An accelerated evaluation method and system for solder joint thermal cycle failure of an in-vehicle controller

Abstract

The application discloses a kind of vehicle controller weld spot thermal cycle failure accelerated evaluation method and system, belong to reliability test technical field, including constructing and applying by local thermal shock and wideband vibration alternately constitute asymmetric alternating stress field;Synchronous monitoring of the resistance of weld spot, acoustic emission and vibration multimode response signal, generate original response dataset;Through multi-source signal fusion analysis to dataset, generate failure mechanism identification result characterized by the evolution type of weld spot microdamage;Based on the result, adaptively adjust the loading parameters of subsequent stress field, form closed-loop feedback optimization;Finally, according to the damage evolution data collected under adaptive loading conditions, combined with the physical damage model matched with the failure mechanism, the accurate evaluation of the life of weld spot is realized.The application uses composite asymmetric alternating stress excitation, combined with multi-source signal fusion, which can realize the identification of vehicle controller weld spot microdamage evolution and life evaluation under accelerated conditions.

Description

Technical Field

[0001] This invention relates to the field of reliability testing technology, and in particular to an accelerated assessment method and system for thermal cycling failure of solder joints in vehicle controllers. Background Technology

[0002] The vehicle controller is a core component of the automotive electronic system. The solder joints on its internal circuit board serve as critical nodes for electrical and mechanical connections, and their reliability directly impacts the safety and performance of the entire vehicle. During vehicle operation, these solder joints are subjected to thermal cycling stress caused by the starting and stopping of power devices and changes in ambient temperature, as well as mechanical vibration stress caused by uneven road surfaces and engine operation. The coupling effect of these two stresses is the main cause of solder joint fatigue failure. Therefore, accurately assessing the lifespan of the solder joints is a crucial step in the reliability design of the controller.

[0003] In related technologies, Chinese invention patent CN116384035A discloses a method for predicting the reliability and lifespan of solder joints in electronic devices under thermo-coupling effects. The method includes: establishing an electronic device packaging model, which includes a chip, solder joints, a copper disk, an intermetallic compound, and a printed circuit board; determining the chip power consumption and ambient temperature based on the electronic device's operating state and environment, and loading the chip power consumption and ambient temperature as initial values ​​into the chip's thermal cycling load; performing thermal stress calculations, extracting the thermal stress response calculation results of the chip solder joints, and determining the stress and strain distribution inside the solder joints and at the connection between the solder joints and the printed circuit board; determining the location of the first critical solder joint in the solder joint array based on the equivalent plastic strain cloud map of the solder joint array; performing thermal fatigue damage calculations of the first critical solder joint based on the Coffin-Manson model; performing vibration fatigue damage calculations of the first critical solder joint based on the Miner linear fatigue damage accumulation criterion, the Manson high-cycle fatigue empirical formula, the Steinberg model, and the three-zone theory; and determining the total damage of the first critical solder joint under the superposition of thermal fatigue damage and vibration fatigue damage using the linear damage superposition method.

[0004] However, the aforementioned existing technical solutions have the following technical defects: Insufficient authenticity and consistency of failure modes: Although existing technical solutions mention thermo-mechanical coupling when calculating thermal fatigue and vibration fatigue damage, their loading methods may not fully simulate the asymmetric alternation and coupling relationship between thermal shock events and high-intensity vibration events experienced by the weld joints of the vehicle controller under real vehicle operating conditions. This may lead to a deviation between the weld joint failure modes induced in accelerated testing and the dominant failure modes under actual service conditions, thus affecting the accuracy and reliability of life assessment results. Accelerated testing efficiency and targeting need improvement: Existing technologies use relatively fixed load spectra for calculations, lacking a loading strategy that can dynamically feedback and adaptively adjust based on the real-time damage state and failure mechanism of the weld joints. This loading method may not be able to accurately apply accelerated energy to the most critical damage evolution path, resulting in low acceleration efficiency, potentially long testing cycles, and limited room for cost optimization. The characterization of damage evolution is not refined enough: Existing technologies focus on damage calculation and life prediction based on models, but may lack refined, multimodal in-situ monitoring and characterization of the entire process of weld joint from microscopic damage initiation to macroscopic crack propagation. For example, they fail to simultaneously monitor signals that can directly reveal the physical mechanism of damage, such as acoustic emission signals, making it difficult to gain in-depth insights into the intrinsic dynamic process of damage evolution and limiting their ability to provide deeper data support for product design optimization. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an accelerated assessment method and system for thermal cycling failure of solder joints in vehicle controllers. By employing composite asymmetric alternating stress excitation and combining multi-source signal fusion, it is possible to identify the microscopic damage evolution of solder joints in vehicle controllers and assess their lifespan under accelerated conditions.

[0006] The above objectives can be achieved through the following approach:

[0007] An accelerated assessment method for thermal cycling failure of solder joints in vehicle controllers includes: constructing and applying an asymmetric alternating stress field composed of alternating local thermal shock and broadband vibration; simultaneously monitoring the resistance, acoustic emission, and multimodal vibration response signals of the solder joints to generate an original response dataset; generating failure mechanism identification results characterizing the micro-damage evolution type of the solder joints through multi-source signal fusion analysis of the dataset; adaptively adjusting the loading parameters of subsequent stress fields based on the results to form a closed-loop feedback optimization; and finally, accurately assessing the solder joint life based on the damage evolution data collected under adaptive loading conditions and a physical damage model matching the failure mechanism.

[0008] Optionally, the construction and application of the asymmetric alternating stress field acting on the solder joints of the vehicle controller includes: acquiring vehicle operating condition data that records the operating status of the power devices of the vehicle controller and the vibration status of the vehicle body, and extracting the temporal relationship between thermal shock events and high-intensity vibration events from the vehicle operating condition data to generate a composite load time sequence spectrum; applying a pulse current to a selected solder joint circuit according to the composite load time sequence spectrum to form a local thermal shock excitation; and applying the broadband vibration excitation with random spectral characteristics at a time stage different from the time stage at which the pulse current is applied, according to the composite load time sequence spectrum; the local thermal shock excitation and the broadband vibration excitation are applied asymmetrically in time, together constituting an asymmetric alternating stress field.

[0009] Optionally, the synchronous monitoring of the multimodal response signal of the vehicle controller solder joint under the action of the asymmetric alternating stress field includes: measuring the current and voltage signals flowing through the selected solder joint circuit and calculating the resistance response data that changes with time; capturing the high-frequency stress wave signal released by the microstructural changes of the solder joint and extracting features from the high-frequency stress wave signal to generate acoustic emission response data; collecting the dynamic response of the vehicle controller under vibration excitation to generate vibration response data; and synchronizing the resistance response data, the acoustic emission response data, and the vibration response data in time to form an original response dataset.

[0010] Optionally, the adaptive adjustment of the loading parameters of the asymmetric alternating stress field in subsequent cycles based on the failure mechanism identification result includes: identifying the current dominant failure mode indicated in the failure mechanism identification result; if the current dominant failure mode is thermal fatigue damage, generating a first adjustment command to enhance the amplitude or rate of change of the local thermal shock excitation in subsequent cycles; if the current dominant failure mode is vibration fatigue damage, generating a second adjustment command to concentrate the energy of the broadband vibration excitation in subsequent cycles to the resonant frequency band of the vehicle controller; and outputting the first adjustment command or the second adjustment command as an adaptive loading command.

[0011] Optionally, the generation of failure mechanism identification results characterizing the micro-damage evolution type of the solder joint includes: extracting acoustic emission feature vectors from the acoustic emission response data from the original response dataset, and simultaneously extracting resistance change features from the resistance response data; matching the acoustic emission feature vectors with a preset feature template library to generate preliminary mechanism discrimination results, wherein the feature template library is associated with acoustic emission signal modes and failure physical processes; and using the damage accumulation process reflected by the resistance change features to verify and correct the preliminary mechanism discrimination results, thereby generating failure mechanism identification results characterizing the micro-damage evolution type of the solder joint.

[0012] Optionally, the generation of failure mechanism identification results characterizing the micro-damage evolution type of the weld joint further includes: when an event indicating the generation of microcracks is detected in the acoustic emission response data and a step change occurs in the synchronous resistance response data, a monitoring enhancement mode is triggered; in the monitoring enhancement mode, the acquisition frequency of the resistance response data is increased, and high-frequency vibration response data within the time window before and after the event is recorded; the high-frequency acquired resistance response data and the high-frequency vibration response data are correlated to generate a damage transient feature package for analyzing the transient physical process of damage, and the damage transient feature package is used to assist in generating the failure mechanism identification results.

[0013] Optionally, the life assessment of the on-board controller solder joint includes: acquiring initial stage damage evolution data under an initial stress level, and acquiring accelerated stage damage evolution data under an enhanced stress level controlled by the adaptive loading command; calculating and updating an acceleration factor used to quantify the acceleration effect online based on the difference in damage accumulation rate between the initial stage damage evolution data and the accelerated stage damage evolution data; and substituting the acceleration factor into the physical damage model to calculate the predicted life of the on-board controller solder joint under the target operating conditions using extrapolation.

[0014] Optionally, the physical damage model that matches the failure mechanism identification result includes: if the failure mechanism identification result indicates that thermal fatigue is dominant, then a lifetime model with inelastic strain accumulation as the damage metric is selected as the physical damage model; if the failure mechanism identification result indicates that vibration fatigue is dominant, then a lifetime model with vibration stress cyclic accumulation as the damage metric is selected as the physical damage model; if the failure mechanism identification result indicates that mixed-mode damage is indicated, then a comprehensive damage accumulation model with coupled thermal stress and vibration stress is selected as the physical damage model.

[0015] Optionally, the method further includes: for solder joints where both resistance monitoring and acoustic emission monitoring are deployed, establishing a correlation mapping relationship between the trend change of the resistance response data and the event energy of the acoustic emission response data; for solder joints where only resistance monitoring is deployed, using the established correlation mapping relationship and the resistance response data of the solder joint, inferring its micro-damage activity level and generating inferred damage state information; and using the inferred damage state information to assist in generating the failure mechanism identification result.

[0016] Based on the same inventive concept, this invention also provides an accelerated assessment system for thermal cycling failure of solder joints in vehicle controllers. The system includes: an asymmetric alternating stress field loading module for constructing and applying an asymmetric alternating stress field to the solder joints of the vehicle controller, wherein the asymmetric alternating stress field is composed of alternating local thermal shock excitation and broadband vibration excitation applied at different time intervals; and a multimodal response signal synchronous monitoring module for synchronously monitoring the multimodal response signals of the solder joints of the vehicle controller under the asymmetric alternating stress field, generating a raw data set including resistance response data, acoustic emission response data, and vibration response data. The system includes: an initial response dataset; a failure mechanism intelligent identification module, used to perform multi-source signal fusion analysis on the initial response dataset to generate failure mechanism identification results characterizing the micro-damage evolution type of the weld joint; an adaptive loading strategy control module, used to adaptively adjust the loading parameters of the asymmetric alternating stress field in subsequent cycles based on the failure mechanism identification results to generate adaptive loading commands; and a lifetime prediction and evaluation module, used to evaluate the lifetime of the vehicle controller weld joint based on the damage evolution data collected under the adaptive loading commands and in combination with a physical damage model that matches the failure mechanism identification results.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] This invention constructs an asymmetric alternating stress field that more closely resembles real-world operating conditions, and combines multimodal response monitoring and failure mechanism identification to ensure that the failure modes induced in accelerated testing are highly consistent with the dominant failure modes of the solder joints under actual service conditions. By selecting a damage model that matches the physical mechanism for life assessment, prediction biases caused by model mismatch are avoided, thus improving the accuracy and reliability of life assessment results.

[0019] This invention proposes a closed-loop adaptive loading strategy that dynamically adjusts the parameters of the subsequent stress field based on the real-time identified dominant mechanism of solder joint damage, precisely applying acceleration energy to the most critical damage evolution path. This targeted acceleration method avoids the inefficiency caused by blindly applying the full load in traditional testing, improving acceleration without sacrificing physical accuracy, thereby shortening the testing cycle and reducing testing costs.

[0020] This invention employs simultaneous monitoring and fusion analysis of multi-modal signals, including resistance, acoustic emission, and vibration, to achieve refined characterization of the entire process of weld joint development, from microscopic damage initiation to macroscopic crack propagation. This not only yields the final lifespan prediction value but also reveals the dynamic process and underlying physical mechanisms of damage evolution. It provides profound physical insights and data support for the design optimization and material selection of vehicle controllers, helping to improve product reliability from the source.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating an accelerated evaluation method for thermal cycling failure of solder joints in an on-board controller according to an embodiment of the present invention.

[0024] Figure 2 This is an asymmetric alternating stress field diagram in an accelerated evaluation method for thermal cycling failure of solder joints in an on-board controller according to an embodiment of the present invention.

[0025] Figure 3 This is a matching diagram of failure mechanism and physical damage model in an accelerated evaluation method for thermal cycling failure of solder joints of vehicle controller according to an embodiment of the present invention.

[0026] Figure 4 This is a module composition diagram of an accelerated evaluation system for thermal cycling failure of solder joints in an on-board controller according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Reference Figure 1 One embodiment of the present invention proposes an accelerated assessment method for thermal cycling failure of solder joints in vehicle controllers. By employing composite asymmetric alternating stress excitation and combining multi-source signal fusion, it is possible to identify the micro-damage evolution of solder joints in vehicle controllers and assess their lifetime under accelerated conditions.

[0029] The method described in this embodiment specifically includes:

[0030] S1. Construct and apply an asymmetric alternating stress field to the solder joints of the vehicle controller. The asymmetric alternating stress field is composed of local thermal shock excitation and broadband vibration excitation applied alternately at different time periods.

[0031] Optionally, constructing and applying the asymmetric alternating stress field acting on the weld joints of the vehicle controller includes:

[0032] Acquire vehicle operating condition data that records the working status of the power devices of the vehicle controller and the vibration status of the vehicle body, and extract the time sequence relationship between thermal shock events and high-intensity vibration events from the vehicle operating condition data to generate a composite load time sequence spectrum.

[0033] According to the composite load timing spectrum, a pulse current is applied to the selected solder joint circuit to form a local thermal shock excitation.

[0034] According to the composite load time sequence spectrum, a broadband vibration excitation with random spectral characteristics is applied at a time stage different from the time stage at which the pulse current is applied;

[0035] The local thermal shock excitation and the broadband vibration excitation are applied asymmetrically in time, together forming an asymmetrical alternating stress field.

[0036] Specifically, asymmetric alternating stress fields such as Figure 2 As shown, a data acquisition system is deployed in real-vehicle testing. For example, a CAN bus analyzer is used to synchronously record the drive signals, current, and temperature data of power devices such as IGBTs or MOSFETs. Simultaneously, vibration data is collected using multi-axis accelerometers placed in the controller housing or its mounting location. The acquired raw data is a high-frequency time-series signal, which needs to be processed to extract key events. Thermal shock events are defined as a rate of temperature change of the power device exceeding a preset threshold, such as a rapid heating or cooling process of 5 degrees Celsius per second. High-intensity vibration events are identified by power spectral density (PSD) analysis of the acceleration signals. When the energy density in a specific frequency band, such as the 50 to 500 Hz range, exceeds a specific threshold, such as 0.1 G² per Hz, it is marked as an event. The system generates a composite load time-series spectrum by analyzing the temporal relationships, delays, and coupling strengths of these events. This time-series spectrum is essentially a digital script file that precisely defines the application time, intensity, and duration of thermal and vibration excitations in subsequent tests.

[0037] A localized thermal shock excitation is applied, utilizing the Joule heating effect to rapidly heat the target solder joint at a microscale, simulating the transient temperature rise caused by the switching action of power devices. The test system, based on instructions from the composite load timing spectrum, injects a large current pulse into the circuit loop containing the target solder joint via a programmable precision power supply. The amplitude of this pulse current is typically in the range of 5 to 15 amperes, and its duration is 100 to 800 milliseconds. The tiny resistance flowing through the solder joint and its pins At this time, instantaneous heat power will be generated. The formula is as follows:

[0038] ,

[0039] in, Instantaneous thermal power, The amplitude of the pulse current is set via a programmable power supply. The equivalent resistance of the solder joint and circuit is an inherent property of the material. This heat power causes a sharp rise in temperature in the solder joint and its surrounding area, resulting in a thermal shock and subsequently generating thermal expansion mismatch stress.

[0040] Broadband vibration excitation is applied at different time stages to simulate the mechanical vibrations generated by a vehicle driving on uneven roads or running an engine. Following the instructions of the composite load time sequence spectrum, within a time window separate from the applied pulse current, the system drives an electromagnetic vibration table to apply random vibrations to a fixture equipped with an onboard controller. The broadband vibration excitation is characterized by its energy distribution over a wide frequency range, such as 20 to 2000 Hz, with its specific power spectral density curve derived from vehicle operating data analysis. This is fundamentally different from single-frequency sinusoidal vibration, enabling more realistic excitation of multiple resonant modes in the controller, thereby generating more complex alternating mechanical stresses at the weld points.

[0041] By strictly executing the composite load timing spectrum through the control system, the local thermal shock excitation and broadband vibration excitation are applied in a time-staggered manner, thus forming the aforementioned asymmetric alternating stress field. For example, a typical loading unit may include a thermal shock lasting 300 milliseconds, followed by a 2-second static cooling period, and then a broadband vibration lasting 5 seconds. This asymmetry in loading timing, along with the differences in amplitude and action mechanism between thermal and mechanical stresses, creates a complex stress environment that highly simulates real-world conditions but also possesses an accelerating effect, laying the foundation for subsequent failure assessment.

[0042] For example, in the accelerated evaluation process of the solder joints of the vehicle controller, the vehicle operating condition data, which records the operating status of the power devices and the vibration status of the vehicle body, is first acquired, and the temperature change rate is extracted from it. The timing relationship between thermal shock events and high-intensity vibration events was analyzed, and a composite load timing spectrum containing excitation time and intensity was generated. During the execution phase, the test system applied this timing spectrum to the equivalent resistance... for Apply amplitude to the selected solder joint circuit 10 The pulsed current lasts for 500ms. Based on the formula, the instantaneous heat power of this solder joint is calculated to be... This simulates localized thermal shock excitation at the microscopic scale, generating thermal expansion mismatch stress. Subsequently, based on the logic of the composite load timing spectrum, the system applies random characteristic broadband vibration excitation with a frequency distribution ranging from 20 to 2000 Hz to the vehicle controller at time stages different from the time stages of the applied pulse current. By applying the aforementioned localized thermal shock excitation and broadband vibration excitation asymmetrically in time—for example, setting a 2-second cooling period after the thermal shock followed by a 5-second vibration—the system successfully constructs an asymmetric alternating stress field acting on the weld joint, providing an excitation basis consistent with real-world operating conditions for subsequent identification of the microscopic damage evolution mechanism.

[0043] S2. Simultaneously monitor the multimodal response signal of the welding point of the vehicle controller under the action of the asymmetric alternating stress field, and generate an original response dataset containing resistance response data, acoustic emission response data and vibration response data;

[0044] Optionally, the synchronous monitoring of the multimodal response signal of the on-board controller solder joints under the asymmetric alternating stress field includes:

[0045] Measure the current and voltage signals flowing through the selected solder joint circuit, and calculate the resistance response data as a function of time;

[0046] High-frequency stress wave signals released by changes in the microstructure of solder joints are captured, and features of the high-frequency stress wave signals are extracted to generate acoustic emission response data.

[0047] The dynamic response of the vehicle controller under vibration excitation is collected to generate vibration response data;

[0048] The resistance response data, the acoustic emission response data, and the vibration response data are time-synchronized and aligned to form the original response dataset.

[0049] Specifically, the system measures and calculates the resistance response data. To accurately track the resistance change caused by the reduction in conductive cross-sectional area due to crack propagation, a four-wire Kelvin measurement method is used. Specifically, a small, constant current (e.g., 10 mA to 100 mA) is applied as a sensing excitation to a selected solder joint circuit using a precision source, while simultaneously measuring the voltage drop using another pair of independent leads located close to the solder joint. This method effectively eliminates interference from the test leads and PCB wiring resistance. The data acquisition unit synchronously records the sensing current at a sampling rate of at least 1 kHz. With voltage And calculates the time-varying properties in real time using the onboard processor. Changing resistance The calculation follows the formula:

[0050] ,

[0051] in, for The resistance of the solder joint circuit at any given time. The voltage value measured at that moment. The current values ​​were measured synchronously. The resulting time series data is the resistance response data, which macroscopically reflects the degree of damage accumulation.

[0052] The system captures acoustic emission response data, listening to high-frequency stress waves released within the solder joint due to microstructural changes such as dislocation movement, grain boundary slip, and microcrack initiation and propagation. One or more broadband piezoelectric acoustic emission sensors, typically operating between 100 kHz and 1 MHz, are mounted on the PCB surface near the target solder joint. The weak electrical signals captured by the sensors are passed through a preamplifier with a gain of 40 to 60 dB, and then filtered by a hardware bandpass filter to remove low-frequency vibration noise and high-frequency electromagnetic interference. The acoustic emission analysis system transiently captures signals exceeding a preset energy threshold, such as 30 dBAE, and extracts key characteristic parameters, such as amplitude, energy, duration, and ring count, forming a series of discrete event records. This set of characteristic parameters constitutes the acoustic emission response data, providing direct clues to identify the specific physical processes of damage.

[0053] Acquire vibration response data from the vehicle controller. This step is used to monitor the dynamic behavior of the controller as a whole and its local areas under broadband vibration excitation. Miniature accelerometers are placed at key measurement points on the controller PCB or housing to acquire the acceleration time history signal of the controller during vibration. The sampling rate must satisfy the Nyquist sampling theorem and is typically set to at least 2.5 times the highest excitation frequency; for example, for a 2000 Hz vibration excitation, the sampling rate should be no less than 5000 Hz. The acquired raw acceleration signal is the vibration response data.

[0054] To ensure accurate analysis of the causal relationships between different physical signals, the three types of data are synchronized in time. All data acquisition channels are driven by a unified master clock signal and respond to the same hardware trigger command to start acquisition. Each acquired data point, whether continuous resistance values, acceleration values, or discrete acoustic emission event characteristics, is assigned a high-precision timestamp. During the data storage phase, these resistance response data, acoustic emission response data, and vibration response data with synchronized timestamps are integrated into a single data structure to form the final raw response dataset, preparing for subsequent multi-source signal fusion analysis.

[0055] For example, in the reproduction experiment of synchronous monitoring of multimodal response signals of on-board controller solder joints, resistance data is first obtained using a four-wire Kelvin measurement method. A constant sensing excitation current is then applied to the selected solder joint circuit. for The instantaneous voltage drop across the solder joint was measured using an independent lead wire. for The resistance response data at that moment was calculated using the resistance calculation formula. The system then uses a broadband piezoelectric acoustic emission sensor positioned near the target weld point to capture the high-frequency stress wave signal released by the structural change. The sensor's operating frequency is set at... to Between, the captured raw electrical signal is passed through a gain of The preamplifier amplifies and extracts key parameters such as energy and amplitude to generate acoustic emission response data. Simultaneously, the system acquires the dynamic response to mechanical vibrations via a miniature accelerometer mounted on the controller housing, targeting a frequency upper limit of [missing information]. Wideband excitation, with a sampling rate set. To generate high-fidelity vibration response data, all acquisition channels of the system are driven by a unified master clock signal and respond to hardware trigger commands. The aforementioned resistance values, acoustic emission events, and acceleration values ​​are assigned timestamps with microsecond precision. After time synchronization and alignment, the final raw response dataset is formed.

[0056] S3. Perform multi-source signal fusion analysis on the original response dataset to generate failure mechanism identification results that characterize the micro-damage evolution type of the solder joint;

[0057] Optionally, the failure mechanism identification results characterizing the micro-damage evolution type of the solder joint include:

[0058] The acoustic emission feature vector of the acoustic emission response data is extracted from the original response dataset, and the resistance change feature of the resistance response data is extracted simultaneously.

[0059] The acoustic emission feature vector is matched with a preset feature template library to generate a preliminary mechanism discrimination result. The feature template library is associated with the acoustic emission signal mode and the failure physical process.

[0060] The damage accumulation process reflected by the resistance change characteristics is used to verify and correct the preliminary mechanism discrimination results, and failure mechanism identification results characterizing the micro-damage evolution type of the solder joint are generated.

[0061] Specifically, the system extracts features from multi-source signals. For the raw response dataset within each acquisition cycle, the analysis system first processes the acoustic emission response data. For each recorded acoustic emission event waveform, its key physical parameters are calculated, such as peak amplitude (typically in the range of 40 to 90 dB), signal energy, rise time, and duration, forming a multi-dimensional acoustic emission feature vector. Simultaneously, the system processes the synchronous resistance response data, calculating its average rate of change over a complete loading cycle—the resistance change characteristic—as well as statistical features such as the standard deviation or the presence of step points during the change process.

[0062] A preliminary mechanism discrimination is performed using a feature template library, a database pre-established through extensive calibration experiments. This library associates different failure physical processes, such as plastic deformation, recrystallization, phase transformation, microcrack initiation and propagation, with the cluster centers or distribution patterns of typical acoustic emission feature vectors they generate. For example, the template library might define acoustic emission signals generated by plastic deformation as having low amplitude and long duration, while crack propagation corresponds to high amplitude and short rise time pulse signals. The analysis system matches the real-time extracted acoustic emission feature vectors with patterns in the template library, typically using the k-nearest neighbor algorithm or support vector machine classifier, and outputs a preliminary mechanism discrimination result, such as "Currently, 70% of the detected events belong to the crack propagation mode, and 30% belong to the plastic deformation mode."

[0063] Verification and correction are performed using resistance change characteristics. Preliminary mechanism identification results, based solely on acoustic emission, may contain ambiguities. The system incorporates resistance change characteristics for logical verification. For example, if the preliminary identification indicates a large number of "crack propagation" events, the corresponding physical phenomenon should be damage to the conductive path, with resistance exhibiting a clear and accelerating upward trend. If the calculated resistance change characteristics are small or zero, the system determines the confidence level of the preliminary results is low, suggesting these acoustic emission signals may originate from noise or other non-damaging structural changes. The system will then correct the mechanism identification results, reducing the weight of "crack propagation." Conversely, if the resistance shows a step increase, even if the acoustic emission system only captures medium-energy events, the resistance data provides strong evidence, and the system will correct and confirm the unstable propagation of macroscopic cracks. In this way, resistance data, as the "ground reality" of damage accumulation, constrains and calibrates the interpretation of acoustic emission events.

[0064] Through the above matching, verification, and correction process, a more reliable and accurate failure mechanism identification result is generated. This result not only indicates the currently dominant damage mode, such as thermal fatigue or vibration fatigue, but may also include a judgment on the stage of damage mode evolution, such as "in the early stage of stable propagation of thermal fatigue cracks," providing a decision-making basis for subsequent adaptive loading strategy adjustments and the selection of life assessment models.

[0065] For example, during the failure mechanism identification process, the system first extracts multi-source signal features from the synchronously acquired raw response dataset. For each captured acoustic emission event waveform, the system calculates its peak amplitude, signal energy, rise time, and duration to construct a multi-dimensional acoustic emission feature vector. Simultaneously, the system analyzes the synchronous resistance response data, calculating its average rate of change and standard deviation over a complete loading cycle, and monitoring for the presence of resistance step points. Next, the system matches the extracted acoustic emission feature vector with a pre-established feature template library created through calibration tests. This template library associates different physical failure processes with acoustic emission modes; for example, it associates high-amplitude, short-rise-time pulse signals with crack propagation, and low-amplitude, long-duration signals with plastic deformation. The system uses a matching algorithm to output preliminary mechanism discrimination results, such as determining that "70% of the currently detected events belong to the crack propagation mode." Finally, the system uses resistance change characteristics as the "ground reality" of damage accumulation to verify and correct the preliminary discrimination results. If preliminary results indicate significant crack propagation, but the synchronously calculated resistance change is minimal, the system determines the confidence level of the judgment to be low, suggesting it may be caused by environmental noise interference, and accordingly reduces the weight of the crack propagation mode. Conversely, if the resistance response data shows a step increase, even if the acoustic emission energy is at a moderate level, the system will correct and confirm the unstable propagation of macroscopic cracks. Through this logical constraint of acoustic emission events on resistance data, the system ultimately generates failure mechanism identification results characterizing the type and specific stage of microscopic damage evolution at the solder joint.

[0066] Optionally, the failure mechanism identification results that characterize the micro-damage evolution type of the solder joint also include:

[0067] When an event indicating the generation of microcracks is detected in the acoustic emission response data and a step change occurs in the synchronous resistance response data, the monitoring enhancement mode is triggered.

[0068] In the enhanced monitoring mode, the frequency of acquiring the resistance response data is increased, and high-frequency vibration response data within the time window before and after the event is recorded;

[0069] The high-frequency acquired resistance response data is correlated with the high-frequency vibration response data to generate a damage transient feature package for analyzing the transient physical process of damage, and the damage transient feature package is used to assist in generating the failure mechanism identification result.

[0070] Specifically, the system continuously and synchronously monitors acoustic emission response data and resistance response data in the background. When the monitoring system identifies a high-energy, short-rise-time pulse event in the acoustic emission response data stream, whose feature vector highly matches the predefined "rapid microcrack propagation" pattern in the feature template library, and at almost the same moment (i.e., the timestamp difference is within 1 millisecond), the system detects a discontinuous step change in the resistance response data exceeding a preset threshold, such as 5% greater than the average of the previous second, the synchronous occurrence of these two events constitutes a sufficient condition for triggering the enhanced monitoring mode. Once triggered, the system immediately performs a mode switch. First, it controls the data acquisition hardware to instantaneously increase the acquisition frequency of the channel responsible for measuring the resistance response data from the conventional 1 kHz level to a higher frequency state, such as 50 kHz to 200 kHz. The purpose of this is to record the detailed process of the resistance step change with sufficient time resolution, including details such as the slope of its rise edge and the presence of oscillations. Simultaneously, the system sends a command to the vibration data acquisition unit to lock and save the high-frequency vibration response data stored in its internal ring buffer within a specific time window centered on the event trigger moment, such as 50 milliseconds before the event to 150 milliseconds after the event. This data segment completely records the transient dynamic environment that leads to or accompanies crack propagation.

[0071] The two newly acquired high-resolution data sets are processed and correlated. The system precisely aligns the time axis of the high-frequency acquired resistance response data with the time axis of the previously saved high-frequency vibration response data. By analyzing the correlation between the two at the microsecond scale, it can be determined whether the abrupt change in resistance is precisely synchronized with a specific impact peak or resonant period in the vibration response. For example, if each tiny jump in resistance corresponds to a peak in the vibration acceleration signal, it can be inferred that the damage is a periodic expansion driven by vibration stress. The results of this correlation analysis, along with the morphological characteristics of the high-frequency resistance curve and transient vibration characteristics, are collectively encapsulated into a structured data unit, namely, the damage transient feature package.

[0072] This damage transient feature package, serving as a high-confidence evidence package, is fed into the decision-making layer for failure mechanism identification. It can strongly confirm or correct preliminary judgments derived from conventional monitoring data. For example, if the initial judgment is that thermal fatigue is dominant, but the damage transient feature package clearly demonstrates a strong coupling relationship between damage propagation and vibration period, the system will correct the failure mechanism identification result to coupled fatigue, or even change it to vibration fatigue dominance, thus providing more accurate guidance for the entire evaluation method.

[0073] For example, in reproducing the correlation between the total acoustic emission energy and the rate of resistance damage accumulation, the system first synchronously acquires multimodal response signals on a calibration solder joint where both resistance and acoustic emission sensors are deployed. Using 100 test cycles as an analysis window, the system calculates the average slope of the resistance response data within that window, thus obtaining the rate of resistance damage accumulation, representing the speed of damage evolution. Simultaneously, the system sums the signal energy of all captured acoustic emission events within that window to obtain the total acoustic emission energy. By continuously recording these sets of data pairs, the system uses a nonlinear regression method to establish a correlation model that characterizes the strong correlation between the total energy and the rate of damage accumulation. When subsequently performing state estimation for an inferred solder joint with only resistance monitoring deployed, the system calculates its measured resistance damage accumulation rate within the current analysis window in real time. Substituting this value into the established model, the system can inversely infer the expected, equivalent level of microscopic damage activity at the inferred solder joint during this period and generate corresponding inferred damage state information. Finally, the system uses this information to help generate the final failure mechanism identification results, enabling accurate prediction of the damage state of more solder joints using a limited sensor deployment.

[0074] S4. Based on the failure mechanism identification results, adaptively adjust the loading parameters of the asymmetric alternating stress field in subsequent cycles to generate adaptive loading instructions;

[0075] Optionally, the adaptive adjustment of the loading parameters of the asymmetric alternating stress field in subsequent cycles based on the failure mechanism identification results includes:

[0076] Identify the current dominant failure mode indicated in the failure mechanism identification results;

[0077] If the current dominant failure mode is thermal fatigue damage, a first adjustment command is generated to enhance the amplitude or rate of change of the local thermal shock excitation in subsequent cycles.

[0078] If the current dominant failure mode is vibration fatigue damage, a second adjustment command is generated to concentrate the energy of the broadband vibration excitation in subsequent cycles into the resonant frequency band of the vehicle controller.

[0079] The first adjustment instruction or the second adjustment instruction is output as an adaptive loading instruction.

[0080] Specifically, the system receives and parses the failure mechanism identification results generated in the previous stage. This result is an identifier that clearly indicates the current dominant failure mode for solder joint damage accumulation, such as "thermal fatigue damage dominant" or "vibration fatigue damage dominant." The control system uses this identifier as a trigger condition to enter the corresponding parameter adjustment logic branch. If the current dominant failure mode is identified as thermal fatigue damage, the system will generate a first adjustment instruction. This instruction aims to increase the severity of local thermal shock excitation in subsequent test cycles. In practice, this instruction adjusts the output parameters of the programmable power supply used to generate local thermal shock excitation. One adjustment method is to increase the amplitude of the pulse current, for example, by 3% to 8% based on the previous cycle, thereby increasing the instantaneous heat generation power according to Joule's law, resulting in a larger temperature rise and thermal stress. Another method is to increase the rise rate of the pulse current, which will allow heat to be injected into the solder joint in a shorter time, increasing the rate of temperature change and thus exacerbating the thermal shock effect. This first adjustment instruction is formatted as a specific parameter setting command for the power supply controller and output as part of the adaptive loading instruction.

[0081] If the current dominant failure mode is identified as vibration fatigue damage, a second adjustment command will be generated. The core objective of this command is to more effectively transfer the energy of the broadband vibration excitation to the weak points of the weld joint, typically near its resonant frequency. First, the previously acquired vibration response data is analyzed, and its power spectral density is calculated using methods such as Fast Fourier Transform (FFT). One or more resonant frequency bands causing the greatest stress at the weld joint are automatically identified; for example, a response peak is identified in the 180-230 Hz range. The second adjustment command will then modify the random vibration load spectrum applied to the vibration table accordingly. Specifically, it generates a new power spectral density (PSD) target curve, which increases the energy density value of the identified resonant frequency bands, such as 180-230 Hz, by, for example, 1.5 to 2.5 times. Simultaneously, the energy of non-critical frequency bands may be appropriately reduced to ensure that the total root mean square (GRMS) vibration remains unchanged or increases only slightly. This redistribution of energy makes the vibration excitation's contribution to the damage more concentrated and efficient. This modified PSD curve is encoded as a command to the vibration control system, output as an adaptive loading command.

[0082] The first or second adjustment command generated by the above logic is uniformly encapsulated into an adaptive loading command and issued to the execution unit of the asymmetric alternating stress field, namely the programmable power supply and vibration table controller, to guide the load application in the next stage of the test cycle. This closed-loop feedback adjustment process is executed periodically throughout the accelerated evaluation test, ensuring that the test stress remains highly correlated with the actual damage evolution path of the weld joint.

[0083] For example, in the reproduction operation of adaptively adjusting loading parameters based on failure mechanism identification results, the system first parses the failure mechanism identification results generated in the previous stage to determine the current dominant failure mode. When the system identifies the current dominant failure mode as thermal fatigue damage, in order to enhance the amplitude of local thermal shock excitation in subsequent cycles, the system generates a first adjustment command. The pulse current amplitude applied in the initial test cycle is set to 10 amperes, and the equivalent resistance of the solder joint and circuit is 0.05 ohms. The initial instantaneous thermal power is calculated to be 5 watts using the physical logic of multiplying the square of the current by the resistance. To accelerate the evaluation process, the system selects a 6% enhancement ratio, adjusting the pulse current amplitude in the next stage to 10.6 amperes, thereby calculating an updated instantaneous thermal power increase to 5.618 watts. If the identification results indicate that the current dominant failure mode has changed to vibration fatigue damage, the system generates a second adjustment command. By analyzing the vibration response data collected earlier, the system identified the resonant frequency band with the highest stress level at the weld point as being in the 180 Hz to 230 Hz range. It then doubled the original 0.1 unit power spectral density energy density value within this band to 0.2 units, thereby achieving precise concentration of excitation energy towards the easily damaged frequency band. Finally, the system outputs either the first or second adjustment command as an adaptive loading command and sends it to the execution unit. This guides the application of the asymmetric alternating stress field in subsequent test cycles, ensuring that the experimental stress remains highly correlated with the actual damage evolution path of the weld point.

[0084] S5. Based on the damage evolution data collected under the adaptive loading command, and combined with the physical damage model that matches the failure mechanism identification result, the life of the on-board controller solder joint is evaluated.

[0085] Optionally, the life assessment of the solder joints of the vehicle controller includes:

[0086] Acquire initial stage damage evolution data under the initial stress level, and acquire accelerated stage damage evolution data under the enhanced stress level controlled by the adaptive loading command.

[0087] Based on the difference in damage accumulation rate between the initial stage damage evolution data and the accelerated stage damage evolution data, the acceleration factor used to quantify the acceleration effect is calculated and updated online.

[0088] Substituting the acceleration factor into the physical damage model, the predicted lifespan of the vehicle controller solder joints under the target operating conditions is calculated by extrapolation.

[0089] Specifically, it is necessary to divide and acquire damage evolution data in two different stages. At the start of the test, the system applies an initial stress level similar to or slightly higher than the target operating condition, for a preset time or number of cycles, such as 50 to 200 cycles. During this period, the system continuously records damage evolution data, such as the resistance change curve with the number of cycles. This data is called the initial stage damage evolution data, which represents the damage accumulation behavior close to the actual operating condition. Subsequently, according to the adaptive loading command, the test is switched to an enhanced stress level. The data acquired under the enhanced stress level is called the accelerated stage damage evolution data.

[0090] Calculate and update the acceleration factor online. Defined as the ratio of the number of cycles required at the initial stress level to the number of cycles required at the increased stress level, under the same damage increment. To improve computational robustness, the damage accumulation rate of the two stages is typically compared. The system calculates the damage accumulation rate separately for the initial stage damage evolution data and the accelerated stage damage evolution data. Taking resistance change as an example, the damage accumulation rate in the initial stage... This can be expressed as the resistance increment during this stage. With the corresponding cycle number The ratio. Similarly, the rate of damage accumulation during the acceleration phase. This can be expressed as the resistance increment during this stage. With the corresponding cycle number The ratio of acceleration factor. The ratio of these two rates is used to estimate the rate, and the calculation follows the formula:

[0091] ,

[0092] in, The acceleration factor is a dimensionless quantity that characterizes the acceleration factor of the accelerated test compared to the initial operating condition. It is the rate of damage accumulation during the acceleration phase. This represents the damage accumulation rate in the initial stage. Both rates are obtained by differentiating the damage indication parameters, such as resistance, collected at the corresponding stage with respect to the cycle number. This calculation process is repeated after each adaptive loading instruction update, ensuring... It can dynamically reflect changes in the acceleration effect.

[0093] The calculated acceleration factor is substituted into the physical damage model for lifetime extrapolation. It is assumed that during the accelerated test, the weld joint undergoes... After several cycles, the failure criterion is met, for example, the resistance increases by 20%. Then, its lifespan at the initial stress level is... A preliminary estimate can be made using the following formula:

[0094] ,

[0095] in, To estimate the lifetime at the initial stress level, To accelerate the lifespan measured during testing, if the initial stress level is not exactly the same as the target operating condition, the selected physical damage model must also be used, in conjunction with the acceleration factor. The slope of the stress-life relationship reflected is further extrapolated to the predicted life under the target operating conditions. This process, by scaling the damage evolution law established through accelerated testing, enables rapid prediction of the long-term reliability of the product.

[0096] For example, in the reproduction process of accelerated life assessment of onboard controller solder joints based on cumulative damage, the system first acquires the initial stage damage evolution data under the initial stress level and measures its damage accumulation rate. The rate is 0.00001 per cycle. Subsequently, the system executes an adaptive loading command to enter the enhanced stress level stage, acquiring damage evolution data during the accelerated phase, and measuring the damage accumulation rate at this point. The acceleration factor is 0.00015 per cycle. The system calculates and updates the acceleration factor used to quantify the acceleration effect online according to the formula. The value is 15. The measured failure cycle number of this solder joint under accelerated testing conditions is known. For 1000 cycles, the system will adjust the acceleration factor. Substituting into the physical damage model, the predicted lifespan of the on-board controller solder joints under the target operating conditions is calculated using the formula extrapolation method. The evaluation process, consisting of 15,000 cycles, accurately captures the impact of stress field adjustments on the damage evolution rate through real-time updated acceleration factors, thereby enabling precise extrapolation of the target weld lifetime under adaptive acceleration conditions.

[0097] Optionally, the physical damage model that matches the failure mechanism identification result includes:

[0098] If the failure mechanism identification results indicate that thermal fatigue is dominant, then the life model with inelastic strain accumulation as the damage metric is selected as the physical damage model.

[0099] If the failure mechanism identification results indicate that vibration fatigue is dominant, then the life model with the cumulative vibration stress cycle as the damage metric is selected as the physical damage model.

[0100] If the failure mechanism identification result indicates mixed-mode damage, then the comprehensive damage accumulation model of coupled thermal stress and vibration stress is selected as the physical damage model.

[0101] Specifically, the matching of failure mechanisms with physical damage models, such as... Figure 3 As shown, if the failure mechanism identification result indicates that thermal fatigue is the dominant factor, the system will automatically select a life model with inelastic strain accumulation as the core damage metric. Commonly used models are the improved Coffin-Manson model or the Darveaux model. The core idea of ​​these models is that the range of inelastic strain caused by the mismatch in the coefficients of thermal expansion of the solder joint due to temperature cycling is... It is the fundamental driving force leading to fatigue damage. Lifespan The relationship between the range of inelastic strain and the range of elastic strain is usually expressed by the formula:

[0102] ,

[0103] in, Failure lifetime, i.e., number of cycles. The range of inelastic strain in each cycle needs to be estimated using finite element analysis or empirical formulas combined with temperature data. and These are the fatigue characteristic constants of the material, which need to be calibrated through material testing. The model is then corrected and solved using collected temperature data and acceleration factors to predict thermal fatigue life.

[0104] If the failure mechanism identification results indicate that vibration fatigue is the dominant factor, the system switches to a life model that uses the cumulative damage metric of cyclic vibration stress. These models, such as the Basquin equation or models based on the Miner cumulative damage criterion, focus on the alternating stress caused by mechanical vibration. For example, the basic form of the stress-based Basquin model is shown in the following formula:

[0105] ,

[0106] in, The stress amplitude can be obtained by combining vibration response data with finite element modal analysis. The fatigue strength coefficient, The values ​​represent fatigue strength exponents, all of which are material constants. In random vibration environments, methods such as the Steinberg three-interval method or Dirlik's method are typically used to transform the power spectral density of broadband random vibration into a series of equivalent stress amplitudes and cycle numbers. Then, Miner's rule is applied to calculate damage accumulation. The system at this point focuses on analyzing vibration response data to drive the calculations of this type of model.

[0107] If the failure mechanism identification indicates complex mixed-mode damage, meaning both thermal and vibrational stresses contribute to the damage, the system will invoke a comprehensive damage accumulation model that couples the two stresses. A common approach is to use a linearly superimposed damage model, such as one that incorporates thermal fatigue damage within the framework of Miner's rule. and vibration fatigue damage Perform linear summation, as shown in the formula:

[0108] ,

[0109] in, This represents the total accumulated damage. When it reaches the critical value of 1, it is considered a failure. and The results were calculated using the aforementioned thermal fatigue model and vibration fatigue model, respectively. More complex models may consider the interaction between two stresses, such as the effect of temperature on the vibration fatigue performance of materials, or the promoting effect of vibration on crack propagation rate. The selection and parameterization of the model will be dynamically adjusted based on the failure mechanism identification results, providing more detailed information, such as the strength of thermal-vibration coupling. Through this adaptive model selection strategy, life assessment is always based on the most relevant physical foundations, ensuring the scientific rigor and accuracy of the entire chain from accelerated testing data to actual operating condition life prediction.

[0110] For example, in the reproduction process of selecting a physical damage model for life assessment based on the failure mechanism identification results, the system first identifies the currently dominant failure mode. When the failure mechanism identification results indicate that thermal fatigue is dominant, the system selects the improved Coffin-Manson model, which uses inelastic strain accumulation as the damage metric, as the physical damage model. The system acquires the temperature data in the current cycle and combines it with the acceleration factor to calculate the inelastic strain range in each cycle. The value is 0.005. The fatigue characteristic constant of the material is known. 2 and It is 0.00025, according to the formula The solution is performed to calculate the failure life. The cycle length is 0.22. If the failure mechanism identification results indicate that vibration fatigue is the dominant factor, the system selects the Basquin model, which uses the cumulative vibration stress cycle as the damage metric. The system obtains the stress amplitude through vibration response data combined with finite element analysis. Given a strength of 50 MPa and known material constants, including fatigue strength coefficient... 500MPa and fatigue strength index It is -0.1, according to the formula The solution is performed to calculate the failure life. The cycle count is 5,000,000,000 cycles. When the failure mechanism identification result indicates mixed-mode damage, the system selects a comprehensive damage accumulation model combining coupled thermal stress and vibration stress. The system uses the aforementioned model to calculate the thermal fatigue damage. 0.4 and vibration fatigue damage It is 0.2, according to the formula The total cumulative damage was calculated. The value is 0.6, and the reliability of the solder joint is evaluated by determining whether it reaches the critical value of 1.

[0111] Optionally, the method further includes:

[0112] For solder joints where both resistance monitoring and acoustic emission monitoring are deployed, a correlation mapping relationship is established between the trend changes of the resistance response data and the event energy of the acoustic emission response data.

[0113] For solder joints where only resistance monitoring is deployed, the established correlation mapping relationship and the resistance response data of the solder joint are used to inversely infer its micro-damage activity level and generate inferred damage state information.

[0114] The inferred damage state information is used to assist in generating the failure mechanism identification results.

[0115] Specifically, a correlation mapping relationship between the two signals is established on "calibration solder joints" where both resistance and acoustic emission sensors are deployed. During the experiment, the system simultaneously acquires resistance response data and acoustic emission response data from these calibration solder joints. The analysis module does not simply correlate instantaneous values ​​but extracts dynamic features that reflect the damage evolution rate. Specifically, the system uses a certain number of cycles as an analysis window and calculates the average slope of the resistance response data within that window, i.e., the damage accumulation rate. Simultaneously, it calculates the total energy of all acoustic emission events within the same window. By continuously calculating and recording the damage accumulation rate and total energy data pairs, the system accumulates a large amount of grouped data. Subsequently, nonlinear regression methods are used, such as using polynomial fitting or training a small neural network, to establish a mathematical model that can characterize the strong correlation between the total energy and the damage accumulation rate, i.e., the correlation mapping relationship.

[0116] Using the established correlation mapping, the state of "inferred solder joints" with only resistance monitoring deployed is estimated. For any inferred solder joint, its resistance damage accumulation rate within the current analysis window is calculated in real time. This measured resistance damage accumulation rate is used as input into the correlation mapping model established in the previous step. The model output is the expected, equivalent microscopic damage activity level of the inferred solder joint within this time period, expressed as an estimated sum of acoustic emission energy. This estimate is encapsulated as inferred damage state information.

[0117] The generated inferred damage state information is incorporated into the failure mechanism identification dataset. During global failure mechanism identification, the decision algorithm possesses not only real acoustic emission data from the calibrated solder joints but also virtual acoustic emission data from a large number of inferred solder joints. This allows the system to more comprehensively evaluate the damage distribution and patterns of the entire controller. For example, if the system finds that the inferred damage state information of multiple solder joints with only resistance monitoring indicates frequent high-energy events, even if the real acoustic emission signals of the calibrated solder joints are not yet significant, it can predict in advance that the controller may be experiencing widely distributed vibration fatigue damage caused by specific vibration modes, thereby enhancing the globality and accuracy of the failure mechanism identification results.

[0118] For example, in the process of establishing a correlation mapping between resistance response data and acoustic emission response data to assist in the reproduction of failure mechanism identification, the system first selects a calibration solder joint equipped with a dual-modal sensor for synchronous data acquisition. The system sets the analysis window to 100 cycles. At the beginning of the window, the initial resistance value of the solder joint is measured, and at the end of the window, the final resistance value is measured. The difference between the two values ​​is divided by the number of cycles to calculate the resistance damage accumulation rate of the calibration solder joint. Simultaneously, the system accumulates the energy integral of all acoustic emission events captured within the analysis window to obtain the total acoustic emission energy. By repeating this process within multiple time windows and using a nonlinear regression method, the system establishes a correlation mapping model that can characterize the correspondence between the resistance damage rate and the total acoustic emission energy. Subsequently, for the inferred solder joint with only resistance monitoring, the system extracts its measured resistance damage accumulation rate within the current analysis window in real time and uses this value as input into the established correlation model to inversely infer the equivalent microscopic damage activity level of the inferred solder joint in the same time period, i.e., the inferred total acoustic emission energy. Finally, the system incorporates the inferred damage state information into the identification dataset, and uses the measured data of the calibrated solder joints and the mapping data of the inferred solder joints to jointly support the generation of failure mechanism identification results, thus achieving accurate determination of the damage evolution type of solder joints on the entire circuit board under limited sensor deployment.

[0119] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides an accelerated evaluation system for thermal cycling failure of solder joints in vehicle controllers, the system comprising:

[0120] An asymmetric alternating stress field loading module is used to construct and apply an asymmetric alternating stress field to the welding points of the vehicle controller. The asymmetric alternating stress field is composed of local thermal shock excitation and broadband vibration excitation applied alternately at different time periods.

[0121] The multimodal response signal synchronous monitoring module is used to synchronously monitor the multimodal response signals of the welding points of the vehicle controller under the action of the asymmetric alternating stress field, and generate a raw response dataset containing resistance response data, acoustic emission response data and vibration response data.

[0122] The intelligent failure mechanism identification module is used to perform multi-source signal fusion analysis on the original response dataset to generate failure mechanism identification results that characterize the micro-damage evolution type of the solder joint.

[0123] An adaptive loading strategy control module is used to adaptively adjust the loading parameters of the asymmetric alternating stress field in subsequent cycles based on the failure mechanism identification results, and generate adaptive loading instructions.

[0124] The life prediction and assessment module is used to assess the life of the on-board controller solder joints based on the damage evolution data collected under the adaptive loading command and in combination with the physical damage model that matches the failure mechanism identification results.

[0125] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0126] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. An accelerated evaluation method for thermal cycling failure of solder joints in vehicle controllers, characterized in that, The method includes: An asymmetric alternating stress field is constructed and applied to the solder joints of the vehicle controller. The asymmetric alternating stress field is composed of local thermal shock excitation and broadband vibration excitation applied alternately at different time periods. The multimodal response signals of the on-board controller solder joints under the action of the asymmetric alternating stress field are monitored synchronously to generate a raw response dataset containing resistance response data, acoustic emission response data and vibration response data; Multi-source signal fusion analysis is performed on the original response dataset to generate failure mechanism identification results characterizing the micro-damage evolution type of the solder joint. This includes: extracting acoustic emission feature vectors from the acoustic emission response data and simultaneously extracting resistance change features from the resistance response data; matching the acoustic emission feature vectors with a preset feature template library to generate preliminary mechanism discrimination results, wherein the feature template library is associated with acoustic emission signal modes and failure physical processes; and verifying and correcting the preliminary mechanism discrimination results using the damage accumulation process reflected by the resistance change features to generate failure mechanism identification results characterizing the micro-damage evolution type of the solder joint. Based on the failure mechanism identification results, the loading parameters of the asymmetric alternating stress field in subsequent cycles are adaptively adjusted to generate an adaptive loading command, which includes: identifying the current dominant failure mode indicated in the failure mechanism identification results; if the current dominant failure mode is thermal fatigue damage, generating a first adjustment command to enhance the amplitude or rate of change of the local thermal shock excitation in subsequent cycles; if the current dominant failure mode is vibration fatigue damage, generating a second adjustment command to concentrate the energy of the broadband vibration excitation in subsequent cycles to the resonant frequency band of the vehicle controller; and outputting the first adjustment command or the second adjustment command as an adaptive loading command. Based on the damage evolution data collected under the adaptive loading command, and combined with the physical damage model that matches the failure mechanism identification results, the life of the on-board controller solder joints is assessed.

2. The accelerated evaluation method for thermal cycling failure of solder joints in an on-board controller according to claim 1, characterized in that, The construction and application of the asymmetric alternating stress field acting on the weld joints of the vehicle controller includes: Acquire vehicle operating condition data that records the working status of the power devices of the vehicle controller and the vibration status of the vehicle body, and extract the time sequence relationship between thermal shock events and high-intensity vibration events from the vehicle operating condition data to generate a composite load time sequence spectrum. According to the composite load timing spectrum, a pulse current is applied to the selected solder joint circuit to form a local thermal shock excitation. According to the composite load time sequence spectrum, a broadband vibration excitation with random spectral characteristics is applied at a time stage different from the time stage at which the pulse current is applied; The local thermal shock excitation and the broadband vibration excitation are applied asymmetrically in time, together forming an asymmetrical alternating stress field.

3. The accelerated evaluation method for thermal cycling failure of solder joints in an on-board controller according to claim 1, characterized in that, The synchronous monitoring of the multimodal response signal of the on-board controller solder joint under the action of the asymmetric alternating stress field includes: Measure the current and voltage signals flowing through the selected solder joint circuit, and calculate the resistance response data as a function of time; High-frequency stress wave signals released by changes in the microstructure of solder joints are captured, and features of the high-frequency stress wave signals are extracted to generate acoustic emission response data. The dynamic response of the vehicle controller under vibration excitation is collected to generate vibration response data; The resistance response data, the acoustic emission response data, and the vibration response data are time-synchronized and aligned to form the original response dataset.

4. The accelerated evaluation method for thermal cycling failure of solder joints in an on-board controller according to claim 1, characterized in that, The failure mechanism identification results that characterize the micro-damage evolution type of the solder joint also include: When an event indicating the generation of microcracks is detected in the acoustic emission response data and a step change occurs in the synchronous resistance response data, the monitoring enhancement mode is triggered. In the enhanced monitoring mode, the frequency of acquiring the resistance response data is increased, and high-frequency vibration response data within the time window before and after the event is recorded; The high-frequency acquired resistance response data is correlated with the high-frequency vibration response data to generate a damage transient feature package for analyzing the transient physical process of damage, and the damage transient feature package is used to assist in generating the failure mechanism identification result.

5. The accelerated evaluation method for thermal cycling failure of solder joints in an on-board controller according to claim 1, characterized in that, The life assessment of the solder joints of the vehicle controller includes: Acquire initial stage damage evolution data under the initial stress level, and acquire accelerated stage damage evolution data under the enhanced stress level controlled by the adaptive loading command. Based on the difference in damage accumulation rate between the initial stage damage evolution data and the accelerated stage damage evolution data, the acceleration factor used to quantify the acceleration effect is calculated and updated online. Substituting the acceleration factor into the physical damage model, the predicted lifespan of the vehicle controller solder joints under the target operating conditions is calculated by extrapolation.

6. The accelerated evaluation method for thermal cycling failure of solder joints in an on-board controller according to claim 1, characterized in that, The physical damage model that matches the failure mechanism identification results includes: If the failure mechanism identification results indicate that thermal fatigue is dominant, then the life model with inelastic strain accumulation as the damage metric is selected as the physical damage model. If the failure mechanism identification results indicate that vibration fatigue is dominant, then the life model with the cumulative vibration stress cycle as the damage metric is selected as the physical damage model. If the failure mechanism identification result indicates mixed-mode damage, then the comprehensive damage accumulation model of coupled thermal stress and vibration stress is selected as the physical damage model.

7. The accelerated evaluation method for thermal cycling failure of solder joints in an on-board controller according to claim 1, characterized in that, The method further includes: For solder joints where both resistance monitoring and acoustic emission monitoring are deployed, a correlation mapping relationship is established between the trend changes of the resistance response data and the event energy of the acoustic emission response data. For solder joints where only resistance monitoring is deployed, the established correlation mapping relationship and the resistance response data of the solder joint are used to inversely infer its micro-damage activity level and generate inferred damage state information. The inferred damage state information is used to assist in generating the failure mechanism identification results.

8. An accelerated evaluation system for thermal cycling failure of solder joints in an on-board controller, applied to the accelerated evaluation method for thermal cycling failure of solder joints in an on-board controller as described in any one of claims 1-7, characterized in that, The system includes: An asymmetric alternating stress field loading module is used to construct and apply an asymmetric alternating stress field to the welding points of the vehicle controller. The asymmetric alternating stress field is composed of local thermal shock excitation and broadband vibration excitation applied alternately at different time periods. The multimodal response signal synchronous monitoring module is used to synchronously monitor the multimodal response signals of the welding points of the vehicle controller under the action of the asymmetric alternating stress field, and generate a raw response dataset containing resistance response data, acoustic emission response data and vibration response data. The intelligent failure mechanism identification module is used to perform multi-source signal fusion analysis on the original response dataset to generate failure mechanism identification results characterizing the micro-damage evolution type of the solder joint. This includes: extracting acoustic emission feature vectors from the acoustic emission response data and simultaneously extracting resistance change features from the resistance response data; matching the acoustic emission feature vectors with a preset feature template library to generate preliminary mechanism discrimination results, where the feature template library is associated with acoustic emission signal patterns and failure physical processes; and verifying and correcting the preliminary mechanism discrimination results using the damage accumulation process reflected by the resistance change features to generate failure mechanism identification results characterizing the micro-damage evolution type of the solder joint. An adaptive loading strategy control module is used to adaptively adjust the loading parameters of the asymmetric alternating stress field in subsequent cycles based on the failure mechanism identification results, and generate adaptive loading instructions, including: identifying the current dominant failure mode indicated in the failure mechanism identification results; if the current dominant failure mode is thermal fatigue damage, generating a first adjustment instruction to enhance the amplitude or rate of change of the local thermal shock excitation in subsequent cycles; if the current dominant failure mode is vibration fatigue damage, generating a second adjustment instruction to concentrate the energy of the broadband vibration excitation in subsequent cycles to the resonant frequency band of the vehicle controller; and outputting the first adjustment instruction or the second adjustment instruction as an adaptive loading instruction. The life prediction and assessment module is used to assess the life of the on-board controller solder joints based on the damage evolution data collected under the adaptive loading command and in combination with the physical damage model that matches the failure mechanism identification results.

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