A Non-destructive Testing Method for Defects in Friction Components Based on Pulse Infrared Thermoimaging

Abstract

The application discloses a kind of based on pulse infrared thermography's nondestructive testing method of friction component defect, the method steps are as follows: the thermal physical parameters of each component material of the friction component with defect are determined by experimental measurement or material manual inquiry;Short pulse infrared thermography detection system and long pulse infrared thermography detection system are constructed;Friction component is detected using short pulse and long pulse infrared thermography detection system respectively, and sequence infrared image and corresponding temperature data are acquired synchronously;In the sequence infrared image collected, the center area of different depth defects on friction component and defect-free area are selected as feature points, and the temperature data of each feature point are extracted to draw temperature-time curve;The collected sequence image data are subjected to data reconstruction and image enhancement processing;Analysis image obtains detection result.The application realizes the accurate detection of different depth defects inside friction component, improves the recognition ability of depth defect, reduces cost and has strong adaptability.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology for friction components, and in particular to a nondestructive testing method for defects in friction components based on pulsed infrared thermography. Background Technology

[0002] As the core actuator of a mechanical braking system, the internal quality of friction components directly determines the operational safety of the equipment. During the manufacturing process of friction components, internal defects are easily generated due to improper process parameters. These defects take various forms, mainly including: friction material detachment due to corrosion, adhesive failure, or thermal / mechanical damage to the backing plate; parallel cracks in the backing plate caused by insufficient stamping pressure during the forming stage; decreased stability of the friction material due to improper pressure, resulting in surface porosity, localized peeling, and edge defects; friction material exceeding the backing plate boundary due to incorrect stamping pressure design; and foreign matter mixed into the friction material, damaging the braking system. If these defects are not detected in time, they can lead to friction material peeling and reduced braking performance during use, and in severe cases, even safety accidents. Therefore, friction component defect detection technology is particularly important for effectively detecting these defects.

[0003] Existing technologies for detecting defects in friction components have significant limitations: the tapping and listening method relies on operator experience, has vague judgment criteria, cannot quantify defect depth, and has a low recognition rate for internal, hidden defects; eddy current-excited infrared thermal imaging technology can only detect localized areas around the coil, resulting in a small detection area, and is greatly affected by electromagnetic interference; traditional short-pulse infrared thermal imaging technology has a short excitation time and low temperature rise, meaning the heat wave can only propagate to the shallow surface of the friction component, failing to meet the needs for detecting deep internal defects. With the standardization of the friction component industry, there is an urgent need for a non-contact, large-area coverage, and high-depth detection method to address the pain points of existing technologies. Summary of the Invention

[0004] The purpose of this invention is to overcome the deficiencies in the aforementioned background technology. This invention proposes a non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging. The method constructs a long-short pulse infrared thermal imaging detection system, compares the temperature field change patterns under short-pulse and long-pulse excitation, and combines logarithmic polynomial data reconstruction and image enhancement techniques to achieve accurate detection of defects of different depths inside friction components, especially improving the ability to identify deep defects, while taking into account both detection efficiency and reliability.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging includes the following steps:

[0007] Step S1: Determine the thermophysical parameters of each component material of the defective friction component through experimental measurement or by consulting a material handbook. The thermophysical parameters include specific heat capacity. ,density and thermal conductivity K And according to the formula Calculate the thermal diffusivity of a material ;

[0008] The morphology of the friction component defects includes, but is not limited to, flat-bottomed holes, bubbles, and delamination. The defect depth includes four gradients: 1 mm, 2 mm, 3 mm, and 4 mm, covering the common internal defect depth range of the friction component to be tested.

[0009] Step S2: Construct a short-pulse infrared thermal imaging detection system and a long-pulse infrared thermal imaging detection system. Both pulse infrared thermal imaging detection systems include an excitation module, an infrared image acquisition module, and a data processing module.

[0010] The excitation module of the short-pulse infrared thermal imaging detection system adopts a flash lamp system and is equipped with a light shield to improve energy utilization. The flash lamp system uses two lamps symmetrically distributed on both sides of the infrared thermal imager. The pulse width is adjustable in the range of 2ms-50ms and the maximum energy is not less than 6000J.

[0011] The excitation module of the long-pulse infrared thermal imaging detection system adopts a halogen lamp system. The halogen lamp uses 4 lamp tubes, with 2 lamp tubes forming a group and symmetrically distributed on both sides of the infrared thermal imager. The inner wall has a diffuse reflection structure to ensure the uniformity of thermal excitation. The excitation time can be adjusted within the range of 1-30s, and the maximum power is not less than 4000W.

[0012] The infrared image acquisition module in both systems uses an uncooled infrared thermal imager with a resolution of no less than 640×512, a noise equivalent temperature difference NETD ≤ 40mK, and an acquisition frame rate of 50Hz. Both systems use the same type of infrared thermal imager to ensure the comparability of the detection data.

[0013] The data processing module is equipped with display and analysis function components in both systems, supporting real-time image display, storage, and temperature data extraction. However, the data processing module of the long-pulse infrared thermal imaging detection system additionally supports polynomial fitting data reconstruction, first-order derivative image generation, and contrast enhancement functions.

[0014] Step S3: Use short-pulse and long-pulse infrared thermal imaging detection systems to detect the friction components, and control the detection environment conditions to be consistent: the ambient temperature is kept constant at 26℃, the ambient humidity is kept at 40%-60%, and the airflow speed is ≤0.5m / s; set the infrared image acquisition time of the two systems to be consistent, both at 80s, and the number of frames acquired is not less than 3500 frames to ensure complete coverage of the entire cycle of thermal excitation and thermal diffusion, and synchronously acquire the sequence of infrared images and corresponding temperature data;

[0015] Step S4: In the sequence of infrared images acquired in step S3, select a central region with an area of ​​not less than 1mm × 1mm and a defect-free region with an area of ​​not less than 10mm × 10mm that is far away from all defects on the friction component as feature points. Extract the temperature data of each feature point throughout the entire acquisition cycle. The data is taken as the average temperature of multiple pixels in the feature point region to reduce the noise influence of a single pixel. Use the obtained data to plot a temperature-time curve and analyze the curve.

[0016] Step S5: Perform data reconstruction and image enhancement processing on the acquired sequence image data;

[0017] Step S51, Data Reconstruction: A logarithmic temperature rise-logarithmic time polynomial fitting method is used, with the fitting formula being...

[0018] ,

[0019] in, Let be the temperature value of the characteristic point at time t. The fitting coefficient is n, which takes the value of 4 or 5. The optimal fitting order is determined by comparing the deviation between the fitted curve and the original temperature data to avoid underfitting when n < 4, which means that the defect temperature change characteristics cannot be captured, or overfitting when n > 5, which means that the fitted curve oscillates violently.

[0020] Step S52, Image Enhancement: Calculate the first-order time derivative of the reconstructed temperature data to generate a first-order derivative image of the temperature change rate distribution. Then, an adaptive histogram equalization algorithm is used to optimize the image grayscale distribution, enhance the grayscale difference between defective and non-defective areas, and improve the clarity of deep defect identification.

[0021] Step S6: Based on the analysis results of the temperature-time curve drawn in Step S4 and the enhanced image analysis in Step S5, the detection results are obtained. The detection capabilities of the two systems are comprehensively compared from four dimensions: maximum detectable defect depth, defect recognition clarity, detection efficiency, and consistency of detection results, providing a reference for subsequent detection.

[0022] Furthermore, the morphology of the defects in the friction component in step S1 includes, but is not limited to, flat-bottomed holes, bubbles, and delamination, and the depth includes four gradients of 1mm, 2mm, 3mm, and 4mm, which need to cover the common internal defect depth range of the friction component to be tested.

[0023] Furthermore, the characteristic is that the heat conduction process of the excitation module in the short-pulse infrared thermal imaging detection system in step S2 follows the short-pulse heat conduction differential equation:

[0024] ,

[0025] in, T For the temperature of the friction components, t For time, x Let be the distance from a point inside the friction component to the surface. Let be the heat source function, with the initial condition being t=0. , The ambient temperature;

[0026] The heat source function in the heat conduction process is:

[0027] ,

[0028] in, q The amount of heat applied per unit area It is the Dirac function;

[0029] The heat conduction process of the excitation module in the long-pulse infrared thermal imaging detection system in step S2 follows the long-pulse heat conduction differential equation:

[0030] ,

[0031] Its boundary conditions are x When =0, That is, the heat flux density is non-zero, and the surface of the friction component continuously absorbs heat. hour d represents the thickness of the friction component, i.e., the back of the friction component is at the same temperature as the ambient temperature.

[0032] Furthermore, in step S3, the sequence of infrared images and corresponding temperature data are stored in a standard format file to facilitate subsequent data retrieval and analysis. The data storage process must retain the acquisition timestamp to ensure accurate correspondence between temperature data and image frames.

[0033] Furthermore, in step S4, the selection of feature points needs to be accurately located using an image coordinate positioning tool. The feature points in the defect center area must be strictly located at the geometric center of the defect to avoid inaccurate temperature data extraction due to positioning deviation, which would affect the curve analysis results.

[0034] Furthermore, the analysis of the temperature-time curve in step S4 is as follows:

[0035] Based on the difference in thermal conduction between the friction component material and the defect region, this study analyzes the separation time of temperature curves for defects at different depths: surface temperature change function of the defect-free region and finite thickness in a short-pulse infrared thermal imaging detection system. d The surface temperature rise functions are as follows:

[0036] ,

[0037] ,

[0038] in, The ratio of energy added per unit area of ​​surface to heat stored per unit volume at the cutoff point;

[0039] In a long-pulse infrared thermal imaging inspection system, the temperature difference between the deep defect region and the defect-free region satisfies a finite thickness requirement. d Surface temperature rise function:

[0040] ,

[0041] The criteria for determining the separation time of curves with different defect depths are: the temperature difference between the defect area and the non-defect area is ≥2℃, and the duration is ≥3 frames of acquisition.

[0042] Furthermore, in step S5, the fitting coefficients are... The rationality verification needs to be combined with the theory of heat conduction to ensure that the temperature change trend corresponding to the fitting coefficient conforms to the heat diffusion law under short pulse or long pulse excitation, and to avoid the reconstructed data deviating from the actual temperature change due to abnormal fitting coefficient.

[0043] Furthermore, in step S6, the consistency of the detection results needs to be achieved through multiple repeated tests. The same friction component should be tested repeatedly using the same detection system at least three times. The deviation of the defect identification position and depth in each test should be calculated and controlled within a preset allowable range.

[0044] Furthermore, the friction components include automotive brake pads and industrial brake friction blocks, which function through friction. The short-pulse and long-pulse infrared thermal imaging detection system adjusts the excitation module parameters and image acquisition parameters according to the structural characteristics and material properties of different friction components to adapt to the detection requirements of different types of friction components.

[0045] Compared with the prior art, the present invention, employing the above technical solution, has the following beneficial effects:

[0046] (1) The present invention proposes a non-destructive testing method for friction component defects based on pulse infrared thermal imaging. By utilizing the continuous thermal excitation characteristics of the long pulse infrared thermal imaging detection system, the heat wave can fully penetrate into the deep layer of the friction material. Even hidden defects with a depth of 4mm can be clearly identified during the cooling stage through temperature-time curve separation and first-order guided image enhancement. The detection capability is stronger and can accurately cover deep defects.

[0047] (2) The present invention proposes a non-destructive testing method for friction component defects based on pulse infrared thermal imaging. Data reconstruction is achieved through temperature-time curves and logarithmic temperature rise-logarithmic time polynomial fitting. Then, the defect display is optimized by first-order image enhancement. No subjective judgment is required throughout the process, avoiding errors caused by differences in operator experience and auditory fatigue. At the same time, the maximum temperature rise of the long pulse infrared thermal imaging detection system significantly increases the temperature difference between defective and non-defective areas, which can effectively resist the interference of ambient temperature fluctuations, making the detection results more reliable and effectively avoiding human and environmental interference.

[0048] (3) The present invention proposes a non-destructive testing method for friction component defects based on pulse infrared thermal imaging. The pulse infrared thermal imaging detection system adopts a "non-contact, large-area coverage" design, which can cover the entire surface of the brake pad in a single test without moving the coil multiple times. The acquisition time for a single brake pad is short. On the other hand, the excitation time and power of the long pulse system can be adjusted to adapt to friction components of different thicknesses and materials, which solves the limitation that eddy current technology is only applicable to brake pads with high metal content. The detection efficiency and applicability are higher.

[0049] (4) The non-destructive testing method for friction component defects based on pulse infrared thermal imaging proposed in this invention has significantly reduced the equipment cost compared to traditional testing methods, resulting in a lower overall investment. Moreover, operators can master the operation of the equipment with simple training, without the need for professional infrared or non-destructive testing qualifications, which greatly reduces the labor cost and operational difficulty of industrial applications. It achieves a good balance between technical performance and cost control, and has the foundation for industrial promotion. Attached Figure Description

[0050] Figure 1 This is a flowchart of the steps of the present invention;

[0051] Figure 2 This is a structural diagram of the brake pad in an embodiment of the present invention;

[0052] Figure 3 This is an external view of the short-pulse infrared thermal imaging detection system in an embodiment of the present invention;

[0053] Figure 4 This is a diagram showing the internal structure of the flash lamp excitation system in the short-pulse infrared thermal imaging detection system according to an embodiment of the present invention.

[0054] Figure 5 This is an external view of the long-pulse infrared thermal imaging detection system in an embodiment of the present invention;

[0055] Figure 6 This is a diagram showing the internal structure of the thermal excitation system in the long-pulse infrared thermal imaging detection system according to an embodiment of the present invention.

[0056] Figure 7 This is an image diagram of the excitation sequence of the short-pulse infrared thermal imaging detection system in this embodiment of the invention;

[0057] Figure 8 This is an image diagram of the excitation sequence of the long-pulse infrared thermal imaging detection system in an embodiment of the present invention;

[0058] Figure 9 These are temperature-time curves for short-pulse and long-pulse excitation in embodiments of the present invention.

[0059] Figure 10 This is an excitation 1D sequence diagram of the short-pulse infrared thermal imaging detection system in an embodiment of the present invention;

[0060] Figure 11 This is an excitation 1D sequence diagram of the long-pulse infrared thermal imaging detection system in an embodiment of the present invention;

[0061] Figure 12 This is a detection effect diagram of the short-pulse infrared thermal imaging detection system in an embodiment of the present invention;

[0062] Figure 13 This is a detection effect diagram of the long pulse infrared thermal imaging detection system in an embodiment of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] A non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging includes the following steps:

[0065] Step S1: Determine the thermophysical parameters of each component material of the defective friction component through experimental measurement or by consulting a material handbook. The thermophysical parameters include specific heat capacity. ,density and thermal conductivity K And according to the formula Calculate the thermal diffusivity of a material ;

[0066] The morphology of the friction component defects includes, but is not limited to, flat-bottomed holes, bubbles, and delamination. The defect depth includes four gradients: 1 mm, 2 mm, 3 mm, and 4 mm, covering the common internal defect depth range of the friction component to be tested.

[0067] Step S2: Construct a short-pulse infrared thermal imaging detection system and a long-pulse infrared thermal imaging detection system. Both pulse infrared thermal imaging detection systems include an excitation module, an infrared image acquisition module, and a data processing module.

[0068] The excitation module of the short-pulse infrared thermal imaging detection system adopts a flash lamp system and is equipped with a light shield to improve energy utilization. The flash lamp system uses two lamps symmetrically distributed on both sides of the infrared thermal imager. The pulse width is adjustable in the range of 2ms-50ms and the maximum energy is not less than 6000J.

[0069] The excitation module of the long-pulse infrared thermal imaging detection system adopts a halogen lamp system. The halogen lamp uses 4 lamp tubes, with 2 lamp tubes forming a group and symmetrically distributed on both sides of the infrared thermal imager. The inner wall has a diffuse reflection structure to ensure the uniformity of thermal excitation. The excitation time can be adjusted within the range of 1-30s, and the maximum power is not less than 4000W.

[0070] The infrared image acquisition module in both systems uses an uncooled infrared thermal imager with a resolution of no less than 640×512, a noise equivalent temperature difference NETD ≤ 40mK, and an acquisition frame rate of 50Hz. Both systems use the same type of infrared thermal imager to ensure the comparability of the detection data.

[0071] The data processing module is equipped with display and analysis function components in both systems, supporting real-time image display, storage, and temperature data extraction. However, the data processing module of the long-pulse infrared thermal imaging detection system additionally supports polynomial fitting data reconstruction, first-order derivative image generation, and contrast enhancement functions.

[0072] Step S3: Use short-pulse and long-pulse infrared thermal imaging detection systems to detect the friction components, and control the detection environment conditions to be consistent: the ambient temperature is kept constant at 26℃, the ambient humidity is kept at 40%-60%, and the airflow speed is ≤0.5m / s; set the infrared image acquisition time of the two systems to be consistent, both at 80s, and the number of frames acquired is not less than 3500 frames to ensure complete coverage of the entire cycle of thermal excitation and thermal diffusion, and synchronously acquire the sequence of infrared images and corresponding temperature data;

[0073] Step S4: In the sequence of infrared images acquired in step S3, select a central region with an area of ​​not less than 1mm × 1mm and a defect-free region with an area of ​​not less than 10mm × 10mm that is far away from all defects on the friction component as feature points. Extract the temperature data of each feature point throughout the entire acquisition cycle. The data is taken as the average temperature of multiple pixels in the feature point region to reduce the noise influence of a single pixel. Use the obtained data to plot a temperature-time curve and analyze the curve.

[0074] Step S5: Perform data reconstruction and image enhancement processing on the acquired sequence image data;

[0075] Step S51, Data Reconstruction: A logarithmic temperature rise-logarithmic time polynomial fitting method is used, with the fitting formula being...

[0076] ,

[0077] in, Let be the temperature value of the characteristic point at time t. The fitting coefficient is n, which takes the value of 4 or 5. The optimal fitting order is determined by comparing the deviation between the fitted curve and the original temperature data to avoid underfitting when n < 4, which means that the defect temperature change characteristics cannot be captured, or overfitting when n > 5, which means that the fitted curve oscillates violently.

[0078] Step S52, Image Enhancement: Calculate the first-order time derivative of the reconstructed temperature data to generate a first-order derivative image of the temperature change rate distribution. Then, an adaptive histogram equalization algorithm is used to optimize the image grayscale distribution, enhance the grayscale difference between defective and non-defective areas, and improve the clarity of deep defect identification.

[0079] Step S6: Based on the analysis results of the temperature-time curve drawn in Step S4 and the enhanced image analysis in Step S5, the detection results are obtained. The detection capabilities of the two systems are comprehensively compared from four dimensions: maximum detectable defect depth, defect recognition clarity, detection efficiency, and consistency of detection results, providing a reference for subsequent detection.

[0080] Furthermore, the morphology of the defects in the friction component in step S1 includes, but is not limited to, flat-bottomed holes, bubbles, and delamination, and the depth includes four gradients of 1mm, 2mm, 3mm, and 4mm, which need to cover the common internal defect depth range of the friction component to be tested.

[0081] Furthermore, the characteristic is that the heat conduction process of the excitation module in the short-pulse infrared thermal imaging detection system in step S2 follows the short-pulse heat conduction differential equation:

[0082] ,

[0083] in, T For the temperature of the friction components, t For time, x Let be the distance from a point inside the friction component to the surface. Let be the heat source function, with the initial condition being t=0. , The ambient temperature;

[0084] The heat source function in the heat conduction process is:

[0085] ,

[0086] in, q The amount of heat applied per unit area It is the Dirac function;

[0087] The heat conduction process of the excitation module in the long-pulse infrared thermal imaging detection system in step S2 follows the long-pulse heat conduction differential equation:

[0088] ,

[0089] Its boundary conditions are x When =0, That is, the heat flux density is non-zero, and the surface of the friction component continuously absorbs heat. hour d represents the thickness of the friction component, i.e., the back of the friction component is at the same temperature as the ambient temperature.

[0090] Furthermore, in step S3, the sequence of infrared images and corresponding temperature data are stored in a standard format file to facilitate subsequent data retrieval and analysis. The data storage process must retain the acquisition timestamp to ensure accurate correspondence between temperature data and image frames.

[0091] Furthermore, in step S4, the selection of feature points needs to be accurately located using an image coordinate positioning tool. The feature points in the defect center area must be strictly located at the geometric center of the defect to avoid inaccurate temperature data extraction due to positioning deviation, which would affect the curve analysis results.

[0092] Furthermore, the analysis of the temperature-time curve in step S4 is as follows:

[0093] Based on the difference in thermal conduction between the friction component material and the defect region, this study analyzes the separation time of temperature curves for defects at different depths: surface temperature change function of the defect-free region and finite thickness in a short-pulse infrared thermal imaging detection system. d The surface temperature rise functions are as follows:

[0094] ,

[0095] ,

[0096] in, The ratio of energy added per unit area of ​​surface to heat stored per unit volume at the cutoff point;

[0097] In a long-pulse infrared thermal imaging inspection system, the temperature difference between the deep defect region and the defect-free region satisfies a finite thickness requirement. d Surface temperature rise function:

[0098] ,

[0099] The criteria for determining the separation time of curves with different defect depths are: the temperature difference between the defect area and the non-defect area is ≥2℃, and the duration is ≥3 frames of acquisition.

[0100] Furthermore, in step S5, the fitting coefficients are... The rationality verification needs to be combined with the theory of heat conduction to ensure that the temperature change trend corresponding to the fitting coefficient conforms to the heat diffusion law under short pulse or long pulse excitation, and to avoid the reconstructed data deviating from the actual temperature change due to abnormal fitting coefficient.

[0101] Furthermore, in step S6, the consistency of the detection results needs to be achieved through multiple repeated tests. The same friction component should be tested repeatedly using the same detection system at least three times. The deviation of the defect identification position and depth in each test should be calculated and controlled within a preset allowable range.

[0102] Furthermore, the friction components include automotive brake pads and industrial brake friction blocks, which function through friction. The short-pulse and long-pulse infrared thermal imaging detection system adjusts the excitation module parameters and image acquisition parameters according to the structural characteristics and material properties of different friction components to adapt to the detection requirements of different types of friction components.

[0103] Example

[0104] The technical solution of the present invention will be further explained below with reference to its application in defect detection in automotive brake pads:

[0105] This automotive brake pad is a low-metal type, with a total thickness of 17mm, a friction material thickness of 12mm, and a steel thickness of 5mm. It has four flat-bottomed hole defects, each with a diameter of 10mm and a depth of 1-4mm. Specific defect depths are as follows... Figure 2 As shown in Table 1, the thermophysical parameters of the steel and friction materials are as follows:

[0106] Table 1

[0107] ,

[0108] Construction of a pulsed infrared thermal imaging detection system: a short-pulse infrared thermal imaging detection system, such as... Figure 3 and Figure 4 As shown, the excitation module uses a dual-lamp flash system with a maximum energy of 6000J and an adjustable pulse width from 2ms to 50ms, equipped with a light shield; the infrared image acquisition module uses an uncooled infrared thermal imager with a resolution of 640×512, NETD≤40mK, and an acquisition frame rate of 50Hz; the data processing module is equipped with a 10.4-inch touch screen, supporting real-time image display, storage, and temperature-time curve plotting; the long-pulse infrared thermal imaging detection system, such as... Figure 5 and Figure 6 As shown: The excitation module uses four halogen lamps, arranged in groups of two, symmetrically distributed on both sides of the thermal imager, with a maximum power of 4000W. The excitation time can be adjusted within the range of 1-30s, and the inner wall has a diffuse reflection structure. The infrared image acquisition module and the short pulse system use the same model of uncooled infrared thermal imager. The data processing module is also equipped with a 10.4-inch touch screen. In addition to the functions of the short pulse system, it also supports logarithmic polynomial fitting data reconstruction, first-order derivative image generation, and contrast enhancement.

[0109] The tests were conducted in a 26℃ constant temperature laboratory with ambient humidity controlled at 45%-55% and airflow velocity ≤0.3m / s. The experimental parameters for the short-pulse infrared thermal imaging detection system were: flash lamp energy 6000J, pulse width 10ms, acquisition time 80s, and 4000 frames. The experimental parameters for the long-pulse infrared thermal imaging detection system were: halogen lamp power 4000W, excitation time 8s, acquisition time 80s, and 4000 frames. The short-pulse excitation sequence images and long-pulse excitation sequence images are shown below. Figure 7 and Figure 8 As shown.

[0110] like Figure 7 At 0.02s: the flash lamp has just ended, and the surface of the specimen appears "granular" due to the difference in material absorption rate; at 1.2s: a 1mm deep defect begins to appear, which is a high-brightness area in the infrared image. Because the defect is heat-insulating, its temperature is higher than that of the non-defect area; at 3.0s: a 2mm deep defect begins to appear, and two defects can be detected at this time; from 7s to 75s: thermal diffusion continues, but 3mm and 4mm deep defects are never detected.

[0111] like Figure 8 At 8.02s after the excitation ended: the 1mm deep defect was clearly visible, and the 2mm deep defect was faintly visible; at 9.2s: the 2mm deep defect was fully visible; at 11.0s: the 3mm deep defect began to appear; at 15.0s: the 3mm defect was obvious, and the 4mm deep defect was faintly visible; at 83s: only 3 defects could still be clearly seen in the original sequence image, and the 4mm defect was not fully visible due to its depth.

[0112] After selecting feature points, a temperature-time curve is plotted, such as... Figure 9 As shown, for the short-pulse infrared thermal imaging detection system: only the temperature drop segment is presented, with a maximum temperature rise of 12℃; only the curves at points A (1mm) and B (2mm) are separated from point E (no defect), while the curves at points C and D are intertwined with point E and cannot be distinguished; for the long-pulse infrared thermal imaging detection system: a temperature rise segment (0-8s) and a drop segment (8-80s) are presented, with a maximum temperature rise of 109℃; points A and B are separated from point E during the excitation period (before 8s), and points C (3mm) and D (4mm) are separated from point E during the cooling phase (about 15s), and all defect curves can be distinguished.

[0113] Data reconstruction was performed only on the temperature decrease segment, using a logarithmic temperature rise-logarithmic time polynomial fitting with n=4 or 5 (to avoid overfitting / underfitting); and the derivative of the reconstructed data was calculated to generate a 1D sequence image, as shown below. Figure 10 and Figure 11 As shown, the short-pulse infrared thermal imaging detection system can only detect 2 defects in the 1D image; the long-pulse infrared thermal imaging detection system can detect 4 defects in the 1D image, with the 4mm deep defect clearly visible.

[0114] The image is enhanced by selecting the moment when it is clearest in the 1D image and applying a contrast enhancement algorithm to achieve the desired detection result, such as... Figure 12 and Figure 13 As shown in the comparison, the short-pulse infrared thermal imaging detection system can only detect 2 defects; the long-pulse infrared thermal imaging detection system shows all 4 defects, and the deepest defect of 4mm has a complete outline.

[0115] The results show that the long-pulse infrared thermal imaging system is more suitable for detecting defects in automotive brake pads, as it has a deeper detection depth and a higher defect detection rate, providing reliable technical support for non-destructive testing of brake pads.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A non-destructive testing method of defects in friction parts based on pulsed infrared thermography, characterized in that, The detection method comprises the following steps: Step S1, determining the thermophysical parameters of each constituent material of the defective friction component, including the specific heat capacity , density , and thermal conductivity K, through experimental measurement or material handbook inquiry, and calculating the thermal diffusivity of the material according to the formula ;​ The morphology of the friction component defect includes but is not limited to flat bottom hole, bubble, delamination, and the defect depth includes four gradients of 1 mm, 2 mm, 3 mm and 4 mm, covering the common internal defect depth range of the friction component to be detected; Step S2, a short pulse infrared thermal imaging detection system and a long pulse infrared thermal imaging detection system are constructed, both of which include an excitation module, an infrared image acquisition module and a data processing module; The excitation module in the short pulse infrared thermal imaging detection system adopts a flash lamp system, and a light shield is configured to improve the energy utilization rate, the flash lamp system adopts double lamp tubes which are symmetrically distributed on both sides of the infrared thermal imager, the pulse width is adjusted in the range of 2 ms-50 ms, and the maximum energy is not less than 6000 J; The excitation module in the long pulse infrared thermal imaging detection system adopts a halogen lamp system, the halogen lamp adopts 4 lamp tubes, each 2 lamp tubes form a group and are symmetrically distributed on both sides of the infrared thermal imager, the inner wall is of a diffuse reflection structure to ensure the uniformity of thermal excitation, the excitation time can be adjusted in the range of 1-30 s, and the maximum power is not less than 4000 W; The infrared image acquisition module in both systems adopts a non-cooled infrared thermal imager, the resolution is not less than 640*512, the noise equivalent temperature difference NETD is less than or equal to 40 mK, the acquisition frame rate is 50 Hz, and the same type of infrared thermal imager is adopted in the two systems to ensure the comparability of the detection data; The data processing module in both systems is configured with display and analysis function components, supports real-time image display, storage and temperature data extraction, but the data processing module of the long pulse infrared thermal imaging detection system additionally supports polynomial fitting data reconstruction, first-order derivative image generation and contrast enhancement functions; Step S3, the short pulse and long pulse infrared thermal imaging detection systems are respectively used for detecting the friction component, the detection environment conditions are controlled to be consistent: the environmental temperature is constant at 26 DEG C, the environmental humidity is kept at 40%-60%, and the air flow speed is less than or equal to 0.5 m / s; the infrared image acquisition time of the two systems is set to be consistent, which is 80 s, the acquisition frame number is not less than 3500 frames, the whole cycle of thermal excitation and thermal diffusion is ensured to be covered, and the sequence infrared images and corresponding temperature data are synchronously acquired; Step S4, in the sequence infrared images acquired in step S3, a center area with an area not less than 1 mm*1 mm of different depth defects on the friction component and a defect-free area with an area not less than 10 mm*10 mm and far away from all defects are selected as feature points, the temperature data of each feature point in the whole acquisition cycle is extracted, the temperature average value of multiple pixel points in the feature point area is taken as the data to reduce the influence of single pixel point noise, the temperature-time curve is drawn by using the obtained data, and the curve graph is analyzed; Step S5, the collected sequence image data is processed by data reconstruction and image enhancement; Step S51, data reconstruction: a logarithmic temperature rise-logarithmic time polynomial fitting method is adopted, and the fitting formula is ， wherein, Ttis the temperature value of the feature point at time t, is a fitting coefficient, and n is 4 or 5; the optimal fitting order is determined by comparing the deviation of the fitting curve and the original temperature data, to avoid underfitting when n<4, i.e. the defect temperature change characteristics cannot be captured, or overfitting when n>5, i.e. the fitting curve oscillates violently; Step S52, image enhancement: first-order time derivation is performed on the reconstructed temperature data to generate a first-order derivative image of the temperature change rate distribution Then, the adaptive histogram equalization algorithm is used to optimize the image gray scale distribution, enhance the gray scale difference between the defect area and the non-defect area, and improve the recognition clarity of the deep depth defects. Step S6: According to the temperature-time curve analysis results drawn in step S4 and the enhanced image analysis in step S5, the detection results are obtained, and the detection capabilities of the two systems are compared from the maximum depth of detectable defects, defect recognition clarity, detection efficiency and detection result consistency to provide reference for subsequent detection.

2. A non-destructive testing method for detecting defects in friction members based on pulsed infrared thermography according to claim 1, characterized in that, The defect forms of the friction component in step S1 include but are not limited to flat bottom hole, bubble, delamination, and the depth includes four gradients of 1mm, 2mm, 3mm and 4mm, which need to cover the common internal defect depth range of the friction component to be detected.

3. A non-destructive testing method for detecting defects in friction members based on pulsed infrared thermography according to claim 1, characterized in that, The heat conduction process of the excitation module in the short pulse infrared thermal imaging detection system in step S2 follows the short pulse heat conduction differential equation: ， where T is the temperature of the friction component, t is time, x is the distance from a point inside the friction component to the surface, is the heat source function, and the initial condition is t = 0 , is the ambient temperature; The heat source function in the heat conduction process is: ， where q is the heat applied per unit area, is the Dirac function; The heat conduction process of the excitation module in the long pulse infrared thermal imaging detection system in step S2 follows the long pulse heat conduction differential equation: ， The boundary condition is x = 0, i.e. the heat flux is non-zero and the surface of the friction component continuously absorbs heat, when d is the thickness of the friction component, i.e. the back of the friction component is at ambient temperature.

4. A non-destructive testing method for detecting defects in friction members based on pulsed infrared thermography according to claim 1, characterized in that, In step S3, the sequence infrared images and corresponding temperature data are stored as standard format files, which is convenient for subsequent data calling and analysis, and the acquisition time stamp needs to be retained during data storage to ensure the accurate correspondence of temperature data and image frames.

5. A non-destructive testing method for detecting defects in friction members based on pulsed infrared thermography according to claim 1, characterized in that, In step S4, the selection of feature points needs to be accurately positioned by image coordinate positioning tool, and the feature points in the center area of the defect need to be strictly located at the geometric center of the defect to avoid inaccurate temperature data extraction due to positioning deviation affecting the curve analysis results.

6. A non-destructive testing method for detecting defects in friction members based on pulsed infrared thermography according to claim 1, characterized in that, The analysis of the temperature-time curve in step S4 is as follows: Based on the difference in heat conduction between the friction component material and the defect area, the separation time of the temperature curve of different depth defects is analyzed: in the short pulse infrared thermal imaging detection system, the surface temperature change function of the defect-free area and the surface temperature rise function at a finite thickness d are respectively: ， ， wherein, is the ratio of the energy added per surface area to the heat stored per unit volume of the section. In the long pulse infrared thermal imaging detection system, the temperature difference between the depth defect area and the defect-free area satisfies the surface temperature rise function at a finite thickness d: ， The separation time of the curve of different defect depths is determined by the following standard: the temperature difference between the defect area and the defect-free area is ≥2℃, and the duration is ≥3 acquisition periods.

7. A non-destructive testing method for detecting defects in friction members based on pulsed infrared thermography according to claim 1, characterized in that, The step S5, the rationality verification of the fitting coefficient The rationality verification of the fitting coefficient in the step S5 needs to be combined with the heat conduction theory to ensure that the temperature change trend corresponding to the fitting coefficient conforms to the heat diffusion law under the short pulse or long pulse excitation, and to avoid that the reconstructed data deviates from the actual temperature change due to the abnormal fitting coefficient.

8. A non-destructive testing method for detecting defects in friction members based on pulsed infrared thermography according to claim 1, characterized in that, In step S6, the detection result consistency needs to be achieved by repeated detection, and the same friction component is detected by the same detection system for not less than 3 times, the deviation of the defect recognition position and depth in each detection is calculated, and the deviation is controlled within the preset allowable range.

9. A non-destructive testing method for detecting defects in friction members based on pulsed infrared thermography according to claim 1, characterized in that, The friction component includes automobile brake pads and industrial brake friction blocks that realize functions by friction, and the short pulse and long pulse infrared thermal imaging detection systems adjust the excitation module parameters and image acquisition parameters according to the structure characteristics and material properties of different friction components to adapt to the detection needs of different types of friction components.

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