Friction part defect nondestructive testing method based on pulse infrared thermal imaging

Abstract

The invention discloses a nondestructive testing method for defects of a friction part based on pulse infrared thermal imaging. The method comprises the following steps: determining thermal physical parameters of each component material of the friction part with the defects through experimental measurement or material manual query; constructing a short-pulse infrared thermal imaging detection system and a long-pulse infrared thermal imaging detection system; a short pulse infrared thermal imaging detection system and a long pulse infrared thermal imaging detection system are respectively adopted to detect the friction part, and sequence infrared images and corresponding temperature data are synchronously obtained; in the collected sequence infrared images, selecting central areas and defect-free areas of different depth defects on the friction part as feature points, and extracting temperature data of each feature point to draw a temperature-time curve; performing data reconstruction and image enhancement processing on the acquired sequence image data; analyzing the image to obtain a detection result. According to the invention, accurate detection of different depth defects in the friction part is realized, the identification capability of the depth defects is improved, the cost is reduced, and the adaptability is high.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nondestructive testing of friction components, and in particular to a nondestructive testing method for defects of friction components based on pulsed infrared thermal imaging. BACKGROUND

[0002] As the core executive component of the mechanical braking system, the internal quality of the friction component directly determines the safety of the equipment operation. During the manufacturing process of the friction component, internal defects are prone to occur due to improper process parameters. The defect forms are diverse, mainly including: shedding of the friction material due to corrosion, adhesive failure or back plate thermal / mechanical damage; parallel cracks of the back plate caused by insufficient stamping pressure during the forming stage; surface porosity, local peeling and edge defects caused by the decline of the stability of the friction material due to improper pressure; friction material exceeding the back plate boundary caused by design error of the stamping pressure; and foreign matter mixed in the friction material damaging the brake system. If these defects are not detected in time, the friction material may peel off during use, the braking efficiency may decrease, and in severe cases, safety accidents may even occur. Therefore, the defect detection technology of the friction component is particularly important to effectively detect the defects of the friction component.

[0003] However, the existing defect detection technology of the friction component has obvious limitations. The knocking sound method relies on the experience of the operator, the judgment standard is ambiguous, the defect depth cannot be quantified, and the recognition rate of internal hidden defects is low. The eddy current excitation infrared thermal imaging technology can only detect the local area around the coil, the detection area is small, and it is greatly affected by electromagnetic interference. The traditional short-pulse infrared thermal imaging technology has short excitation time and low temperature rise, and the thermal wave can only propagate to the superficial layer of the friction component, which cannot meet the detection requirements of deep internal defects. With the standardized development of the friction component industry, there is an urgent need for a non-contact, large-area coverage, high-depth detection capability nondestructive testing method to solve the pain points of the existing technology. SUMMARY

[0004] The purpose of the present application is to overcome the defects in the background art. The present application provides a nondestructive testing method for defects of friction components based on pulsed infrared thermal imaging. The method compares the temperature field variation under short-pulse and long-pulse excitation by constructing a long-short pulse infrared thermal imaging detection system, combines logarithmic polynomial data reconstruction and image enhancement technology, and realizes accurate detection of internal defects of different depths of the friction component, especially improves the recognition ability of deep defects, and at the same time considers the detection efficiency and reliability.

[0005] The present application adopts the following technical solutions to solve the above technical problems: A nondestructive testing method for defects of friction components based on pulsed infrared thermal imaging, comprising the following steps: Step S1, determine the thermal physical parameters of each component material of the defective friction component through experimental measurement or material manual query, the thermal physical parameters include specific heat capacity , density and thermal conductivity K , and calculate the thermal diffusion coefficient of the material according to the formula ; ; The morphology of the defect of the friction component includes but is not limited to flat bottom hole, bubble, delamination, and the defect depth includes four gradients of 1mm, 2mm, 3mm and 4mm, covering the common internal defect depth range of the friction component to be detected; Step S2, construct short pulse infrared thermal imaging detection system and long pulse infrared thermal imaging detection system, both of which contain excitation module, infrared image acquisition module and data processing module; The excitation module in the short pulse infrared thermal imaging detection system adopts a flash lamp system, which is configured with a light shield to improve energy utilization. The flash lamp system adopts double lamp tubes symmetrically distributed on both sides of the infrared thermal imager, the pulse width is adjusted within the range of 2ms-50ms, and the maximum energy is not less than 6000J; The excitation module in the long pulse infrared thermal imaging detection system adopts a halogen lamp system, which adopts 4 lamp tubes, each 2 lamp tubes as a group and symmetrically distributed on both sides of the infrared thermal imager, the inner wall is of 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; The infrared image acquisition module in both systems adopts a non-cooled infrared thermal imager with a resolution not less than 640×512, a noise equivalent temperature difference NETD≤40mK, and a collection frame rate of 50Hz. The same type of infrared thermal imager is used 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, which support 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, use short pulse and long pulse infrared thermal imaging detection system to detect the friction component respectively, control the consistent detection environment conditions: the environment temperature is constant at 26℃, the environment humidity is kept at 40%-60%, and the air flow speed is ≤0.5m / s; Set the infrared image acquisition time of the two systems to be consistent, both for 80s, the acquisition frame number is not less than 3500 frames, which ensures complete coverage of the whole period of thermal excitation and thermal diffusion, and synchronously acquires sequence infrared images and corresponding temperature data; Step S4, in the sequence infrared image collected in step S3, a center area with an area not less than 1mm*1mm of different depth defects on the friction component and a defect-free area with an area not less than 10mm*10mm and far away from all defects are selected as feature points, temperature data of each feature point in the entire collection period is extracted, the data is the average temperature of multiple pixel points in the feature point area to reduce the influence of single pixel point noise, a temperature-time curve is drawn by using the obtained data, and the curve graph is analyzed; Step S5, data reconstruction and image enhancement processing are performed on the collected sequence image data; Step S51, data reconstruction: a logarithmic temperature rise-logarithmic time polynomial fitting method is adopted, and the fitting formula is wherein, T(t) is 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 derivative is performed on the reconstructed temperature data to generate a first-order derivative image of 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 defect-free area, and improve the recognition clarity of deep depth defects; Step S6: the detection results are obtained according to the analysis results of the temperature-time curve drawn in step S4 and the enhanced image in step S5, and the detection capabilities of the two systems are compared from four dimensions of the maximum depth of detectable defects, defect recognition clarity, detection efficiency and detection result consistency to provide reference for subsequent detection.

[0006] Further, the morphology of the defect of the friction component in step S1 includes but is not limited to flat bottom hole, bubble and delamination, and the depth includes four gradients of 1mm, 2mm, 3mm and 4mm, which needs to cover the common internal defect depth range of the friction component to be detected.

[0007] Further, in the short pulse infrared thermal imaging detection system in step S2, the heat conduction process of the excitation module follows the short pulse heat conduction differential equation: wherein, T T is the temperature of the friction component, t t is the 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​​ , The ambient temperature is; The heat source function in the heat conduction process is: , Wherein, q The heat applied per unit area, The Dirac function is; 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, That is, the heat flow density is non-zero, and the surface of the friction component continuously absorbs heat, , d is the thickness of the friction component, that is, the back surface of the friction component is consistent with the ambient temperature.

[0008] Further, in step S3, the sequence of infrared images and corresponding temperature data are stored as standard format files, which facilitates subsequent data calling and analysis, and the collection time stamp needs to be retained during data storage to ensure the accurate correspondence of temperature data and image frames.

[0009] Further, in step S4, the selection of feature points needs to be accurately positioned by an image coordinate positioning tool, and the feature points in the defect center area 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.

[0010] Further, 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 temperature curve separation time of defects with different depths 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 of the finite thickness d are respectively: , , Wherein, The ratio of the energy added per unit area to the heat storage per unit volume of the section; In the long-pulse infrared thermal imaging detection system, the temperature difference between the deep defect area and the defect-free area satisfies the surface temperature rise function of the finite thickness d : , The separation time of curves 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 cycles.​

[0011] Further, 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 short pulse or long pulse excitation, so that the reconstructed data deviates from the actual temperature change due to abnormal fitting coefficient is avoided.

[0012] Further, the consistency of the detection result in the step S6 needs to be realized by repeated detection, and the same detection system is used to repeat the detection of the same friction part 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.

[0013] Further, the friction part includes automobile brake pads and industrial brake friction blocks to realize the function of friction, and 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 parts to adapt to the detection needs of different types of friction parts.

[0014] Compared with the prior art, the above technical scheme has the following beneficial effects: (1) The nondestructive testing method for defects of friction parts based on pulse infrared thermal imaging is proposed, the continuous thermal excitation characteristics of the long pulse infrared thermal imaging detection system are used to make the thermal wave fully penetrate to the deep layer of the friction material layer, even the hidden defects with a depth of 4mm can be clearly identified through temperature-time curve separation and first-order derivative image enhancement in the cooling stage, and the detection capacity is stronger, and the depth defects can be accurately covered.

[0015] (2) The nondestructive testing method for defects of friction parts based on pulse infrared thermal imaging is proposed, the data reconstruction is realized through temperature-time curve and logarithmic temperature rise-logarithmic time polynomial fitting, and then the defect display is optimized through first-order derivative image enhancement, the whole process does not need artificial subjective judgment, and the errors caused by the experience difference of the operators and auditory fatigue are avoided; at the same time, the maximum temperature rise of the long pulse infrared thermal imaging detection system significantly enlarges the temperature difference between the defect and non-defect areas, which can effectively resist the interference of environmental temperature fluctuation, the detection result is more reliable, and the artificial and environmental interference is effectively avoided.

[0016] (3) The nondestructive testing method for defects of friction parts based on pulse infrared thermal imaging is proposed, the pulse infrared thermal imaging detection system adopts the design of "non-contact and large-area coverage", and the whole surface of the brake pad can be covered in a single detection without moving the coil multiple times, and the acquisition time of 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 parts with different thicknesses and different materials, and the limitation of the eddy current technology that is only applicable to brake pads with high metal content is solved, and the detection efficiency and applicability are higher.​

[0017] (4) The nondestructive testing method for friction part defects based on the short pulse infrared thermal imaging, which has a lower cost than the traditional testing method, and the overall investment is lower. The operator can master the operation of the equipment after simple training, without professional infrared or nondestructive testing qualification, which greatly reduces the labor cost and operation difficulty of industrial application, realizes a good balance between technical performance and cost control, and has the basis for industrial popularization. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a step flow chart of the present application; Figure 2 is a brake pad structure diagram in the embodiment of the present application; Figure 3 is an appearance diagram of the short pulse infrared thermal imaging detection system in the embodiment of the present application; Figure 4 is an internal structure diagram of the flash excitation system in the short pulse infrared thermal imaging detection system in the embodiment of the present application; Figure 5 is an appearance diagram of the long pulse infrared thermal imaging detection system in the embodiment of the present application; Figure 6 is an internal structure diagram of the thermal excitation system in the long pulse infrared thermal imaging detection system in the embodiment of the present application; Figure 7 is an excitation sequence image diagram of the short pulse infrared thermal imaging detection system in the embodiment of the present application; Figure 8 is an excitation sequence image diagram of the long pulse infrared thermal imaging detection system in the embodiment of the present application; Figure 9 is a temperature-time curve analysis diagram of the short pulse and long pulse excitation in the embodiment of the present application; Figure 10 is an excitation 1D sequence diagram of the short pulse infrared thermal imaging detection system in the embodiment of the present application; Figure 11 is an excitation 1D sequence diagram of the long pulse infrared thermal imaging detection system in the embodiment of the present application; Figure 12 is a detection effect diagram of the short pulse infrared thermal imaging detection system in the embodiment of the present application; Figure 13 is a detection effect diagram of the long pulse infrared thermal imaging detection system in the embodiment of the present application. DETAILED DESCRIPTION

[0019] 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.

[0020] A non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging includes the following steps: 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 ; 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. 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. 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. 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 in each 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. 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. 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. Step S3, respectively, using short pulse and long pulse infrared thermal imaging detection system for detection of friction parts, control the detection environment conditions are consistent: the ambient temperature is constant at 26℃, the ambient humidity is kept at 40%-60%, the air flow velocity is less than or equal to 0.5m / s; Set two sets of system infrared image acquisition time is consistent, both for 80s, the number of frames is not less than 3500 frames, to ensure complete coverage of the whole cycle of thermal excitation and heat diffusion, synchronous acquisition of sequence infrared image and corresponding temperature data; Step S4, in the sequence infrared image collected in step S3, select the area of the center region of the friction part with different depth defects not less than 1mm×1mm, and a defect-free area not less than 10mm×10mm away from all defects as feature points, extract the temperature data of each feature point in the whole acquisition period, take the average temperature of multiple pixel points in the feature point area to reduce the influence of single pixel point noise, draw the temperature-time curve using the data obtained, and analyze the curve; Step S5, data reconstruction and image enhancement processing of the collected sequence image data; Step S51, data reconstruction: using logarithmic temperature rise-logarithmic time polynomial fitting method, the fitting formula is , Wherein, is the temperature value of the feature point at time t, is the fitting coefficient, 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, that is, the defect temperature change characteristics cannot be captured, or overfitting when n>5, that is, the fitting curve oscillates violently; Step S52, image enhancement: first-order time derivative of the reconstructed temperature data is generated , then adaptive histogram equalization algorithm is used to optimize the image gray scale distribution, enhance the gray scale difference between the defect area and the defect-free area, and improve the recognition clarity of deep defects; Step S6: according to the analysis results of temperature-time curve drawn in step S4 and the enhanced image analysis in step S5, the detection results are obtained, and the detection ability of the two systems is compared from four dimensions of the maximum depth of detectable defects, defect recognition clarity, detection efficiency and detection result consistency, to provide reference for subsequent detection.

[0021] Further, the morphology of the defects of the friction part in step S1 includes but is not limited to flat bottom hole, bubble, delamination, and the depth includes four gradients of 1mm, 2mm, 3mm and 4mm, which needs to cover the common internal defect depth range of the friction part to be detected.

[0022] 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: , 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; The heat source function in the heat conduction process is: , in, q The amount of heat applied per unit area It is the Dirac function; In step S2, the heat conduction process of the excitation module in the long-pulse infrared thermal imaging detection system follows the long-pulse heat conduction differential equation: , 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.

[0023] 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.

[0024] 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.

[0025] Furthermore, the analysis of the temperature-time curve in step S4 is as follows: 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: , , in, the ratio of the energy added per unit area of surface to the heat stored per unit volume of the section; In the long-pulse infrared thermal imaging detection system, the temperature difference between the deep defect area and the non-defect area meets the limited thickness d The surface temperature rise function is: , The separation time determination criterion of the curves of different defect depths is that the temperature difference between the defect area and the non-defect area is greater than or equal to 2 DEG C, and the duration is greater than or equal to 3 frame acquisition periods.

[0026] Further, in the step S5, the rationality verification of the fitting coefficient 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 short-pulse or long-pulse excitation, so as to avoid that the reconstructed data deviates from the actual temperature change due to abnormal fitting coefficient.

[0027] Further, the consistency of the detection result in the step S6 needs to be realized through repeated detection, and the same detection system is used to repeatedly detect the same friction part 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.

[0028] Further, the friction part includes automobile brake pads and industrial brake friction blocks that realize functions through friction, and 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 parts, so as to adapt to the detection needs of different types of friction parts.

[0029] Embodiment The technical solution of the application will be further described below in combination with the defect detection of the application applied in automobile brake pads: The automobile brake pad in this application is a little metal type automobile brake pad, the total thickness is 17 mm, the friction material thickness is 12 mm, the steel thickness is 5 mm, there are 4 flat bottom hole defects, the defect diameter is 10 mm, the defect depth is 1-4 mm, and the specific defect depth is shown in Figure 2 Table 1 shows the thermal physical parameters of the steel and the friction material: Table 1 , The pulse infrared thermal imaging detection system is built: the short-pulse infrared thermal imaging detection system, such as Figure 3 and Figure 4As shown, the excitation module adopts a double-lamp flash system, with a maximum energy of 6000J, a pulse width adjustable in the range of 2ms-50ms, and a light shield configured; the infrared image acquisition module adopts a non-cooled infrared thermal imager with a resolution of 640x512, a NETD≤40mK, and a frame frequency of 50Hz; the data processing module is configured with a 10.4-inch touch display screen, supporting real-time image display, storage, and temperature-time curve drawing; the long-pulse infrared thermal imaging detection system, as shown in Figure 5 and Figure 6 : the excitation module adopts 4 halogen lamp tubes, 2 of which form a group and are symmetrically distributed on both sides of the thermal imager, with a maximum power of 4000W and an excitation time adjustable in the range of 1-30s, and the inner wall is of a diffuse reflection structure; the infrared image acquisition module uses the same model non-cooled infrared thermal imager as the short-pulse system; the data processing module is also configured with a 10.4-inch touch display screen, which, in addition to the functions of the short-pulse system, also supports logarithmic polynomial fitting data reconstruction, first-order derivative image generation, and contrast enhancement.

[0030] The detection was carried out in a 26℃ constant-temperature laboratory, with the environmental humidity controlled at 45%-55% and the air flow speed ≤0.3m / s. For the short-pulse infrared thermal imaging detection system, the experimental parameters were set as: flash light energy 6000J, pulse width 10ms, acquisition time 80s, and acquisition frame number 4000 frames; for the long-pulse infrared thermal imaging detection system, the experimental parameters were set as: halogen lamp power 4000W, excitation time 8s, acquisition time 80s, and acquisition frame number 4000 frames. The short-pulse excitation sequence image and the long-pulse excitation sequence image are shown in Figure 7 and Figure 8 .

[0031] As shown in Figure 7 , at 0.02s: the flash light just ended, and the surface of the test piece showed "graininess" due to the difference in material absorption rate; at 1.2s: the 1mm deep defect began to appear, showing a high-intensity area in the infrared image, and because the defect was heat-insulating, the temperature was higher than that of the non-defect area; at 3.0s: the 2mm deep defect began to appear, and at this time, 2 defects could be detected; from 7s to 75s: the heat diffusion continued, but the 3mm and 4mm deep defects were still not detected.

[0032] As shown in Figure 8 , after the excitation ended at 8.02s: the 1mm deep defect was clearly visible, and the 2mm deep defect was faintly visible; at 9.2s: the 2mm deep defect was completely 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: in the original sequence image, only 3 defects could be clearly seen, and the 4mm defect was not completely visible due to its depth.

[0033] After feature point selection, the temperature-time curve was drawn, as shown inFigure 9 As shown in Figs. 2 and 3, for the short-pulse infrared thermal imaging detection system: only the temperature drop section is presented, and the maximum temperature rise is 12℃; only the curves of points A (1mm) and B (2mm) are separated from point E (no defect), and the curves of points C and D are intertwined with the curve of point E, and cannot be distinguished; for the long-pulse infrared thermal imaging detection system: both the temperature rise section (0-8s) and the temperature drop section (8-80s) are presented, and the maximum temperature rise is 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 temperature drop stage (about 15s), and all defect curves can be distinguished.

[0034] Only the data of the temperature drop section are reconstructed, a logarithmic temperature rise-logarithmic time polynomial fitting is adopted, n=4 or 5 is selected (to avoid overfitting / underfitting), and the reconstructed data are differentiated to generate a 1D sequence image as shown in Figs. 4 and 5. Figure 10 Figure 11 As shown in Figs. 4 and 5, only 2 defects can be detected in the 1D image of the short-pulse infrared thermal imaging detection system; 4 defects can be detected in the 1D image of the long-pulse infrared thermal imaging detection system, and the 4mm deep defect is clearly presented. The most clear moment of the 1D image is selected, a contrast enhancement algorithm is adopted for image enhancement, and the detection effect is obtained as shown in Figs. 6 and 7. Figure 12 Figure 13 As shown in Figs. 6 and 7, the image of the short-pulse infrared thermal imaging detection system still only detects 2 defects; the image of the long-pulse infrared thermal imaging detection system displays all 4 defects, and the profile of the 4mm deep defect is complete. It can be seen from the results that the long-pulse infrared thermal imaging system is more suitable for defect detection of automobile brake pads, has a deeper detection depth and a higher defect detection rate, and can provide reliable technical support for nondestructive detection of brake pads.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.​​

Claims

1. A non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging, characterized in that, The detection method includes the following steps: 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 ; 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. 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. 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. 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 in each 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. 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. 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. 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; 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. Step S5: Perform data reconstruction and image enhancement processing on the acquired sequence image data; Step S51, Data Reconstruction: A logarithmic temperature rise-logarithmic time polynomial fitting method is used, with the fitting formula being... ， 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. 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. 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.

2. The non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging according to claim 1, characterized in that, The morphology of the defects in the friction component in step S1 includes, but is not limited to, flat-bottomed holes, bubbles, and delamination. The depth includes four gradients: 1 mm, 2 mm, 3 mm, and 4 mm, which need to cover the common internal defect depth range of the friction component to be tested.

3. The non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging 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: ， 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. , Ambient temperature; The heat source function in the heat conduction process is: ， in, q The amount of heat applied per unit area It is the Dirac function; In step S2, the heat conduction process of the excitation module in the long-pulse infrared thermal imaging detection system follows the long-pulse heat conduction differential equation: ， 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.

4. The non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging according to claim 1, characterized in that, 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.

5. The non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging according to claim 1, characterized in that, In step S4, the selection of feature points needs to be accurately located using an image coordinate positioning tool. 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.

6. The non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging 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 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: ， ， in, The ratio of energy added per unit area of ​​surface to heat stored per unit volume at the cutoff point; 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: ， 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.

7. The non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging according to claim 1, characterized in that, 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.

8. The non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging according to claim 1, characterized in that, In step S6, the consistency of the detection results needs to be achieved through repeated detection. The same friction component should be repeatedly detected no less than 3 times using the same detection system. The deviation of the defect identification position and depth in each detection should be calculated and controlled within the preset allowable range.

9. The non-destructive testing method for defects in friction components based on pulsed infrared thermal imaging according to claim 1, characterized in that, In step S6, the consistency of the detection results needs to be achieved through repeated detection. The same friction component should be repeatedly detected no less than 3 times using the same detection system. The deviation of the defect identification position and depth in each detection should be calculated and controlled within the preset allowable range.

Images