is rounded down to 6 steps for coarse adjustment, and the remaining 0.006 degrees is completed through fine adjustment. The generated control parameters include 6 steps for positive X-axis adjustment, 0.006 degrees for fine adjustment, medium speed for adjustment, and an accuracy requirement of ±0.01 degrees.

[0036] This invention effectively solves the testing error problem caused by inaccurate indentation angle of the indenter during load application by using multi-parameter data fusion and dynamic control algorithms. Through closed-loop control and real-time adjustment, high-precision control of the indenter angle is ensured, thereby improving the reliability and reproducibility of the test data.

[0037] Step 4: Drive the adjustment mechanism to perform tilt angle calibration based on the indenter tilt angle control parameters to obtain standardized indentation shape data.

[0038] In one specific embodiment, the process of performing step 4 may specifically include the following steps: (1) Extract the adjustment direction and adjustment range from the indenter tilt angle control parameters; (2) Drive the pressure head adjustment mechanism with a stepper motor to perform dynamic tilt calibration according to the adjustment direction and adjustment range, and collect the calibrated indentation shape data; (3) The indentation shape data is normalized using a preset standardization algorithm to generate standardized indentation shape data; (4) Based on the standardized indentation shape data, determine whether the indentation shape conforms to the preset shape standard. If yes, output the standardized indentation shape data. If no, re-calibrate and test the tilt angle until the indentation shape conforms to the shape standard.

[0039] Specifically, the stepper motor control signal generates corresponding drive pulses based on the extracted adjustment direction (e.g., clockwise or counterclockwise) and adjustment amplitude (e.g., angle in degrees), driving the stepper motor to precisely adjust the position of the indenter. During the adjustment process, the stepper motor sends control pulses to the indenter at a preset frequency to complete the precise angle adjustment. Real-time acquisition of the calibrated indentation shape data ensures that the indenter's tilt angle meets standard requirements after each adjustment. The acquired data includes the indentation depth, shape, and distribution characteristics on the material surface.

[0040] A pre-defined standardization algorithm is used to normalize the calibrated indentation shape data to ensure the consistency and comparability of the test data. The normalization process includes: standardizing the indentation shape data: establishing a standard coordinate system with the geometric center of the indentation as the origin, and transforming the coordinate values ​​of all indentation contour points to this unified standard coordinate system; indentation contour fitting: using the least squares method to fit the indentation contour, selecting appropriate basis functions based on the shape of the indentation during the fitting process; and normalizing the indentation size parameters: using the characteristic dimensions of a standard indentation as a benchmark, converting the length, width, depth, and other parameters of the actual indentation into dimensionless relative values. The standard indentation is typically an indentation with typical characteristics obtained under specific test conditions (such as standard loads and loading speeds). Standardized indentation shape data mainly includes: standardized coordinate data, fitted curve parameters (i.e., parameters of a continuous mathematical curve obtained by fitting using the least squares method. For circular indentations, these may be parameters such as the center coordinates and radius of the circle; for square indentations, these may be parameters such as the vertex coordinates of the polygon) and normalized size parameters (dimensionless relative values ​​obtained after normalizing the indentation size parameters, such as normalized length, width, and depth).

[0041] A quality evaluation index system for indentation shape is established, defining key indicators such as indentation roundness error, symmetry deviation, and edge smoothness, and setting acceptable numerical ranges for each indicator. For example, roundness error is quantified by calculating the standard deviation of the distance from the actual contour point to the fitted circle center, and symmetry deviation is evaluated by comparing the geometric characteristics of the indentation in different directions. The values ​​of each indicator in the standardized indentation shape data are compared item by item with preset standards. If all standards are met, the standardized indentation shape data is output and recorded. If any indicator fails to meet the standard, the system triggers a recalibration and retesting process to ensure that the final indentation shape meets the specified shape standards. Through this closed-loop control, the system can precisely adjust the indenter tilt angle to ultimately obtain the required indentation shape.

[0042] In a preferred embodiment, if a certain indicator does not meet the standard, the possible causes of the unqualified indentation shape are analyzed, and a calibration strategy is determined based on the analysis results.

[0043] Specifically, possible reasons for unqualified indentation shape include: inaccurate tilt angle calibration and uneven load distribution. Inaccurate tilt angle calibration manifests as asymmetrical indentations, irregular edges, and geometric dimensional deviations, mainly due to improper execution of the adjustment mechanism. Uneven load distribution manifests as inconsistent indentation depths and localized deformation, mainly due to substandard optimization parameters. If the problem is due to inaccurate tilt angle calibration, recalculate the tilt angle adjustment amount (return to step 3) and correct the indenter posture. If the problem is due to uneven load distribution, re-optimize the load application parameters (return to step 2) and adjust the application speed / force value.

[0044] Preferably, the tilt adjustment is calculated first (return to step 3). If multiple calibrations are still ineffective, the load application parameters are re-optimized (return to step 2). The specific selection needs to be combined with the root cause analysis of the non-compliant indicators.

[0045] Through multi-level dynamic adjustment and precise control, the problems of inaccurate indentation angle and non-standard indentation shape during load application are solved. By using precise stepper motor drive and efficient normalization through standardized algorithms, the system achieves high-precision indentation testing and reliable data output, significantly improving the accuracy and repeatability of the testing process. Furthermore, closed-loop control and real-time feedback ensure the dynamic nature and accuracy of indenter angle adjustment, thereby effectively improving the reliability and quality control level of material performance testing.

[0046] Step 5: Extract geometric features from the standardized indentation shape data and combine them with vibration data to calculate the vibration influence coefficient and obtain the vibration suppression requirement parameters.

[0047] In one specific embodiment, the process of performing step 5 may specifically include the following steps: (1) Acquire vibration data collected in real time by vibration sensors; (2) Based on geometric features and vibration data, the degree of deformation at the edge of the indentation is calculated by weighted combination; (3) Based on the degree of deformation, a preset vibration influence model is used to calculate the vibration influence coefficient; (4) Determine whether the vibration influence coefficient is greater than the second preset threshold. If so, generate vibration suppression requirement parameters that characterize the edge deformation problem.

[0048] Specifically, in terms of geometric feature extraction, the standardized indentation shape data undergoes edge detection processing to identify the indentation boundary contour. Specifically, the Canny edge detection algorithm is used to extract the contour information of the indentation edge. Further, based on the indentation boundary contour line, geometric parameters such as the perimeter, area, roundness, ellipticity, and rate of curvature change of the indentation edge are calculated. These parameters quantify the morphological characteristics of the indentation, constructing a five-dimensional feature vector, which is then normalized to obtain the geometric features describing the indentation shape. Real-time data collected by vibration sensors is used to capture vibration interference that may be introduced during the test. Vibration data is monitored by a triaxial accelerometer to obtain acceleration information in the X, Y, and Z directions. The time-domain signal is converted into a frequency-domain signal using Fourier transform to identify the main vibration frequency components. For example, the sensor sampling frequency is set to 1000Hz.

[0049] The deformation degree of the indentation edge is calculated by weighted combination of geometric features and vibration data. This process utilizes a weighted summation method for multidimensional feature data, where the correlation between different geometric feature parameters and vibration intensity is weighted and adjusted. For example, the correlation between geometric features such as roundness deviation, ellipticity deviation, and rate of change of curvature and vibration intensity is assigned different weights to reflect the contribution of each factor to the deformation degree. For instance, the weight of roundness deviation is set to 0.3, the weight of ellipticity deviation is 0.25, the weight of rate of change of curvature is 0.2, and the weight of vibration intensity is 0.25. The formula for calculating the deformation degree D is: D = 0.3 × roundness deviation + 0.25 × ellipticity deviation + 0.2 × rate of change of curvature + 0.25 × vibration intensity.

[0050] The vibration impact model is constructed based on a multiple linear regression method. By combining the degree of deformation with characteristics such as vibration frequency and vibration acceleration, the model calculates the comprehensive degree of vibration's influence on the indentation shape. The vibration impact coefficient reflects the degree of interference of vibration on the indentation shape, providing a basis for further vibration suppression. For example, the model expression is Y = a1×D + a2×F1 + a3×A + b, where Y is the vibration impact coefficient, D is the degree of deformation, F1 is the dominant frequency vibration intensity, A is the vibration acceleration amplitude, a1, a2, and a3 are regression coefficients, and b is a constant term. The regression coefficients are determined by least-squares fitting of historical test data. The dominant frequency vibration intensity and vibration acceleration amplitude are extracted from the vibration data, combined with the deformation degree value, and the vibration impact coefficient is calculated according to the model expression. Preferably, boundary processing is performed on the calculation results to ensure that the vibration impact coefficient value is between 0 and 2.

[0051] Preferably, the above vibration influence model is a material-adaptive vibration influence model, and its regression coefficients are adjusted according to different material types. For example, for soft materials such as rubber or plastics, which exhibit significant viscoelasticity and are more affected by vibration frequency, a2 (weight of dominant frequency vibration intensity) is adjusted; for hard materials such as steel or alloys, which are brittle and sensitive, high-frequency vibrations easily induce microcracks, so a3 (weight of vibration acceleration) is increased. Due to their high hardness and large elastic modulus, vibration mainly leads to macroscopic deformation, so a1 (weight of deformation degree) is increased; for ceramic materials, which are brittle and sensitive, high-frequency vibrations easily induce microcracks, so a3 (weight of vibration acceleration) is increased. This adaptive adjustment ensures the accuracy and relevance of the vibration influence coefficient calculation.

[0052] When the calculated vibration influence coefficient exceeds the second preset threshold (trigger threshold, used to initiate the suppression process), it indicates that the indentation edge shape is significantly affected by vibration, resulting in obvious deformation. At this point, vibration suppression requirement parameters are generated, including the suppression intensity level and the required vibration frequency range. These parameters are output as a data package, containing information such as the suppression intensity level, target frequency range, and expected suppression effect. Based on different vibration influence coefficients, the vibration suppression requirement parameters can be categorized into three levels: mild, moderate, and severe. The specific suppression intensity and frequency range are determined based on actual test conditions and material properties. For example, the second preset threshold is 1.0; coefficients between 1.0 and 1.3 correspond to mild suppression, between 1.3 and 1.6 to moderate suppression, and above 1.6 to severe suppression.

[0053] By combining and optimizing multiple data sources, the problem of indentation shape distortion caused by vibration during material mechanics testing has been solved, thereby effectively improving the stability and reliability of data during testing and increasing testing accuracy. It has significant technical advantages, especially in precision material testing and high-precision application scenarios.

[0054] Step 6: Based on the vibration suppression requirement parameters, adjust the response characteristics of the damping mechanism to determine the real-time control parameters for vibration suppression.

[0055] In one specific embodiment, the process of performing step 6 may specifically include the following steps: (1) Extract the vibration influence coefficient from the vibration suppression requirement parameters; (2) Based on the vibration influence coefficient, the response parameters of the damping mechanism are calculated using an adaptive damping algorithm; (3) Adjust the damping coefficient of the damping mechanism based on the response parameters and collect new vibration data; (4) Calculate the updated vibration influence coefficient based on the new vibration data; (5) Determine whether the updated vibration influence coefficient is less than the third preset threshold. If so, use the response parameter as the real-time control parameter for vibration suppression.

[0056] Specifically, a vibration influence coefficient is extracted from the vibration suppression requirement parameters. This coefficient reflects the degree of influence of vibration on the accuracy of the indentation shape during the test. Based on this coefficient, an adaptive damping algorithm is used to calculate the response parameters of the damping mechanism. An adaptive damping algorithm is constructed based on the PID control principle. First, a mapping relationship between the vibration influence coefficient and the damping response characteristics is established. The proportional element determines the basic damping strength based on the deviation between the current vibration influence coefficient and the target value. The integral element accumulates historical deviation information to eliminate steady-state error, and the derivative element predicts vibration change trends to provide proactive adjustment. Next, the frequency characteristic parameters of the damping response are calculated based on the numerical range of the vibration influence coefficient. This method allows the system to flexibly adjust the damping response at different frequency bands to ensure the vibration suppression effect reaches the expected level. For example, when the vibration influence coefficient is greater than 0.8, it indicates strong high-frequency vibration interference, and the response parameters focus on suppressing high-frequency vibrations above 100Hz. When the vibration influence coefficient is between 0.3 and 0.8, it mainly targets mid-frequency vibration adjustment between 20Hz and 100Hz. Finally, based on the rate of change of the vibration influence coefficient, an exponential decay function is used to calculate the time constant of the damping response. A smaller time constant is used for a large rate of change to achieve a fast response, while a larger time constant is used for a small rate of change to ensure system stability. This determines the response speed parameters of the damping mechanism, and finally, the response parameters of the damping mechanism are calculated. The response parameters of a damping mechanism include damping strength, frequency response parameters, and response speed parameters.

[0057] After calculating the response parameters, the damping coefficient of the damping mechanism is adjusted based on these parameters. This process requires targeted adjustments based on the frequency characteristics of the response parameters. For cases requiring high-frequency vibration suppression, the viscous damping coefficient is increased to provide stronger velocity-dependent damping force, thus suppressing high-frequency vibrations. For mid-to-low-frequency vibrations, the structural damping coefficient is primarily adjusted to alter the system's inherent damping characteristics, thereby effectively controlling mid-to-low-frequency vibrations. Simultaneously, a graded adjustment strategy for the damping coefficient is implemented, adjusting by different multiples based on the damping requirement indicated by the response parameters. When strong damping is required, the damping coefficient is adjusted by a 1.5-fold increase; when medium damping is required, it is adjusted by a 1.2-fold increase; and when slight damping adjustment is required, it is fine-tuned by a 1.1-fold increase. This adjustment method, combining frequency band differentiation with graded amplitudes, avoids drastic changes in the damping coefficient that could impact the testing process. It ensures targeted attenuation of vibration energy while preventing abrupt coefficient changes from introducing secondary interference, ultimately achieving a stable and smooth vibration suppression effect.

[0058] After the damping coefficient is adjusted, further vibration data is collected. During this process, the system monitors changes in vibration amplitude and spectral characteristics, analyzing this data to determine whether the current vibration interference has been effectively suppressed. If the collected vibration data indicates that the interference level is still high, the system will recalculate and adjust the response parameters until the vibration influence coefficient drops below a preset threshold. When the updated vibration influence coefficient is less than the third preset threshold (convergence threshold, used to terminate the optimization process), it indicates that vibration suppression has reached an effective level. At this point, the current response parameters are determined as the real-time control parameters for vibration suppression, and these parameters are output to the control unit of the damping device. The control unit makes final adjustments to the damping system based on these parameters to ensure that the impact of vibration is minimized during the test.

[0059] For example, during testing, a calculated vibration influence coefficient of 0.85 indicates strong vibration interference. Based on the adaptive damping algorithm, the damping gain calculated by the proportional element is 2.3. Further integral and derivative adjustments enable the system to respond quickly and adapt to vibration changes. Within the high-frequency range of vibration interference (e.g., 180Hz, 320Hz), the algorithm increases the damping coefficient, effectively suppressing vibrations at these frequencies. After adjustment, the vibration influence coefficient drops to 0.18, far below the preset threshold of 0.2, indicating that the system has successfully suppressed vibration interference.

[0060] This dynamic adjustment mechanism allows for flexible control of damping parameters based on real-time vibration data, effectively suppressing vibration interference and ensuring that the indentation shape data in the Vickers hardness test is not affected by unnecessary vibration. This improves the stability and adaptability of the testing process, thereby enhancing testing accuracy.

[0061] Step 7: Drive the damping device to perform high-frequency vibration smoothing operation according to the real-time control parameters to obtain stable test process data.

[0062] Specifically, the damping device is driven to perform high-frequency vibration smoothing operation based on real-time control parameters. This aims to reduce or eliminate the interference of high-frequency vibrations on the testing process by adjusting the damping mechanism. The real-time control parameters include the vibration influence coefficient and the response characteristics of the damping system. These data are processed by an algorithm and converted into control signals for the damping device. By continuously monitoring vibration data and combining it with the real-time calculated control parameters, the damping device is instructed to enhance its damping effect in the high-frequency vibration range, smoothing vibration fluctuations. In practice, the damping device adjusts its damping coefficient according to the control parameters, ensuring that high-frequency vibrations are rapidly absorbed and converted when applied to the test sample, thus avoiding the influence of vibration interference on the Vickers hardness test data.

[0063] This technical feature effectively eliminates test data fluctuations caused by high-frequency vibration, ensuring the stability and accuracy of the testing process, especially in high-precision hardness testing, providing more reliable experimental data. Through this technology, the system not only achieves dynamic balance and response but also improves test repeatability and consistency through precise vibration control, thus effectively solving the error problem caused by vibration interference in high-precision testing.

[0064] Step 8: Extract material surface characteristics and mechanical property parameters from the test process data, and calculate the co-optimization parameters through dynamic parameter adjustment model to obtain the final test control parameters.

[0065] In one specific embodiment, the process of performing step 8 may specifically include the following steps: (1) Input the material surface roughness and mechanical property parameters into the pre-established dynamic parameter adjustment model; (2) The model calculation parameters for the combined optimization of load and tilt angle are adjusted by dynamically adjusting the parameters; (3) Based on the collaborative optimization parameters, generate the final test control parameters including the load application speed, force value and tilt angle adjustment.

[0066] Specifically, the test data is first preprocessed to remove noise and outliers, ensuring data reliability. Based on this, material surface roughness and mechanical property parameters, such as elastic modulus and hardness values, are extracted from the test data.

[0067] The pre-processed material surface roughness (e.g., root mean square value or average deviation value) and mechanical property parameters (elastic modulus, hardness, etc.) are input into a pre-trained multilayer neural network model. This model performs nonlinear feature extraction using a three-hidden-layer architecture containing 12-8-6 neurons, and calculates intermediate feature vectors through forward propagation of the neural network. Subsequently, a mathematical model of the coupling relationship between load and tilt angle is established based on the output of the neural network, considering the influence of load application on the tilt angle accuracy of the indenter, as well as the reaction effect of tilt angle changes on the uniformity of load distribution. By defining a load-tilt angle coupling coefficient matrix, the sensitivity of tilt angle adjustment under different load levels is described, and the spatial gradient vector of the load distribution is calculated to obtain the direction and amplitude information of load changes. Next, a tilt angle compensation function is established to automatically calculate the required tilt angle adjustment based on the load distribution gradient. Finally, a genetic algorithm is used for multi-objective optimization, with load uniformity (variance coefficient) and tilt accuracy (root mean square of angle deviation) as fitness functions. The algorithm is solved iteratively through roulette wheel selection, single-point crossover and Gaussian mutation operations of 50 populations. The final output is the combination of load application speed, target force value and tilt adjustment parameters that make the two work together optimally.

[0068] Based on the co-optimization results, test control parameters are generated, including load application speed, target force value, and indentation angle adjustment. These parameters reflect the optimal speed and accuracy requirements for load application during the test, as well as the control requirements for adjusting the indentation angle, thereby ensuring the standardization of indentation shape and the stability of test data. This process aims to dynamically adjust the load application and indenter indentation angle according to the surface and mechanical properties of the material to ensure high accuracy and consistency in Vickers hardness testing.

[0069] The above describes the Vickers hardness test control method based on multi-parameter optimization in the embodiments of this application. The following describes the Vickers hardness test control system based on multi-parameter optimization in the embodiments of this application. Please refer to [link to relevant documentation]. Figure 2 The present application provides a schematic diagram of an embodiment of a Vickers hardness test control system based on multi-parameter optimization. The system includes: The load assessment module 10 is used to collect force distribution data during the load application process through sensors, calculate the global load uniformity coefficient, and obtain a load application uniformity assessment report.

[0070] The parameter optimization module 20 is used to adjust the load application parameters according to the load application uniformity evaluation report and determine the optimized load application parameters.

[0071] The tilt angle calculation module 30 is used to extract velocity and force data from the optimized load application parameters, and calculate the tilt angle adjustment amount by combining the tilt angle data to obtain the head tilt angle control parameters.

[0072] The calibration execution module 40 is used to drive the adjustment mechanism to perform tilt angle calibration according to the indenter tilt angle control parameters, so as to obtain standardized indentation shape data.

[0073] The vibration analysis module 50 is used to extract geometric features from standardized indentation shape data and, in combination with vibration data, calculate the vibration influence coefficient to obtain vibration suppression requirement parameters.

[0074] The damping control module 60 is used to adjust the response characteristics of the damping mechanism according to the vibration suppression requirement parameters and determine the real-time control parameters for vibration suppression.

[0075] The vibration suppression module 70 is used to drive the damping device to perform high-frequency vibration smoothing operation according to real-time control parameters, so as to obtain stable test process data.

[0076] The collaborative optimization module 80 is used to extract material surface characteristics and mechanical property parameters from the test process data, and calculate collaborative optimization parameters through dynamic parameter adjustment model to obtain the final test control parameters.

[0077] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0078] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A Vickers hardness test control method based on multi-parameter optimization, characterized in that, include: Step 1: Collect force distribution data during the load application process using sensors, calculate the global load uniformity coefficient, and obtain a load application uniformity assessment report; Step 2: Adjust the load application parameters according to the load application uniformity evaluation report to determine the optimized load application parameters; Step 3: Extract velocity and force data from the optimized load application parameters, and calculate the tilt angle adjustment amount by combining the tilt angle data to obtain the indenter tilt angle control parameters; Step 4: Drive the adjustment mechanism to perform tilt angle calibration based on the indenter tilt angle control parameters to obtain standardized indentation shape data; Step 5: Extract geometric features from the standardized indentation shape data, and combine them with vibration data to calculate the vibration influence coefficient and obtain the vibration suppression requirement parameters; Step 6: Adjust the response characteristics of the damping mechanism based on the vibration suppression requirement parameters to determine the real-time control parameters for vibration suppression; Step 7: Drive the damping device to perform high-frequency vibration smoothing operation according to the real-time control parameters to obtain stable test process data; Step 8: Extract material surface characteristics and mechanical property parameters from the test process data, and calculate the collaborative optimization parameters through dynamic parameter adjustment model to obtain the final test control parameters.

2. The control method as described in claim 1, characterized in that, Step 1 includes: The root mean square error and average force value at each sensor location are calculated based on the force distribution data. Calculate the standard deviation of the root mean square of all force values ​​and the average of the average of all force values, and define the ratio of the average to the standard deviation as the global load uniformity coefficient; Determine whether the global load uniformity coefficient is less than a first preset threshold. If yes, generate a first evaluation result characterizing uneven load distribution; otherwise, generate a second evaluation result characterizing uniform load distribution. Output a load application uniformity assessment report based on the evaluation results.

3. The control method as described in claim 1, characterized in that, Step 2 includes: Extract the global load uniformity coefficient from the load application uniformity assessment report; Based on the global load uniformity coefficient, a servo control system is used to adjust the load application speed, obtain the adjusted load application speed, and collect new force distribution data. Based on the new force distribution data, the coefficient of variation at each sensor location is calculated, and the reciprocal of the coefficient of variation is used as the single-point load stability coefficient. Determine whether the single-point load stability coefficient meets the preset optimization conditions. If so, use the adjusted load application speed and the new force distribution data as the optimized load application parameters.

4. The control method as described in claim 1, characterized in that, Step 3 includes: Acquire the tilt angle data collected in real time by the indenter tilt angle sensor; The theoretical tilt angle value is obtained based on the velocity value and the force value data, and the difference between the theoretical tilt angle value and the tilt angle data is used as the tilt angle deviation value of the indenter. Based on the tilt angle deviation value, the tilt angle adjustment amount is calculated using a preset tilt angle adjustment algorithm; Based on the tilt angle adjustment amount, the indenter tilt angle control parameters, including the adjustment direction and adjustment range, are generated.

5. The control method as described in claim 4, characterized in that, Step 4 includes: Extract the adjustment direction and adjustment range from the indenter tilt angle control parameters; The pressure head adjustment mechanism is driven by a stepper motor to perform dynamic tilt angle calibration according to the adjustment direction and adjustment range, and the calibrated indentation shape data is collected. The indentation shape data is normalized using a preset standardization algorithm to generate standardized indentation shape data; Based on the standardized indentation shape data, determine whether the indentation shape conforms to the preset shape standard. If yes, output the standardized indentation shape data. If no, re-calibrate and test the tilt angle until the indentation shape conforms to the shape standard.

6. The control method as described in claim 1, characterized in that, Step 5 includes: Acquire the vibration data collected in real time by the vibration sensor; Based on the geometric features and the vibration data, the degree of deformation at the indentation edge is calculated by weighted combination; For the degree of deformation, a preset vibration influence model is used to calculate the vibration influence coefficient; Determine whether the vibration influence coefficient is greater than a second preset threshold. If so, generate the vibration suppression requirement parameter that characterizes the existence of edge deformation problems.

7. The control method according to claim 1, characterized in that, Step 6 includes: Extract the vibration influence coefficient from the vibration suppression requirement parameters; Based on the vibration influence coefficient, the response parameters of the damping mechanism are calculated using an adaptive damping algorithm; The damping coefficient of the damping mechanism is adjusted based on the response parameters, and new vibration data is collected. Based on the new vibration data, the updated vibration influence coefficient is calculated; Determine whether the updated vibration influence coefficient is less than a third preset threshold. If so, use the response parameter as the real-time control parameter for vibration suppression.

8. The control method as described in claim 1, characterized in that, Step 8 includes: The surface roughness and mechanical property parameters of the material are input into a pre-established dynamic parameter adjustment model; The model is adjusted using the dynamic parameters to calculate the co-optimization parameters of load and tilt angle; Based on the aforementioned collaborative optimization parameters, final test control parameters are generated, including load application speed, force value, and tilt angle adjustment.

9. A Vickers hardness test control system based on multi-parameter optimization, used to implement the method as described in any one of claims 1 to 8, characterized in that, The system includes: The load assessment module is used to collect force distribution data during the load application process through sensors, calculate the global load uniformity coefficient, and obtain a load application uniformity assessment report. The parameter optimization module is used to adjust the load application parameters according to the load application uniformity evaluation report and determine the optimized load application parameters. The tilt angle calculation module is used to extract velocity and force data from the optimized load application parameters, and calculate the tilt angle adjustment amount by combining the tilt angle data to obtain the indenter tilt angle control parameters; The calibration execution module is used to drive the adjustment mechanism to perform tilt angle calibration according to the indenter tilt angle control parameters, so as to obtain standardized indentation shape data; The vibration analysis module is used to extract geometric features from the standardized indentation shape data and, in combination with vibration data, calculate the vibration influence coefficient to obtain vibration suppression requirement parameters. The damping control module is used to adjust the response characteristics of the damping mechanism according to the vibration suppression requirement parameters and determine the real-time control parameters for vibration suppression. The vibration suppression module is used to drive the damping device to perform high-frequency vibration smoothing operation according to the real-time control parameters, so as to obtain stable test process data. The collaborative optimization module is used to extract material surface characteristics and mechanical property parameters from the test process data, calculate collaborative optimization parameters through dynamic parameter adjustment model, and thus obtain the final test control parameters.

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