A method for suppressing secondary damage during variable parameter drilling of composite materials with delamination damage

Abstract

The present invention discloses a method for suppressing secondary damage during variable-parameter drilling in repairing composite materials containing delamination damage, comprising: discovering key features of the axial force curve during drilling of composite materials containing delamination damage, revealing the variation pattern of the drilling axial force with process parameters, and adjusting the process parameters; proposing an equivalent delamination factor to quantitatively evaluate delamination damage, establishing a correlation model between axial force and delamination damage, and adjusting the axial force value; establishing a support vector regression prediction model to improve the intelligent means of drilling axial force prediction and quality assessment; developing a damage interface identification algorithm to determine the location of parameter changes; constructing a multivariate linear regression model to predict the axial force time domain curve, and then setting the damage interface algorithm threshold. This method can cope with various delamination damages, realize intelligent optimization of drilling process parameters in composite material repair, and minimize the expansion of initial delamination damage and the generation of new delamination damage, thereby reducing repair costs, improving repair efficiency, and ensuring repair quality.

Description

Technical Field

[0001] The invention belongs to the technical field of composite material mechanical repair, and in particular relates to a method for suppressing secondary damage during variable parameter drilling of composite material containing delamination damage. Background Art

[0002] Carbon fiber composites are increasingly being used in aerospace due to their high specific stiffness, high specific strength, designability, and fatigue resistance. Composites exhibit anisotropy in their macroscopic mechanical properties, with high in-plane strength and weaker strength through the thickness, particularly between layers, which is the composite's weakest point. During the composite molding process, uneven resin flow and pressure can easily form resin-rich regions between layers, leading to differential shrinkage between layers. Incomplete curing of the material, the ingress of foreign particles, and uneven heating of the equipment can also cause interlaminar debonding, leading to initial interlaminar damage or cracks in composite structures early in service. During aircraft service, composite components are frequently subjected to alternating high and low temperatures, high humidity, and complex loads. This not only causes existing interlaminar delamination damage to propagate under extreme conditions, but also leads to the formation of new delamination damage. These delaminations reduce strength and stiffness, severely impairing the overall performance of the composite component and posing significant safety risks. To reduce costs and cycle times, it is necessary to repair composite components with delamination defects in situ rather than simply replacing them.

[0003] Generally, drilling and riveting at the delamination damage site and its envelope area can inhibit the expansion of delamination damage and restore the overall strength and stiffness of the component. Therefore, when aircraft composite components with delamination damage need to be repaired by riveting, it is inevitable to make connection holes in the damaged area. However, due to anisotropy and heterogeneity, composite materials are usually difficult to process and are prone to burrs, tears and delamination, which seriously weakens the assembly reliability and service performance stability of the components. In addition, drilling in the delamination area may aggravate the original delamination damage and even cause the structural component to fail directly into two sub-components. Therefore, there is an urgent need to study methods for suppressing secondary damage in drilling repair of composite materials with delamination damage, which is of great significance to improving repair quality and extending component life.

[0004] At present, research on drilling of composite materials mainly focuses on theoretical models of axial forces and drilling experiments to explore factors affecting drilling quality. Research considering initial delamination defects mainly focuses on material property evaluation, such as strength, stiffness and fatigue. Among them, compression load and fatigue load are the most sensitive factors affecting the delamination expansion behavior of structural parts in engineering applications. Existing research is of great significance to promoting the development of cutting technology and the widespread application of composite materials in various fields. However, few studies on drilling of composite materials take into account the delamination defects inside the material. In the in-situ drilling and riveting repair of composite parts with delamination damage, the existing delamination damage changes the stress state of the repair area during the processing. The original drilling and riveting technology and process methods for assembly and connection of carbon fiber composite materials cannot adapt to composite materials with delamination damage, and it is difficult to solve the problems of low quality and large damage in the repair of parts with delamination damage. Therefore, the present invention proposes a method for suppressing secondary damage by variable parameter drilling for repairing composite materials with delamination damage. Summary of the Invention

[0005] In response to the deficiencies of the above-mentioned existing technologies, the present invention provides a method for suppressing secondary damage during variable parameter drilling during the repair of composite materials containing delamination damage, which can effectively avoid the expansion of initial delamination damage caused by drilling during the repair of composite materials containing delamination damage and reduce the formation of new delamination damage at the outlet.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0007] A method for suppressing secondary damage during variable parameter drilling of composite materials containing delamination damage comprises the following steps:

[0008] S1. Set reasonable process parameter gradients to conduct drilling experiments on composite materials with delamination damage; use intact composite materials as a control group to study the effect of defects on the time-domain curve of the axial force during composite drilling; analyze the influence of different process parameters on the axial force during drilling of delaminated composite materials, extract the characteristic data of the average value of the stable section of the drilling axial force curve; analyze the correlation between the drilling axial force and process parameters, and construct a process database;

[0009] S2. Use ultrasonic phased array nondestructive testing to detect interlaminar damage in composite materials containing delamination damage. Use a digital ultra-depth-of-field microscope to take photos of the exit damage and perform image processing on the photos. Propose an equivalent delamination factor to quantitatively evaluate the exit delamination damage, analyze the correlation between exit delamination damage and process parameters, and construct a quality database.

[0010] S3. Construct a support vector regression model and determine its hyperparameters through a grid search algorithm. The model uses speed, feed, and quantitative characteristics of delamination defects as inputs, and drilling axial force and equivalent delamination factor as outputs to predict drilling axial force and exit delamination damage under different process parameters.

[0011] S4. Analyze the correlation between outlet delamination damage and drilling axial force, fit the drilling axial force and outlet delamination damage data by the least squares method, and establish a quantitative correlation model between outlet delamination damage and drilling axial force;

[0012] S5. Analyze the trend characteristics of the drilling axial force time domain curve under different process parameters, establish a multivariate linear regression model, predict the key characteristic inflection points of the drilling axial force time domain curve, and predict the change trend of the drilling axial force time domain curve under different process parameters through the key characteristic inflection points;

[0013] S6. Calculate the difference of the drilling axial force with respect to the time integral to determine the damage interface identification algorithm. Combined with the quantitative correlation model between the outlet delamination damage and the drilling axial force in step 4 above, determine the drilling axial force value that needs to be adjusted by the variable parameter strategy. Combined with the correlation relationship between the drilling axial force and the process parameters in step 1 above, determine the amount of process parameters that need to be adjusted by the variable parameter strategy. Combined with the correlation model between the outlet delamination damage and the process parameters in step 2, predict the degree of outlet delamination damage after variable parameter drilling.

[0014] S7. The damage interface parameters are controlled by the variable parameter drilling strategy to avoid the expansion of the initial delamination damage during the drilling process and inhibit the formation of new delamination damage at the exit.

[0015] Furthermore, the method also requires software and hardware, including: a drilling end and its controller, a force measuring instrument and data processing software.

[0016] Furthermore, step S1 includes the following sub-steps:

[0017] S11. Build a drilling experimental platform, design a composite material drilling experimental plan, and use a dynamometer to collect drilling axial force curves under different process parameters;

[0018] S12, selecting a low-pass filtering algorithm to filter the collected drilling axial force data, filtering out interference signals such as high-frequency noise in the signal, and obtaining a drilling axial force characteristic signal;

[0019] S13, extracting drilling axial force data in the stable section and calculating the average drilling axial force;

[0020] S14. Reveal the variation of drilling axial force with process parameters.

[0021] Furthermore, step S2 includes the following sub-steps:

[0022] S21. After drilling, interlayer damage is scanned using ultrasonic phased array nondestructive testing, and exit damage photos are taken using a digital ultra-depth-of-field microscope.

[0023] S22. Perform image processing on the scanned and photographed exit damage photos and calculate the equivalent delamination factor;

[0024] S23. Analyze the variation of equivalent stratification factors with process parameters and build a quality database.

[0025] Furthermore, step S5 includes the following sub-steps:

[0026] S51, extracting the drilling axial force value at the key characteristic inflection point of the drilling axial force time domain curve;

[0027] S52. Use the multiple linear regression model to construct an empirical relationship formula between the drilling axial force value at the key characteristic inflection point of the drilling axial force time domain curve and the feed speed and rotation speed, and verify the accuracy of the multiple linear regression model.

[0028] Furthermore, step S6 includes the following sub-steps:

[0029] S61. Predicting the axial force time domain curve from the process parameters using a drilling axial force time domain curve prediction model;

[0030] S62. For the predicted drilling axial force time domain curve, integrate the drilling axial force value every Δt time interval, compare the current drilling axial force integral value within the Δt time interval with the previous drilling axial force integral value within the Δt time interval, and calculate the difference between the two.

[0031] S63. For the real-time collected drilling axial force time domain curve, the drilling axial force value is integrated every Δt time. The drilling axial force integral value within the current Δt time is compared with the drilling axial force integral value within the previous Δt time, and the difference between the two is calculated. If the absolute value of the difference exceeds the set threshold, the point can be determined as the damage location; the threshold is set to the drilling axial force integral difference within adjacent Δt time calculated for the predicted drilling axial force time domain curve in step S62.

[0032] S64. Determine the drilling axial force value that needs to be adjusted for the variable parameter strategy based on a quantitative correlation model between exit delamination damage and drilling axial force; determine the amount of process parameters that need to be adjusted for the variable parameter strategy based on a correlation relationship between process parameters and drilling axial force characteristics; and predict the degree of exit delamination damage after variable parameter drilling based on the correlation model between exit delamination damage and process parameters.

[0033] Furthermore, step S7 is specifically as follows:

[0034] The drilling end and its controller execute corresponding instructions according to the variable parameter strategy to complete the variable parameter drilling process; the variable parameter drilling strategy is used to achieve parameter control of the damage interface, thereby avoiding the expansion of initial delamination damage during the drilling process and suppressing the formation of new delamination damage at the exit.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The provided variable parameter drilling secondary damage suppression method for repairing composite materials with delamination damage can target delamination damage of any shape, size, and position; the location of parameter change is determined by the damage interface recognition algorithm, and variable parameter regulation is realized by combining the process parameter-drilling axial force-exit delamination damage correlation model, avoiding the expansion of initial delamination defects and severe delamination damage at the drilling exit caused by improper drilling process during mechanical repair of composite materials, and even causing the damaged component to be directly scrapped and unable to be repaired.

[0037] The variable parameter drilling strategy can monitor and process the drilling force signal in real time, make real-time decisions and provide feedback on the drilling process. It is an important means to achieve personalized, automated and intelligent composite material repair, which is conducive to reducing repair costs, improving repair efficiency and ensuring repair quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The time domain curves of axial force during drilling of intact composite materials and composite materials with delamination damage are shown;

[0039] Figure 2 The delamination damage morphology and equivalent delamination factor calculation method of composite material drilling exit are presented;

[0040] Figure 3 It is the principle of support vector regression model;

[0041] Figure 4 The process of optimizing support vector regression model hyperparameters for the grid search algorithm;

[0042] Figure 5 Prediction process for support vector regression model;

[0043] Figure 6 The axial force prediction results for drilling composite materials with delamination damage are shown;

[0044] Figure 7 The prediction results of equivalent delamination factor for composite materials with delamination damage;

[0045] Figure 8 The correlation between the equivalent delamination factor of composite materials with delamination damage and the drilling axial force;

[0046] Figure 9This is the prediction result of the time domain curve of the axial force when drilling composite materials with delamination damage;

[0047] Figure 10 Flowchart of the online damage location identification algorithm;

[0048] Figure 11 The results are used to verify the accuracy of the damage location identification algorithm under the variable speed strategy;

[0049] Figure 12 To verify the accuracy of the damage location identification algorithm under the variable feed strategy;

[0050] Figure 13 The results are provided to verify the accuracy of the damage location identification algorithm under variable speed and feed strategies;

[0051] Figure 14 Implementation process of variable parameter drilling strategy for composite materials robot;

[0052] Figure 15 Comparison of hole wall and outlet damage effects between fixed parameter hole making and variable parameter hole making. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] Although the steps in the present invention are arranged with numbers, they are not intended to limit the order of the steps. Unless the order of the steps is clearly stated or the execution of a step requires other steps as a basis, the relative order of the steps can be adjusted. It is understood that the term "and / or" used herein refers to and covers any and all possible combinations of one or more of the associated listed items.

[0055] like Figures 1 to 15 The present invention provides a method for suppressing secondary damage during variable parameter drilling of composite materials containing delaminated damage. The hardware and software devices required to implement the method include a drilling end and its controller, a force measuring instrument, and data processing software.

[0056] In this embodiment, the method specifically includes the following steps:

[0057] S1. Set reasonable process parameter gradients to conduct drilling experiments on composite materials with delamination damage; use intact composite materials as a control group to study the effect of defects on the time-domain curve of the axial force during composite drilling; analyze the influence of different process parameters on the axial force during drilling of delaminated composite materials, extract the characteristic data of the average value of the stable section of the drilling axial force curve; analyze the correlation between the drilling axial force and process parameters, and construct a process database;

[0058] As a preferred implementation of step S1, it specifically includes:

[0059] S11. Build a drilling experimental platform, design a composite material drilling experimental plan, and use a dynamometer to collect drilling axial force curves under different process parameters. In this embodiment, the dynamometer acquisition frequency is set to 10KHz, and the acquisition time is calculated according to the composite material plate thickness and feed speed. ,in, t is the acquisition time, in seconds (s); h is the thickness of the plate, in millimeters (mm); f is the feed rate in millimeters per minute (mm / min);

[0060] S12, selecting a low-pass filtering algorithm to filter the collected drilling axial force data, filtering out interference signals such as high-frequency noise in the signal, and obtaining a drilling axial force characteristic signal; Figure 1 As shown in the figure, when drilling a composite material with delamination damage, unlike the intact composite material, the curve will show an obvious concave feature when drilling to the defect location, and the damage interface can be identified and determined based on the concave feature;

[0061] S13, extracting drilling axial force data in the stable section and calculating the average drilling axial force;

[0062] S14. Reveal the variation of drilling axial force with process parameters.

[0063] S2. Use ultrasonic phased array nondestructive testing to detect interlaminar damage in composite materials containing delamination damage. Use a digital ultra-depth-of-field microscope to take photos of the exit damage and perform image processing on the photos. Propose an equivalent delamination factor to quantitatively evaluate the exit delamination damage, analyze the correlation between exit delamination damage and process parameters, and construct a quality database.

[0064] As a preferred implementation of step S2, it specifically includes:

[0065] S21. After drilling, interlayer damage is scanned using ultrasonic phased array nondestructive testing, and exit damage photos are taken using a digital ultra-depth-of-field microscope.

[0066] S22. Perform image processing on the scanned and photographed exit damage photos and calculate the equivalent delamination factor; e.g. Figure 2 As shown in FIG, the calculation method of the composite material drilling outlet delamination damage morphology and equivalent delamination factor used in this embodiment is:

[0067] ;

[0068] in, is the equivalent stratification factor, is the nominal diameter of the hole, is the equivalent layered diameter; by equating the layered area to a circle concentric with the standard aperture, the diameter of the circle is the equivalent layered diameter, that is:

[0069] ;

[0070] ;

[0071] in, is the delamination damage area around the hole, It is the standard hole area formed around the hole.

[0072] S23. Analyze the variation of equivalent stratification factors with process parameters and build a quality database.

[0073] S3. Construct a support vector regression model and determine its hyperparameters through a grid search algorithm. The model uses speed, feed, and quantitative characteristics of delamination defects as inputs, and drilling axial force and equivalent delamination factor as outputs to predict drilling axial force and exit delamination damage under different process parameters.

[0074] The support vector machine regression model constructed in this embodiment is as follows Figure 3 As shown. The experimental data is divided into a training set and a test set, which are used to build the model and verify the accuracy of the model respectively. Support vector regression is a regression method based on support vector machine. The construction of support vector regression model is essentially a process of selecting kernel function and optimizing parameters. Its principle is to map nonlinear space to high-dimensional feature space through nonlinear mapping and search for the optimal linear function (hyperplane) in the feature space. Figure 3 (a) is the data of nonlinear space, Figure 3 (b) is a high-dimensional feature space data. In the feature space, the support vector regression model defines the variables , as long as the deviation between the predicted value and the true value does not exceed , then the prediction result is considered accurate. Otherwise, the loss needs to be calculated and ( is the distance from the sampling point outside the upper decision boundary to the upper decision boundary, is the distance from the sampling point outside the lower decision boundary to the lower decision boundary), the optimization goal of the support vector regression model is: to make the interval Maximize the total loss minimize.

[0075] For the constructed support vector regression model, the hyperparameters are the main factors affecting the model fitting accuracy. The grid search algorithm is used to optimize the regularization parameters of the support vector regression model.C and kernel function parameters The flowchart of the grid search algorithm to optimize SVR hyperparameters is as follows: Figure 4 The grid search algorithm is a parameter optimization algorithm that uses a cross-validation scheme. That is, within a certain parameter range, different parameters are arranged and combined according to a specified step size, each set of parameter combinations is tested, and the parameter set with the best performance index is used as the final parameter value.

[0076] The process of prediction through the constructed support vector regression model is as follows Figure 5 As shown in the figure. The spindle speed, feed rate and layer width in the test set are used as input parameters of the optimal SVR model. The output result is the drilling axial force or equivalent layer factor. The prediction performance of the SVR model is evaluated by comparing the difference between the output value and the true value in the test set. In the data analysis method, the mean absolute error (MAE), root mean square error (RMS) and determination coefficient are usually used to calculate the difference between the two sets of data. As an evaluation indicator. Among them, MAE is used to quantify the absolute value of the error between the predicted value and the observed value, and RMSE is used to quantify the absolute error between the predicted value and the observed value. Used to judge the degree of fit between the model and the curve trend, the formula of the above performance indicators is given as follows:

[0077] ;

[0078] ;

[0079] ;

[0080] in, represents a set of observations, Represents the predicted value of this group of data, represents the average value of the data set. m Indicates the number of this set of data.

[0081] Through the above SVR model, such as Figure 6 As shown in Figure 1, the drilling axial force under different process parameters is predicted. The errors between the predicted values ​​and the experimental values ​​are shown in Table 1 below. The maximum error is 10.18%.

[0082] Table 1 Errors between predicted and experimental drilling axial forces under different process parameters

[0083] Serial number Spindle speed (r / min) Feed speed (mm / min) Layer width (mm) Predicted value Experimental value error 1 6000 500 10 71.48 71.00 0.68% 2 6000 900 10 103.63 108.33 -4.54% 3 8000 500 10 59.90 53.80 10.18% 4 6000 100 0 30.17 30.45 -0.94% 5 6000 500 0 67.07 61.92 7.68% 6 6000 700 0 84.10 84.86 -0.90% 7 3500 500 0 79.78 81.09 -1.64%

[0084] like Figure 7 As shown in Figure 2, the equivalent delamination factors under different process parameters are predicted. The errors between the predicted values ​​and the experimental values ​​are given in Table 2 below, with the maximum error being 3.79%.

[0085] Table 2 Errors between predicted and experimental values ​​of equivalent delamination factors under different process parameters

[0086] Serial number Spindle speed (r / min) Feed speed (mm / min) Layer width (mm) Predicted value Experimental value error 1 6000 500 10 1.3370 1.3340 0.23% 2 6000 700 10 1.3893 1.4170 -1.95% 3 4500 500 10 1.3856 1.3840 0.12% 4 8000 500 10 1.2954 1.3260 -2.31% 5 6000 100 0 1.1490 1.1720 -1.96% 6 6000 900 0 1.4114 1.4670 -3.79% 7 6000 500 20 1.3635 1.3810 -1.27%

[0087] Since the equivalent delamination factor is used to quantitatively evaluate the exit delamination damage, step S3 ultimately achieves the establishment of correlations between the drilling axial force and process parameters, and between the exit delamination factor and process parameters for the composite material containing delamination damage.

[0088] S4. Analyze the correlation between outlet delamination damage and drilling axial force, fit the drilling axial force and outlet delamination damage data by the least squares method, and establish a quantitative correlation model between outlet delamination damage and drilling axial force;

[0089] In this embodiment, Figure 8 As shown, Figure 8 (a) is the relationship between the equivalent delamination factor and the drilling axial force, Figure 8 (b) is the correlation analysis result between the equivalent delamination factor and the drilling axial force. Figure 8 From (a), we can see that the equivalent delamination factor and the drilling axial force are positively correlated. By fitting the positive correlation between the two using the least squares method, we get the following relationship:

[0090] ;

[0091] in, represents the equivalent stratification factor, The above relationship provides a theoretical basis for the subsequent secondary damage suppression in variable parameter drilling.

[0092] S5. Analyze the trend characteristics of the drilling axial force time domain curve under different process parameters, establish a multivariate linear regression model, predict the key characteristic inflection points of the drilling axial force time domain curve, and predict the change trend of the drilling axial force time domain curve under different process parameters through the key characteristic inflection points;

[0093] As a preferred implementation of step S5, it specifically includes:

[0094] S51, extracting the drilling axial force value at the key characteristic inflection point of the drilling axial force time domain curve;

[0095] S52. Use the multiple linear regression model to construct an empirical relationship formula between the drilling axial force value at the key characteristic inflection point of the drilling axial force time domain curve and the feed speed and rotation speed, and verify the accuracy of the multiple linear regression model.

[0096] In this embodiment, Figure 9The prediction results of the time domain curve of the axial force during drilling of composite materials with delamination damage are given. Composite materials with delamination defects have four characteristic inflection points. The empirical formulas for fitting the characteristic inflection points using the multivariate linear regression model are:

[0097] ;

[0098] in, Represents the drilling axial forces at the four characteristic inflection points A, B, C, and D, respectively. n Indicates the spindle speed (unit: r / min), f Indicates the spindle feed speed (unit: mm / min).

[0099] According to the above empirical formula, the drilling axial force curve is predicted under the parameters of speed 6000r / min and feed speed 500mm / min as follows Figure 9 As shown; the error between the predicted value and the experimental value is given in Table 3 below, and the error is controlled at 8.52%;

[0100] Table 3 Errors between the predicted and experimental values ​​of the drilling axial force at each characteristic inflection point of the drilling axial force time domain curve under the parameters of a rotation speed of 6000 r / min and a feed rate of 500 mm / min

[0101] Feature Points Predicted value Experimental value error <![CDATA[Fitting degree R 2 > P-value Point A 44.887 42.993 4.22% 0.9252 0.0015 Point B 70.109 64.133 8.52% 0.9191 0.0019 Point C 57.800 53.017 8.28% 0.9342 0.0011 Point D 58.104 54.220 6.68% 0.9181 0.0019

[0102] S6. Calculate the difference of the drilling axial force with respect to the time integral to determine the damage interface identification algorithm. Combined with the quantitative correlation model between the outlet delamination damage and the drilling axial force in step 4 above, determine the drilling axial force value that needs to be adjusted by the variable parameter strategy. Combined with the correlation relationship between the drilling axial force and the process parameters in step 1 above, determine the amount of process parameters that need to be adjusted by the variable parameter strategy. Combined with the correlation model between the outlet delamination damage and the process parameters in step 2, predict the degree of outlet delamination damage after variable parameter drilling.

[0103] As a preferred implementation mode of step S6, it specifically includes:

[0104] S61. Predicting the axial force time domain curve from the process parameters using a drilling axial force time domain curve prediction model;

[0105] S62. For the predicted drilling axial force time domain curve, integrate the drilling axial force value every Δt time interval, compare the current drilling axial force integral value within the Δt time interval with the previous drilling axial force integral value within the Δt time interval, and calculate the difference between the two.

[0106] S63. For the real-time acquired drilling axial force time domain curve, integrate the drilling axial force value at intervals of Δt. Compare the current drilling axial force integral value within Δt with the previous drilling axial force integral value within Δt, and calculate the difference between the two. If the absolute value of the difference exceeds a set threshold, the point is determined to be a damage location; the threshold is set to the drilling axial force integral difference within adjacent Δt periods calculated for the predicted drilling axial force time domain curve in step S62.

[0107] S64. Determine the drilling axial force value that needs to be adjusted by the variable parameter strategy based on the quantitative correlation model between the exit delamination damage and the drilling axial force; determine the amount of process parameters that need to be adjusted by the variable parameter strategy based on the correlation relationship between the process parameters and the drilling axial force characteristics; and predict the degree of exit delamination damage after variable parameter drilling based on the correlation model between the exit delamination damage and the process parameters.

[0108] In this embodiment, Figure 10 This is the flow chart of the damage interface identification algorithm. The specific process is as follows:

[0109] (1) Before the tool starts cutting the material, mark the tool position as Position = 0;

[0110] (2) Current time As the 0 point, the drilling axial force value within the Δt time is integrated to obtain The integral value at the moment , and then after Δt time arrives At this moment, the integral value of the drilling axial force is calculated as , calculate the integral difference .

[0111] (3) When Position=0 and Δ F > threshold 1, it can be determined that the tool begins to contact the upper surface of the material and drill. At this time, the tool position is marked as Position = 1; continue with the integration algorithm in step 2. When Position = 1 and Δ F > threshold 2, it can be determined that the tool has reached the damaged position and the tool position is marked as Position = 2; continue with the integration algorithm in step 2. When Position = 2 and Δ F When the value is less than the threshold value 3, it can be determined that the tool has drilled out the last layer of material of the laminate, and the tool position is marked as Position=3.

[0112] (4) When Position = 3, the algorithm successfully identifies the three drilling processes: the tool reaches the upper surface of the laminate and starts drilling, the tool reaches the damage position, and the tool drills out of the laminate.

[0113] In order to verify the accuracy of the online damage interface identification algorithm, the parameters were changed on the damage interface. By monitoring the drilling axial force curve in the variable parameter experiment, it was compared with the drilling axial force curve under fixed parameters, and the test results were analyzed. Figure 11-13 As shown in the figure, when drilling holes at the damage location with variable parameters, the axial drilling force at the damage interface under the three different variable parameter strategies is lower than that under the fixed parameter drilling method. This shows that the variable parameter strategy effectively reduces the axial drilling force at the damage location, achieving secondary damage suppression, and also proves that the recognition algorithm proposed in this invention is relatively accurate in identifying the damage interface.

[0114] S7: The drilling end and its controller execute the corresponding instructions according to the variable parameter strategy, completing the variable parameter drilling process. The variable parameter drilling strategy enables the control of damage interface parameters, thereby preventing the expansion of initial delamination damage during the drilling process and suppressing the formation of new delamination damage at the exit.

[0115] like Figure 14 As shown, the implementation process of the variable parameter drilling strategy of the composite robot adopted in this embodiment. The robot system uses ultrasonic phased array sensors to detect the depth, position and shape of the internal delamination damage of aviation composite components. The damage data is transmitted to the composite intelligent repair software for processing to generate drilling and riveting points. The robot moves to the corresponding point according to the numerical control file, and adjusts the posture of the end effector to ensure the accuracy of the hole making normal; the axes of the robot are locked, and the end effector starts the hole making task. First, the end pressure foot pushes out and presses the composite surface with the set pressure, the spindle rotates and the spindle feed motor fast-forwards, and the drilling axial force collection, filtering and damage interface recognition algorithm programs start running, and the drilling axial force is processed in real time. When feeding to Position=1, the tool has just touched the composite material. At this time, the hole making parameters use the first set of processing parameters; when monitoring Position=2, it can be determined that the tool has reached the damage position. At this time, the second set of processing parameters is used to make holes until it reaches Position=3 and ends the hole making. The robot moves to the planned points in sequence to make holes and rivets. The processing test results are shown in Figure 15 As shown in the images, the exit delamination damage caused by fiber pullout is essentially eliminated in the upper and lower regions of the hole wall during variable parameter drilling, demonstrating improved exit delamination damage. The hole diameter error for variable parameter drilling of composite materials is within 8-15 μm, with a hole precision reaching H8 and a hole production efficiency of 3 seconds per hole. No severe secondary damage was observed during the test, demonstrating excellent hole quality.

[0116] In summary, this method can realize intelligent optimization of drilling process parameters in composite material repair for delamination damage of any shape, size, and position, and minimize the expansion of initial delamination damage and the generation of new delamination damage at the exit, thereby reducing repair costs, improving repair efficiency, and ensuring repair quality.

[0117] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0118] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for suppressing secondary damage during variable parameter drilling of composite materials containing delamination damage, characterized in that: The following steps are involved: S1. Set reasonable process parameter gradients to conduct drilling experiments on composite materials with delamination damage; use intact composite materials as a control group to study the impact of defects on the time-domain curve of the axial force during drilling of composite materials; analyze the influence of different process parameters on the axial force during drilling of delaminated composite materials, and extract the characteristic data of the average value of the stable section of the drilling axial force curve; Analyze the correlation between drilling axial force and process parameters and build a process database; S2. Use ultrasonic phased array nondestructive testing to detect interlaminar damage in composite materials containing delamination damage. Use a digital ultra-depth-of-field microscope to take photos of the exit damage and perform image processing on the photos. Propose an equivalent delamination factor to quantitatively evaluate the exit delamination damage, analyze the correlation between exit delamination damage and process parameters, and construct a quality database. S3. Construct a support vector regression model and determine its hyperparameters through a grid search algorithm. The model uses speed, feed, and quantitative characteristics of delamination defects as inputs, and drilling axial force and equivalent delamination factor as outputs to predict drilling axial force and exit delamination damage under different process parameters. S4. Analyze the correlation between the outlet delamination damage and the drilling axial force, fit the data of the drilling axial force and the outlet delamination damage by the least squares method, and establish a quantitative correlation model between the outlet delamination damage and the drilling axial force; S5. Analyze the trend characteristics of the drilling axial force time domain curve under different process parameters, establish a multivariate linear regression model, predict the key characteristic inflection points of the drilling axial force time domain curve, and predict the change trend of the drilling axial force time domain curve under different process parameters through the key characteristic inflection points; S6. Calculate the difference of the drilling axial force with respect to the time integral to determine the damage interface identification algorithm. Combined with the quantitative correlation model between the outlet delamination damage and the drilling axial force in step S4, determine the drilling axial force value that needs to be adjusted by the variable parameter strategy. Combined with the correlation between the drilling axial force and the process parameters in step S1, determine the amount of process parameters that need to be adjusted by the variable parameter strategy. Combined with the correlation between the outlet delamination damage and the process parameters in step S2, predict the degree of outlet delamination damage after variable parameter drilling. S7. The damage interface parameters are controlled by the variable parameter drilling strategy to avoid the expansion of the initial delamination damage during the drilling process and inhibit the formation of new delamination damage at the exit.

2. The method for suppressing secondary damage during variable parameter drilling of composite materials with delamination damage according to claim 1, characterized in that: The method also requires hardware and software, including: a drilling end and its controller, a force measuring instrument and data processing software.

3. The method for suppressing secondary damage during variable parameter drilling of composite materials with delamination damage according to claim 2, characterized in that: Step S1 specifically includes: S11. Build a drilling experimental platform, design a composite material drilling experimental plan, and use a dynamometer to collect drilling axial force curves under different process parameters; S12, selecting a low-pass filtering algorithm to filter the collected drilling axial force data, filtering out high-frequency noise interference signals in the signal, and obtaining a drilling axial force characteristic signal; S13, extracting drilling axial force data in the stable section and calculating the average drilling axial force; S14. Reveal the variation of drilling axial force with process parameters.

4. The method for suppressing secondary damage during variable parameter drilling of composite materials with delamination damage according to claim 2, characterized in that: Step S2 specifically includes: S21. After drilling, interlayer damage is scanned using ultrasonic phased array nondestructive testing, and exit damage photos are taken using a digital ultra-depth-of-field microscope. S22. Perform image processing on the scanned and photographed exit damage photos and calculate the equivalent delamination factor; S23. Analyze the variation of equivalent stratification factors with process parameters and build a quality database.

5. The method for suppressing secondary damage during variable parameter drilling of composite materials with delamination damage according to claim 2, characterized in that: Step S5 specifically includes: S51, extracting the drilling axial force value at the key characteristic inflection point of the drilling axial force time domain curve; S52. Use the multiple linear regression model to construct an empirical relationship formula between the drilling axial force value at the key characteristic inflection point of the drilling axial force time domain curve and the feed speed and rotation speed, and verify the accuracy of the multiple linear regression model.

6. The method for suppressing secondary damage during variable parameter drilling of composite materials with delamination damage according to claim 2, characterized in that: Step S6 specifically includes: S61. Predicting the axial force time domain curve from the process parameters using a drilling axial force time domain curve prediction model; S62. For the predicted drilling axial force time domain curve, integrate the drilling axial force value every Δt time interval, compare the current drilling axial force integral value within the Δt time interval with the previous drilling axial force integral value within the Δt time interval, and calculate the difference between the two. S63. For the real-time acquired drilling axial force time domain curve, integrate the drilling axial force value every Δt period. Compare the current drilling axial force integral value within the Δt period with the previous drilling axial force integral value within the Δt period. Calculate the difference between the two. If the absolute value of the difference exceeds a set threshold, the point is determined to be a damage location. The threshold is set to the drilling axial force integral difference within adjacent Δt periods calculated for the predicted drilling axial force time domain curve in step S62. S64. Determine the drilling axial force value that needs to be adjusted by the variable parameter strategy based on the quantitative correlation model between the exit delamination damage and the drilling axial force; determine the amount of process parameters that need to be adjusted by the variable parameter strategy based on the correlation between the process parameters and the drilling axial force characteristics; and predict the degree of exit delamination damage after variable parameter drilling based on the correlation between the exit delamination damage and the process parameters.

7. The method for suppressing secondary damage during variable parameter drilling of composite materials with delamination damage according to claim 2, characterized in that: Step S7 is specifically as follows: The drilling end and its controller execute corresponding instructions according to the variable parameter strategy to complete the variable parameter drilling process; the variable parameter drilling strategy is used to achieve parameter control of the damage interface, thereby avoiding the expansion of initial delamination damage during the drilling process and suppressing the formation of new delamination damage at the exit.

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