Vacuum brazing quality detection method of heat dissipation structure and related equipment

Abstract

The invention discloses a vacuum brazing quality detection method for a heat dissipation structure and related equipment, and the method comprises the steps: obtaining heat flux density distribution data of the heat dissipation structure, and dividing the heat dissipation structure into different functional regions according to a thermal efficiency index; differential thermal pulses are applied to the heat dissipation structure in the vacuum brazing process, and thermal pulse response data and technological process monitoring data are obtained; calculating a thermal response characteristic parameter, comparing the thermal response characteristic parameter with a preset parameter range, identifying an abnormal state and generating a process adjustment instruction; performing time-space synchronization processing on the thermal response characteristic parameters and technological process monitoring data, and determining defect types, positions and severity; and performing a thermal load test to obtain heat conduction characteristic data, and quantitatively evaluating the structural integrity and the heat conduction performance of the brazed joint surface. According to the method, the defects of the internal joint surface can be detected in real time in the vacuum brazing process, the heat dissipation performance is accurately evaluated, and the limitation of a traditional detection method in coping with a complex geometric structure is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of vacuum brazing quality detection of heat dissipation structures, and in particular to a vacuum brazing quality detection method of a heat dissipation structure and related equipment. Background Art

[0002] Vacuum brazing of heat dissipation structures involves melting and flowing a brazing filler metal (such as aluminum, copper, or their alloys) in a vacuum environment to achieve high-strength, high-thermal-conductivity connections within complex internal channels and multi-layer structures. Because heat dissipation structures typically include multiple layers of fins, microchannels, and differentiated geometric layouts, the vacuum brazing process requires precise control of temperature, pressure, and atmosphere to ensure uniform distribution of the brazing filler metal across all joints and sufficient metal bonding, resulting in excellent thermal conductivity and overall structural reliability.

[0003] However, traditional detection methods such as X-rays, ultrasound, and airtightness testing all have obvious shortcomings when dealing with the complex internal structure of heat dissipation structures: X-rays have limited penetration ability in areas with thicker walls, ultrasound echoes reflected in multi-layer structures are too complex and difficult to analyze, and airtightness testing can only detect through-holes or obvious cracks, but cannot identify small cracks and incompletely fused areas. In addition, these detection methods are mostly performed offline after welding is completed, and cannot identify and locate welding defects in real time during the vacuum brazing process. Once defects are formed, they cannot be intervened in time, which not only leads to increased production costs, but also significantly increases the scrap rate. Especially in heat dissipation structures, even extremely small welding defects may form thermal resistance, seriously weaken the heat conduction efficiency and affect the overall heat dissipation performance of the system. Therefore, there is an urgent need for a high-precision, real-time quality detection method that can take into account the complex geometric characteristics of the heat dissipation structure and the characteristics of the vacuum brazing process to achieve real-time detection of internal joint surface defects and accurate evaluation of heat dissipation performance. Summary of the Invention

[0004] The main purpose of the present invention is to develop a non-destructive detection method that can detect internal joint surface defects in real time and accurately evaluate the heat dissipation performance based on comprehensive consideration of the complex geometric shape unique to the heat dissipation structure and the characteristics of the vacuum brazing process, thereby significantly improving the production efficiency and product reliability of the heat dissipation structure.

[0005] A first aspect of the present invention provides a method for detecting the quality of vacuum brazing of a heat dissipation structure, the method comprising: Acquiring heat flux density distribution data of the heat dissipation structure, calculating a thermal efficiency index of each area based on the heat flux density distribution data, and dividing the heat dissipation structure into different functional areas based on the thermal efficiency index; According to the division of the different functional areas, a standardized heat pulse is applied to the heat dissipation structure during the vacuum brazing process to obtain heat pulse response data and process monitoring data; Calculating thermal response characteristic parameters based on the thermal pulse response data, comparing the thermal response characteristic parameters with a preset parameter range, identifying abnormal conditions during the vacuum brazing process, and generating process adjustment instructions; adjusting vacuum brazing process parameters according to the process adjustment instructions, combining the thermal response characteristic parameters with the process monitoring data for spatiotemporal synchronization processing, extracting defect characteristic parameters from the processed data, and determining the defect type, location, and severity of the vacuum brazing joint surface; Based on the defect characteristic parameters and the type, location and severity of the defect, a heat load test is performed to obtain the thermal conductivity characteristic data of the heat dissipation structure and quantitatively evaluate the structural integrity and thermal conductivity performance of the brazing joint surface.

[0006] Preferably, the obtaining of heat flux density distribution data of the heat dissipation structure, calculating the thermal efficiency index of each area according to the heat flux density distribution data, and dividing the heat dissipation structure into different functional areas according to the thermal efficiency index include: Obtain the design parameters of the heat dissipation structure and extract the internal microchannel dimensions, fin layout, and multi-layer structure thickness data of the heat dissipation structure; Calculate the heat conduction path length and heat conduction cross-sectional area of each area of the heat dissipation structure based on the extracted structural data; Obtain the material thermal conductivity and operating temperature difference data of each area of the heat dissipation structure, and calculate the heat flux density distribution data of each area of the heat dissipation structure by combining the heat conduction path length and heat conduction cross-sectional area; Based on the obtained heat flux density distribution data and the geometric dimensions of each area, the thermal efficiency index of each area is calculated according to the formula: thermal efficiency index = regional heat flux density × heat conduction cross-sectional area / heat conduction path length; Calculating the total thermal efficiency value of the heat dissipation structure, wherein the total thermal efficiency value is the sum of the thermal efficiency indexes of all regions; Calculate the percentage of each region's thermal efficiency index to the total thermal efficiency value to obtain the thermal efficiency contribution rate of each region; According to the thermal efficiency contribution rate, the area with a thermal efficiency contribution rate greater than 30% is divided into a high-efficiency area, the area with a thermal efficiency contribution rate between 10% and 30% is divided into a standard functional area, and the area with a thermal efficiency contribution rate less than 10% is divided into an auxiliary area, completing the division of different functional areas of the heat dissipation structure.

[0007] Preferably, applying standardized heat pulses to the heat dissipation structure during vacuum brazing according to the division of the different functional areas, and obtaining heat pulse response data and process monitoring data, includes: According to the division of different functional areas, the density ratio of heat pulse monitoring points in high-performance area, standard functional area and auxiliary area is determined to be 5:3:1, and differentiated heat pulse parameters are designed, including: applying pulse width of 3 seconds and power density of 2W / cm2 to the high-performance area. 2 A heat pulse with a pulse width of 4 seconds and a power density of 1.5 W / cm2 is applied to the standard functional area. 2 A heat pulse with a pulse width of 5 seconds and a power density of 1 W / cm was applied to the auxiliary area. 2 heat pulses; Determining four key temperature points in the vacuum brazing process, the four key temperature points including 15°C before the solder melting point, the solder melting point, the midpoint of the holding temperature, and 15°C after the solder solidification point, and applying the differentiated heat pulse parameters to the heat dissipation structure at the four key temperature points; Record the temperature rise curve, peak time and cooling rate data of each monitoring point after the heat pulse is applied, calculate the heat conduction time constant and thermal diffusion coefficient of each point, and form the heat pulse response data; The temperature field distribution data, acoustic signal data and stress distribution data of the entire vacuum brazing process are obtained, and the acquired data are filtered, denoised and normalized to form process monitoring data.

[0008] Preferably, the acquisition of temperature field distribution data, acoustic signal data and stress distribution data of the entire vacuum brazing process, filtering, denoising and normalizing the acquired data to form process monitoring data includes: During the vacuum brazing process, the key phase transition temperature points of the heat dissipation structure are identified. Based on the phase change characteristics of the brazing filler metal, the vacuum brazing process is divided into the solid-phase heating stage, the solid-liquid coexistence stage, the liquid phase flow stage, and the solidification and crystallization stage. Different temperature acquisition strategies are used in each stage to obtain the original data of the temperature field distribution. Acoustic signals during vacuum brazing are collected at multiple scales and segments. High-frequency sampling is used to obtain raw acoustic signal data during the solder melting period. Wavelet decomposition is performed on the collected raw acoustic signal data to separate the interface formation acoustic signal, solder flow acoustic signal, and bubble formation acoustic signal, generating acoustic feature data. The stress distribution raw data obtained during vacuum brazing are used to extract the local area of the brazing interface. The stress component ratio and stress gradient of each monitoring point are calculated according to the interface geometry to form stress characteristic data. The raw data of the temperature field distribution are subjected to median filtering and denoising, the acoustic feature data are subjected to bandpass filtering, and the stress feature data are subjected to statistical filtering. The three types of processed data are synchronously aligned and dimensionally normalized according to timestamps to form process monitoring data.

[0009] Preferably, calculating a thermal response characteristic parameter based on the thermal pulse response data, comparing the thermal response characteristic parameter with a preset parameter range, identifying an abnormal state during vacuum brazing, and generating a process adjustment instruction includes: Performing time series analysis on the thermal pulse response data to calculate the temperature rise rate, peak temperature, decay constant and thermal response delay time to form thermal response characteristic parameters; According to the material combination, bonding interface characteristics and geometric structure data of the heat dissipation structure, combined with the statistical results of normal brazing samples, the preset parameter range of the thermal response characteristic parameters of each monitoring point is determined; Comparing the thermal response characteristic parameter with a preset parameter range, calculating the parameter deviation value and the deviation duration, and identifying abnormal monitoring points where the parameter deviation exceeds a threshold; Analyze the distribution characteristics and deviation patterns of the abnormal monitoring points, and determine that the abnormality of the temperature rise rate being more than 20% lower than the lower limit of the preset range and the thermal response delay time being more than 15% higher than the upper limit of the preset range is poor interface contact; the abnormality of the temperature rise rate being within the preset range but the attenuation constant being more than 25% higher than the upper limit of the preset range is insufficient solder flow; and the abnormality of the peak temperature fluctuation amplitude being more than 30% higher than the preset stable range is bubble formation; Based on the determined abnormality type, location, and severity, corresponding process adjustment instructions are generated, including: generating pressure adjustment instructions for areas with poor interface contact, generating holding time adjustment instructions for areas with insufficient solder flow, and generating vacuum adjustment instructions for areas with bubble formation.

[0010] Preferably, adjusting the vacuum brazing process parameters according to the process adjustment instruction, combining the thermal response characteristic parameters with the process monitoring data for spatiotemporal synchronization processing, extracting defect characteristic parameters from the processed data, and determining the defect type, location, and severity of the vacuum brazing joint surface include: According to the process adjustment instructions, the vacuum brazing process parameters were quantitatively adjusted, wherein the pressure was adjusted in a step range of 0.3-0.8 MPa, the holding time was adjusted in a step range of 20-40 seconds, and the vacuum degree was adjusted in a step range of 0.3×10 -3 -0.8×10 -3 Adjust the step range of Pa to obtain the adjusted temperature change data during brazing; Aligning and transforming the thermal response characteristic parameters and the process monitoring data according to time tags and spatial coordinates to eliminate time differences and position deviations in data acquisition and generate a spatiotemporal synchronized data set; Analyzing the spatiotemporal synchronized data set, extracting a temperature gradient distribution coefficient, an acoustic emission signal characteristic spectrum, and a stress distribution coefficient, and combining the temperature gradient distribution coefficient, the acoustic emission signal characteristic spectrum, and the stress distribution coefficient to form a defect characteristic parameter group; According to the characteristic combination and distribution pattern of the defect characteristic parameter group, the defect type of the vacuum brazing joint surface is judged, the spatial position coordinates and area range of the defect are determined, and the defect severity index is calculated according to the numerical intensity of the defect characteristic parameter group to determine the defect type, location and severity of the vacuum brazing joint surface.

[0011] Preferably, the heat load test is performed based on the defect characteristic parameters and the type, location and severity of the defect to obtain the heat conduction characteristic data of the heat dissipation structure and quantitatively evaluate the structural integrity and heat conduction performance of the brazing joint surface, including: Design a thermal load test plan based on the defect characteristic parameters and the type, location, and severity of the defect, and determine the layout of test points. The density of test points in the area surrounding the defect is 2-3 times that of the defect-free area. Apply a gradient heat load to the heat dissipation structure, gradually increasing the heat load from 30% to 120% of the rated load of the heat dissipation structure. Maintain each load level for 20-30 minutes. Record the temperature change curve, heat flux density distribution, and steady-state temperature difference data of each test point to determine the maximum heat load value of the heat dissipation structure. Conduct thermal cycle tests on the heat dissipation structure, completing 10-20 heating and cooling cycles under rated load, recording the temperature change curve in each cycle, calculating the temperature fluctuation amplitude and thermal response time, and forming thermal cycle characteristic data; Calculate the effective thermal conductivity, temperature uniformity and thermal response time of the heat dissipation structure based on the temperature change curve, heat flux density distribution, steady-state temperature difference data, maximum heat load value and thermal cycle characteristic data to form thermal conduction characteristic data; The thermal conductivity characteristic data is compared with the rated performance parameters of the heat dissipation structure, and the effective thermal conductivity deviation rate, temperature uniformity deviation rate, and thermal response time deviation rate are calculated. The structural integrity score is calculated based on the effective thermal conductivity deviation rate, temperature uniformity deviation rate, and thermal response time deviation rate. The thermal conductivity performance score is calculated based on the maximum heat load value, temperature uniformity, and thermal cycle characteristic data to quantitatively evaluate the structural integrity and thermal conductivity performance of the brazing joint surface.

[0012] A second aspect of the present invention provides a vacuum brazing quality detection device for a heat dissipation structure, the vacuum brazing quality detection device for a heat dissipation structure comprising: a heat flow analysis module for obtaining heat flux density distribution data of the heat dissipation structure, calculating a thermal efficiency index of each area based on the heat flux density distribution data, and dividing the heat dissipation structure into different functional areas based on the thermal efficiency index; a heat pulse detection module, configured to apply standardized heat pulses to the heat dissipation structure during the vacuum brazing process according to the division of the different functional areas, and obtain heat pulse response data and process monitoring data; an abnormality identification module, configured to calculate a thermal response characteristic parameter based on the thermal pulse response data, compare the thermal response characteristic parameter with a preset parameter range, identify abnormal conditions during the vacuum brazing process, and generate a process adjustment instruction; a defect characterization module, configured to adjust vacuum brazing process parameters according to the process adjustment instructions, combine the thermal response characteristic parameters with the process monitoring data for spatiotemporal synchronization processing, extract defect characteristic parameters from the processed data, and determine the defect type, location, and severity of the vacuum brazing joint surface; The performance evaluation module is used to perform a heat load test based on the defect characteristic parameters and the type, location and severity of the defect, obtain the thermal conductivity characteristic data of the heat dissipation structure, and quantitatively evaluate the structural integrity and thermal conductivity performance of the brazing joint surface.

[0013] The third aspect of the present invention provides a vacuum brazing quality inspection device for a heat dissipation structure, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected through lines; the at least one processor calls the instructions in the memory so that the vacuum brazing quality inspection device for the heat dissipation structure performs the steps of the above-mentioned vacuum brazing quality inspection method for the heat dissipation structure.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer, causes the computer to execute the steps of the above-mentioned method for detecting the quality of vacuum brazing of a heat dissipation structure.

[0015] This method first calculates the thermal efficiency index of each region and performs regional segmentation based on the heat flux density distribution within the heat dissipation structure, combined with the material thermal conductivity and multi-layer geometric layout information. This step enables early identification of key areas with significant impact on heat dissipation, laying the foundation for subsequent application of differentiated heat pulses at different locations. When the heat dissipation structure produces different thermal response characteristics under heating conditions, these characteristics can be compared with pre-set reference ranges to promptly detect potential lack of fusion, bubbles, and other internal joint defects. Simultaneously, multiple data sets, including temperature field distribution, acoustic signals, and stress distribution, are monitored in real time during the welding process. These data are then synchronized and noise filtered to effectively detect abnormal fluctuations caused by minor defects. Targeted parameter adjustment instructions, such as fine-tuning pressure, holding time, or vacuum level, are then generated based on the specific anomaly type. This means that if signs of defective joints appear, offline testing is no longer necessary after the entire weld is completed. Instead, corrections can be made during the welding process, preventing further expansion of defects and minimizing the difficulty of subsequent repairs and scrap rates.

[0016] After completing the real-time monitoring and adjustment of the joint surface, in order to ensure the reliability and stability of the final heat dissipation effect, this method also rigorously evaluates the heat dissipation performance after welding by applying incremental heat loads and conducting thermal cycle tests. Since the hidden dangers of the internal joint surface have been accurately diagnosed and corrected in the previous steps, the temperature distribution, heat flux density and steady-state temperature difference recorded in the thermal load and thermal cycle stages can accurately reflect the impact of residual defects on heat dissipation performance. If there are still local poor joints or microcracks, these locations often have abnormal temperature field distributions under higher loads or multiple thermal cycles, which provides a direct basis for further improving structural design or optimizing welding processes. Through this complete monitoring and evaluation process, it is possible to quickly capture and intervene in subtle welding defects in complex geometric structures, and at the same time accurately quantify the heat dissipation performance, thereby significantly improving production efficiency and product reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of an embodiment of a method for detecting the quality of vacuum brazing of a heat dissipation structure according to an embodiment of the present invention; Figure 2A schematic diagram of an embodiment of a vacuum brazing quality detection device for a heat dissipation structure according to an embodiment of the present invention; Figure 3 Schematic diagram of an embodiment of a vacuum brazing quality inspection device for a heat dissipation structure in an embodiment of the present invention.

[0019] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0022] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, and must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0023] An embodiment of the present application provides a method for detecting the quality of vacuum brazing of a heat dissipation structure. Figure 1 A flow chart of a method for detecting the quality of vacuum brazing of a heat dissipation structure provided in one embodiment of the present application. In this embodiment, the method includes: See also Figure 1 , obtaining heat flux density distribution data of the heat dissipation structure, calculating the thermal efficiency index of each area according to the heat flux density distribution data, and dividing the heat dissipation structure into different functional areas according to the thermal efficiency index; In one embodiment of the present invention, obtaining heat flux density distribution data of the heat dissipation structure, calculating a thermal efficiency index of each region based on the heat flux density distribution data, and dividing the heat dissipation structure into different functional regions based on the thermal efficiency index includes: Obtain the design parameters of the heat dissipation structure and extract the internal microchannel dimensions, fin layout, and multi-layer structure thickness data of the heat dissipation structure; Calculate the heat conduction path length and heat conduction cross-sectional area of each area of the heat dissipation structure based on the extracted structural data; Obtain the material thermal conductivity and operating temperature difference data of each area of the heat dissipation structure, and calculate the heat flux density distribution data of each area of the heat dissipation structure by combining the heat conduction path length and heat conduction cross-sectional area; Based on the obtained heat flux density distribution data and the geometric dimensions of each area, the thermal efficiency index of each area is calculated according to the formula: thermal efficiency index = regional heat flux density × heat conduction cross-sectional area / heat conduction path length; Calculating the total thermal efficiency value of the heat dissipation structure, wherein the total thermal efficiency value is the sum of the thermal efficiency indexes of all regions; Calculate the percentage of each region's thermal efficiency index to the total thermal efficiency value to obtain the thermal efficiency contribution rate of each region; According to the thermal efficiency contribution rate, the area with a thermal efficiency contribution rate greater than 30% is divided into a high-efficiency area, the area with a thermal efficiency contribution rate between 10% and 30% is divided into a standard functional area, and the area with a thermal efficiency contribution rate less than 10% is divided into an auxiliary area, completing the division of different functional areas of the heat dissipation structure.

[0024] Specifically, the 3D model data of the heat dissipation structure is first derived using a computer-aided design (CAD) system. Geometric analysis software is then used to extract the internal microchannel dimensional data, including parameters such as channel width, height, length, and bend angle. For the heat dissipation fin layout, image recognition technology combined with a laser scanning measurement system is used to obtain geometric parameters such as fin spacing, height, thickness, and arrangement. The thickness data of the multilayer structure is measured using a high-precision ultrasonic thickness gauge or tomography technology, using differentiated measurement frequencies for different material layers to ensure measurement accuracy. For example, a liquid-cooled heat sink may contain microchannels with a diameter of 0.8-2mm, fins with a thickness of 0.2-0.5mm, and a multilayer composite structure with a thickness ranging from 0.3-1.5mm. This precise data is an important foundation for subsequent thermal performance calculations. The purpose of obtaining these design parameters is to establish a precise geometric model of the heat dissipation structure, which is crucial for accurately calculating thermal conductivity characteristics, as heat dissipation performance is closely related to geometric parameters. In particular, for every 10% reduction in microchannel diameter, flow resistance increases by approximately 21%, while heat transfer efficiency improves by approximately 15%.

[0025] Calculating the heat conduction path length and heat conduction cross-sectional area based on the extracted structural data is a key step in thermal analysis. The heat conduction path length refers to the actual distance that heat travels from the heat source to the heat dissipation medium, which is determined using a heat streamline tracing algorithm. The system first uses finite element analysis software to establish a three-dimensional thermal network model of the heat dissipation structure, identify the main heat flow direction, and then measures the actual distance from the heat source to each heat dissipation surface along the heat flow direction to obtain the heat conduction path length. The heat conduction cross-sectional area is the effective cross-sectional area through which the heat flow passes. The heat dissipation structure is divided into multiple microelements using meshing technology, and the average cross-sectional area on each heat flow path is calculated. For complex heat dissipation structures, the equivalent thermal resistance method is used to process parallel and series heat paths to ensure the accuracy of the calculation results. For example, for a multi-layer microchannel heat sink, the heat conduction path length in the central area may be 3-5mm, while the edge area may reach 8-12mm; the heat conduction cross-sectional area in the central area may be 400-600mm. 2 , while the edge area may be 200-300mm 2 The technical value of this step lies in its ability to quantify the thermal conductivity characteristics within the heat dissipation structure. Considering that the heat flow in the heat dissipation structure is not uniformly distributed but rather has a significant concentration phenomenon, accurately calculating these parameters can identify the thermal conduction bottleneck area and provide data support for the subsequent thermal efficiency index calculation.

[0026] Obtaining the material thermal conductivity and operating temperature difference data, and combining the above calculation results to determine the heat flux density distribution is the core of achieving accurate thermal performance analysis. The material thermal conductivity data is obtained through thermal physical property testing instruments. For composite materials or special alloys, the laser flash method is used to measure their thermal diffusivity, and then the thermal conductivity is calculated by combining the density and specific heat capacity. The operating temperature difference data is obtained by installing high-precision thermocouples or infrared thermal imagers at key positions of the heat dissipation structure, measuring the temperature of each point under simulated actual working conditions, and calculating the temperature difference between adjacent areas. The heat flux density is calculated using Fourier's heat conduction law. The regional thermal conductivity, regional operating temperature difference, heat conduction path length and heat conduction cross-sectional area are substituted into the formula q=λ×ΔT / L (where q is the heat flux density, λ is the material thermal conductivity, ΔT is the operating temperature difference, and L is the heat conduction path length) to calculate the heat flux density distribution data for each region. For example, the thermal conductivity of the copper material area is about 380-400W / (m·K), and that of the aluminum material area is about 200-220W / (m·K). Under typical working conditions, the temperature difference in the core area may reach 30-40°C, and that in the edge area may be 10-15°C. The significance of this step is to calculate the heat flux distribution under real working conditions through actual measurement data, rather than relying solely on theoretical models. Considering that the heat dissipation structure in actual work often faces non-uniform heat source distribution, this heat flux density calculation based on measured data can more accurately reflect the actual heat dissipation performance, especially for heat source power density reaching 100W / cm 2Above high power cooling system.

[0027] Based on the heat flux density distribution and geometric dimension data obtained from the aforementioned analysis, calculating the thermal efficiency index is an innovative method for assessing regional heat dissipation contributions. The thermal efficiency index is calculated using the formula: Thermal efficiency index = regional heat flux density × heat conduction cross-sectional area / heat conduction path length. This index comprehensively considers regional heat flow and thermal resistance characteristics, providing a visual reflection of the regional contribution to overall heat dissipation performance. The calculation utilizes a regional integration method, dividing the heat dissipation structure into hundreds of microregions. The thermal efficiency index is calculated for each region, and then a thermal efficiency distribution cloud map is generated. The thermal efficiency index is expressed in W / m, which physically represents the heat conduction capacity per unit width. For example, for a high-performance heat sink, the thermal efficiency index of the central microchannel region may reach 800-1200 W / m, while the edge connection region may only be 100-300 W / m. The calculation of the thermal efficiency index transcends the limitations of traditional thermal analysis, which focuses solely on the temperature field or the heat flow field. It innovatively combines heat flux, geometric characteristics, and thermal resistance characteristics to form a comprehensive performance indicator. This method is particularly suitable for evaluating heat dissipation systems with complex structures because it can accurately reflect the differences in heat dissipation efficiency in different areas and identify the key areas that contribute most to heat dissipation performance.

[0028] Calculating the total thermal efficiency (TTE) of a heat dissipation structure is a crucial step in quantifying its overall cooling capacity. This TTE is equal to the sum of the thermal efficiency indices of all regions. This calculation utilizes a numerical integration method, dividing the heat dissipation structure into thousands of tiny cells. The TTE index for each cell is calculated separately and then summed. To ensure integration accuracy, the mesh is refined using adaptive meshing techniques, increasing the mesh density in areas with large heat flux gradients. Parallel computing is used to accelerate large-scale data processing and ensure computations are completed within a reasonable timeframe. The TTE represents the overall heat transfer capability of the heat dissipation structure under given operating conditions, also expressed in W / m². For example, a heat dissipation structure designed for an 80W processor might have a TTE in the range of 5000-8000 W / m². Calculating the TTE establishes a benchmark for measuring the overall performance of the heat dissipation structure, providing a reference for subsequent regional contribution analysis. Compared to traditional methods that evaluate heat dissipation performance solely based on maximum or average temperatures, the TTE considers the characteristics of the heat flow paths within the heat dissipation structure, providing a more comprehensive reflection of the actual performance of the heat dissipation structure.

[0029] Calculating the thermal efficiency contribution rate is fundamental to implementing a differentiated testing strategy. The thermal efficiency contribution rate is calculated by dividing the regional thermal efficiency index by the total thermal efficiency value, multiplied by 100%, representing the percentage contribution of that region to overall heat dissipation performance. The calculation utilizes a regional partitioning technique, dividing the heat dissipation structure into 10-20 major regions based on functional and geometric characteristics. The sum of the thermal efficiency index for each region is calculated and then divided by the overall thermal efficiency value to obtain the thermal efficiency contribution rate for each region. To ensure the reliability of the results, a Monte Carlo method is used for uncertainty analysis to assess the impact of measurement and calculation errors on the contribution rate calculation. For example, in a typical microchannel heat sink, the core channel region may contribute 40-50% to the thermal efficiency, the substrate heat transfer region 20-30%, and the connecting structure and edge regions may contribute only 5-10%. The innovation of calculating the thermal efficiency contribution rate lies in quantifying the heat dissipation performance as the relative contribution of each region. This approach overcomes the difficulty in distinguishing the importance of regions in traditional thermal analysis. By calculating the thermal efficiency contribution rate, the key areas that have the greatest impact on overall heat dissipation performance can be intuitively identified, providing a scientific basis for subsequent detection resource allocation.

[0030] Dividing functional areas based on thermal efficiency contribution is a key step in achieving differentiated testing. Using a threshold segmentation method, regions with a thermal efficiency contribution greater than 30% are classified as high-efficiency zones, those between 10% and 30% as standard functional zones, and those less than 10% as auxiliary zones. The region division process utilizes a computer-aided decision-making system, combined with an expert experience database, to ensure the rationality of the division results. To improve the accuracy of the division, the system considers the spatial continuity and functional integrity of the regions to avoid over-segmentation. After the division is completed, pseudo-color marking technology is used to visually display the different functional areas on the three-dimensional model of the heat dissipation structure. For example, for a processor heat sink, the central microchannel array might be classified as a high-efficiency zone, the heat pipe connection area as a standard functional zone, and the edge fixing structure as an auxiliary zone. The technical value of this division method lies in the rational allocation of testing resources. As the high-efficiency zone is a key area for thermal performance, allocating more testing resources to it maximizes testing efficiency and accuracy. Especially for large, complex heat dissipation structures, comprehensive inspection of all areas is often cost-prohibitive and unnecessary. A differentiated inspection strategy based on thermal efficiency contribution can significantly reduce inspection costs while ensuring quality. The 30% and 10% thresholds were set based on statistical analysis of extensive heat dissipation structure performance test data. Practice has shown that areas with a thermal efficiency contribution exceeding 30% generally have a decisive impact on overall performance, while areas with a thermal efficiency contribution below 10%, even if defective, have a relatively limited impact on overall performance.

[0031] Please continue reading Figure 1, according to the division of the different functional areas, applying standardized heat pulses to the heat dissipation structure during the vacuum brazing process, and obtaining heat pulse response data and process monitoring data; In one embodiment of the present invention, applying standardized heat pulses to the heat dissipation structure during vacuum brazing according to the division of the different functional areas, and obtaining heat pulse response data and process monitoring data, includes: According to the division of different functional areas, the density ratio of heat pulse monitoring points in high-performance area, standard functional area and auxiliary area is determined to be 5:3:1, and differentiated heat pulse parameters are designed, including: applying pulse width of 3 seconds and power density of 2W / cm2 to the high-performance area. 2 A heat pulse with a pulse width of 4 seconds and a power density of 1.5 W / cm2 is applied to the standard functional area. 2 A heat pulse with a pulse width of 5 seconds and a power density of 1 W / cm was applied to the auxiliary area. 2 heat pulses; Determining four key temperature points in the vacuum brazing process, the four key temperature points including 15°C before the solder melting point, the solder melting point, the midpoint of the holding temperature, and 15°C after the solder solidification point, and applying the differentiated heat pulse parameters to the heat dissipation structure at the four key temperature points; Record the temperature rise curve, peak time and cooling rate data of each monitoring point after the heat pulse is applied, calculate the heat conduction time constant and thermal diffusion coefficient of each point, and form the heat pulse response data; The temperature field distribution data, acoustic signal data and stress distribution data of the entire vacuum brazing process are obtained, and the acquired data are filtered, denoised and normalized to form process monitoring data.

[0032] Specifically, the implementation process of determining the density ratio of thermal pulse monitoring points and differentiated thermal pulse parameters according to the division of different functional areas first establishes a monitoring network based on the high-efficiency area, standard functional area and auxiliary area divided in the early stage. During the specific implementation, a multi-channel thermal pulse control system is used to arrange micro thermal pulse generators and temperature sensor arrays on the surface of the heat dissipation structure. The monitoring point density ratio is set to 5:3:1, which means that within a unit area, the number of monitoring points in the high-efficiency area is 1.67 times that of the standard functional area and 5 times that of the auxiliary area. For example, for a total area of 100cm 2 For the heat dissipation structure, assuming the total number of monitoring points is 100, the high-efficiency area (occupies 25% of the area) will be equipped with about 56 monitoring points, the standard functional area (occupies 50% of the area) will be equipped with about 33 monitoring points, and the auxiliary area (occupies 25% of the area) will be equipped with about 11 monitoring points. The design of differentiated thermal pulse parameters is tailored to the thermal characteristics of different areas: the high-efficiency area applies a pulse width of 3 seconds and a power density of 2W / cm 2The heat pulse applied in the standard functional area is 4 seconds in width and 1.5W / cm in power density. 2 The auxiliary zone is applied with a heat pulse of 5 seconds and a power density of 1W / cm 2 The core consideration of this differentiated design is to match the heat dissipation capacity and thermal response characteristics of different areas. The high-efficiency area has a higher heat conduction efficiency and requires a higher power density (2W / cm 2 ) can produce an obvious thermal response, and a shorter pulse width (3 seconds) can improve the time resolution of the detection; on the contrary, the thermal conduction efficiency of the auxiliary area is low, and a lower power density (1W / cm 2 ) and a longer pulse width (5 seconds) ensure that heat is fully diffused and detected. This differentiated design not only improves detection efficiency but also prevents excessive heat pulses from causing unnecessary thermal stress damage to the heat dissipation structure itself.

[0033] Determining the four key temperature points in the vacuum brazing process and applying differentiated heat pulses at these points are the core steps to achieve real-time detection. These four key temperature points are determined by analyzing the phase change characteristics of the solder, which are 15°C before the solder melting point, the solder melting point, the mid-point temperature of the insulation, and 15°C after the solder solidification point. In actual operation, a high-precision temperature control system and multi-point temperature monitoring equipment are used to track the temperature changes in the vacuum brazing furnace in real time. When it is detected that the temperature reaches the preset key point, the heat pulse generation system is automatically triggered to apply the aforementioned differentiated heat pulse parameters to the heat dissipation structure. For example, for aluminum-silicon solder with a melting point of 600°C, the four key temperature points are 585°C, 600°C, 620°C (assuming the insulation temperature is 640°C) and 550°C. The purpose of applying heat pulses at these four temperature points is to capture the interface formation process of the solder under different states. At 15°C before the solder melting point, the solder is solid but near its melting point. Applying a heat pulse at this point can monitor interfacial contact. At the solder melting point, the solder begins to melt and flow. Applying a heat pulse at this point can reflect the initial flow state. At the midpoint of the holding period, the solder is completely melted and fully fluid. Applying a heat pulse at this point can monitor solder filling. At 15°C after the solder solidification point, the solder has solidified but remains hot. Applying a heat pulse at this point can monitor the quality of the final joint interface. These four specific temperature points were chosen over others because experimental studies have shown that the physical state changes corresponding to these points are the most critical and can best reflect changes in joint quality. In particular, the two temperatures, 15°C before the solder melting point and 15°C after the solder solidification point, are in the critical phase transition region, experiencing dramatic changes in thermophysical parameters and providing the highest sensitivity for defect detection.

[0034] Recording the thermal response data of each monitoring point after the application of the heat pulse is an important part of forming the basis for defect judgment. In actual implementation, a high-speed thermal imager and a micro-thermocouple array are used to synchronously record the temperature changes of each monitoring point on the surface of the heat dissipation structure. The acquisition frequency of the thermal imager is set to above 50Hz to ensure that the rapid thermal response process is captured. For each monitoring point, the system records three types of key data: temperature rise curve (temperature-time curve of the entire process from the application of the heat pulse to temperature stabilization), peak time (the time interval from the application of the heat pulse to the temperature reaching the highest point) and cooling rate (the temperature drop value per unit time after the peak temperature). Based on these raw data, the system obtains the following formula: t=T max (1-e -（t / τ） ); where τ is the heat conduction time constant. The thermal diffusivity characterizes the material's ability to conduct heat and is calculated by analyzing the slope of the cooling curve. For example, for a properly brazed aluminum heat sink, the typical heat conduction time constant in the high-efficiency zone is approximately 0.8-1.2 seconds, and the thermal diffusivity is approximately 8-10×10 -5 m 2 / s; in areas with unfilled defects, the heat conduction time constant may increase to 1.5-2.5 seconds, and the thermal diffusion coefficient may decrease to 4-6×10 -5 m 2 / s. This significant difference provides a crucial basis for defect identification. The unique value of thermal pulse response testing lies in its ability to reveal the internal joint state through heat transfer characteristics without damaging the brazed structure. Thermal pulse response testing can provide an effective detection tool, particularly in complex heat dissipation structures where traditional X-ray or ultrasonic testing struggles to penetrate or resolve areas.

[0035] Acquiring and processing multi-source data from the entire vacuum brazing process is the foundation for comprehensive process monitoring. During the vacuum brazing process, three monitoring systems are deployed simultaneously: a temperature field monitoring system, an acoustic signal monitoring system, and a stress monitoring system. Temperature field monitoring uses a multi-point thermocouple array and an infrared thermal imager to record temperature changes in various parts of the heat dissipation structure in real time. Acoustic signal monitoring utilizes a highly sensitive piezoelectric sensor array to capture the weak sound waves generated during the brazing process. Stress monitoring employs a fiber optic strain sensor network to measure changes in stress distribution during the brazing process. The acquired raw data typically contains various interferences and noise and requires processing before analysis. Temperature field data is filtered using a median filter to remove spikes and noise. Acoustic signal data is filtered using a bandpass filter (typical frequency range: 50kHz-500kHz) to remove ambient noise and equipment vibration. Stress data is filtered using statistical filtering to eliminate random fluctuations. The filtered data also requires normalization to bring the data of different physical quantities into a standard range of 0-1 to facilitate subsequent comprehensive analysis. For example, temperature data can be calculated using the formula (TT min) / (T max -T min ) are normalized, and the acoustic signal intensity and stress values are also processed in a similar way. In addition, the system will synchronize and align the three types of data according to the timestamp to ensure that different physical phenomena can be accurately associated during analysis. The key value of multi-source data fusion is that it provides a complementary approach to defect detection: temperature field data mainly reflects thermal conduction anomalies, acoustic signals can capture transient events such as bubble formation and interface desorption, and stress data can reflect stress concentration and residual stress distribution inside the structure. This multi-dimensional monitoring greatly improves the comprehensiveness and accuracy of defect detection, especially for defect types that are difficult to fully identify with traditional single detection methods, such as unfused interfaces, bubble inclusions, and poor contact.

[0036] In one embodiment of the present invention, the temperature field distribution data, acoustic signal data and stress distribution data of the entire vacuum brazing process are obtained, and the obtained data are filtered, de-noised and normalized to form process monitoring data, including: During the vacuum brazing process, the key phase transition temperature points of the heat dissipation structure are identified. Based on the phase change characteristics of the brazing filler metal, the vacuum brazing process is divided into the solid-phase heating stage, the solid-liquid coexistence stage, the liquid phase flow stage, and the solidification and crystallization stage. Different temperature acquisition strategies are used in each stage to obtain the original data of the temperature field distribution. Acoustic signals during vacuum brazing are collected at multiple scales and segments. High-frequency sampling is used to obtain raw acoustic signal data during the solder melting period. Wavelet decomposition is performed on the collected raw acoustic signal data to separate the interface formation acoustic signal, solder flow acoustic signal, and bubble formation acoustic signal, generating acoustic feature data. The stress distribution raw data obtained during vacuum brazing are used to extract the local area of the brazing interface. The stress component ratio and stress gradient of each monitoring point are calculated according to the interface geometry to form stress characteristic data. The raw data of the temperature field distribution are subjected to median filtering and denoising, the acoustic feature data are subjected to bandpass filtering, and the stress feature data are subjected to statistical filtering. The three types of processed data are synchronously aligned and dimensionally normalized according to timestamps to form process monitoring data.

[0037] Specifically, identifying the key phase change temperature points of the heat dissipation structure during vacuum brazing is the basis for realizing stage-by-stage temperature field monitoring. This step pre-analyzes the thermal physical properties of the solder through differential scanning calorimetry (DSC) and thermogravimetric analyzer (TGA) to determine its melting point, solidification point and phase change temperature range. During the actual brazing process, a high-precision thermocouple array and infrared thermal imaging system are used to monitor the temperature changes of the heat dissipation structure in real time. When a sudden change in the temperature change rate or a peak in heat absorption / release is detected, the system automatically identifies the key phase change temperature point, and then divides the entire vacuum brazing process into four key stages: solid-phase heating stage (from room temperature to 15°C before the solder melting point), solid-liquid coexistence stage (15°C before the solder melting point to the complete melting temperature of the solder), liquid phase flow stage (from complete liquefaction of the solder to the beginning of solidification) and solidification crystallization stage (from the beginning of solidification to room temperature). Differentiated temperature acquisition strategies are employed for different stages: a low sampling frequency (1 Hz) and a large grid spacing (10 mm × 10 mm) are used during the solid-phase heating phase; the sampling frequency is increased to 5 Hz and the monitoring grid is reduced to 5 mm × 5 mm during the solid-liquid coexistence phase; the highest sampling frequency (10 Hz) and the densest monitoring grid (2 mm × 2 mm) are used during the liquid phase flow phase; and a moderate sampling frequency (3 Hz) and a monitoring grid (5 mm × 5 mm) are used during the solidification and crystallization phase. For aluminum-silicon brazing alloy (melting point 577°C), the temperature ranges for the four stages are: solid-phase heating (25°C–562°C), solid-liquid coexistence (562°C–585°C), liquid phase flow (585°C–550°C), and solidification and crystallization (550°C–100°C). This differentiated acquisition strategy ensures high-precision data acquisition during critical stages (such as solid-liquid coexistence and liquid phase flow) while avoiding the data redundancy and processing burden associated with high-frequency sampling throughout the entire process. Especially in the liquid phase flow stage, improving the spatial and temporal resolution can accurately capture transient phenomena in the solder flow and filling process, such as local flow stagnation or uneven filling, which are often early indicators of potential defects.

[0038] Multi-scale segmented acquisition and processing of acoustic signals during vacuum brazing is an important means of identifying the formation of microscopic defects. During the implementation process, a high-sensitivity piezoelectric acoustic sensor array (frequency response range 20kHz-1MHz) is installed on the outer wall of the vacuum brazing equipment, and the sound waves are transmitted from the brazing cavity to the sensor through the waveguide rod. A multi-scale segmented acquisition strategy is adopted, specifically: a low-frequency sampling rate (100kHz) is used in non-critical stages (the initial stage of solid-phase heating and the late stage of cooling), and a high-frequency sampling rate (500kHz-1MHz) is used in the solder melting range (i.e., the solid-liquid coexistence stage and the initial stage of liquid phase flow). The collected raw acoustic signal data is pre-amplified and analog-to-digital converted, and then input into the digital signal processing system. The system uses a multi-resolution wavelet decomposition algorithm (using the Daubechies wavelet basis) to decompose the original acoustic signal into sub-signals of different frequency bands. Through spectral and time domain feature analysis, it separates three types of characteristic acoustic signals: interface formation acoustic signals (typical frequency range 50-100kHz, with a continuous weak-amplitude waveform), solder flow acoustic signals (typical frequency range 100-300kHz, with an intermittent medium-amplitude waveform), and bubble formation acoustic signals (typical frequency range 300-600kHz, with a sudden high-amplitude waveform). For example, during the brazing process of an aluminum microchannel heat sink, when the solder begins to flow and fill the microchannel at 590°C, the acoustic monitoring system detects a continuous flow signal of approximately 150kHz. When the temperature reaches 610°C and residual flux is locally present, the system captures the acoustic characteristics of bubble collapse at approximately 400kHz. The core value of this acoustic feature recognition technology lies in its ability to monitor in real time microscopic physical processes that are invisible to the naked eye, especially three phenomena closely related to brazing quality: interface formation, solder flow, and bubble formation. Because these acoustic events have unique spectral characteristics, they can be effectively separated and identified through wavelet decomposition, even if they overlap in time. The choice of high-frequency sampling rate (500kHz-1MHz) is based on the inherent frequency characteristics of acoustic events. The characteristic frequencies of key defect events such as bubble formation and rupture are mostly distributed in the range of 300-600kHz, and a sampling rate of at least 500kHz is required to accurately capture these high-frequency signals.

[0039] Processing and analyzing the raw stress distribution data acquired during vacuum brazing is another important dimension in assessing joint quality. The stress monitoring system primarily consists of a fiber Bragg grating (FBG) sensor network embedded or attached to the surface of the heat dissipation structure and acquiring local strain data by measuring wavelength variations in the optical fiber. The digital speckle correlation system uses a high-resolution camera to record the deformation of the surface speckle pattern and calculate the full-field strain distribution. The system first extracts the acquired stress distribution data from a local region of the brazed joint interface. Specifically, the stress data within a ±5 mm radius, centered around the pre-determined joint interface location, are analyzed. The extracted local region data undergoes a coordinate transformation based on the interface geometry, mapping the complex three-dimensional interface into a two-dimensional parameter space for unified processing. Two key stress characteristic parameters are then calculated: the stress component ratio (the ratio of the primary stress to the secondary stress) and the stress gradient (the rate of stress change per unit distance). The stress component ratio reflects the balance of stress. An ideal brazed joint interface has a stress component ratio close to 1, indicating uniform stress distribution. The stress gradient reflects the degree of stress concentration. High stress gradients often indicate potential stress concentration areas, representing a high risk for defect formation. Taking the brazing of microchannel heat sinks as an example, the stress component ratio of a normal joint interface is generally in the range of 0.8-1.2, and the stress gradient is less than 0.5MPa / mm; when an incomplete fusion area occurs, the stress component ratio may deviate to below 0.5 or above 1.5, and the stress gradient may exceed 2MPa / mm. The technical effect of stress characteristic data analysis is that it provides a direct means of evaluating the structural integrity of the brazing joint interface. Compared with temperature and acoustic data, which reflect thermal conductivity and acoustic properties, stress data directly reflects the mechanical properties and potential mechanical reliability issues of the interface. In particular, stress gradient analysis can effectively identify stress concentration areas. Even if these areas do not have obvious defects in the early stage, they may fail first due to fatigue or thermal cycling during subsequent use. The selection of ±5mm as the local area extraction range is based on experimental studies showing that the stress-affected area of the brazing joint is usually most significant within 5mm on both sides of the interface.

[0040] Filtering, aligning, and standardizing the three types of raw data are key steps in generating comprehensive process monitoring data. The raw temperature field distribution data is first de-noised using a median filter algorithm. This involves replacing the original value with the median of the five preceding and succeeding time points in the temperature time series for each measurement point, effectively eliminating random interference and sudden changes. After separation, the acoustic feature data is processed using a bandpass filter. Frequency bands are set for different types of acoustic signals: a 50-100kHz bandpass filter is used for the interface formation signal, a 100-300kHz bandpass filter is used for the solder flow signal, and a 300-600kHz bandpass filter is used for the bubble formation signal, effectively filtering out out-of-band interference. Statistical filtering is used for stress feature data. By calculating the mean and standard deviation within a local region, outliers that deviate from the mean by more than three standard deviations are eliminated, preserving the true stress distribution characteristics. The three types of filtered data differ in acquisition time and spatial location and require synchronization alignment based on timestamps. Based on the acquisition timestamps of each data stream, the system uses an interpolation algorithm to unify data from different sampling frequencies onto the same time base, ensuring that data points correspond to the same physical moment. Furthermore, data normalization is performed to account for dimensional differences in different physical quantities: temperature data is normalized using linear normalization, acoustic signal intensity data using logarithmic normalization, and stress data using maximum normalization. The normalized data are mapped to a range of 0–1, facilitating subsequent comprehensive analysis and comparison. In practical applications, after synchronous alignment and normalization, the temperature, acoustic, and stress data of an aluminum-copper dissimilar metal brazed joint clearly revealed an anomaly at 590°C: a localized drop in temperature (normalized value from 0.85 to 0.78), accompanied by an acoustic signal peak in the 350kHz band (normalized value reaching 0.95) and a sudden increase in stress gradient (normalized value increasing from 0.3 to 0.75), indicating a bubble formation event. The technical benefit of this multi-source data fusion is that it provides comprehensive brazing process monitoring capabilities. Different types of data complement and verify each other, significantly improving the reliability and accuracy of defect identification. In particular, synchronous alignment ensures the accuracy of the temporal correlation between different physical phenomena, revealing the physical mechanisms and evolution of defect formation. For example, bubble formation often manifests first as an acoustic signal anomaly, followed by a local temperature anomaly, and finally an abnormal stress distribution.

[0041] Please continue reading Figure 1 , calculating thermal response characteristic parameters according to the thermal pulse response data, comparing the thermal response characteristic parameters with a preset parameter range, identifying abnormal conditions during vacuum brazing, and generating process adjustment instructions; In one embodiment of the present invention, calculating a thermal response characteristic parameter based on the thermal pulse response data, comparing the thermal response characteristic parameter with a preset parameter range, identifying an abnormal state during vacuum brazing, and generating a process adjustment instruction includes: Performing time series analysis on the thermal pulse response data to calculate the temperature rise rate, peak temperature, decay constant and thermal response delay time to form thermal response characteristic parameters; According to the material combination, bonding interface characteristics and geometric structure data of the heat dissipation structure, combined with the statistical results of normal brazing samples, the preset parameter range of the thermal response characteristic parameters of each monitoring point is determined; Comparing the thermal response characteristic parameter with a preset parameter range, calculating the parameter deviation value and the deviation duration, and identifying abnormal monitoring points where the parameter deviation exceeds a threshold; Analyze the distribution characteristics and deviation patterns of the abnormal monitoring points, and determine that the abnormality of the temperature rise rate being more than 20% lower than the lower limit of the preset range and the thermal response delay time being more than 15% higher than the upper limit of the preset range is poor interface contact; the abnormality of the temperature rise rate being within the preset range but the attenuation constant being more than 25% higher than the upper limit of the preset range is insufficient solder flow; and the abnormality of the peak temperature fluctuation amplitude being more than 30% higher than the preset stable range is bubble formation; Based on the determined abnormality type, location, and severity, corresponding process adjustment instructions are generated, including: generating pressure adjustment instructions for areas with poor interface contact, generating holding time adjustment instructions for areas with insufficient solder flow, and generating vacuum adjustment instructions for areas with bubble formation.

[0042] Specifically, time series analysis of thermal pulse response data first requires processing the temperature-time series data collected at each monitoring point after the thermal pulse is applied. The specific implementation process uses a digital signal processing system. After inputting the original temperature time series, the four key thermal response characteristic parameters are calculated through numerical differentiation, fitting analysis and feature extraction algorithms. The temperature rise rate refers to the rate of temperature rise per unit time after the thermal pulse is applied. It is obtained by calculating the slope of the temperature curve in the initial stage (0-1 second) after the pulse is applied, and the unit is ℃ / second. The peak temperature refers to the highest value reached by the temperature after the thermal pulse is applied. It is directly obtained by searching for the maximum value of the temperature time series, and the unit is ℃. The decay constant is a parameter that characterizes the temperature drop rate. It is obtained by fitting the temperature drop curve after the thermal pulse stops to an exponential decay function, specifically: T(t)=T0+ΔT×e -（t / τ）; where τ is the decay constant, in seconds, and its physical meaning is the time required for the temperature to drop to 36.8% (1 / e) of the initial increment. The thermal response delay time refers to the time interval from the application of the heat pulse to the start of a significant temperature rise, which is determined by analyzing the inflection point of the temperature curve, in seconds. In a specific example, for an aluminum-copper joint heat sink, a 2W / cm 2 After a thermal pulse, the typical temperature rise rate is about 15-20°C / second, the peak temperature is 10-15°C higher than the ambient temperature, the decay constant is about 1.2-1.8 seconds, and the thermal response delay time is about 0.1-0.3 seconds. These four parameters together constitute the thermal response characteristic parameter set, which reflects the thermal conduction characteristics from different angles: the temperature rise rate and thermal response delay time mainly reflect the initial stage characteristics of thermal conduction and are closely related to the contact thermal resistance; the peak temperature reflects the heat absorption and diffusion capacity; the decay constant reflects the heat dissipation capacity and heat storage characteristics. This multi-parameter characterization method can comprehensively characterize the thermal response process and enhance the ability to identify different types of defects, especially defects caused by different mechanisms such as poor interface contact and insufficient solder filling.

[0043] Determining the preset parameter range of the thermal response characteristic parameters of each monitoring point is a key step in establishing a defect judgment benchmark. The specific implementation process first collects detailed material combination data of the heat dissipation structure, including the type of substrate (such as aluminum, copper or its alloys), solder composition and its thermophysical parameters, interface treatment method and coating type, etc. Then combine the bonding interface characteristic data, including the bonding surface roughness, solder thickness, interface metal mutual solution zone thickness, etc. At the same time, consider the geometric structure data, including the bonding area, thermal conduction path length and structural complexity. Based on these basic data, combined with a large amount of experience to prove that the quality of normal brazing samples is qualified, a statistical analysis is carried out to establish a standard library of thermal response characteristic parameters of various heat dissipation structures in different functional areas. The specific method is to conduct thermal pulse tests on multiple qualified samples of each type of structure, collect thermal response data of hundreds of monitoring points, calculate the average value and standard deviation of each parameter, and use the average value ± 2 times the standard deviation as the preset parameter range. For example, for microchannel heat sinks, the preset range for the temperature rise rate in the high-efficiency zone is 16-22°C / second, the preset range for the peak temperature is ambient temperature +12-16°C, the preset range for the attenuation constant is 1.3-1.7 seconds, and the preset range for the thermal response delay time is 0.15-0.25 seconds. The corresponding parameter ranges for the standard functional zone are 12-18°C / second, ambient temperature +8-12°C, 1.5-2.0 seconds, and 0.2-0.3 seconds, respectively. This parameter range determination method, based on material properties, structural characteristics, and normal sample statistics, overcomes the subjectivity of simple empirical judgments and the limitations of a single standard, and can adapt to heat dissipation systems with different material combinations and structural designs. Importantly, this method takes into account the differences between different functional areas of the heat dissipation structure and establishes personalized judgment criteria for each monitoring point, avoiding one-size-fits-all erroneous judgments and improving the accuracy of defect identification.

[0044] Comparing thermal response characteristic parameters with preset parameter ranges is the core process for identifying abnormal conditions. A real-time data processing system is used during implementation to compare the four thermal response characteristic parameters of each monitoring point with the corresponding preset parameter ranges, calculating the parameter deviation value and deviation duration. The parameter deviation value is defined as the ratio of the difference between the measured value and the midpoint of the preset range to the width of the preset range. For example, if the temperature rise rate at a point is 14°C / second, and the preset range is 16-22°C / second (the midpoint of the range is 19°C / second, and the range width is 6°C / second), the temperature rise rate deviation value is (14-19) / 6 = -0.83. The deviation duration refers to the length of time that the parameter continuously deviates from the preset range and is calculated through multiple consecutive measurements. The system sets a deviation threshold. When the absolute value of the deviation of any parameter exceeds the set threshold (usually 0.5, meaning the deviation from the midpoint of the preset range exceeds half the width of the preset range), the monitoring point is marked as an abnormal point. For example, during the vacuum brazing process of a microchannel radiator, the temperature rise rate of monitoring point No. 15 was continuously lower than the lower limit of the preset range by 3°C / second, the deviation value was -0.75, exceeded the threshold value of 0.5, and lasted for 15 seconds. The system identified it as an abnormal monitoring point. The advantage of this parameter deviation analysis method is that it takes into account both the magnitude of the deviation and the persistence of the deviation, and can effectively distinguish between instantaneous fluctuations and real anomalies. In particular, the introduction of the deviation duration eliminates the influence of measurement noise and environmental interference, and improves the reliability of the judgment. The threshold is set to 0.5 instead of other values, which is determined based on a large number of brazing quality statistical analysis results. This value can ensure the identification of real defects while avoiding misjudgments caused by oversensitivity, achieving the best balance between detection sensitivity and accuracy.

[0045] Analyzing the distribution characteristics and deviation patterns of abnormal monitoring points is a key step in determining the defect type. During implementation, the identified abnormal monitoring points are first visually marked on the three-dimensional model of the heat dissipation structure to analyze their spatial distribution characteristics, such as whether they are clustered, distributed along a specific direction, or located at the edge of the structural feature. Then, a pattern matching analysis is performed on the four parameter deviations of each abnormal point and compared with a pre-established defect feature database. The specific judgment criteria are: when the temperature rise rate is more than 20% lower than the lower limit of the preset range (i.e., the measured value is less than 80% of the preset lower limit) and the thermal response delay time exceeds the upper limit of the preset range by more than 15% (i.e., the measured value is greater than 115% of the preset upper limit), it is judged as poor interface contact; when the temperature rise rate is within the preset range but the attenuation constant exceeds the upper limit of the preset range by more than 25%, it is judged as insufficient solder flow; when the peak temperature fluctuation amplitude (i.e., the difference between the maximum and minimum values) exceeds the preset stable range by more than 30%, it is judged as bubble formation. For example, during the brazing process of a microchannel heat sink, a set of monitoring points located at the corners showed a temperature rise rate of only 75% of the preset lower limit (significantly low), and a thermal response delay time of 126% of the preset upper limit (significantly high). The system identified this as poor interface contact. Another set of monitoring points in the central area showed a normal temperature rise rate, but a decay constant of 138% of the preset upper limit (significantly high). The system identified this as insufficient solder flow. Another set of dispersed monitoring points showed peak temperature fluctuations reaching 165% of the preset stable range (extremely unstable). The system identified this as bubble formation. This deviation pattern-based defect type determination method is based on a deep understanding of the physical mechanisms of defects: poor interface contact increases contact thermal resistance, making it difficult for heat to conduct quickly, thus manifesting as a low temperature rise rate and a long thermal response delay time. In areas with insufficient solder flow, the heat conduction path is distorted and discontinuous, making it difficult for heat to diffuse effectively, thus manifesting as a large decay constant. In areas where bubbles form, due to the thermal instability of the bubbles and the local convection effect, heat transfer exhibits significant fluctuations, manifesting as unstable peak temperatures. The set percentage thresholds (20%, 15%, 25%, 30%) can provide reliable defect classification results under various heat dissipation structures and brazing process conditions.

[0046] Generating process adjustment instructions based on the identified abnormality type is a key step in achieving real-time intervention. During implementation, the process parameter adjustment module of the expert system architecture is used to automatically generate corresponding process adjustment instructions based on the identified defect type, location and severity. For areas with poor interface contact, the system generates a pressure adjustment instruction, specifically to increase the local or overall brazing pressure. The adjustment range is determined according to the severity of the poor contact. Generally, 0.3-0.8MPa is added to the original pressure to ensure sufficient contact between the joint surfaces. For areas with insufficient solder flow, the system generates a holding time adjustment instruction to extend the duration of the high-temperature holding stage. The adjustment range is to increase by 20-40 seconds to ensure that the solder is fully melted and flows to fill the gap. For areas where bubbles form, the system generates a vacuum adjustment instruction to increase the vacuum pump speed or extend the vacuum time to increase the vacuum level by 0.3×10 -3 -0.8×10 -3 Pa, strengthen the removal of gases and volatiles. For example, during the vacuum brazing process of a large heat sink array, the system detected serious poor interface contact in the edge area at the melting point of the solder, and immediately generated a pressure adjustment instruction to increase the pressure from the original 0.5MPa to 1.1MPa; at the same time, it detected moderate solder flow deficiency in the central area and generated an adjustment instruction to extend the holding time by 30 seconds; slight bubble formation was detected at several discrete points, and an adjustment instruction to increase the vacuum degree by 0.4×10 -3 Pa adjustment instructions. These three instructions are prioritized and transmitted to the control system of the vacuum brazing equipment for execution, realizing real-time adjustment of the brazing process. The technical advantage of this differentiated process adjustment method based on defect type is that it can solve problems caused by different mechanisms in a targeted manner, avoiding the negative impact that may be caused by simple and crude full parameter adjustment. In particular, the selection of the three parameters of pressure, holding time and vacuum degree directly corresponds to the three key physical processes of interface contact, solder flow and gas removal, forming a logically complete and targeted process control system. Parameter adjustment step (0.3-0.8MPa, 20-40 seconds, 0.3×10 -3 -0.8×10 - 3 The design of Pa) takes into account the effectiveness and safety of adjustments, ensuring that problems can be solved without introducing new risks.

[0047] Please continue reading Figure 1 , adjusting the vacuum brazing process parameters according to the process adjustment instruction, combining the thermal response characteristic parameters with the process monitoring data for spatiotemporal synchronization processing, extracting defect characteristic parameters from the processed data, and determining the defect type, location and severity of the vacuum brazing joint surface; In one embodiment of the present invention, adjusting the vacuum brazing process parameters according to the process adjustment instruction, combining the thermal response characteristic parameters with the process monitoring data for spatiotemporal synchronization processing, extracting defect characteristic parameters from the processed data, and determining the defect type, location, and severity of the vacuum brazing joint surface include: According to the process adjustment instructions, the vacuum brazing process parameters were quantitatively adjusted, wherein the pressure was adjusted in a step range of 0.3-0.8 MPa, the holding time was adjusted in a step range of 20-40 seconds, and the vacuum degree was adjusted in a step range of 0.3×10 -3 -0.8×10 -3 Adjust the step range of Pa to obtain the adjusted temperature change data during brazing; Aligning and transforming the thermal response characteristic parameters and the process monitoring data according to time tags and spatial coordinates to eliminate time differences and position deviations in data acquisition and generate a spatiotemporal synchronized data set; Analyzing the spatiotemporal synchronized data set, extracting a temperature gradient distribution coefficient, an acoustic emission signal characteristic spectrum, and a stress distribution coefficient, and combining the temperature gradient distribution coefficient, the acoustic emission signal characteristic spectrum, and the stress distribution coefficient to form a defect characteristic parameter group; According to the characteristic combination and distribution pattern of the defect characteristic parameter group, the defect type of the vacuum brazing joint surface is judged, the spatial position coordinates and area range of the defect are determined, and the defect severity index is calculated according to the numerical intensity of the defect characteristic parameter group to determine the defect type, location and severity of the vacuum brazing joint surface.

[0048] Specifically, quantitatively adjusting vacuum brazing process parameters based on process adjustment instructions is a real-time intervention step to ensure brazing quality. This is implemented using computer-controlled precision vacuum brazing equipment equipped with a programmable logic controller (PLC) and a multi-parameter closed-loop control system capable of receiving and executing process adjustment instructions in real time. When the system receives a pressure adjustment instruction, it adjusts the pressure via a servo motor-driven pressure mechanism, with the adjustment step size controlled within a range of 0.3-0.8 MPa. For example, an increase of 0.3 MPa is required when a minor contact failure is detected, and an increase of 0.8 MPa is required when a severe contact failure is detected. For the insulation time adjustment instruction, the system extends the duration of the high-temperature insulation phase by modifying the time parameters in the temperature control program. The adjustment step size ranges from 20-40 seconds, such as an increase of 20 seconds for areas with minor solder flow deficiency and 40 seconds for areas with large areas of insufficient flow. For the vacuum level adjustment instruction, the system increases the vacuum level within the vacuum chamber by increasing the vacuum pump speed or extending the vacuum time. The adjustment step size is controlled within a range of 0.3×10 -3 -0.8×10 -3 Pa range, such as increasing 0.3×10-3 Pa, for serious bubble areas, it increases by 0.8×10 -3 After the parameter adjustment is executed, the system continues to obtain temperature change data of the brazing process after the adjustment through the temperature sensor array to monitor the adjustment effect. For example, during the brazing process of a high-efficiency radiator, the system detected poor interface contact in the top area and immediately increased the pressure from 0.6MPa to 1.2MPa (an increase of 0.6MPa); at the same time, insufficient brazing material flow was found in the middle area, and the holding time was extended from 60 seconds to 90 seconds (an increase of 30 seconds); and bubble formation was found in the heat sink fin area, and the vacuum degree was increased from 1.5×10 -3 Pa increased to 2.0×10 -3 Pa (increase 0.5×10 -3 Pa). These adjustment parameter ranges are set based on the physical properties of the solder and the mechanical properties of the joint. A pressure step of 0.3-0.8 MPa can effectively improve the interface contact without causing deformation; a holding time step of 20-40 seconds is sufficient to promote solder flow without excessively consuming production time; 0.3×10 -3 -0.8×10 -3 The vacuum degree adjustment step of Pa can enhance gas removal without overloading the equipment.

[0049] The spatiotemporal synchronization of thermal response characteristic parameters with process monitoring data is a key step in achieving multi-source data fusion. This implementation utilizes a spatiotemporal data alignment system. First, a unified time stamp is added to each data source. A high-precision clock signal is used to provide a synchronized time reference for all sensors, ensuring that the collected data has accurate timestamps. Then, based on the spatial coordinates of each sensor, a mapping relationship is established between the sensor position and the three-dimensional model of the heat dissipation structure, constructing a unified spatial coordinate system. To address time differences in data acquisition, linear or spline interpolation methods are used for time alignment, unifying data with different sampling frequencies to the same time point. To address spatial position deviations, a spatial coordinate transformation algorithm is used to transform the thermal response data, temperature field data, acoustic signal data, and stress distribution data into a unified spatial reference system. Geometric correction is then used to eliminate positional and angular errors among the sensors. For example, in brazing monitoring of a multi-layer microchannel heat sink, the thermal pulse response data is sampled at a frequency of 10 Hz, while the acoustic signal is sampled at a frequency of 500 kHz, resulting in a time difference of up to five orders of magnitude. Furthermore, the spatial resolution of the thermal imager is 0.5 mm / pixel, while the positioning accuracy of the acoustic sensor is approximately 5 mm, resulting in significant spatial deviations. Through spatiotemporal synchronization, the system aligns these heterogeneous data to a unified frequency of 1 Hz along the time axis, spatially converts them into a rectangular coordinate system with the center of the heat sink as the origin, and achieves accuracy of 1 mm. This ultimately generates a spatiotemporal synchronized dataset covering the entire brazing process. This spatiotemporal synchronization overcomes the greatest obstacle to traditional multi-source data fusion—temporal and spatial inconsistencies—making it possible to analyze correlations between different physical quantities. In particular, it can reveal key information such as the temporal relationship and spatial evolution of defect formation, such as the sequence of thermal and acoustic anomalies and the spatial coupling of stress concentration and temperature gradients.

[0050] Analyzing and extracting defect characteristic parameters from a spatiotemporally synchronized data set is a key step in defect characterization. A feature extraction system is employed. The system first calculates the temperature gradient distribution coefficient—that is, the spatial gradient vector of the temperature field data is calculated. The consistency of the gradient direction and the rate of change of the gradient amplitude are then analyzed to form a dimensionless coefficient characterizing the uniformity of the temperature field. Specifically, the temperature gradient vector is calculated for each monitoring point in the two-dimensional temperature field. The directional consistency and amplitude variation of the gradient vector within the local region are then analyzed. In normal brazing areas, the temperature gradient distribution is uniform and consistent in direction, with a temperature gradient distribution coefficient close to 1. In defective areas, however, the temperature gradient direction is disordered and the amplitude varies dramatically, with the temperature gradient distribution coefficient deviating from 1. The characteristic spectrum of the acoustic emission signal is then analyzed. A short-time Fourier transform is performed on the acoustic signal data to calculate the energy density spectrum. The energy distribution ratio and spectral shape parameters of characteristic frequency bands are extracted to form a spectral characteristic vector describing the acoustic characteristics. The acoustic emission signal spectrum during a normal brazing process exhibits regular variations, whereas defect formation is often accompanied by abnormal increases or decreases in energy in specific frequency bands. Finally, the stress distribution coefficient is calculated, and the stress field data is analyzed for the rate of change of the principal stress direction and the stress concentration factor, forming a parameter set that characterizes the stress distribution characteristics. While the stress distribution in a normal brazing area is uniform and the principal stress direction changes gently, defective areas often exhibit stress concentration and a sharp change in the principal stress direction. These three characteristic parameters (temperature gradient distribution coefficient, acoustic emission signal characteristic spectrum, and stress distribution coefficient) are combined to form a multidimensional defect characteristic parameter set. For example, during the brazing process of an aluminum radiator, the system detected a temperature gradient distribution coefficient of 0.45 (far below the normal value of 1) in one area, an abnormally high energy ratio of the acoustic emission signal in the 350-450kHz frequency band, and a five-fold increase in the rate of change of the principal stress direction from the stress distribution coefficient. This set of characteristic parameters clearly indicates the presence of a bubble defect in this area. This multi-physical quantity characteristic parameter extraction method breaks through the limitations of single physical quantity detection. By comprehensively analyzing data from three dimensions: heat, sound, and force, it significantly improves the accuracy and comprehensiveness of defect identification, especially for complex defect types and multiple defects.

[0051] Judging the defect type and evaluating its severity based on the defect characteristic parameter group is the final step of quality diagnosis. Pattern recognition and machine learning methods are used in implementation. First, a defect feature library is established, which contains the characteristic parameter group patterns of various typical defects. Then, pattern matching and cluster analysis are performed on the currently detected defect characteristic parameter group, and the defect type is judged based on the similarity of the feature combination. For example, the combination feature of low temperature gradient distribution coefficient, enhanced high-frequency components in acoustic emission signals and uneven stress distribution usually corresponds to bubble-type defects; the combination feature of low temperature gradient distribution coefficient, enhanced low-frequency components in acoustic emission signals and relatively uniform stress distribution is a typical representation of insufficient solder defects. The spatial position coordinates of the defect are directly determined by the coordinates of the abnormal point of the characteristic parameter, and the area range is gradually expanded to the boundary where the characteristic parameters return to normal through the regional growing algorithm, with the abnormal center as the seed point, and the actual coverage area of the defect is calculated. The defect severity index is calculated based on the numerical strength of the defect characteristic parameter group. The specific formula is defect severity index = ∑ i w i ×(|P i -P i,norm | / P i,norm ), where w i is the weight coefficient of each characteristic parameter, P i is the measured characteristic parameter value, P i,norm is the parameter value of the normal area. According to the calculated severity index, the defects are divided into three levels: mild (0-0.3), moderate (0.3-0.7) and severe (0.7-1.0). For example, in the brazing process monitoring of a large radiator array, the system detected a temperature gradient distribution coefficient of 0.35 in the right edge area, the energy ratio of the acoustic emission signal in the 300-400kHz frequency band increased by 180%, and the stress distribution coefficient showed that the local stress concentration factor reached 3.2. The feature combination was determined to be a bubble-type defect, with spatial position coordinates of (x=85mm, y=120mm, z=5mm) and an area range of approximately 12mm 2 , the calculated severity index is 0.82, which is classified as severe. This defect diagnosis method based on multi-source data can not only accurately identify the defect type, but also quantitatively assess the location, scope, and severity of the defect, providing a comprehensive basis for subsequent quality control decisions. In particular, the introduction of the defect severity index converts multidimensional physical parameters into a single evaluation indicator, simplifying the decision-making process and improving the efficiency of quality management. The three-level defect classification (minor, moderate, and severe) is consistent with the quality grading method commonly used in industrial production, facilitating integration with existing quality management systems.

[0052] Please continue reading Figure 1According to the defect characteristic parameters and the type, location and severity of the defect, a heat load test is performed to obtain the thermal conductivity characteristic data of the heat dissipation structure and quantitatively evaluate the structural integrity and thermal conductivity performance of the brazing joint surface.

[0053] In one embodiment of the present invention, the heat load test is performed based on the defect characteristic parameters and the type, location and severity of the defect to obtain the heat conduction characteristic data of the heat dissipation structure and quantitatively evaluate the structural integrity and heat conduction performance of the brazing joint surface, including: Design a thermal load test plan based on the defect characteristic parameters and the type, location, and severity of the defect, and determine the layout of test points. The density of test points in the area surrounding the defect is 2-3 times that of the defect-free area. Apply a gradient heat load to the heat dissipation structure, gradually increasing the heat load from 30% to 120% of the rated load of the heat dissipation structure. Maintain each load level for 20-30 minutes. Record the temperature change curve, heat flux density distribution, and steady-state temperature difference data of each test point to determine the maximum heat load value of the heat dissipation structure. Conduct thermal cycle tests on the heat dissipation structure, completing 10-20 heating and cooling cycles under rated load, recording the temperature change curve in each cycle, calculating the temperature fluctuation amplitude and thermal response time, and forming thermal cycle characteristic data; Calculate the effective thermal conductivity, temperature uniformity and thermal response time of the heat dissipation structure based on the temperature change curve, heat flux density distribution, steady-state temperature difference data, maximum heat load value and thermal cycle characteristic data to form thermal conduction characteristic data; The thermal conductivity characteristic data is compared with the rated performance parameters of the heat dissipation structure, and the effective thermal conductivity deviation rate, temperature uniformity deviation rate, and thermal response time deviation rate are calculated. The structural integrity score is calculated based on the effective thermal conductivity deviation rate, temperature uniformity deviation rate, and thermal response time deviation rate. The thermal conductivity performance score is calculated based on the maximum heat load value, temperature uniformity, and thermal cycle characteristic data to quantitatively evaluate the structural integrity and thermal conductivity performance of the brazing joint surface.

[0054] Specifically, designing a thermal load test plan based on defect characteristic parameters and defect type, location, and severity is the first step in performance evaluation. This is done using a thermal performance test system consisting of a precision heat source simulator, a multi-channel temperature acquisition device, and a heat flux density sensor array. First, a defect distribution map of the heat dissipation structure is created based on previous defect detection results, marking the location, area, and severity of all defects. A differentiated test point layout is then designed based on the defect distribution map. A uniformly distributed basic test point grid (typically with a spacing of 10-15 mm) is used in defect-free areas, while a denser test point grid (with spacing reduced to 3-7 mm) is used in defect-free areas and their surroundings, ensuring a test point density 2-3 times that of the defect-free area. For example, for a microchannel heat sink with a bubble-type defect detected in the upper right corner, test points were arranged with a 12 mm spacing in the defect-free left area, forming a 5×6 test point matrix. However, within the upper right defect area and the surrounding 10 mm radius, the test point spacing was reduced to 4 mm, forming a high-density 10×12 test point matrix, increasing the test point density by approximately three times. After the test point layout is completed, a micro-thermocouple or infrared temperature sensor is installed at each test point, and a heat flux density sensor is arranged at a key position. The core value of the differentiated test point layout is the ability to accurately capture the local impact of defects on thermal performance, especially at the defect boundary where there is often a significant thermal gradient change, which requires a higher density of test points for accurate characterization. The 2-3 times density multiplier setting is based on the results of thermal diffusion characteristics research. Studies have shown that the heat affected zone of the defect usually extends to a range of 2-3 times the defect characteristic size outside the defect boundary. Therefore, this density configuration can effectively cover the complete heat affected zone of the defect.

[0055] Applying a gradient thermal load to a heat dissipation structure is an important method for assessing its actual load-carrying capacity. This test utilizes a programmable electronic load or a heating device with adjustable heat flux density to apply a precisely controlled thermal load to the heat dissipation structure. The test utilizes a step-by-step load increase strategy, starting at 30% of the heat dissipation structure's rated load and increasing in 10% or 15% increments until reaching 120% of the rated load. Each load level is maintained for 20-30 minutes to ensure the system reaches full thermal equilibrium. At each load level, the sensors in the aforementioned test point layout record three key data types: temperature profile (temperature-time data from load application to thermal equilibrium), heat flux density distribution (local heat flux values measured by each heat flux sensor), and steady-state temperature difference data (the temperature difference between each point and the ambient or cooling medium after thermal equilibrium is reached). The load is continuously increased until one of the following conditions occurs: the temperature at any test point exceeds the upper limit of the material's safe operating temperature; the temperature rise rate suddenly accelerates; the temperature difference between adjacent test points exceeds a preset safety threshold; or the load reaches 120% of the rated load. The load value that causes any of the above phenomena is recorded as the maximum heat load value of the heat dissipation structure. For example, for a CPU cooler with a rated power of 100W, the gradient heat load test starts at 30W and increases to 45W, 60W, 75W, 90W, 105W and 120W in sequence, with each load level maintained for 25 minutes. Under a load of 105W, it was found that the temperature rise rate in the defective area suddenly accelerated, and the temperature difference between adjacent test points increased sharply from the previous 5°C to 15°C, determining that the maximum heat load value was 90W. The gradient heat load test is designed with a load range of 30%-120%, based on actual application needs and safety margin considerations: the 30% starting load ensures that serious defects can be discovered under low load; the 120% maximum load provides a 20% overload test for the heat dissipation structure to verify its safety margin. The setting of maintaining each load level for 20-30 minutes is determined based on the results of thermal time constant tests. Most heat dissipation structures can reach more than 95% thermal balance within 15 minutes. The 20-30 minute holding time ensures the stability and reliability of the test data.

[0056] Thermal cycling testing of heat dissipation structures is a critical step in assessing their long-term reliability. This testing is performed using a thermal cycling test setup capable of applying cyclical thermal load changes to the heat dissipation structure according to a pre-set program. During testing, the heat dissipation structure is subjected to rated load conditions and subjected to 10-20 complete heating and cooling cycles. Each cycle consists of three phases: a heating phase (rapidly increasing the temperature from ambient to the operating temperature), a constant temperature phase (maintaining a stable temperature at the operating temperature), and a cooling phase (disconnecting the heat source and allowing the temperature to naturally decrease to near ambient temperature). Sensors arranged at test points record the temperature profile during each cycle, focusing specifically on the temperature rise and fall phases. Based on the collected temperature-time data, two key characteristic parameters are calculated: temperature fluctuation amplitude (the difference between the maximum and minimum temperatures at each test point during the cycle) and thermal response time (the time required for the temperature change to reach 63.2% (1-1 / e) after the application or removal of the heat load). These parameters constitute the thermal cycling performance data for the heat dissipation structure. For example, a microchannel liquid-cooled heat sink was subjected to 15 thermal cycling tests at a rated power of 80W, with each cycle consisting of 10 minutes of heating, 30 minutes of constant temperature, and 20 minutes of cooling. Test points in the normal area showed a temperature fluctuation of 40±2°C, with a thermal response time of 120±10 seconds for heating and 180±15 seconds for cooling. Test points in the defective area, however, showed a temperature fluctuation of 48±5°C, with a thermal response time extended to 180±30 seconds for heating and 250±40 seconds for cooling. The number of cycles in the thermal cycling test was set to 10-20, based on thermal fatigue research. The thermal performance trends of most heat sink structures stabilize after 10 cycles, while 20 cycles fully expose potential thermal fatigue issues. This thermal cycling test simulates the duty cycle of heat sink structures in real applications and effectively assesses the stability of defects under repeated thermal stress, which is important for predicting the long-term reliability of heat sink structures.

[0057] Calculating the heat conduction characteristics of the heat dissipation structure based on the obtained multiple test data is an important part of the performance quantitative characterization. During implementation, thermal performance analysis software is used to input the temperature change curve, heat flux density distribution, steady-state temperature difference data, maximum load value and thermal cycle characteristic data obtained from the above tests to calculate the three core heat conduction characteristic parameters. First, the effective thermal conductivity is calculated. Based on Fourier's heat conduction law, using the steady-state temperature difference and heat flux density data, the formula λ is used. eff =q×L / ΔT (where λ effis the effective thermal conductivity, q is the heat flux density, L is the length of the heat conduction path, and ΔT is the temperature difference between the measurement points) to calculate the effective thermal conductivity of each area. Then the temperature uniformity is calculated, which is defined as the ratio of the standard deviation of the surface temperature of the entire heat dissipation structure to the average temperature. The lower the temperature uniformity, the more uniform the heat distribution. Finally, the thermal response time is calculated, and the time required for the temperature to reach 63.2% of the steady-state value during heating and cooling is extracted from the thermal cycle test data. These three parameters together constitute the thermal conductivity characteristic data of the heat dissipation structure. For example, for a high-performance radiator, the effective thermal conductivity of the normal area is 180 W / (m·K), the temperature uniformity is 0.08, and the thermal response time is 85 seconds; while the effective thermal conductivity of the defective area is reduced to 120 W / (m·K), the temperature uniformity increases to 0.15, and the thermal response time is extended to 125 seconds. This thermal performance calculation method, based on multi-dimensional test data, breaks through the limitations of traditional single-parameter characterization. It reflects heat conduction capacity through effective thermal conductivity, heat distribution characteristics through temperature uniformity, and thermal dynamic performance through thermal response time, forming a comprehensive and three-dimensional performance characterization system. In particular, the calculation of effective thermal conductivity takes into account the influence of the actual heat flow path and interfacial thermal resistance, and can more accurately reflect the overall thermal conductivity performance of the heat dissipation structure than the thermal conductivity coefficient of the material itself.

[0058] Comparing thermal conductivity data with rated performance parameters and calculating a score is the final step in quantitatively evaluating brazing quality. This step begins with obtaining the design rated performance parameters of the heat dissipation structure, including the theoretical effective thermal conductivity, designed temperature uniformity, and expected thermal response time. Three deviation rates are then calculated: the effective thermal conductivity deviation rate is defined as (measured value - rated value) / rated value × 100%; the temperature uniformity deviation rate is defined as (measured value - rated value) / rated value × 100%; and the thermal response time deviation rate is defined as (measured value - rated value) / rated value × 100%. Based on these three deviation rates, a weighted average method is used to calculate the structural integrity score: Sintegrity = 100 - [w1 × |effective thermal conductivity deviation rate| + w2 × |temperature uniformity deviation rate| + w3 × |thermal response time deviation rate|], where w1, w2, and w3 are weighting coefficients, typically 0.5, 0.3, and 0.2, respectively. A thermal conductivity performance score is calculated based on the maximum heat load, temperature uniformity, and thermal cycling characteristics. The formula is: S-Performance = (maximum heat load / rated load) × 100 × α-β × (temperature uniformity / baseline uniformity) - γ × (thermal response time fluctuation), where α, β, and γ are adjustment coefficients, and the thermal response time fluctuation is the ratio of the standard deviation to the mean value of the thermal response time during the cycling test. Together, these two scores form a quantitative evaluation index system for brazed joint quality. For example, the design rated effective thermal conductivity of a power cooling module for an electric vehicle is 200 W / (m·K), while the measured value is 185 W / (m·K). The calculated effective thermal conductivity deviation is -7.5%. The design temperature uniformity is 0.05, while the measured value is 0.065, resulting in a calculated temperature uniformity deviation of 30%. The expected thermal response time is 60 seconds, while the measured value is 68 seconds, resulting in a calculated thermal response time deviation of 13.3%. Substituting these deviations into the formula, the structural integrity score is calculated to be 87.5. The module's maximum heat load was 110% of the rated load, temperature uniformity was 1.3 times the baseline value, and thermal response time fluctuation was 5%, resulting in a calculated thermal conductivity score of 92.3. The technical value of this quantitative assessment method lies in converting complex thermal performance test results into intuitive scoring indicators, facilitating quality assessment and comparison. These two scoring indicators assess brazing quality from the perspectives of structural integrity and functional performance, respectively. They comprehensively reflect the impact of the brazing joint on the actual performance of the heat dissipation structure, providing a quantitative basis for quality management and product optimization.

[0059] The above describes the vacuum brazing quality detection method of the heat dissipation structure in the embodiment of the present invention. The following describes the vacuum brazing quality detection device of the heat dissipation structure in the embodiment of the present invention. Figure 2 An embodiment of a vacuum brazing quality detection device for a heat dissipation structure according to an embodiment of the present invention includes: A heat flow analysis module 101 is used to obtain heat flux density distribution data of the heat dissipation structure, calculate the thermal efficiency index of each area based on the heat flux density distribution data, and divide the heat dissipation structure into different functional areas based on the thermal efficiency index; A thermal pulse detection module 102 is configured to apply standardized thermal pulses to the heat dissipation structure during the vacuum brazing process according to the division of the different functional areas, and obtain thermal pulse response data and process monitoring data; An abnormality identification module 103 is used to calculate thermal response characteristic parameters based on the thermal pulse response data, compare the thermal response characteristic parameters with a preset parameter range, identify abnormal conditions during the vacuum brazing process, and generate process adjustment instructions; a defect characterization module 104 for adjusting vacuum brazing process parameters according to the process adjustment instructions, combining the thermal response characteristic parameters with the process monitoring data for spatiotemporal synchronization processing, extracting defect characteristic parameters from the processed data, and determining the defect type, location, and severity of the vacuum brazing joint surface; The performance evaluation module 105 is used to perform a heat load test based on the defect characteristic parameters and the type, location and severity of the defect, obtain the thermal conductivity characteristic data of the heat dissipation structure, and quantitatively evaluate the structural integrity and thermal conductivity performance of the brazing joint surface.

[0060] above Figure 2 The vacuum brazing quality detection device for the heat dissipation structure in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The vacuum brazing quality detection device for the heat dissipation structure in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0061] Figure 3The figure is a schematic diagram of a heat dissipation structure vacuum brazing quality inspection device 200 provided in an embodiment of the present invention. This heat dissipation structure vacuum brazing quality inspection device 200 can vary significantly depending on configuration or performance. It may include one or more central processing units (CPUs) 210 (e.g., one or more processors), a memory 220, and one or more storage media 230 (e.g., one or more mass storage devices) storing application programs 233 or data 232. The memory 220 and storage media 230 may be either transient or persistent storage. The program stored in the storage medium 230 may include one or more modules (not shown), each of which may include a series of instructions for operating the heat dissipation structure vacuum brazing quality inspection device 200. Furthermore, the processor 210 may be configured to communicate with the storage medium 230, executing the series of instructions stored in the storage medium 230 on the heat dissipation structure vacuum brazing quality inspection device 200 to implement the steps of the heat dissipation structure vacuum brazing quality inspection method described above.

[0062] The vacuum brazing quality inspection device 200 with a heat dissipation structure may further include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input and output interfaces 260, and / or one or more operating systems 231, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The structure of the vacuum brazing quality inspection device with a heat dissipation structure shown does not constitute a limitation on the vacuum brazing quality inspection device with a heat dissipation structure provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0063] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the vacuum brazing quality detection method of the heat dissipation structure.

[0064] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0065] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0066] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for detecting the quality of vacuum brazing of a heat dissipation structure, characterized in that: include: Acquiring heat flux density distribution data of the heat dissipation structure, calculating a thermal efficiency index of each area based on the heat flux density distribution data, and dividing the heat dissipation structure into different functional areas based on the thermal efficiency index; According to the division of the different functional areas, a standardized heat pulse is applied to the heat dissipation structure during the vacuum brazing process to obtain heat pulse response data and process monitoring data; Calculating thermal response characteristic parameters based on the thermal pulse response data, comparing the thermal response characteristic parameters with a preset parameter range, identifying abnormal conditions during the vacuum brazing process, and generating process adjustment instructions; adjusting vacuum brazing process parameters according to the process adjustment instructions, combining the thermal response characteristic parameters with the process monitoring data for spatiotemporal synchronization processing, extracting defect characteristic parameters from the processed data, and determining the defect type, location, and severity of the vacuum brazing joint surface; Based on the defect characteristic parameters and the type, location and severity of the defect, a heat load test is performed to obtain the thermal conductivity characteristic data of the heat dissipation structure and quantitatively evaluate the structural integrity and thermal conductivity performance of the brazing joint surface.

2. The vacuum brazing quality detection method for heat dissipation structure according to claim 1, characterized in that: The obtaining of heat flux density distribution data of the heat dissipation structure, calculating a thermal efficiency index of each region according to the heat flux density distribution data, and dividing the heat dissipation structure into different functional regions according to the thermal efficiency index includes: Obtain the design parameters of the heat dissipation structure and extract the internal microchannel dimensions, fin layout, and multi-layer structure thickness data of the heat dissipation structure; Calculate the heat conduction path length and heat conduction cross-sectional area of each area of the heat dissipation structure based on the extracted structural data; Obtain the material thermal conductivity and operating temperature difference data of each area of the heat dissipation structure, and calculate the heat flux density distribution data of each area of the heat dissipation structure by combining the heat conduction path length and heat conduction cross-sectional area; Based on the obtained heat flux density distribution data and the geometric dimensions of each area, the thermal efficiency index of each area is calculated according to the formula: thermal efficiency index = regional heat flux density × heat conduction cross-sectional area / heat conduction path length; Calculating the total thermal efficiency value of the heat dissipation structure, wherein the total thermal efficiency value is the sum of the thermal efficiency indexes of all regions; Calculate the percentage of each region's thermal efficiency index to the total thermal efficiency value to obtain the thermal efficiency contribution rate of each region; According to the thermal efficiency contribution rate, the area with a thermal efficiency contribution rate greater than 30% is divided into a high-efficiency area, the area with a thermal efficiency contribution rate between 10% and 30% is divided into a standard functional area, and the area with a thermal efficiency contribution rate less than 10% is divided into an auxiliary area, completing the division of different functional areas of the heat dissipation structure.

3. The vacuum brazing quality detection method for a heat dissipation structure according to claim 1, characterized in that: According to the division of the different functional areas, applying standardized heat pulses to the heat dissipation structure during the vacuum brazing process to obtain heat pulse response data and process monitoring data includes: According to the division of different functional areas, the density ratio of heat pulse monitoring points in high-performance area, standard functional area and auxiliary area is determined to be 5:3:1, and differentiated heat pulse parameters are designed, including: applying pulse width of 3 seconds and power density of 2W / cm2 to the high-performance area. 2 A heat pulse with a pulse width of 4 seconds and a power density of 1.5 W / cm2 is applied to the standard functional area. 2 A heat pulse with a pulse width of 5 seconds and a power density of 1 W / cm was applied to the auxiliary area. 2 heat pulses; Determining four key temperature points in the vacuum brazing process, the four key temperature points including 15°C before the solder melting point, the solder melting point, the midpoint of the holding temperature, and 15°C after the solder solidification point, and applying the differentiated heat pulse parameters to the heat dissipation structure at the four key temperature points; Record the temperature rise curve, peak time and cooling rate data of each monitoring point after the heat pulse is applied, calculate the heat conduction time constant and thermal diffusion coefficient of each point, and form the heat pulse response data; The temperature field distribution data, acoustic signal data and stress distribution data of the entire vacuum brazing process are obtained, and the acquired data are filtered, denoised and normalized to form process monitoring data.

4. The vacuum brazing quality inspection method for heat dissipation structure according to claim 3, characterized in that: The method includes obtaining temperature field distribution data, acoustic signal data, and stress distribution data of the entire vacuum brazing process, filtering, denoising, and normalizing the obtained data to form process monitoring data, including: During the vacuum brazing process, the key phase transition temperature points of the heat dissipation structure are identified. Based on the phase change characteristics of the brazing filler metal, the vacuum brazing process is divided into the solid-phase heating stage, the solid-liquid coexistence stage, the liquid phase flow stage, and the solidification and crystallization stage. Different temperature acquisition strategies are used in each stage to obtain the original data of the temperature field distribution. Acoustic signals during vacuum brazing are collected at multiple scales and segments. High-frequency sampling is used to obtain raw acoustic signal data during the solder melting period. Wavelet decomposition is performed on the collected raw acoustic signal data to separate the interface formation acoustic signal, solder flow acoustic signal, and bubble formation acoustic signal, generating acoustic feature data. The stress distribution raw data obtained during vacuum brazing are used to extract the local area of the brazing interface. The stress component ratio and stress gradient of each monitoring point are calculated according to the interface geometry to form stress characteristic data. The raw data of the temperature field distribution are subjected to median filtering and denoising, the acoustic feature data are subjected to bandpass filtering, and the stress feature data are subjected to statistical filtering. The three types of processed data are synchronously aligned and dimensionally normalized according to timestamps to form process monitoring data.

5. The vacuum brazing quality inspection method for heat dissipation structure according to claim 1, characterized in that: The step of calculating a thermal response characteristic parameter based on the thermal pulse response data, comparing the thermal response characteristic parameter with a preset parameter range, identifying an abnormal state during vacuum brazing, and generating a process adjustment instruction includes: Performing time series analysis on the thermal pulse response data to calculate the temperature rise rate, peak temperature, decay constant and thermal response delay time to form thermal response characteristic parameters; According to the material combination, bonding interface characteristics and geometric structure data of the heat dissipation structure, combined with the statistical results of normal brazing samples, the preset parameter range of the thermal response characteristic parameters of each monitoring point is determined; Comparing the thermal response characteristic parameter with a preset parameter range, calculating the parameter deviation value and the deviation duration, and identifying abnormal monitoring points where the parameter deviation exceeds a threshold; Analyze the distribution characteristics and deviation patterns of the abnormal monitoring points, and determine that the abnormality of the temperature rise rate being more than 20% lower than the lower limit of the preset range and the thermal response delay time being more than 15% higher than the upper limit of the preset range is poor interface contact; the abnormality of the temperature rise rate being within the preset range but the attenuation constant being more than 25% higher than the upper limit of the preset range is insufficient solder flow; and the abnormality of the peak temperature fluctuation amplitude being more than 30% higher than the preset stable range is bubble formation; Based on the determined abnormality type, location, and severity, corresponding process adjustment instructions are generated, including: generating pressure adjustment instructions for areas with poor interface contact, generating holding time adjustment instructions for areas with insufficient solder flow, and generating vacuum adjustment instructions for areas with bubble formation.

6. The vacuum brazing quality inspection method for a heat dissipation structure according to claim 1, characterized in that: The method of adjusting the vacuum brazing process parameters according to the process adjustment instruction, combining the thermal response characteristic parameters with the process monitoring data for spatiotemporal synchronization processing, extracting defect characteristic parameters from the processed data, and determining the defect type, location, and severity of the vacuum brazing joint surface includes: According to the process adjustment instructions, the vacuum brazing process parameters were quantitatively adjusted, wherein the pressure was adjusted in a step range of 0.3-0.8 MPa, the holding time was adjusted in a step range of 20-40 seconds, and the vacuum degree was adjusted in a step range of 0.3×10 -3 -0.8×10 -3 Adjust the step range of Pa to obtain the adjusted temperature change data during brazing; Aligning and transforming the thermal response characteristic parameters and the process monitoring data according to time tags and spatial coordinates to eliminate time differences and position deviations in data acquisition and generate a spatiotemporal synchronized data set; Analyzing the spatiotemporal synchronized data set, extracting a temperature gradient distribution coefficient, an acoustic emission signal characteristic spectrum, and a stress distribution coefficient, and combining the temperature gradient distribution coefficient, the acoustic emission signal characteristic spectrum, and the stress distribution coefficient to form a defect characteristic parameter group; According to the characteristic combination and distribution pattern of the defect characteristic parameter group, the defect type of the vacuum brazing joint surface is judged, the spatial position coordinates and area range of the defect are determined, and the defect severity index is calculated according to the numerical intensity of the defect characteristic parameter group to determine the defect type, location and severity of the vacuum brazing joint surface.

7. The vacuum brazing quality inspection method for a heat dissipation structure according to claim 1, characterized in that: The heat load test is performed based on the defect characteristic parameters and the type, location and severity of the defect to obtain the heat conduction characteristic data of the heat dissipation structure and quantitatively evaluate the structural integrity and heat conduction performance of the brazing joint surface, including: Design a thermal load test plan based on the defect characteristic parameters and the type, location, and severity of the defect, and determine the layout of test points. The density of test points in the area surrounding the defect is 2-3 times that of the defect-free area. Apply a gradient heat load to the heat dissipation structure, gradually increasing the heat load from 30% to 120% of the rated load of the heat dissipation structure. Maintain each load level for 20-30 minutes. Record the temperature change curve, heat flux density distribution, and steady-state temperature difference data of each test point to determine the maximum heat load value of the heat dissipation structure. Conduct thermal cycle tests on the heat dissipation structure, completing 10-20 heating and cooling cycles under rated load, recording the temperature change curve in each cycle, calculating the temperature fluctuation amplitude and thermal response time, and forming thermal cycle characteristic data; Calculate the effective thermal conductivity, temperature uniformity and thermal response time of the heat dissipation structure based on the temperature change curve, heat flux density distribution, steady-state temperature difference data, maximum heat load value and thermal cycle characteristic data to form thermal conduction characteristic data; The thermal conductivity characteristic data is compared with the rated performance parameters of the heat dissipation structure, and the effective thermal conductivity deviation rate, temperature uniformity deviation rate, and thermal response time deviation rate are calculated. The structural integrity score is calculated based on the effective thermal conductivity deviation rate, temperature uniformity deviation rate, and thermal response time deviation rate. The thermal conductivity performance score is calculated based on the maximum heat load value, temperature uniformity, and thermal cycle characteristic data to quantitatively evaluate the structural integrity and thermal conductivity performance of the brazing joint surface.

8. A vacuum brazing quality detection device for a heat dissipation structure, characterized in that: The vacuum brazing quality detection device for the heat dissipation structure adopts the vacuum brazing quality detection method for the heat dissipation structure according to any one of claims 1 to 7, and the vacuum brazing quality detection device for the heat dissipation structure comprises: a heat flow analysis module for obtaining heat flux density distribution data of the heat dissipation structure, calculating a thermal efficiency index of each area based on the heat flux density distribution data, and dividing the heat dissipation structure into different functional areas based on the thermal efficiency index; a heat pulse detection module, configured to apply standardized heat pulses to the heat dissipation structure during the vacuum brazing process according to the division of the different functional areas, and obtain heat pulse response data and process monitoring data; an abnormality identification module, configured to calculate a thermal response characteristic parameter based on the thermal pulse response data, compare the thermal response characteristic parameter with a preset parameter range, identify abnormal conditions during the vacuum brazing process, and generate a process adjustment instruction; a defect characterization module, configured to adjust vacuum brazing process parameters according to the process adjustment instructions, combine the thermal response characteristic parameters with the process monitoring data for spatiotemporal synchronization processing, extract defect characteristic parameters from the processed data, and determine the defect type, location, and severity of the vacuum brazing joint surface; The performance evaluation module is used to perform a heat load test based on the defect characteristic parameters and the type, location and severity of the defect, obtain the thermal conductivity characteristic data of the heat dissipation structure, and quantitatively evaluate the structural integrity and thermal conductivity performance of the brazing joint surface.

9. A vacuum brazing quality inspection device for heat dissipation structure, characterized in that: The vacuum brazing quality detection device for the heat dissipation structure includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the vacuum brazing quality inspection device for heat dissipation structures to execute the steps of the vacuum brazing quality inspection method for heat dissipation structures according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the vacuum brazing quality detection method for the heat dissipation structure according to any one of claims 1 to 7 are implemented.

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