Automobile wire harness connector crimping quality traceability and failure active suppression method

Abstract

The invention discloses an automobile wire harness connector crimping quality traceability and failure active suppression method, which comprises the following steps of: digitally modeling and solidifying parameters in a crimping process, constructing a process parameter quality index mapping model through process trial production, and determining process parameter intervals and quality judgment thresholds of various products; crimping quality full-batch digital traceability is carried out, crimping process data is collected in real time, the quality grade is automatically judged, a unique traceability code is generated for each connector, and full-process traceability is realized; micro-motion corrosion is actively inhibited in a use stage, and a circuit load is actively adjusted in an over-limit state to inhibit corrosion and send an early warning; based on precise maintenance of the traceability data, traceability and early warning information are associated to construct a maintenance evaluation model, a hierarchical maintenance strategy is formulated, and the model and a threshold are continuously optimized through a data closed loop. Full-batch monitoring of crimping quality, active suppression of failure risks and accurate formulation of maintenance strategies are realized, and reliability and full-life-cycle management efficiency of an electrical system are improved.

Description

Technical Field

[0001] This invention relates to the field of automotive wiring harness manufacturing and maintenance technology, specifically a method for tracing the crimping quality of automotive wiring harness connectors and actively suppressing failures. Background Technology

[0002] Automotive wiring harnesses are the core connection components of automotive electrical systems, and connectors, as key nodes for transmitting signals and electrical energy, directly determine the stability, reliability, and safety of the automotive electrical system through their crimping quality. With the rapid development of automobiles towards intelligence and electrification, the number of onboard electrical devices has increased significantly, and the application scenarios for wiring harness connectors have become increasingly complex. They must not only withstand harsh environments such as vibration and temperature changes during vehicle operation but also meet the high-precision requirements of high-frequency signal transmission. This places higher standards on their crimping quality and overall lifecycle reliability.

[0003] In existing technologies, the quality control of crimping in automotive wiring harness connectors largely relies on sampling inspection after trial production. This involves determining the range of process parameters by testing quality indicators such as contact resistance and pull-out force, followed by mass production based on these fixed parameters. However, this approach has drawbacks: firstly, sampling inspection cannot cover the entire batch, making it difficult to detect crimping defects in individual connectors. Furthermore, the lack of precise records of crimping process parameters for each connector makes it impossible to trace the entire process and pinpoint the root cause when quality issues arise. Secondly, the determination of existing process parameters largely depends on accumulated experience, lacking a precise mapping model between process parameters and quality indicators. This results in a lack of data support for process parameter optimization, making it difficult to adapt to connector product requirements with different core attributes, such as terminal materials and wire cross-sectional areas.

[0004] During the use of connectors, vibrations generated during vehicle operation can easily cause fretting wear at the connection points between connector terminals and wires, leading to fretting corrosion. This results in increased contact resistance, unstable signal transmission, and in severe cases, circuit breaks, affecting the operation of core vehicle functions. Currently, the industry primarily employs a reactive maintenance strategy for such failures, replacing or repairing faulty connectors only after a failure occurs. This approach fails to proactively warn and mitigate failures before they occur, increasing safety risks during vehicle operation. Furthermore, existing maintenance strategies are mostly periodic preventative maintenance, failing to incorporate historical data on connector crimping quality and actual operating conditions. This leads to inaccurate timing of maintenance, unreasonable allocation of maintenance resources, and issues of over-maintenance or under-maintenance.

[0005] Furthermore, in existing technologies, crimping quality data, operating condition data, and maintenance data are isolated from each other and do not form a complete data loop. This makes it impossible to optimize crimping process parameters and failure warning thresholds through subsequent maintenance data, resulting in insufficient iterative optimization capabilities of the technical solution. Summary of the Invention

[0006] The purpose of this invention is to provide a method for tracing the crimping quality of automotive wiring harness connectors and actively suppressing failures, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for tracing the crimping quality and actively suppressing failures of automotive wiring harness connectors, comprising the following steps: S1: Digital modeling and parameter solidification of the crimping process, classifying product types according to the core attributes of the connector, collecting multiple sets of process parameters and corresponding quality index data through process trial production, constructing a process parameter-quality index mapping model, determining the process parameter range and quality judgment threshold for each product type based on the mapping model, and assigning a unique process code to each product type. S2: Full batch digital traceability of crimping quality. The crimping equipment is upgraded with sensor integration to collect crimping process data of each connector in real time. The crimping process data is compared with the optimal process parameter range and quality judgment threshold in the mapping model to automatically determine the quality level of each connector. A unique traceability code containing product type, process code, crimping time, quality level and equipment number is generated for each connector and marked. At the same time, the traceability data is stored in the cloud database to realize full process traceability. S3: Active suppression of fretting corrosion during use. A critical vibration frequency for fretting corrosion is preset based on the vibration characteristics of the connector installation location. Vibration data of the connector installation location is collected in real time by the vehicle-mounted sensor and compared with the critical vibration frequency. When the vibration data exceeds the critical value, the corresponding circuit load output is adjusted by the vehicle-mounted control unit to suppress fretting corrosion, and a risk warning message is sent at the same time. When the vibration data returns to below the critical value, the circuit load is automatically restored to the normal level. S4: Precise maintenance based on traceability data. A maintenance assessment model is constructed by linking the traceability data of the connectors with the vibration risk warning information sent by the vehicle system. Based on the maintenance assessment model, maintenance levels are divided and corresponding maintenance strategies are formulated. After the maintenance operation is performed, the maintenance-related data is entered into the cloud database to complete the data closure and optimize the mapping model and critical vibration frequency threshold.

[0008] Preferably, the core attributes of the connector in S1 include the terminal material and the cross-sectional area of ​​the wire. The terminal material includes at least one of brass, phosphor bronze, and titanium copper. The process parameters include at least the wire stripping length, crimping pressure, and crimping speed. The quality indicators include at least the contact resistance and the pull-out force.

[0009] Preferably, the process trial production described in S1 is implemented as follows: for each product type, multiple sets of different parameter combinations are selected within the preset process parameter range for press-fitting trials. The contact resistance and pull-out force data of each set of trial connectors are tested. Each set of process parameters is associated with the corresponding test data to form a sample dataset. Based on the sample dataset, a process parameter-quality index mapping model is constructed using a data fitting method.

[0010] Preferably, the specific content of the sensor integration transformation of the crimping equipment described in S2 is as follows: a high-definition industrial camera, a pressure sensor, and a displacement sensor are integrated into the automated crimping equipment. The high-definition industrial camera is used to collect image data of the wire stripping length, the pressure sensor is used to collect crimping pressure curve data, and the displacement sensor is used to collect crimping displacement data. The crimping process data is the collection of the above-mentioned image data, pressure curve data, and displacement data.

[0011] Preferably, the quality grade determination rule in S2 is as follows: if the crimping process data fully conforms to the optimal process parameter range and the quality indicators meet the quality judgment threshold, it is determined to be grade A; if the crimping process data has a slight deviation but the quality indicators still meet the quality judgment threshold, it is determined to be grade B; if the crimping process data exceeds the optimal process parameter range or the quality indicators do not meet the quality judgment threshold, it is determined to be grade C. When storing the traceability data, the traceability data corresponding to the grade C connector needs to be marked and the rejection process needs to be triggered.

[0012] Preferably, the traceability code in S2 is marked by laser marking, and the marking position is on the surface of the connector sheath. The marking process does not change the original structure and performance parameters of the connector. The cloud database adopts an encrypted storage method and supports unique query and retrieval of traceability data through the traceability code.

[0013] Preferably, the preset method for the critical vibration frequency in S3 is as follows: based on the classification of the connector installation location, the fretting corrosion degree data of the connector at different vibration frequencies are collected through bench vibration test, and the vibration frequency corresponding to the fretting corrosion degree reaching the preset deterioration standard is taken as the critical vibration frequency; the connector installation location includes at least three categories: engine compartment, cockpit, and chassis, and different installation locations correspond to different critical vibration frequencies.

[0014] Preferably, the specific method for adjusting the load output of the corresponding circuit in S3 is as follows: under the premise of not affecting the operation of the vehicle's core functions, the load of the corresponding circuit is temporarily reduced by 10%-20% to reduce the current density at the terminal contact point; the risk warning information is sent to the vehicle manufacturer's backend through the vehicle-mounted remote information processing terminal, and the warning information includes the connector's traceability code, installation location, and real-time vibration data.

[0015] Preferably, the maintenance level classification rules and corresponding maintenance strategies described in S4 are as follows: (1) Level 1 maintenance: The connector quality level is A and there is no vibration risk warning information. The maintenance strategy is to clean the impurities on the connector surface and check the locking status. (2) Level 2 maintenance: If the quality level of the connector is B and the number of vibration risk warning messages is 1-3, the maintenance strategy is to re-test the contact resistance of the connector. If the contact resistance meets the preset usage threshold, it can continue to be used; otherwise, the crimping process is re-executed. (3) Level 3 maintenance: If the quality level of the connector is B and the number of vibration risk warning messages is not less than 4, or if the quality level is C, the maintenance strategy is to replace the connector as a whole.

[0016] Preferably, the data closed-loop implementation process in S4 is as follows: the maintenance level, maintenance operation content, maintenance effect verification data and failure cause analysis data during the maintenance process are entered into the cloud database. At each preset interval, the process parameter-quality index mapping model is modified based on the newly added data, and the critical vibration frequency threshold for fretting corrosion of connectors in different installation positions is optimized.

[0017] Compared with existing technologies, the beneficial effects of this invention are: digital modeling and parameter solidification of the crimping process, full batch digital traceability of crimping quality, active suppression of micro-corrosion during use, and precise maintenance steps based on traceability data. By constructing a process parameter-quality index mapping model, full batch quality monitoring is achieved. Combined with real-time data acquisition and active suppression mechanisms, the risk of failure is reduced. This solves the problems of insufficient sampling inspection and data isolation in existing technologies. It has the advantages of realizing full batch digital monitoring of crimping quality and active failure suppression, improving the reliability of electrical systems and maintenance efficiency. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the crimping quality traceability and active failure suppression method according to an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 A method for tracing the source of crimping quality and actively suppressing failures in automotive wiring harness connectors includes the following steps: S1: Digital modeling and parameter solidification of the crimping process. Product types are classified according to the core attributes of the connectors. Multiple sets of process parameters and corresponding quality index data are collected through process trial production. A process parameter-quality index mapping model is constructed. Based on the mapping model, the process parameter range and quality judgment threshold of each product type are determined, and a unique process code is assigned to each product type. S2: Full batch digital traceability of crimping quality. The crimping equipment is upgraded with sensor integration to collect crimping process data of each connector in real time. The crimping process data is compared with the optimal process parameter range and quality judgment threshold in the mapping model to automatically determine the quality level of each connector. A unique traceability code containing product type, process code, crimping time, quality level and equipment number is generated for each connector and marked. At the same time, the traceability data is stored in the cloud database to realize full process traceability. S3: Active suppression of fretting corrosion during use. Based on the vibration characteristics of the connector installation location, a critical vibration frequency for fretting corrosion is preset. Vibration data of the connector installation location is collected in real time by the vehicle-mounted sensor and compared with the critical vibration frequency. When the vibration data exceeds the critical value, the corresponding circuit load output is adjusted by the vehicle-mounted control unit to suppress fretting corrosion, and a risk warning information is sent at the same time. When the vibration data returns to below the critical value, the circuit load is automatically restored to the normal level. S4: Precise maintenance based on traceability data. A maintenance assessment model is constructed by linking the traceability data of the connectors with the vibration risk warning information sent by the vehicle system. Based on the maintenance assessment model, maintenance levels are divided and corresponding maintenance strategies are formulated. After the maintenance operation is performed, the maintenance-related data is entered into the cloud database to complete the data closure and optimize the mapping model and critical vibration frequency threshold.

[0021] For ease of understanding, the following explains some key terms in this embodiment: Digital modeling and parameter solidification of the crimping process refers to the process of quantifying and standardizing key parameters that affect the quality of crimping by conducting data analysis and model building of the crimping process, and solidifying them into executable process specifications or procedures to ensure the stability and consistency of the crimping process.

[0022] A process parameter-quality index mapping model is a data correlation model established between crimping process parameters, such as wire stripping length and crimping pressure, and the final quality index of the connector, such as contact resistance and pull-out force. This model can reveal the impact of different combinations of process parameters on product quality, thereby guiding process optimization and quality control.

[0023] Full-batch digital traceability of crimping quality refers to the real-time collection, recording, and storage of crimping process data for each connector on the production line, and the generation of a unique identifier for it, so as to track and query the production information and quality status of the connector throughout the product lifecycle.

[0024] A traceability code is a unique identifier generated for each connector, containing key information such as product type, process code, crimping time, quality grade, and equipment number. This code is used to identify the connector and trace its data.

[0025] The critical vibration frequency for fretting corrosion refers to the vibration frequency threshold at which fretting corrosion begins to occur on the contact surface of a connector terminal under specific vibration conditions, or when the degree of fretting corrosion reaches a preset deterioration standard. When the actual vibration frequency exceeds this critical value, the risk of fretting corrosion increases.

[0026] An onboard control unit (OCU) is an electronic control module installed inside a vehicle that is responsible for receiving sensor data, executing control commands, and managing various vehicle functions. In this embodiment, it is used to adjust the circuit load output based on vibration data.

[0027] A maintenance assessment model is a comprehensive evaluation model that combines various data, such as connector manufacturing quality data and vibration risk warning information during use, to assess the current condition and potential failure risks of connectors. This model is used to guide the classification of maintenance levels and the formulation of maintenance strategies.

[0028] Data closed loop refers to the integration of data from the entire lifecycle of a product, from design, production, use to maintenance, and through data analysis and feedback mechanisms, the maintenance and failure data are applied in reverse to optimize production process parameters, early warning thresholds, and other aspects, forming a continuous improvement cycle.

[0029] This embodiment provides a method for tracing the quality of automotive wiring harness connector crimping and actively suppressing failures. Specifically, during the digital modeling and parameter solidification stage of the crimping process, product types can be classified according to the general physical characteristics of the connectors, such as their size, connector type, or main application scenarios. All small signal connectors can be grouped into one category, and large power connectors into another. During trial production, multiple different combinations of process parameters can be manually set, adjusting the crimping stroke and crimping time of the crimping equipment. Each trial-produced connector is manually inspected, recording basic quality indicators such as whether its appearance is acceptable and whether the wires are secure. This data is then manually entered into the system. Based on the collected process parameters and quality indicator data, a process parameter-quality indicator mapping model can be constructed using a data fitting method. For example, the data fitting method can employ a multiple linear regression algorithm. Stripping length, crimping pressure, and crimping speed are used as independent variables, and contact resistance and pull-out force are used as dependent variables. Based on the sample dataset obtained from trial production, the regression coefficients are solved using the least squares method, thereby establishing a mathematical model that can be used for prediction. The model can be constructed using algorithms such as linear regression, multinomial regression, or artificial neural networks, rather than linear regression. Observations revealed a trend relationship between crimping stroke and wire pull-out force. After constructing a preliminary mapping model, a general operating range of process parameters can be set for each product type based on experience or industry standards, and a basic quality acceptance standard can be determined. The crimping stroke should be within a certain millimeter range, and the wire pull-out force should be greater than a certain Newton value. Subsequently, a short, easily identifiable identifier, such as A01, B02, etc., is assigned to each product type for differentiation and management during production.

[0030] Furthermore, in the stage of full-batch digital traceability of crimping quality, the crimping equipment is sensor-integrated and transformed, that is, a high-definition industrial camera, a pressure sensor, and a displacement sensor are integrated on the automatic crimping equipment. The high-definition industrial camera is installed above the wire stripping station and is used to collect images of the wires after wire stripping. The pressure sensor is integrated on the force application mechanism of the crimping die and is used to collect the pressure curve data of the entire crimping process. The displacement sensor is installed on the crimping punch and is used to collect the crimping displacement data. These data are stored in a local data file. The collected crimping process data can be simply logically judged against the preset process parameter range and quality standards. It is judged whether the travel switch is triggered within the specified time and whether the current reading is within the normal fluctuation range. Based on the above comparison results, a simple judgment rule can be set. Those with all basic parameters within the normal range are judged as qualified, otherwise they are judged as unqualified. A string containing its basic production information is generated for each completed connector. A unique traceability code is generated for each connector by laser marking. Through a fiber laser marking machine, two-dimensional code or character information including product type, process code, crimping time, quality grade, and equipment number is etched on the specified surface position of the connector sheath, and this process ensures that the internal structure and electrical performance of the connector are not damaged. At the same time, all the collected traceability data can be regularly uploaded to a centralized server for subsequent query.

[0031] The system automatically compares the real-time collected crimping process data, including the measured value of the wire stripping length, the pressure curve shape, the displacement curve, with the optimal process parameter range of the corresponding product type. At the same time, it evaluates whether it meets the quality judgment threshold based on the mapping relationship between the process data and the quality index. Those that fully conform to the range and are evaluated to meet the threshold are judged as Class A; those with slight deviations in the process data, such as individual parameters at the edge of the range but with a normal curve shape and still evaluated to meet the threshold, are judged as Class B; those with process data exceeding the range or not meeting the threshold are judged as Class C.

[0032] Furthermore, regarding the active suppression of fretting corrosion during use, an empirical upper limit for vibration frequency can be set for connectors in different installation locations based on vehicle design specifications or industry-standard guidelines. For connectors in the engine compartment, the permissible vibration frequency may be higher than that of connectors in the passenger compartment. During vehicle operation, vibration data at the connector installation locations is periodically collected using universal vibration sensors installed on the vehicle, such as accelerometers. This data is then compared numerically with a preset critical vibration frequency. When the detected vibration data exceeds the critical value, the onboard control unit adjusts the corresponding circuit load output. That is, while ensuring the normal operation of core vehicle functions such as power, braking, and steering, a control command is sent to the power management module of the circuit to temporarily reduce the load current by 10%-20% to reduce the current density at the terminal contacts. Simultaneously, an alarm is issued via indicator lights or a buzzer on the vehicle's instrument panel. When the vibration sensor detects that the vibration data remains below the critical value for a period of time, the onboard control unit can automatically restore previously shut-off auxiliary electrical equipment, returning the circuit load to normal.

[0033] Finally, in the precise maintenance phase based on traceability data, the production batch information of connectors can be manually matched with vibration warning information in vehicle fault records to form a simple association table. Based on this association table, basic maintenance rules can be set. For connectors that have previously issued vibration warnings, inspection is recommended; for those with multiple warnings, replacement is recommended. After completing maintenance work, maintenance personnel manually record the maintenance date, maintenance content, and replaced parts in a paper form, and this information is periodically entered into a centralized server by a designated person. Upon receiving maintenance effectiveness verification data entered during the maintenance process, such as post-maintenance retested contact resistance and failure cause analysis data, the cloud database matches this data with the original process parameters associated with the corresponding traceability code to form a new sample dataset. The system automatically runs an optimization program at preset intervals, such as quarterly: First, based on the expanded sample set, incremental learning algorithms such as recursive least squares are used to correct the coefficients of the process parameter-quality index mapping model; second, the correlation between the frequency and amplitude of vibration early warning information and subsequent maintenance level under different installation positions is statistically analyzed, and the critical vibration frequency threshold for fretting corrosion at each installation position is dynamically adjusted and optimized accordingly.

[0034] This method achieves precise control of process parameters for different types of connectors by digitally modeling and solidifying the crimping process, solving the problems of reliance on traditional experience and poor adaptability. Full-batch digital traceability compensates for the shortcomings of sampling inspection, ensuring the quality traceability of each connector. During vehicle use, proactively suppressing fretting corrosion transforms traditional passive maintenance into proactive prevention, improving driving safety. Finally, through traceability data-driven precision maintenance and data closure, continuous optimization of process parameters and early warning thresholds is achieved, overcoming the limitations of isolated data and insufficient iteration.

[0035] In some of the solutions mentioned above in this application, product types are classified according to the core attributes of connectors and a process parameter-quality index mapping model is constructed to optimize the crimping process. However, in this process, since the specific ranges of core attributes, process parameters and quality indicators are not clearly defined, the model construction lacks specificity and is difficult to effectively adapt to the needs of connector products with different terminal materials and wire cross-sectional areas, thereby affecting the accuracy of crimping quality judgment and the efficiency of process optimization.

[0036] In this regard, this application further proposes that in step S1, the core attributes of the connector include the terminal material and the cross-sectional area of ​​the wire, the terminal material includes at least one of brass, phosphor bronze, and titanium copper, the process parameters include at least wire stripping length, crimping pressure, and crimping speed, and the quality indicators include at least contact resistance and pull-out force.

[0037] The core attributes of a connector refer to the fundamental characteristics used to distinguish different connector product types. These attributes form the basis for constructing a process parameter-quality indicator mapping model, ensuring that the model can be customized and optimized for different product characteristics. In addition to terminal material and wire cross-sectional area, core connector attributes may also include, for example, terminal plating type, insulation material type, or connector structural form.

[0038] The terminal material is a key material constituting the conductive part of the connector, and its physical and electrochemical properties directly affect the connector's conductivity, mechanical strength, and corrosion resistance. In this application, the terminal material includes at least one of brass, phosphor bronze, and titanium copper. Brass is widely used due to its good conductivity and machinability; phosphor bronze has higher strength and elasticity, making it suitable for applications requiring good elasticity and fatigue resistance; and titanium copper performs excellently in high-temperature and high-strength applications. In addition, the terminal material can also be beryllium copper alloy, which has extremely high strength and excellent conductivity, suitable for high-performance requirements, or other copper alloys, such as high-conductivity copper alloys, to meet specific electrical performance requirements.

[0039] The conductor cross-sectional area refers to the cross-sectional area of ​​the conductor connected to the connector terminal, which determines the conductor's current-carrying capacity and its compatibility with the terminal. Conductor cross-sectional area is typically expressed in millimeters (mm²) or American Wire Gauge (AWG), such as 0.5mm², 0.75mm², 1.0mm², 1.5mm², 2.5mm², etc. Precisely defining the conductor cross-sectional area helps ensure a good mechanical and electrical connection between the terminal and the conductor during crimping.

[0040] The process parameters are controllable variables during the crimping process and directly affect the crimping quality. In this application, the process parameters include at least wire stripping length, crimping pressure, and crimping speed. Wire stripping length refers to the length of the wire insulation layer that is stripped; its precise control is crucial to ensuring sufficient contact between the wire conductor and the crimping area of ​​the terminal. The wire stripping length can be precisely set using an automated wire stripping machine, such as 3mm, 4mm, or 5mm. Crimping pressure refers to the force applied by the crimping equipment to the terminal and the wire; it affects the degree of deformation and contact tightness of the crimping area. Crimping pressure can be adjusted and controlled using a hydraulic system, pneumatic system, or mechanical cam mechanism, and can be set to a specific force value of Newtons (N) or kilonewtons (kN). Crimping speed refers to the speed at which the crimping die closes; it affects the flowability of the material and the heat generated during the crimping process. Crimping speed can be controlled by adjusting the motor speed or hydraulic flow rate, and can be set to millimeters per second (mm / s) or cycles per second (cycles / s). In addition to the above parameters, process parameters may also include crimping height, crimping width, crimping die type, or wire insertion depth, etc.

[0041] The quality indicators are key parameters used to evaluate the performance of crimped connections. In this application, the quality indicators include at least contact resistance and pull-out force. Contact resistance refers to the electrical contact impedance between the connector terminal and the conductor, and is an important indicator of the reliability of the electrical connection. Contact resistance is usually measured using the four-wire method, and the unit is milliohms or microohms. Pull-out force is the force required to pull the crimped conductor out of the terminal, and is an important indicator of the strength of the mechanical connection. Pull-out force is usually measured using a tensile testing machine, and the unit is Newton-N or kilogram-force (kgf). In addition to the above indicators, quality indicators may also include crimp cross-section analysis to check the geometry and material filling of the crimped area, or visual inspection to assess appearance defects.

[0042] The above technical solution clarifies the specific range of core attributes, process parameters, and quality indicators for connectors in step S1, making the digital modeling and parameter solidification of the crimping process more targeted and operable. By classifying connectors according to core attributes such as terminal material and wire cross-sectional area, it is possible to establish exclusive process parameter-quality indicator mapping models for different types of products, avoiding the limitations of a one-size-fits-all model when dealing with diverse products. Brass terminals and phosphor bronze terminals may have different deformation characteristics and required pressures during crimping. By refining attribute classification, more precise stripping length, crimping pressure, and crimping speed ranges can be set for each material. At the same time, using contact resistance and pull-out force as core quality indicators can directly reflect the electrical performance and mechanical strength of the crimped connection, ensuring that the mapping model can accurately capture the impact of process parameter changes on the final product quality. This detailed definition and classification allows the constructed mapping model to more effectively adapt to the crimping requirements of different connector products, improving the accuracy of crimping quality judgment and the efficiency of process optimization, and providing a more solid data foundation for subsequent quality traceability and proactive failure prevention.

[0043] In some of the embodiments described above in this application, a process pilot production is proposed to collect process parameters and quality index data to construct a mapping model. However, the specific implementation method of the process pilot production is not clear, which may lead to unsystematic and incomplete data collection, affecting the accuracy and reliability of the mapping model.

[0044] In this regard, this application further proposes the following method for implementing the process trial production described in S1: For each product type, select multiple sets of different parameter combinations within the preset process parameter range for press-fitting trial production, test the contact resistance and pull-out force data of each set of trial-produced connectors, associate each set of process parameters with the corresponding test data to form a sample dataset, and construct a process parameter-quality index mapping model based on the sample dataset using a data fitting method.

[0045] Product types are categorized based on the core attributes of connectors, aiming to differentiate connectors of different specifications or application scenarios. Besides terminal materials and conductor cross-sectional areas mentioned above, product types can also be classified according to rated current, rated voltage, operating temperature range, protection level, or connector form. This classification ensures that subsequent optimization of process parameters can accurately adapt to the characteristic requirements of various connectors. The preset process parameter range is usually determined based on existing production experience, equipment capacity limitations, material supplier recommendations, or industry standards, aiming to define a reasonable and operable parameter space. Selecting multiple sets of different parameter combinations for crimping trials aims to systematically explore the impact of process parameters on product quality. This can be achieved through various experimental design methods, such as orthogonal experimental design to efficiently cover the parameter space within a limited number of tests, or uniform design to evenly distribute test points within the parameter space to obtain more comprehensive data. Crimping trials refer to small-batch production using crimping equipment in actual or simulated production environments, according to selected parameter combinations, to obtain connector samples with different process parameter backgrounds.

[0046] For each set of prototype connectors, contact resistance and pull-out force data were tested. Contact resistance is a key indicator for evaluating the electrical performance of the connector. This can be achieved using the four-wire method, employing a dedicated contact resistance tester to accurately measure the resistance at the connection between the connector terminal and the wire, thus eliminating the influence of lead resistance. Pull-out force data is a key indicator for evaluating the mechanical connection strength of the connector. This is typically achieved using a tensile testing machine, applying a pull-out test to the crimped connector at a preset tensile speed until the wire separates from the terminal, and recording the maximum tensile force at this point. Correlating each set of process parameters with the corresponding test data to form a sample dataset involves mapping the specific process parameters used in each prototype, such as wire stripping length, crimping pressure, and crimping speed, to the contact resistance and pull-out force data measured for that set of prototype connectors. This can be automatically completed by a data acquisition system, storing the parameters and results in a structured database to form a complete dataset containing input process parameters and output quality indicators. Based on the aforementioned sample dataset, a process parameter-quality index mapping model is constructed using data fitting methods. The aim is to learn the intrinsic relationship between process parameters and quality indicators from the sample data. Besides the data fitting methods mentioned above, various statistical or machine learning methods can also be employed, including statistical models such as multiple linear regression, multinomial regression, and support vector regression, or machine learning algorithms such as artificial neural networks and decision tree regression, to establish a model capable of predicting connector quality indicators under given process parameters. This model can be a mathematical function expression, a set of decision rules, or a set of neural network weights.

[0047] Through the above technical solution, this application clarifies the specific implementation path of process pilot production and solves the problems of unsystematic and incomplete data collection in traditional process pilot production. By conducting systematic parameter combination trials for each product type and accurately detecting key quality indicators, the comprehensiveness and accuracy of the sample dataset are ensured. On this basis, a process parameter-quality indicator mapping model is constructed using a data fitting method, which can more scientifically and accurately reveal the quantitative relationship between process parameters and product quality. This provides a solid data foundation and model support for the subsequent digital modeling and parameter solidification of the pressing process, thereby effectively improving the accuracy and reliability of the mapping model and laying the foundation for full-batch digital traceability of pressing quality.

[0048] In some of the embodiments described above in this application, a sensor integration modification is proposed for crimping equipment to collect crimping process data in real time. However, the lack of specific sensor integration methods and data collection details in its implementation may lead to incomplete or inaccurate data collection, affecting the reliability of quality judgment and the accuracy of traceability.

[0049] In response, this application further proposes specific details of the sensor integration modification of the crimping equipment described in S2, aiming to ensure the comprehensiveness and accuracy of the crimping process data through refined sensor deployment. This modification includes integrating a high-definition industrial camera, a pressure sensor, and a displacement sensor into the automated crimping equipment. The high-definition industrial camera is used to acquire stripping length image data, the pressure sensor is used to acquire crimping pressure curve data, and the displacement sensor is used to acquire crimping displacement data. The crimping process data is the collection of the aforementioned image data, pressure curve data, and displacement data.

[0050] The automated crimping equipment typically refers to a mechanical device capable of automatically completing the crimping operation of wire harness connectors. It integrates functions such as feeding, wire stripping, and terminal crimping, aiming to improve production efficiency and consistency in crimping quality. This equipment can employ a servo motor drive system controlled by a PLC or industrial PC to achieve precise control of the crimping action; alternatively, it can be a flexible crimping workstation built on a robotic arm or collaborative robot platform, capable of adapting to rapid switching and crimping of different types of connectors.

[0051] To achieve visual monitoring of the crimping process, this application integrates a high-definition industrial camera into the automated crimping equipment. This camera is a digital camera specifically designed for industrial inspection and image acquisition, featuring high resolution, high frame rate, and good environmental adaptability. The camera can connect to an industrial computer via interfaces such as USB 3.0, GigE Vision, or CoaXPress, and utilize machine vision software for image processing and analysis. Alternatively, it can employ global shutter or rolling shutter technology, selecting an appropriate exposure mode based on the dynamic characteristics of the crimping action to ensure clear, motion-free images. This high-definition industrial camera is primarily used to acquire image data of the wire stripping length, a key parameter affecting crimping quality; excessively long or short strips can lead to poor contact or short circuits. After the wire stripping station is completed, the camera captures images, automatically identifying the boundary between the wire insulation layer and the conductor using image processing algorithms, and calculating the stripping length. Alternatively, it can be combined with backlighting or ring lighting to optimize image contrast, ensuring clear visibility of the stripped edges and improving the accuracy of image data analysis.

[0052] This application also integrates a pressure sensor for real-time monitoring of the force applied to the connector during the crimping process. This pressure sensor is a device that converts pressure signals into electrical signals. Piezoelectric or strain gauge pressure sensors can be used, installed at key stress points in the crimping mold or mechanism to directly measure the crimping force. Alternatively, a pressure transmitter in a hydraulic or pneumatic system can be used to indirectly calculate the crimping force by monitoring system pressure and outputting the signal to the data acquisition module. This pressure sensor is primarily used to collect crimping pressure curve data, which reflects the trend of force change over time or displacement during the crimping process and is an important basis for evaluating the stability and quality of the crimping process. The pressure sensor transmits real-time pressure signals to a data acquisition card for continuous sampling at a preset sampling frequency, forming a pressure-time curve. Alternatively, the data acquisition system can simultaneously record the displacement data of the crimping punch, thereby generating a pressure-displacement curve, which more intuitively reflects the material deformation and mechanical response during the crimping process.

[0053] Furthermore, this application integrates a displacement sensor for monitoring the precise displacement of the crimping die or punch. This displacement sensor is a device used to measure changes in the position or distance of an object, designed to ensure the accuracy of crimping depth and stroke. Linear encoders or magnetostrictive displacement sensors can be used, directly mounted on the motion axis of the crimping punch or die, providing high-precision position feedback. Alternatively, laser displacement sensors or eddy current displacement sensors can be used to measure the relative displacement of the crimped components non-contactly, suitable for applications requiring high precision. This displacement sensor primarily collects crimping displacement data, recording the precise stroke of the crimping punch from its initial position to its final crimped position. This data is crucial for controlling the crimping depth and ensuring the tightness of the connection between the terminal and the wire. The displacement sensor outputs a position signal in real time, which is digitized by the data acquisition module and correlated with a timestamp to form a displacement-time curve. Alternatively, during the crimping process, multiple displacement thresholds can be set, triggering data recording or process parameter adjustments when the punch reaches a specific displacement point, achieving finer crimping control.

[0054] Ultimately, the crimping process data comprises the aforementioned stripping length image data, crimping pressure curve data, and crimping displacement data. Integrating this multi-source data forms a comprehensive crimping process dataset, designed to record and describe the crimping status of each connector from multiple dimensions. A unified data management platform is used to synchronously collect, timestamp-align, and store data from different sensors, ensuring data integrity and correlation. Alternatively, a structured database or time-series database can be used to store this multimodal data, and a data index can be established to facilitate subsequent querying, analysis, and traceability.

[0055] By integrating a high-definition industrial camera, pressure sensor, and displacement sensor into an automated crimping machine, these sensors respectively acquire image data of wire stripping length, crimping pressure curve data, and crimping displacement data. This data set serves as the crimping process data, enabling comprehensive, accurate, and multi-dimensional data acquisition for each connector's crimping process. The high-definition industrial camera provides visual verification of wire stripping length, effectively avoiding potential defects caused by improper stripping. The pressure sensor monitors the dynamic changes in crimping force in real time, ensuring the mechanical stability of the crimping process. The displacement sensor accurately records the crimping stroke, guaranteeing the accuracy of the crimping depth. This multi-sensor fusion data acquisition method overcomes the limitations of insufficient data from a single sensor, improving the completeness and accuracy of the crimping process data. This provides a solid data foundation for subsequent quality level determination, greatly enhancing the reliability of quality assessment. Simultaneously, this detailed crimping process data provides refined support for the quality traceability of each connector, enabling more precise identification of the root cause of quality problems and optimizing the entire quality management system.

[0056] In some of the embodiments described above in this application, a quality grade determination is proposed to achieve full batch digital traceability. However, in the process of its implementation, the lack of specific determination rules may lead to inconsistent determination results or failure to effectively identify defective products, affecting subsequent rejection and maintenance.

[0057] In response, this application further proposes the following rules for determining the quality level as described in S2: If the crimping process data fully conforms to the optimal process parameter range and the quality indicators meet the quality judgment threshold, it is judged as Grade A; if the crimping process data has a slight deviation but the quality indicators still meet the quality judgment threshold, it is judged as Grade B; if the crimping process data exceeds the optimal process parameter range or the quality indicators do not meet the quality judgment threshold, it is judged as Grade C. When storing traceability data, the traceability data corresponding to Grade C connectors needs to be marked and the rejection process needs to be triggered.

[0058] To ensure an objective and consistent assessment of the crimping quality of each connector and to provide an accurate basis for subsequent quality traceability, maintenance, and optimization, this application establishes a clear set of quality level determination rules. These rules, based on a comprehensive analysis of crimping process data and final quality indicators, aim to classify the quality status of connectors into different levels, thereby achieving refined management. Products can be categorized as qualified, pending, or unqualified according to preset quality standards.

[0059] When the crimping process data fully conforms to the optimal process parameter range and the quality indicators meet the quality judgment threshold, the connector is classified as Grade A. This indicates that the connector strictly followed the optimal process requirements during production, and its key performance indicators also reached the highest standards. If process parameters such as wire stripping length, crimping pressure, and crimping speed are all within the preset optimal range, and quality indicators such as contact resistance and pull-out force are all better than or equal to the set qualified thresholds, then it can be classified as Grade A. Connectors of this grade are generally considered high-quality products and can be used directly.

[0060] When there are slight deviations in the crimping process data but the quality indicators still meet the quality judgment threshold, the connector is judged as Grade B. This means that although some parameters in the production process may fluctuate slightly, the performance of the final product still meets the usage requirements. The wire stripping length may slightly exceed the optimal range, but the contact resistance and pull-out force are still within the acceptable range. This judgment mechanism allows for a certain degree of process tolerance, avoiding excessive rejection of qualified products due to minor deviations, while still identifying potential process stability issues.

[0061] When the crimping process data exceeds the range of process parameters or the quality indicators fail to meet the quality judgment threshold, the connector is judged as Grade C. This grade indicates that the crimping quality of the connector has obvious defects and may not meet the usage requirements. The crimping pressure deviates significantly from the optimal range, or key quality indicators such as contact resistance and pull-out force fail to meet the qualified standards. The Grade C judgment aims to clearly identify unqualified products, prevent them from entering the market, and thus ensure the overall product quality and safety in use.

[0062] For connectors classified as Grade C, their corresponding traceability data must be specially marked during storage, and a rejection process must be triggered immediately. The purpose of this is to ensure that all non-conforming products are promptly identified, isolated, and removed from the production line, preventing them from entering subsequent assembly or use stages. Marking can be achieved by adding a specific status field to the traceability data, and the rejection process can include measures such as automatic alarms, production line shutdowns, manual re-inspection, and physical removal. This approach effectively prevents defective products from leaving the production line and provides accurate failure data for subsequent quality analysis and process improvement.

[0063] Through the above technical solution, this application provides a clear and quantifiable set of quality grade judgment rules, effectively solving the problems of inconsistent quality judgment standards and strong subjectivity in traditional methods. By comparing the crimping process data and quality indicators with preset optimal ranges and judgment thresholds, connectors can be accurately divided into three quality grades: A, B, and C. Grade A judgment ensures the identification of high-quality products, while Grade B judgment, under the premise of allowing certain process fluctuations, still ensures that product performance meets standards and avoids unnecessary waste. Most importantly, Grade C judgment can promptly and accurately identify non-conforming products and, by marking traceability data and triggering a rejection process, prevents defective products from entering subsequent stages from the source, greatly improving product quality reliability. This graded judgment mechanism not only provides each connector with a clear quality identity but also provides a solid data foundation for subsequent quality traceability, maintenance strategy formulation, and process optimization, thereby improving the overall quality management level and failure prevention capability of automotive wiring harness connectors.

[0064] In some of the embodiments described above in this application, a method of generating a traceability code and marking each connector to achieve full-process traceability is proposed. However, during its implementation, the marking method may damage the original structure or performance parameters of the connector, leading to a decrease in reliability. Furthermore, database storage poses security risks and cannot ensure the unique query and retrieval of traceability data.

[0065] In this regard, this application further proposes that in step S2, the traceability code is marked by laser marking, the marking location is on the surface of the connector sheath, and the marking process does not change the original structure and performance parameters of the connector. The cloud database adopts an encrypted storage method and supports unique query and retrieval of traceability data through the traceability code.

[0066] The traceability code is marked using laser marking. Laser marking is a non-contact marking technology that uses a high-energy-density laser beam to locally irradiate the material surface, causing physical or chemical changes and forming a permanent mark. Fiber laser marking machines can be used to precisely control the path, energy, and frequency of the laser beam to etch clear characters, barcodes, or QR codes onto the connector surface. Ultraviolet laser marking machines can also be used, utilizing their cold-working characteristics to finely mark heat-sensitive sheath materials, effectively avoiding material damage caused by thermal effects. The marking location is the connector sheath surface. The connector sheath surface refers to the outer insulating protective layer of the connector, usually made of polymer materials. The sheath surface is chosen as the marking location because it typically has sufficient space for marking, and as a non-core functional component, the surface marking will not directly affect the internal electrical connections or mechanical locking functions of the connector. A dedicated marking area can be reserved on the flat side or top of the sheath to ensure the marking is clearly visible and not obstructed by other components during assembly or use. Marking can also be done in specific grooves or raised areas of the sheath for further protection. The marking process does not alter the original structure or performance parameters of the connector. This means that during laser marking, key parameters such as the connector's geometry, material physicochemical properties, electrical performance, and mechanical performance remain within the design tolerances. This is achieved by precisely setting process parameters such as laser power, pulse width, and scanning speed to ensure that laser energy acts only on the very shallow surface of the sheath material, without causing deep ablation, melting, or structural deformation. Simultaneously, selecting a laser wavelength that matches the sheath material minimizes the heat-affected zone, thus ensuring zero impact on the overall performance of the connector during marking. The cloud database uses encrypted storage. A cloud database refers to a database service deployed on a remote server cluster, providing data storage and access functions via the network. Encrypted storage means that data is encrypted before being written to the cloud database or while stored in the database to prevent unauthorized access, theft, or tampering. All traceability data can be encrypted using industry-standard symmetric encryption algorithms, with an independent key management system responsible for key generation, storage, and rotation. Alternatively, transparent data encryption functionality provided by database service providers can be utilized to automatically encrypt and decrypt data at the storage layer, transparent to the application layer. Unique querying and retrieval of traceability data is supported through traceability codes. This allows the system to accurately and efficiently retrieve all associated production, quality, and usage stage data based on the unique traceability code of each connector, and provide it to authorized users or systems for access.A unique index can be created for the traceability code field in the cloud database to ensure the uniqueness of each traceability code and optimize query performance. At the same time, a secure API interface can be developed to allow authorized external systems to call and obtain the corresponding traceability data by passing the traceability code as a parameter, and to perform strict authentication and access control.

[0067] Through the above technical solution, this application effectively solves the problems that traditional marking methods may damage connector performance and database storage security. Laser marking, as a non-contact marking technology, ensures the formation of a clear and permanent traceability code on the connector sheath surface without introducing any physical damage or altering its original structure and performance parameters, thus maintaining the long-term reliability of the connector. Furthermore, storing traceability data in a cloud database using encrypted storage greatly enhances data security and confidentiality, effectively preventing the risk of data leakage or tampering. Combined with the function of unique query and retrieval through the traceability code, this application ensures that information about each connector throughout its entire lifecycle from production to use can be accurately and securely traced, providing a reliable data foundation for subsequent quality analysis, fault diagnosis, and maintenance, thereby improving the integrity and reliability of the entire automotive wiring harness connector crimping quality traceability and proactive failure prevention method.

[0068] In some of the embodiments described above in this application, a method for presetting a critical vibration frequency is proposed to actively suppress fretting corrosion during vehicle operation. However, in its implementation, the method of presetting the critical vibration frequency does not take into account the differences in vibration characteristics at different installation locations, resulting in inaccurate setting of the critical value and inability to effectively adapt to connectors at different locations, thereby affecting the suppression effect.

[0069] In response, this application further proposes a preset method for the critical vibration frequency in S3: based on the classification of the connector installation location, data on the degree of fretting corrosion of the connector at different vibration frequencies are collected through bench vibration tests, and the vibration frequency corresponding to the degree of fretting corrosion reaching the preset deterioration standard is taken as the critical vibration frequency; the connector installation location includes at least three categories: engine compartment, cockpit, and chassis, and different installation locations correspond to different critical vibration frequencies.

[0070] Classification based on connector installation location refers to categorizing connectors according to their physical location within the vehicle. This classification aims to identify differences in the vibration environment of different areas within the vehicle, providing a targeted basis for subsequent critical vibration frequency setting. Besides the three typical locations—engine compartment, cockpit, and chassis—further subdivisions can be made based on vehicle structural characteristics, such as the interior of doors, behind the dashboard, and under seats, as the vibration modes and intensities may differ in each area. Additionally, classification can also be based on the type of system the connector connects to, such as powertrain connectors, braking system connectors, and infotainment system connectors, because different systems may have different sensitivities to vibration or be situated in different environments.

[0071] Collecting fretting corrosion data of connectors at different vibration frequencies through bench vibration testing refers to using controlled experimental methods to simulate the vibration environment of actual vehicle operation and quantify the fretting corrosion of connectors under different vibration conditions. This step aims to obtain objective, quantitative data to establish the relationship between vibration frequency and fretting corrosion degree. Bench vibration testing can use an electric or hydraulic vibration table, simulating actual working conditions by setting different sweep frequency ranges, vibration accelerations, and vibration directions, such as X, Y, and Z triaxial or multi-axis composite vibrations. Fretting corrosion degree data can be collected in various ways, including periodically measuring changes in connector contact resistance, observing the wear morphology of terminal contact surfaces using optical or scanning electron microscopy, and performing chemical composition analysis to detect corrosion products.

[0072] Using the vibration frequency corresponding to the point where fretting corrosion reaches a preset degradation standard as the critical vibration frequency means defining an acceptable upper limit for fretting corrosion and determining the threshold vibration frequency based on this. This step aims to ensure that the setting of the critical vibration frequency has a clear failure criterion, avoiding subjective judgment. The preset degradation standard can be formulated according to industry standards or internal company specifications, such as an increase in contact resistance exceeding a certain percentage, a wear depth on the terminal surface exceeding a certain micrometer value, or the appearance of visible corrosion products covering a certain proportion of the area. The critical vibration frequency can be determined through data fitting, statistical analysis, or a combination of expert experience and experimental data. When the contact resistance first exceeds the preset degradation standard, the vibration frequency at this point is recorded as the critical value.

[0073] The connector installation locations include at least three categories: engine compartment, driver's compartment, and chassis. These categories provide typical examples that clearly define the main areas of difference in vibration environment within the vehicle. These categories can be further refined; for example, the engine compartment can be divided into areas near the engine body and areas near the body structure, because the vibration transmission path and attenuation characteristics differ in different locations.

[0074] Different installation locations correspond to different critical vibration frequencies, emphasizing the customization and regional specificity of critical vibration frequencies. By conducting independent bench vibration tests on each category of installation locations, data on the degree of fretting corrosion were obtained, and their respective critical vibration frequencies were determined independently. This ensures that the critical values ​​accurately reflect the actual vibration environment and the withstand capability of the connectors at each installation location.

[0075] Through the above technical solution, this application can scientifically preset the critical vibration frequency, effectively solve the problem of inaccurate preset methods, and improve the pertinence and reliability of fretting corrosion suppression.

[0076] Based on the classification of connector installation locations, the differences in vibration characteristics of different locations such as the engine compartment, driver's cabin, and chassis are fully considered, making the setting of critical frequencies more targeted and able to accurately match actual working conditions. Simultaneously, data on the degree of fretting corrosion of connectors at different vibration frequencies is collected through bench vibration tests. Using experimental data rather than empirical values ​​makes the setting of critical frequencies more objective and reliable. The vibration frequency corresponding to the degree of fretting corrosion reaching the preset degradation standard is used as the critical vibration frequency, defining a clear degradation threshold and ensuring that the critical value has a quantifiable basis. Different installation locations correspond to different critical vibration frequencies, realizing customized settings and optimizing suppression strategies for each location, thereby improving the overall accuracy and adaptability of active suppression. When the vibration data collected in real time by the on-board sensors exceeds the critical value set for that location, the risk of fretting corrosion can be more accurately judged, allowing timely adjustment of the corresponding circuit load output through the on-board control unit, effectively suppressing the occurrence and development of fretting corrosion, extending the service life of connectors, and ensuring the long-term stable operation of the vehicle's electrical system.

[0077] In some of the solutions mentioned above in this application, it is proposed to suppress fretting corrosion and send risk warning information to actively prevent failure by adjusting the circuit load output. In this process, the specific method of adjusting the load output is not clearly defined, which may lead to improper load adjustment and affect the normal operation of the vehicle's core functions, or insufficient suppression effect. At the same time, the content of the risk warning information lacks standardized definition and may lack key identifiers such as the unique traceability code of the connector and installation location details, resulting in the maintenance response not being able to accurately match the actual working conditions and delaying fault handling.

[0078] In response, this application further proposes a specific method for adjusting the load output of the corresponding circuit in the active suppression of staged micro-motion corrosion in S3: under the premise of not affecting the operation of the vehicle's core functions, the load of the corresponding circuit is temporarily reduced by 10%-20% to reduce the current density at the terminal contact. The risk warning information is sent to the vehicle manufacturer's backend through the vehicle-mounted remote information processing terminal. The warning information includes the traceability code of the connector, the installation location, and real-time vibration data.

[0079] Without affecting the core functions of the vehicle, the onboard control unit monitors various key operating parameters of the vehicle in real time before performing load adjustments. These parameters include engine speed, vehicle speed, brake pressure, steering angle, and airbag status. This ensures that any load adjustment operation will not negatively impact or interfere with the vehicle's core functions such as power, braking, steering, and safety. This can be achieved through a preset priority management strategy, prioritizing the operation of core functions when they conflict with load adjustment requirements, or through redundant design to ensure that even if the load on some circuits is reduced, core functions can still operate normally through backup paths or redundant power supplies.

[0080] The temporary 10%-20% load reduction of the corresponding circuit was determined based on extensive experimental data and simulation analysis. This aims to effectively suppress fretting corrosion while minimizing the impact on non-core functions. The vehicle control unit can achieve this temporary load reduction by sending commands to the target circuit's power management module to adjust the duty cycle of its pulse width modulation signal, or by controlling a variable resistor to precisely regulate the current flowing through the circuit. Besides the 10%-20% range, other optimized ranges such as 5%-15% or 15%-25% can be dynamically adjusted based on different connector types, installation locations, and fretting corrosion risk levels to achieve the best balance between suppression and functional impact. This measure aims to reduce the current density at the terminal contacts, as fretting corrosion is closely related to the current density at the terminal contact surface. Excessive current density accelerates oxidation and wear at the contact points, forming an insulating layer and increasing contact resistance. By reducing the overall circuit load, the current flowing through the connector terminal contact area can be effectively reduced, thereby slowing down the electrochemical reaction and mechanical wear of the contact surface material and suppressing the occurrence and development of fretting corrosion.

[0081] The risk warning information is sent to the vehicle manufacturer's backend via an in-vehicle telematics terminal, which is an intelligent in-vehicle device integrating a global positioning system, mobile communication module, and data acquisition and processing unit. This terminal is responsible for collecting vehicle operating data, diagnostic information, and the risk warning information generated by this method in real time, and transmitting this data encrypted to the vehicle manufacturer's backend server via a wireless communication network. The vehicle manufacturer's backend is a centralized data management and analysis platform based on a cloud computing architecture, capable of receiving, storing, and processing massive amounts of vehicle data, and triggering corresponding maintenance scheduling or service responses according to preset rules.

[0082] The warning message includes the connector's traceability code, installation location, and real-time vibration data. The traceability code is a unique identifier generated for each connector in step S2, containing key information such as product type, process code, crimping time, quality grade, and equipment number. Including this traceability code in the risk warning message allows vehicle manufacturers' back-end systems or maintenance personnel to quickly trace the connector's complete production history and quality data, providing accurate background information for subsequent fault diagnosis and maintenance decisions. The installation location refers to the connector's physical deployment position within the vehicle, such as the engine compartment-left front headlight wiring harness connector, the cockpit-instrument panel power interface, or the chassis-ABS sensor connector. Specifying the installation location in the warning message helps maintenance personnel quickly pinpoint the specific connector causing the problem, avoiding time-consuming troubleshooting in complex wiring harness layouts and improving maintenance efficiency. Real-time vibration data refers to parameters such as vibration frequency, vibration amplitude, and vibration acceleration collected in real-time by onboard sensors at the connector's installation location. These data directly reflect the actual vibration conditions currently experienced by the connector. Including real-time vibration data in the early warning information can help maintenance personnel assess the severity and duration of vibration risks. Combined with the quality level information in the traceability code, it can more comprehensively determine the failure risk of connectors and formulate targeted maintenance strategies.

[0083] Through the above technical solution, this application effectively solves the problems of load adjustment potentially affecting core vehicle functions and the lack of accuracy in early warning information during the active suppression of fretting corrosion. By temporarily reducing the load of the corresponding circuit by 10%-20% without affecting the operation of core vehicle functions, this application ensures that critical systems such as the vehicle's power, braking, and steering can operate stably while suppressing fretting corrosion, avoiding safety hazards caused by improper load adjustment. This precise load adjustment strategy can effectively reduce the current density at terminal contacts, thereby slowing down the occurrence and development of fretting corrosion and extending the service life of connectors. Simultaneously, by sending risk warning information including connector traceability codes, installation locations, and real-time vibration data to the vehicle manufacturer's backend via an onboard remote information processing terminal, this application achieves accurate early warning of potential failure risks. The introduction of traceability codes enables maintenance personnel to quickly trace the production quality history of connectors, rapidly locate fault points by combining installation location information, and assess risk levels based on real-time vibration data. This allows for the development of more targeted and efficient maintenance strategies, avoiding blind troubleshooting and unnecessary waste of resources, greatly improving the timeliness and accuracy of maintenance, and ultimately ensuring the long-term reliable operation of automotive electrical systems.

[0084] In some of the solutions mentioned above in this application, a maintenance assessment model based on traceability data is proposed to classify maintenance levels and formulate maintenance strategies. However, the lack of specific classification rules and strategy details may lead to inaccurate judgment of maintenance timing, unreasonable allocation of maintenance resources, and problems of over-maintenance or under-maintenance.

[0085] In response, this application further proposes the following rules for classifying maintenance levels as described in S4, and the corresponding maintenance strategies: (1) Level 1 maintenance: The connector quality level is A and there is no vibration risk warning information. The maintenance strategy is to clean the impurities on the connector surface and check the locking status. (2) Level 2 maintenance: If the quality level of the connector is B and the number of vibration risk warning messages is 1-3, the maintenance strategy is to re-test the contact resistance of the connector. If the contact resistance meets the preset usage threshold, it can continue to be used; otherwise, the crimping process is re-executed. (3) Level 3 maintenance: If the quality level of the connector is B and the number of vibration risk warning messages is not less than 4, or if the quality level is C, the maintenance strategy is to replace the connector as a whole.

[0086] The maintenance level classification rules and corresponding maintenance strategies refer to the systematic definition of different maintenance levels and their corresponding specific operation plans based on the initial crimping quality level of the connector and the accumulation of vibration risk warning information during use. Its purpose is to provide a scientific, data-driven decision-making framework for the maintenance of automotive wiring harness connectors, enabling precise maintenance. These classification rules can be implemented based on a pre-set logical judgment model, such as through an expert system or decision tree algorithm, mapping the input quality level and the number of warning messages to a specific maintenance level. Alternatively, machine learning models can be used to learn and optimize the maintenance level classification boundaries and strategy recommendations based on a large amount of historical data.

[0087] For Level 1 maintenance: The connector quality level is A and there are no vibration risk warnings. The maintenance strategy is to clean the connector surface to remove impurities and check the locking status. A Level 1 quality level and no vibration risk warnings indicate that the connector had optimal crimping quality during production and no fretting corrosion risk was detected during use. This is typically determined by querying the connector's traceability data from a cloud database and vibration risk warnings from onboard sensors. When this condition is met, the maintenance strategy of cleaning the connector surface and checking the locking status aims to perform basic preventative maintenance. This can be done manually by visually inspecting the connector and using a specialized cleaning agent to remove dust, oil, and other impurities from the connector sheath surface. Simultaneously, check the locking mechanism of the connector for integrity and the secure connection to prevent potential problems caused by external environmental factors or mechanical loosening. Alternatively, for easily accessible connectors, automated cleaning robots can be used for surface cleaning, and the locking status can be checked using an integrated vision system.

[0088] For Level 2 maintenance: If the connector quality grade is B and the vibration risk warning messages have been issued 1-3 times, the maintenance strategy is to re-test the connector contact resistance. If the contact resistance meets the preset usage threshold, continued use is allowed; otherwise, the crimping process is re-executed. The condition that the connector quality grade is B and the vibration risk warning messages have been issued 1-3 times indicates a slight deviation in the crimping process during production and a small number of fretting corrosion risk warnings during use. This is typically determined through statistical analysis of historical warning data using a maintenance assessment model. When this condition is met, the maintenance strategy of re-testing the connector contact resistance is to assess whether the connector's actual electrical performance has been affected. Maintenance personnel can use a high-precision contact resistance tester to measure the terminal contact resistance of the connector and compare it with the preset usage threshold. If the measurement result still meets the threshold, the connector is functioning normally and can continue to be used; conversely, if the contact resistance exceeds the threshold, it indicates that the connector performance has deteriorated and the crimping process needs to be re-executed. This may include removing the original terminals and re-crimping new terminals, or replacing part of the wiring harness. Another approach is to have the vehicle automatically perform online or offline testing of the contact resistance of specific connectors through the on-board diagnostic system while in diagnostic mode.

[0089] For Level 3 maintenance: If the connector quality level is B and the number of vibration risk warning messages is no less than 4, or if the quality level is C, the maintenance strategy is to replace the entire connector. The conditions for this are that the connector quality level is B and the number of vibration risk warning messages is no less than 4, or the quality level is C, indicating that the connector has obvious quality defects during production, or although the initial quality is acceptable, frequent fretting corrosion risk warnings occur 4 or more times during use, indicating that its reliability has seriously declined. This is also determined by a maintenance assessment model that comprehensively judges the quality level and the number of warnings. When this condition is met, the maintenance strategy of replacing the entire connector is to completely eliminate potential fault hazards. Maintenance personnel will remove the connector from the wiring harness according to its model and installation location, and install a brand new, compliant connector to ensure the reliability of the electrical connection. Another approach is that for highly integrated modular connectors, the entire module can be replaced directly.

[0090] Through the aforementioned technical solution, this application can construct refined maintenance level classification rules and corresponding maintenance strategies based on the initial crimping quality level of connectors and vibration risk warning information during the usage phase. This differentiated maintenance scheme allows connectors with a quality level of A and no vibration risk to undergo only basic cleaning and locking checks, avoiding unnecessary over-maintenance and thus saving maintenance costs and resources. For connectors with a quality level of B and a few vibration risk warnings, their performance status is accurately determined by re-testing contact resistance, avoiding blind replacement and ensuring the effectiveness and economy of maintenance. For connectors with a quality level of C or frequent vibration risk warnings, a complete replacement strategy is adopted, fundamentally eliminating high-risk hazards and greatly improving the operational reliability and safety of automotive electrical systems. This data-driven maintenance decision-making mechanism effectively solves the problems of inaccurate maintenance timing judgment, unreasonable resource allocation, and over- or under-maintenance in traditional maintenance, achieving precise and intelligent maintenance strategies.

[0091] In some of the embodiments described above in this application, a data closed loop is proposed to optimize the mapping model and critical frequency. However, the implementation process lacks specific implementation details, such as data entry details and periodic optimization mechanisms. This results in the data closed loop being executed unsystematic, making it impossible to efficiently utilize maintenance data to reverse-optimize process parameters and early warning thresholds, thereby limiting the iterative optimization capability of the technical solution.

[0092] In response, this application further proposes the following implementation process for the data closed loop described in S4: the maintenance level, maintenance operation content, maintenance effect verification data and failure cause analysis data during the maintenance process are entered into the cloud database, and the process parameter-quality index mapping model is modified based on the newly added data at each preset interval, while optimizing the critical vibration frequency threshold for fretting corrosion of connectors in different installation positions.

[0093] Maintenance level is a quantitative assessment of the current state and required level of maintenance of the connector. It can be determined based on the connector's quality level, the frequency of vibration risk warnings, and other operational data. It can be categorized into Level 1 (light maintenance), Level 2 (moderate maintenance), Level 3 (heavy maintenance), or more precisely, through multi-dimensional grading based on fault type and severity. Maintenance operation content refers to the specific repair or maintenance activities performed for different maintenance levels. This may include, but is not limited to, cleaning impurities from the connector surface, checking the locking status, retesting the connector contact resistance, replacing specific components, or replacing the entire connector. Detailed recording of these operations helps in subsequent analysis of maintenance effectiveness and optimization of maintenance strategies. Maintenance effectiveness verification data consists of various indicators used to evaluate whether the maintenance operations have achieved the expected results. After performing cleaning or adjustment operations, the connector's contact resistance, pull-out force, or vibration data in the actual operating environment can be retested to see if it has returned to normal. Verification data can also be obtained through visual inspection, functional testing, etc. Failure cause analysis data involves investigating and recording the root causes of connector failure or performance degradation. This can include Failure Mode and Effects Analysis (FMEA) reports, physical inspection results of failed parts, environmental factor analysis, and correlation analysis with crimping process data and usage history data. It records whether the failure was caused by fretting corrosion, crimping defects, material aging, or external damage. Entering the above maintenance-related data into a cloud database refers to structured storage in a remote server cluster. This can be achieved through automatic uploading via onboard diagnostic systems, manual entry by maintenance personnel via mobile terminals, or batch import via the service station management system interface. Cloud databases typically offer high availability, scalability, and data security, supporting centralized data management and remote access.

[0094] The process parameter-quality indicator mapping model is periodically revised based on newly added data. The preset period refers to the time interval or data volume threshold at which the system automatically triggers parameter revisions. This can be set to monthly or triggered when the amount of newly added maintenance data reaches a certain threshold. This periodic revision ensures the model reflects the latest production and usage data in a timely manner, maintaining its accuracy and effectiveness. Newly added data refers to all maintenance-related data collected since the last model revision, including maintenance level, maintenance operation content, maintenance effect verification data, and failure cause analysis data. This data is combined with the data in the original process parameter-quality indicator mapping model to update the model's parameters. Parameter revision involves adjusting and optimizing the internal parameters of the existing process parameter-quality indicator mapping model based on the newly added data. This can be achieved through various machine learning or statistical methods. For models built based on regression analysis, least squares or gradient descent methods can be used to refit the parameters; for neural network-based models, incremental learning or retraining of some network layers can be performed. The goal of the revision is to enable the model to more accurately predict the quality indicators of connectors under different process parameters.

[0095] This application also optimizes the critical vibration frequency thresholds for fretting corrosion of connectors in different installation locations. Optimization refers to adjusting the preset critical vibration frequency thresholds for fretting corrosion based on actual usage and maintenance data to better reflect actual operating conditions. This can be achieved through statistical analysis of the correlation between historical vibration data and failure events, or by using machine learning algorithms to identify new failure modes and corresponding vibration characteristics. If a connector in a certain installation location is found to frequently experience fretting corrosion even at vibration frequencies below the current critical value, the critical vibration frequency threshold for that location should be appropriately lowered. For connectors in different installation locations, the vibration environment and fretting corrosion susceptibility vary, thus requiring different critical vibration frequency thresholds. The optimization process adjusts the thresholds for these different locations separately to improve the accuracy and specificity of the early warning system. For example, the threshold for the engine compartment may need to be higher than that for the cockpit because its vibration environment is more severe.

[0096] Through the above technical solution, this application effectively solves the problems of unsystematic execution of data closure and low utilization of maintenance data by constructing a complete data closed loop, thereby improving the iterative optimization capability of the technical solution. Maintenance levels, maintenance operation content, maintenance effect verification data, and failure cause analysis data during the maintenance process are comprehensively and structurally entered into the cloud database, ensuring the complete recording and traceability of all key maintenance information, providing a solid data foundation for subsequent analysis and optimization. Based on this, the system can, at preset intervals, correct the parameters of the process parameter-quality index mapping model based on these newly added maintenance data. This means that the model is no longer static, but can dynamically learn and adapt to new situations and problems arising in actual production and use, thereby continuously improving the accuracy of crimping quality prediction. When it is found that connectors produced under a certain combination of process parameters exhibit a higher failure risk in actual use, the model can more accurately identify and avoid these undesirable process parameter combinations through parameter correction, guiding optimization in the production process. Simultaneously, this solution can also optimize the critical vibration frequency threshold for fretting corrosion of connectors in different installation positions based on actual maintenance data. This makes the early warning mechanism for fretting corrosion more accurate and intelligent, dynamically adjusting the warning criteria based on actual failure conditions under different environments, avoiding over-warning or under-warning. If a connector at a certain installation location frequently experiences fretting corrosion under vibration conditions below the original threshold, the system will automatically lower the critical threshold for that location, thereby achieving earlier and more accurate risk warnings and making proactive fretting corrosion suppression more effective during the usage phase. In summary, through comprehensive collection of maintenance data, periodic model correction, and dynamic threshold optimization, this application achieves deep integration and continuous improvement between crimp quality traceability, proactive failure suppression, and maintenance strategies, forming a self-learning and self-optimizing intelligent system that greatly improves the full lifecycle reliability and maintenance efficiency of automotive wiring harness connectors.

[0097] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for tracing the crimping quality and actively suppressing failures of automotive wiring harness connectors, characterized in that, Includes the following steps: S1: Digital modeling and parameter solidification of the crimping process, classifying product types according to the core attributes of the connector, collecting multiple sets of process parameters and corresponding quality index data through process trial production, constructing a process parameter-quality index mapping model, determining the process parameter range and quality judgment threshold for each product type based on the mapping model, and assigning a unique process code to each product type. S2: Full batch digital traceability of crimping quality. The crimping equipment is upgraded with sensor integration to collect crimping process data of each connector in real time. The crimping process data is compared with the optimal process parameter range and quality judgment threshold in the mapping model to automatically determine the quality level of each connector. A unique traceability code containing product type, process code, crimping time, quality level and equipment number is generated for each connector and marked. At the same time, the traceability data is stored in the cloud database to realize full process traceability. S3: Active suppression of fretting corrosion during use. A critical vibration frequency for fretting corrosion is preset based on the vibration characteristics of the connector installation location. Vibration data of the connector installation location is collected in real time by the vehicle-mounted sensor and compared with the critical vibration frequency. When the vibration data exceeds the critical value, the corresponding circuit load output is adjusted by the vehicle-mounted control unit to suppress fretting corrosion, and a risk warning message is sent at the same time. When the vibration data returns to below the critical value, the circuit load is automatically restored to the normal level. S4: Precise maintenance based on traceability data. A maintenance assessment model is constructed by linking the traceability data of the connectors with the vibration risk warning information sent by the vehicle system. Based on the maintenance assessment model, maintenance levels are divided and corresponding maintenance strategies are formulated. After the maintenance operation is performed, the maintenance-related data is entered into the cloud database to complete the data closure and optimize the mapping model and critical vibration frequency threshold.

2. The method for tracing the crimping quality and actively suppressing failure of automotive wiring harness connectors according to claim 1, characterized in that, The core attributes of the connector described in S1 include the terminal material and the cross-sectional area of ​​the wire. The terminal material includes at least one of brass, phosphor bronze, and titanium copper. The process parameters include at least the wire stripping length, crimping pressure, and crimping speed. The quality indicators include at least the contact resistance and pull-out force.

3. The method for tracing the crimping quality and actively suppressing failure of automotive wiring harness connectors according to claim 1, characterized in that, The process trial production described in S1 is implemented as follows: For each product type, multiple sets of different parameter combinations are selected within the preset process parameter range for press-fitting trials. Contact resistance and pull-out force data are detected for each set of trial connectors. Each set of process parameters is associated with the corresponding test data to form a sample dataset. Based on the sample dataset, a process parameter-quality index mapping model is constructed using a data fitting method.

4. The method for tracing the crimping quality and actively suppressing failure of automotive wiring harness connectors according to claim 1, characterized in that, The specific content of the sensor integration transformation of the crimping equipment described in S2 is as follows: a high-definition industrial camera, a pressure sensor, and a displacement sensor are integrated into the automated crimping equipment. The high-definition industrial camera is used to collect image data of the stripping length, the pressure sensor is used to collect crimping pressure curve data, and the displacement sensor is used to collect crimping displacement data. The crimping process data is the collection of the above-mentioned image data, pressure curve data, and displacement data.

5. The method for tracing the crimping quality and actively suppressing failure of automotive wiring harness connectors according to claim 1, characterized in that, The quality grade determination rules in S2 are as follows: if the crimping process data fully conforms to the optimal process parameter range and the quality indicators meet the quality judgment threshold, it is determined to be Grade A; if the crimping process data has a slight deviation but the quality indicators still meet the quality judgment threshold, it is determined to be Grade B; if the crimping process data exceeds the optimal process parameter range or the quality indicators do not meet the quality judgment threshold, it is determined to be Grade C. When storing the traceability data, the traceability data corresponding to Grade C connectors needs to be marked and the rejection process needs to be triggered.

6. The method for tracing the crimping quality and actively suppressing failure of automotive wiring harness connectors according to claim 1, characterized in that, The traceability code described in S2 is marked by laser marking, with the marking location being the surface of the connector sheath, and the marking process does not change the original structure and performance parameters of the connector; the cloud database adopts an encrypted storage method, supporting unique query and retrieval of traceability data through the traceability code.

7. The method for tracing the crimping quality and actively suppressing failure of automotive wiring harness connectors according to claim 1, characterized in that, The preset method for the critical vibration frequency mentioned in S3 is as follows: based on the classification of the connector installation location, the fretting corrosion degree data of the connector at different vibration frequencies are collected through bench vibration test, and the vibration frequency corresponding to the fretting corrosion degree reaching the preset deterioration standard is taken as the critical vibration frequency; the connector installation location includes at least three categories: engine compartment, cockpit, and chassis, and different installation locations correspond to different critical vibration frequencies.

8. The method for tracing the crimping quality and actively suppressing failure of automotive wiring harness connectors according to claim 1, characterized in that, The specific method for adjusting the load output of the corresponding circuit as described in S3 is as follows: under the premise of not affecting the operation of the vehicle's core functions, the load of the corresponding circuit is temporarily reduced by 10%-20% to reduce the current density at the terminal contact point; the risk warning information is sent to the vehicle manufacturer's backend through the vehicle-mounted remote information processing terminal, and the warning information includes the connector's traceability code, installation location, and real-time vibration data.

9. The method for tracing the crimping quality and actively suppressing failure of automotive wiring harness connectors according to claim 1, characterized in that, The maintenance level classification rules and corresponding maintenance strategies described in S4 are as follows: (1) Level 1 maintenance: The connector quality level is A and there is no vibration risk warning information. The maintenance strategy is to clean the impurities on the connector surface and check the locking status. (2) Level 2 maintenance: If the quality level of the connector is B and the number of vibration risk warning messages is 1-3, the maintenance strategy is to re-test the contact resistance of the connector. If the contact resistance meets the preset usage threshold, it can continue to be used; otherwise, the crimping process is re-executed. (3) Level 3 maintenance: If the quality level of the connector is B and the number of vibration risk warning messages is not less than 4, or if the quality level is C, the maintenance strategy is to replace the connector as a whole.

10. The method for tracing the crimping quality and actively suppressing failure of automotive wiring harness connectors according to claim 1, characterized in that, The data closed-loop implementation process described in S4 is as follows: the maintenance level, maintenance operation content, maintenance effect verification data and failure cause analysis data during the maintenance process are entered into the cloud database. At each preset interval, the process parameter-quality index mapping model is modified based on the newly added data, and the critical vibration frequency threshold for fretting corrosion of connectors in different installation positions is optimized.

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