Terminal pressure monitoring method and system

Abstract

The present invention relates to the field of pressure monitoring and quality control technology, and in particular to a terminal pressure monitoring method and system. The method comprises the following steps: obtaining good product pressure data, and generating a pressure curve based on the good product pressure data to obtain good product pressure curve data; during the production process of the terminal machine, collecting the pressure curve data of the crimping process in real time, and performing a curve comparison based on the pressure curve data and the good product pressure curve data to obtain curve difference data; performing anomaly judgment based on the curve difference data to obtain terminal pressure anomaly data; and performing terminal pressure monitoring alarms and shutdown auxiliary operations based on the terminal pressure anomaly data. The present invention not only improves the quality and stability of terminal crimping during the production process, but also greatly reduces failures and downtime during the production process through intelligent anomaly detection and automatic response mechanisms, thereby improving overall production efficiency and production quality.

Description

Technical Field

[0001] The present invention relates to the technical field of pressure monitoring and quality control, and in particular to a terminal pressure monitoring method and system. Background Art

[0002] In modern industrial production, the crimping quality of terminal crimping machines has a crucial impact on the performance and reliability of the final product, especially in sectors with stringent safety and long-term stability requirements, such as automotive, aerospace, and electronics manufacturing. As a critical process widely used in electrical connections, terminal crimping quality not only impacts the product's electrical performance (such as on-resistance and conductive stability) but also directly affects its mechanical properties (such as tensile strength and vibration resistance). Therefore, effective monitoring and precise control of terminal crimping quality are crucial for improving product quality and ensuring product safety. In actual quality inspection processes, traditional methods for controlling terminal crimping quality, such as fixed threshold methods that determine whether a crimp is acceptable by setting a pressure range, are insufficient. This approach ignores the dynamic changes in material properties and process conditions, easily leading to false positives (e.g., misjudging a qualified crimp as abnormal) or false negatives (e.g., overlooking actual anomalies). Summary of the Invention

[0003] In order to solve the above technical problems, the present invention proposes a terminal pressure monitoring method and system to solve at least one of the above technical problems.

[0004] The present application provides a terminal pressure monitoring method, comprising the following steps:

[0005] Step S1: obtaining good product pressure data, and generating a pressure curve based on the good product pressure data to obtain good product pressure curve data;

[0006] Step S2: During the production process of the terminal machine, pressure curve data of the crimping process is collected in real time, and curve comparison is performed based on the pressure curve data and the pressure curve data of good products to obtain curve difference data;

[0007] Step S3: performing abnormality judgment based on the curve difference data to obtain abnormal terminal pressure data;

[0008] Step S4: Perform terminal pressure monitoring alarm and shutdown auxiliary operations based on the abnormal terminal pressure data.

[0009] The terminal machine in the present invention always keeps accurate tracking of the crimping process during the production process to ensure that the pressure change of each crimping meets the preset good product standard. This reduces potential quality fluctuations in the production process and improves production stability. By real-time acquisition and comparison of the crimping pressure curve, any deviation in the crimping pressure can be quickly discovered during the production process. The abnormal judgment of the curve difference data can effectively distinguish normal fluctuations from potential fault signals. For example, if there is an equipment failure or material problem, the system can quickly issue an alarm and trigger a shutdown operation to avoid the continued production of unqualified products. By triggering alarms and shutdown auxiliary operations based on abnormal terminal pressure data, early warnings are provided to the production line to prevent problems from expanding, effectively reducing downtime and defective products in the production process.

[0010] Preferably, step S1 is specifically:

[0011] Step S11: Obtaining good product pressure data;

[0012] Step S12: extracting the crimping system conditions and crimping mode according to the good product pressure data, and obtaining crimping system condition data and crimping mode data, respectively. The crimping system condition data includes geometric dimension data of the terminal and the wiring harness, terminal material property data, crimping die structure data, and force transmission path data. The crimping mode data includes load application mode data, load loading path data, loading rate data, loading direction data, and loading duration data during the crimping process.

[0013] Step S13: Acquire initial state data of good products, and perform initial state modeling based on the initial state data of good products to obtain an initial state model of good products;

[0014] Step S14: performing material constitutive relationship processing according to terminal material properties to obtain material constitutive relationship data;

[0015] Step S15: constructing a mechanical equation based on the contact pressure mode data to obtain contact pressure mechanical equation data;

[0016] Step S16: constructing a contact model according to the contact pressure mode data to obtain a contact pressure contact model;

[0017] Step S17: performing effect modeling based on the material constitutive relationship data, the contact pressure equation data, the contact pressure model, and the good product initial state model to obtain a good product state model;

[0018] Step S18: numerically discretizing the good product state model to obtain a discretized good product state model;

[0019] Step S19: Calculate the pressure distribution of the discretized good product state model to obtain good product pressure distribution data;

[0020] Step S110: performing curve fitting on the good product pressure distribution data to obtain good product pressure curve data.

[0021] The present invention deeply considers all factors that affect pressure changes during the crimping process by extracting the crimping system condition data (such as the geometric dimensions of the terminals and wire harnesses, material properties, etc.) and the crimping method data (such as the load application method, loading path, etc.). By separately processing the material constitutive relationship and the crimping method, the stress-strain performance and contact behavior of the material during the crimping process can be accurately simulated. The present invention enables the generated pressure curve to better reflect the specific conditions in actual production, rather than just standardized hypothetical data, thereby improving the accuracy and predictive ability of the overall model. By constructing mechanical equations and processing contact models, the force transmission and contact behavior between the terminals and wire harnesses during the crimping process are taken into account, thereby enhancing the physical realism of the model. By numerically discretizing and solving the good product state model, the good product pressure distribution data is obtained, and then the accurate good product pressure curve data is obtained by curve fitting.

[0022] Preferably, the contact model is constructed as follows:

[0023] Performing nonlinear elastic contact processing on the contact pressure mode data to obtain nonlinear elastic contact data, and performing nonlinear elastic feature extraction on the nonlinear elastic contact data to obtain nonlinear elastic feature data;

[0024] Performing elastic-plastic contact processing according to terminal material characteristic data and contact pressure method data to obtain elastic-plastic contact data, and performing elastic-plastic feature extraction on the elastic-plastic contact data to obtain elastic-plastic feature data;

[0025] Performing viscous contact processing according to terminal material characteristic data and connection and pressing method data to obtain viscous contact data, and performing viscous feature extraction on the viscous contact data to obtain viscous feature data;

[0026] A preliminary contact model is constructed based on nonlinear elastic characteristic data, elastic-plastic characteristic data and viscous characteristic data to obtain a preliminary contact model;

[0027] The contact behavior of the preliminary contact model is corrected to obtain the contact-pressure contact model.

[0028] In this invention, various contact characteristics (such as the nonlinear properties of the elastic phase, the hardening behavior of the plastic phase, and the damping effect of the viscous phase) are individually extracted and analyzed, significantly improving the model's accuracy. Specifically, the model captures the true stress-strain relationship in the elastic phase, avoiding the oversimplification of traditional linear elastic models. The hardening characteristics of the plastic region are modeled in detail, accurately simulating the material's yield behavior and plastic deformation. The energy loss and damping behavior during viscous contact are analyzed, reflecting the material's response characteristics under dynamic loading conditions. Each type of contact characteristic is constructed based on the terminal material properties and the contact pressure method data, ensuring that the model can dynamically adapt to different material types (such as copper and aluminum alloys) and process conditions (such as loading rate and loading path). By extracting nonlinear elastic characteristic data (such as nonlinear stiffness and maximum strain) and elastoplastic characteristic data (such as yield point and hardening coefficient), key features of the contact process can be clearly captured. Integrating these different characteristic data (nonlinear elasticity, elastoplasticity, and viscosity) into a unified contact model avoids the limitations of a single model and enables more accurate analysis of contact mechanics. Comprehensive modeling can accurately predict the pressure distribution and deformation behavior of terminals and wire harnesses during the crimping process, providing strong support for mechanical property monitoring during the production process.

[0029] Preferably, the nonlinear elastic contact processing is specifically as follows:

[0030] Perform preliminary contact area identification based on the contact pressure mode data to obtain preliminary contact area data;

[0031] Perform a preliminary analysis of nonlinear characteristics based on preliminary contact area data to obtain nonlinear characteristic area data;

[0032] Perform stress-strain curve fitting based on the nonlinear characteristic region data to obtain nonlinear stress-strain curve data;

[0033] The nonlinear contact stiffness is calculated based on the nonlinear stress-strain curve data to obtain the nonlinear contact stiffness data;

[0034] The nonlinear normal force is calculated based on the nonlinear contact stiffness data to obtain the nonlinear normal force curve data;

[0035] The nonlinear stress-strain curve data and the nonlinear normal force curve data are integrated to obtain the nonlinear elastic contact data.

[0036] The nonlinear elastic contact processing in the present invention can accurately simulate the nonlinear behavior of the contact area force changing with deformation during the contact process. Through the gradual analysis of the contact area, stress-strain relationship, contact stiffness and other aspects, the nonlinear characteristics of the material in actual production can be more accurately reflected, rather than relying solely on the linear model. Through accurate contact area data, the stress changes and contact behavior of the material during contact can be more effectively evaluated, errors can be reduced, and the authenticity of the model can be improved. The nonlinear stress-strain curve obtained by fitting can accurately reflect the stress-strain relationship of the terminal material during the contact process. Through nonlinear calculation, over-simplified stiffness calculations and mechanical models are avoided, making the model more realistic, especially in complex crimping processes, the force transmission and material behavior can be more accurately simulated. By integrating the nonlinear stress-strain curve data and the nonlinear normal force curve data, nonlinear elastic contact data is obtained.

[0037] Preferably, the elastic-plastic contact treatment is specifically:

[0038] Extract yield behavior characteristics based on terminal material characteristic data and connection and pressure method data to obtain yield behavior characteristic data;

[0039] Calculating the elastic stiffness characteristics of the yield behavior characteristic data to obtain elastic stiffness characteristic data;

[0040] Fitting the elastic stress-strain curve to the elastic stiffness characteristic data to obtain the elastic stage stress-strain curve data;

[0041] According to the stress-strain curve data of the elastic stage, the plastic region is divided to obtain the plastic region data;

[0042] Generate a hardening curve based on the stress-strain curve data of the elastic stage and the plastic region data to obtain the plastic region hardening curve data;

[0043] The elastic-plastic contact data are obtained by integrating the stress-strain curve data in the elastic stage and the hardening curve data in the plastic region.

[0044] In the present invention, the yield point and elastic stage of the terminal material during the stress process can be accurately identified by extracting and analyzing the yield behavior characteristics. By accurately dividing the elastic stage and the plastic stage, it can be ensured that the model has a higher degree of fit to the behavior of the material, so that the mechanical behavior of the crimping process can be more accurately predicted, and a clear division of the elastic and plastic stages of the material during the stress process is achieved. The generation of the hardening curve is a key step in elastic-plastic contact modeling. It can describe the hardening behavior of the material after entering the plastic stage from the elastic stage, and reflect the stress growth law after the material yields. By integrating the stress-strain curve of the elastic stage and the hardening curve of the plastic region, the elastic-plastic contact data is obtained, thereby providing more refined control parameters for the crimping pressure control, mold design and material selection in the production process.

[0045] Preferably, the plastic region is divided into:

[0046] According to the stress-strain curve data of the elastic stage, the nonlinear elastic region is identified to obtain the nonlinear elastic boundary point data;

[0047] Perform elastic zone endpoint verification on nonlinear elastic boundary point data to obtain elastic zone endpoint data;

[0048] Determine the starting point of the plastic region based on the stress-strain curve data of the elastic stage to obtain the starting point data of the plastic region;

[0049] The elastic-plastic transition interval data is obtained by performing mixed regression calculation based on the elastic zone end point data and the plastic zone start point data.

[0050] Perform strain hardening on the elastic-plastic transition interval data to obtain the elastic-plastic transition optimization data;

[0051] The elastic-plastic transition optimization data is divided into plastic dissipation intervals to obtain plastic dissipation interval data;

[0052] Rate correction is performed based on the plastic dissipation interval data and loading rate data to obtain the plastic zone data.

[0053] In the present invention, by performing nonlinear elastic region identification on the stress-strain curve data of the elastic stage, the transition region between the elastic stage and the plastic stage can be accurately distinguished. By combining the end point of the elastic zone and the starting point of the plastic zone, the hybrid regression calculation method is used to accurately obtain the elastic-plastic transition interval, effectively capturing the subtle changes of the material from the elastic stage to the plastic stage. By strain hardening treatment, the modeling of the elastic-plastic transition interval is optimized, which can better reflect the strain hardening behavior of the material in the plastic stage. Rate correction is performed based on the plastic dissipation interval data and the loading rate data, so that the method can accurately adapt to the changes in loading rate during the production process. Since the loading rate changes greatly during the terminal crimping process, rate correction can significantly improve the adaptability and accuracy of the model, especially under the changing conditions in actual production.

[0054] Preferably, the adhesive contact treatment is specifically:

[0055] A preliminary estimate of the viscosity coefficient is made based on the terminal material characteristic data and the connection and pressure method data to obtain preliminary viscosity parameter data;

[0056] Perform loading rate weighting processing based on preliminary viscosity parameter data and loading rate data to obtain rate-sensitive viscosity parameter data;

[0057] Calculate the time relaxation characteristics based on the rate-sensitive viscosity parameter data to obtain relaxation characteristic data;

[0058] Perform double-path integral calculation on the relaxation characteristic data to obtain the viscous hysteresis loop characteristic data;

[0059] Dynamic contact force is calculated based on relaxation characteristic data and rate-sensitive viscosity parameter data to obtain dynamic viscous damping data;

[0060] The relaxation characteristic data, viscous hysteresis loop characteristic data and dynamic viscous damping data are partitioned and integrated to obtain the viscous contact data.

[0061] In the present invention, a preliminary estimate of the viscosity coefficient is made by combining the terminal material characteristic data and the connection and pressing method data. In the case of different materials and different connection and pressing methods, an effective preliminary parameter estimate can be provided, which helps to improve the calculation accuracy and reduce errors. Through rate weighting processing, the change of loading rate can be corrected, and the influence of loading rate on viscosity behavior is taken into account, so that the model can be adaptively adjusted under different production conditions, especially suitable for situations where the loading rate fluctuates greatly during the crimping process, thereby ensuring the accuracy and stability of the simulation results. By calculating the time relaxation characteristics of rate-sensitive viscosity parameters, the behavior of the material under long-term load or changing rate can be captured, which helps to simulate the stress relaxation phenomenon of the terminal material during the crimping process, especially under high temperature or long-term loading conditions, and can reflect the real process of material performance changing over time, avoiding errors caused by ignoring the relaxation effect. The viscosity hysteresis loop characteristic data is obtained by dual-path integral calculation, which can fully reflect the effects of energy loss, deformation hysteresis, etc. during the viscous contact process. The formation of the hysteresis loop reflects the internal friction and energy conversion of the material during the force-bearing process, thereby accurately simulating the influence of temperature and pressure changes on the material during the crimping process. Based on the rate-sensitive viscosity parameters and relaxation characteristic data, the dynamic contact force is calculated, and the dynamic viscous damping is further derived. Under different loading and rate conditions, the damping effect in the actual contact process is simulated, accurately describing the friction and energy loss between materials.

[0062] Preferably, step S2 is specifically:

[0063] Step S21: During the production process of the terminal machine, real-time collection of pressure curve data of the crimping process;

[0064] Step S22: performing point-by-point difference calculation based on the pressure curve data and the good product pressure curve data to obtain production pressure difference data;

[0065] Step S23: Calculating the coefficient of variation based on the production pressure difference data to obtain production fluctuation characteristic data;

[0066] Step S24: performing weighted adjustment on the preset pressure threshold data according to the production fluctuation characteristic data to obtain first pressure threshold weighted data;

[0067] Step S25: comparing the production pressure difference data according to the first pressure threshold weighted data to obtain first curve difference data;

[0068] Step S26: extracting characteristic points of the pressure curve according to the pressure curve data and the qualified product pressure curve data, and obtaining pressure curve characteristic point data and qualified product pressure curve characteristic point data respectively;

[0069] Step S27: Calculating characteristic differences based on the pressure curve characteristic point data and the good product pressure curve characteristic point data to obtain characteristic difference data;

[0070] Step S28: performing weighted adjustment on the preset pressure threshold data according to the characteristic difference data to obtain second pressure threshold weighted data;

[0071] Step S29: comparing the production pressure difference data according to the second pressure threshold weighted data to obtain second curve difference data;

[0072] Step S210: performing difference stability calculation based on the first curve difference data and the second curve difference data to obtain difference stability data;

[0073] Step S211: performing difference weight calculation based on the difference stability data to obtain difference weight data;

[0074] Step S212: performing weighted fusion on the first curve difference data and the second curve difference data according to the difference weight data to obtain curve difference data.

[0075] The present invention collects the pressure curve data during the crimping process in real time and performs point-by-point difference calculations with the good product pressure curve data, thereby being able to monitor the pressure changes at each crimping point during the production process with high precision. By calculating the coefficient of variation of the production pressure difference data, the pressure fluctuation characteristics during the production process can be effectively analyzed. Based on the pressure fluctuation characteristic data during the production process, the preset pressure threshold is dynamically weighted and adjusted to avoid misjudgment or missed judgment problems caused by fixed thresholds. By extracting key feature points (such as peaks, turning points, etc.) in the pressure curve and performing difference calculations with the good product curve feature points, it is possible to focus on the key parts of the pressure changes, avoid the computational burden brought by the full curve comparison, improve computational efficiency, and increase sensitivity to key differences. By weighted fusion of different difference data (such as the first curve difference and the second curve difference), the deviation caused by a single threshold or feature comparison method can be effectively eliminated.

[0076] Preferably, step S3 is specifically:

[0077] Step S31: performing curve anomaly classification based on the curve difference data to obtain curve anomaly classification data;

[0078] Step S32: extracting frequency features from the curve abnormality classification data to obtain curve abnormality frequency feature data;

[0079] Step S33: performing terminal pressure abnormality event mapping according to the curve abnormality classification data and the curve abnormality frequency characteristic data to obtain terminal pressure abnormality data.

[0080] The present invention classifies anomalies based on curve difference data, distinguishing between different types of anomalies (such as pressure fluctuations, sudden pressure changes, and persistently high or low pressure). This classification identifies the specific anomaly causing the pressure change, helping users quickly pinpoint the problem and avoid simple, generalized false alarms. By extracting the frequency characteristics of curve anomalies (such as the frequency of abnormal fluctuations and the frequency of periodic changes), sensitivity to specific anomaly patterns can be further enhanced. Different types of anomalies may have different frequency characteristics. For example, sustained pressure deviations may have a lower frequency, while short, sudden pressure fluctuations may exhibit a higher frequency. Therefore, frequency feature extraction allows for more precise identification of different types of anomalies, avoiding missed or false alarms caused by simple pressure difference analysis. By extracting and analyzing anomaly frequencies, underlying patterns can be identified, enabling prediction of future pressure anomalies. Mapping terminal pressure anomaly events based on curve anomaly classification data and frequency feature data allows for matching anomalies with actual production processes and equipment status.

[0081] Preferably, the present application further provides a terminal pressure monitoring system for executing the terminal pressure monitoring method described above, the terminal pressure monitoring system comprising:

[0082] A good product pressure curve generation module is used to obtain good product pressure data and generate a pressure curve based on the good product pressure data to obtain good product pressure curve data;

[0083] The curve comparison module is used to collect the pressure curve data of the crimping process in real time during the production process of the terminal machine, and perform curve comparison based on the pressure curve data and the pressure curve data of good products to obtain curve difference data;

[0084] The terminal pressure abnormality judgment module is used to make abnormality judgment based on the curve difference data to obtain the terminal pressure abnormality data;

[0085] The terminal pressure monitoring alarm and shutdown auxiliary operation module is used to perform terminal pressure monitoring alarm and shutdown auxiliary operations based on abnormal terminal pressure data.

[0086] The present invention has the beneficial effect of establishing a highly accurate pressure reference model by acquiring good product pressure data and generating a good product pressure curve. This good product pressure curve not only reflects the pressure variation over time during the normal crimping process, but also details the pressure fluctuations under different operating conditions. By comparing it with the real-time pressure curve data collected during the production process, the differences between the two can be accurately compared and curve difference data can be obtained. In conventional pressure monitoring, anomaly detection is often based on simple threshold comparisons, but this method is susceptible to interference from various factors during the production process, leading to false positives or missed negatives. By introducing point-by-point difference and coefficient of variation calculations, combined with an anomaly detection mechanism, the present invention can dynamically adapt to the pressure fluctuation characteristics during the production process. When pressure fluctuations are large during different production stages or due to environmental changes, the system can automatically adjust the judgment criteria to ensure the stability and accuracy of the monitoring system. Feature point extraction and frequency feature extraction can extract key variation patterns (such as peaks, sudden changes, and fluctuations) in the pressure curve from the overall noise, enhancing sensitivity to key anomaly patterns. Frequency feature extraction further helps distinguish between periodic changes, short-term fluctuations, and long-term trends, enabling the system to more accurately identify different types of anomalies and reduce misjudgments caused by overreliance on a single judgment criterion. Through an early anomaly detection and warning mechanism based on curve difference data, the present invention can respond promptly to terminal pressure deviations. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0088] Figure 1 A flowchart showing the steps of a terminal pressure monitoring method according to an embodiment is shown;

[0089] Figure 2 A flowchart showing the steps of a method for generating a good product pressure curve according to an embodiment is shown;

[0090] Figure 3 A flow chart showing the steps of a curve comparison method according to one embodiment is shown;

[0091] Figure 4 A flowchart of a method for determining abnormal terminal pressure according to an embodiment is shown. DETAILED DESCRIPTION

[0092] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0093] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0094] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0095] See also Figures 1 to 4 , the present application provides a terminal pressure monitoring method, comprising the following steps:

[0096] Step S1: obtaining good product pressure data, and generating a pressure curve based on the good product pressure data to obtain good product pressure curve data;

[0097] Specifically, pressure data is collected during the crimping process of qualified terminals in actual production. A sensor (such as a pressure sensor) records the pressure changes during the contact between the terminal and the crimping machine in real time, obtaining a series of pressure values. Using this data, a pressure curve is generated. Pressure data can be represented as a time-pressure series. ,in Indicates time, Indicates at a point in time The pressure value of the qualified terminal is generated by the pressure sequence of all qualified terminals. Smoothing, denoising and fitting into a standard good product pressure curve .

[0098] Step S2: During the production process of the terminal machine, pressure curve data of the crimping process is collected in real time, and curve comparison is performed based on the pressure curve data and the pressure curve data of good products to obtain curve difference data;

[0099] Specifically, during the production process, the pressure sensor on the terminal machine continues to collect real-time pressure data of the terminal crimping process being produced, generating a real-time pressure curve By calculating the real-time pressure curve and good product pressure curve The difference is calculated by the following formula:

[0100] ;

[0101] The difference data obtained This is the curve comparison result. If the difference is too large, it means there is an abnormality in the terminal crimping process.

[0102] Step S3: performing abnormality judgment based on the curve difference data to obtain abnormal terminal pressure data;

[0103] Specifically, according to the curve difference , define a threshold When the difference at a certain moment is greater than the threshold, it is considered that an abnormality has occurred at that moment. The specific judgment process uses the conditional judgment formula: ;

[0104] If there is a difference exceeding the threshold within a certain time window (for example, 10 consecutive time points), it can be determined that there is an abnormality in the terminal crimping pressure.

[0105] Step S4: Perform terminal pressure monitoring alarm and shutdown auxiliary operations based on the abnormal terminal pressure data.

[0106] Specifically, once abnormal data is detected, an alarm can be triggered through the system, and automatic shutdown or auxiliary operation can be performed according to the set rules. Specifically, the abnormal data is connected to the production line control system to automatically issue an alarm signal or automatically shut down the production line.

[0107] Preferably, step S1 is specifically:

[0108] Step S11: Obtaining good product pressure data;

[0109] Specifically, the pressure sensor in the crimping machine is used to obtain the pressure data of good products during the standard crimping process. The high-precision pressure sensor records the pressure value at each time point during the terminal crimping process. , ,…, Obtain pressure data at all times , ,…, The recorded pressure data sequence constitutes the pressure data set of the good product .

[0110] Step S12: extracting the crimping system conditions and crimping mode according to the good product pressure data, and obtaining crimping system condition data and crimping mode data, respectively. The crimping system condition data includes geometric dimension data of the terminal and the wiring harness, terminal material property data, crimping die structure data, and force transmission path data. The crimping mode data includes load application mode data, load loading path data, loading rate data, loading direction data, and loading duration data during the crimping process.

[0111] Specifically, the specific crimping machine crimping operation parameters are extracted based on the good product pressure data to obtain the crimping system condition data and crimping method data. The crimping system condition data includes information such as the geometric dimensions of the terminal and the wire harness, material properties, the structure of the crimping die, and the force transmission path. The terminal geometric dimension data includes the terminal's external dimensions, thickness, aspect ratio, etc., which are obtained through 3D scanning equipment or numerical modeling tools. For example, if the terminal geometric dimensions are , , Indicates the length, width and thickness of the terminal. The terminal material characteristic data involves the elastic modulus of the material , Poisson's ratio , yield strength Data such as the crimping die's geometry and stiffness are obtained through standard material datasheets or experimental measurements. Crimping die structural data describes the die's geometry and stiffness, extracted through CAD modeling or scanned data. Force transfer path data involves the transfer of pressure from the crimping die to the terminal. Mechanical analysis is used to determine the contact geometry and pressure distribution path between the die and the terminal.

[0112] The crimping method data includes information such as the load application method, path, loading rate, loading direction, and duration during the crimping process. Load application methods include linear or segmented application, and are determined through simulation experiments. Load path data describes the force propagation path during the load application process. Load rate data, provided by the machine control system, is the rate of change of force during the loading process. Load direction data describes the direction of force applied during the crimping process, whether it is forward or oblique. Load duration data, provided through experiments or production records, is the duration of the loading process.

[0113] Step S13: Acquire initial state data of good products, and perform initial state modeling based on the initial state data of good products to obtain an initial state model of good products;

[0114] Specifically, the initial state data of good products includes the geometric dimensions, material properties, and environmental conditions of the terminal in an unstressed state. Modeling is performed using this data, for example, through finite element modeling technology, to obtain the deformation and stress distribution of the terminal in the initial state.

[0115] Step S14: performing material constitutive relationship processing according to terminal material properties to obtain material constitutive relationship data;

[0116] Specifically, the material constitutive relation is a mathematical model that describes the relationship between material stress and strain. For example, if the material follows the elastic constitutive relation, the stress-strain relationship is given by Hooke's law: ,in is stress, is the elastic modulus of the material, For strain.

[0117] Step S15: constructing a mechanical equation based on the contact pressure mode data to obtain contact pressure mechanical equation data;

[0118] Specifically, based on the crimping method data, the mechanical equations for the terminal crimping process are constructed. The contact force transmission process is based on empirical mechanical equations found in the data query, such as static equilibrium and stiffness distribution. The equations for the crimping process are constructed using the mechanical equilibrium equations. The pressure distribution at the contact point is uniform or modeled according to a certain functional distribution form: ,in is the pressure distribution in the contact area, is the spatial coordinate. Alternatively, the contact pressure can be expressed as and displacement To express the relationship between: .

[0119] Step S16: constructing a contact model according to the contact pressure mode data to obtain a contact pressure contact model;

[0120] Specifically, the contact model is constructed based on the physical properties of material contact and deformation. Linear or nonlinear contact mechanics models are used to describe the pressure distribution when two objects come into contact. During the contact process between the terminal and the mold, the contact model is simulated and optimized using the finite element method (FEM), taking into account factors such as friction and elastic deformation.

[0121] Specifically, a contact algorithm (such as penalty function method or Lagrange multiplier method) is used to process the contact behavior between the terminal and the mold to ensure that the model can correctly capture the changes in contact force. Friction model: Use the Coulomb friction model ( ),in is the friction coefficient, is the contact force. The magnitude of the friction force is proportional to the relative sliding velocity of the contact surface. The contact pressure distribution function is used to describe the pressure between the terminal and the mold. The pressure is assumed to follow a linear distribution and be symmetrical at the initial contact stage. The pressure follows a linear distribution, that is, within the contact area, the pressure gradually changes from the contact point to the edge along the contact surface, and the change pattern is linear. It can be expressed as: ,in is the distance from the center on the contact surface The contact pressure at position is the maximum pressure at the center of the contact area, is the center of the contact surface, It is the total length of the contact area, which refers to the width or diameter of the contact area between the terminal and the mold. Apply an external force to simulate the actual working state when the terminal contacts the mold. For example, apply an axial pressure (for example, 500 N) to simulate the assembly force of the terminal. The contact surface of the mold is fixed to simulate that it does not move during the contact process. There is no initial gap between the contact surfaces, that is, the terminal and the mold are in contact under zero load. The key results such as contact pressure distribution, friction force, elastic deformation, etc. are solved by finite element software (such as ANSYS, Abaqus, etc.). Solve the deformation and contact force distribution during the contact process between the terminal and the mold. Analyze the pressure changes in the contact area and update the contact state between the contact surfaces. Based on the contact mechanics model, calculate the friction between the terminal and the mold, and evaluate its influence on the deformation.

[0122] Step S17: performing effect modeling based on the material constitutive relationship data, the contact pressure equation data, the contact pressure model, and the good product initial state model to obtain a good product state model;

[0123] Specifically, the effects modeling is performed by combining the constitutive relationship of the terminal material, the joint stress equations, the contact model, and the initial state data of the good product. Numerical analysis methods (such as finite element analysis) are used to solve the mechanical model, and the stress distribution and deformation of the terminal during the crimping process are determined. A mathematical representation of the good product state model is obtained through some form of stress-strain calculation.

[0124] Step S18: numerically discretizing the good product state model to obtain a discretized good product state model;

[0125] Specifically, the solution requires discretization of the continuous model. This discretization involves meshing space and time to form a finite element mesh. The number of meshes is N, with each mesh node corresponding to a finite element solution. Shape functions are interpolated between these nodes to create a discretized good-quality state model.

[0126] Step S19: Calculate the pressure distribution of the discretized good product state model to obtain good product pressure distribution data;

[0127] Specifically, the pressure distribution data of the terminal during the crimping process is obtained by solving the discretized good product state model. Numerical methods (such as direct solutions or iterative methods) are used to solve the linear or nonlinear equations to obtain the pressure distribution data for each node.

[0128] Step S110: performing curve fitting on the good product pressure distribution data to obtain good product pressure curve data.

[0129] Specifically, the discretized pressure data is subjected to curve fitting, such as polynomial fitting or least squares fitting, by fitting the discrete data points. Fitting is performed to obtain a smooth pressure curve The goal of the fitting process is to minimize the error between the fitting curve and the actual measured data: , get the pressure curve data of good products .

[0130] Preferably, the contact model is constructed as follows:

[0131] Performing nonlinear elastic contact processing on the contact pressure mode data to obtain nonlinear elastic contact data, and performing nonlinear elastic feature extraction on the nonlinear elastic contact data to obtain nonlinear elastic feature data;

[0132] Specifically, nonlinear elastic contact processing mainly considers the contact mechanics between the terminal and the mold. During the contact process, the relationship between pressure and deformation is no longer linear, but has certain nonlinear characteristics. The deformation of the contact area follows the behavior of nonlinear elastic materials and is described using the nonlinear elastic constitutive relationship: ,in is the nonlinear elastic contact data, is the local strain at the contact point, is the strain-dependent elastic modulus, Is a nonlinear index that describes the degree of nonlinearity of the material. It is determined by fitting experimental data or based on the characteristics of the material. and After the nonlinear elastic contact processing, when extracting characteristic data, the relationship between contact pressure and strain is analyzed and the nonlinear characteristics are extracted using curve fitting technology to obtain nonlinear characteristic data, such as the maximum contact pressure. , the strain in the contact area , and the nonlinear exponent of deformation .

[0133] Performing elastic-plastic contact processing according to terminal material characteristic data and contact pressure method data to obtain elastic-plastic contact data, and performing elastic-plastic feature extraction on the elastic-plastic contact data to obtain elastic-plastic feature data;

[0134] Specifically, elastic-plastic contact treatment considers that materials will yield during contact, with local regions undergoing plastic deformation. The stress-strain relationship of the material requires simultaneous calculation of both the elastic and plastic parts. The plastic region is described using the following elastic-plastic constitutive model:

[0135] ;

[0136] ;

[0137] in is the elastic-plastic contact data, is the elastic modulus, For strain, is the yield strain, is the yield stress, is the hardening coefficient. The elastic part follows Hooke's law, while the plastic part calculates the hardening behavior of the material after yielding. The elastic-plastic contact feature extraction process solves the maximum stress value, yield strain and hardening coefficient of the contact area. , features are used to describe the deformation and yield characteristics of the material during the contact process. Through numerical analysis, the stress and strain distribution in the contact area is obtained, and then the elastic-plastic characteristic data is extracted.

[0138] Performing viscous contact processing according to terminal material characteristic data and connection and pressing method data to obtain viscous contact data, and performing viscous feature extraction on the viscous contact data to obtain viscous feature data;

[0139] Specifically, the viscous contact treatment considers the friction and velocity-dependent viscous effects during contact. The mathematical model of viscous contact behavior is related to the relative velocity between the contacting surfaces and is described by the following model:

[0140] ;

[0141] in, is the friction force, is the viscosity coefficient, is the relative velocity of the contact point. For the contact process with non-constant velocity, numerical solutions are performed based on different contact velocities and viscosity coefficients. The viscosity feature extraction process obtains the viscosity coefficient by analyzing the relationship between the friction force and relative velocity in the contact area. The characteristic data can be used to describe the energy loss and dynamic response during the contact process.

[0142] A preliminary contact model is constructed based on nonlinear elastic characteristic data, elastic-plastic characteristic data and viscous characteristic data to obtain a preliminary contact model;

[0143] Specifically, the contribution of each part can be adjusted by the weighting coefficient, and the weight is , , , the total contact force is: , For the initial contact model, is the nonlinear elastic characteristic data, is the elastic-plastic characteristic data, is the viscosity characteristic data.

[0144] The contact behavior of the preliminary contact model is corrected to obtain the contact-pressure contact model.

[0145] Specifically, after the preliminary contact model is constructed, it is corrected to adjust for errors or non-ideal behaviors that exist during the actual contact process. The correction process is achieved by analyzing the geometry of the actual contact area, the stress distribution in the contact area, and the contact force, and adjusting the boundary conditions to more realistically reflect the mechanical behavior during the actual contact process. If there is a strong friction effect in the actual contact, the friction model is modified to make it more consistent with the actual situation. For high strain or large deformation, the material parameters in the nonlinear elastic model (such as elastic modulus, nonlinear index, etc.) are adjusted according to experimental data. After these corrections, the contact pressure contact model is obtained, which can more accurately describe the contact behavior between the terminal and the mold.

[0146] Preferably, the nonlinear elastic contact processing is specifically as follows:

[0147] Perform preliminary contact area identification based on the contact pressure mode data to obtain preliminary contact area data;

[0148] Specifically, preliminary contact area identification is used to determine the area of ​​contact between the terminal and the mold. This process relies on data from the crimping method, including the geometric characteristics of the contact area, the loading direction, and the loading rate. The contact area is determined by the relative position and velocity between the contact surfaces. By simulating the contact surface between the terminal and the mold during the crimping process, the data for preliminary contact area identification can be represented as a region in two-dimensional or three-dimensional space. For example, using the basic theory of contact mechanics, a contact area is defined when the crimping force on the contact surface exceeds a certain threshold.

[0149] Perform a preliminary analysis of nonlinear characteristics based on preliminary contact area data to obtain nonlinear characteristic area data;

[0150] Specifically, based on the preliminary contact area data, a preliminary analysis of nonlinear characteristics is performed. The purpose of the analysis is to determine whether the material in the contact area exhibits obvious nonlinear characteristics. Nonlinear behavior is caused by the stress-strain relationship of the material, especially under large deformation or high stress conditions. The stress distribution in the contact area is analyzed and compared with the elastic modulus of the material. By calculating the strain and the corresponding stress , the nonlinear characteristic data in the region are obtained, marked as ,in Indicates that the point has nonlinear characteristics, and 0 indicates linear characteristics.

[0151] Perform stress-strain curve fitting based on the nonlinear characteristic region data to obtain nonlinear stress-strain curve data;

[0152] Specifically, the stress-strain curve is fitted using data from the nonlinear characteristic region. The relationship between stress and strain in the contact region follows the nonlinear material constitutive relationship (such as hyperbola, power law, etc.). The stress-strain relationship is fitted using experimental data or numerical simulation. The nonlinear stress-strain relationship is represented by the following power law model:

[0153] ;

[0154] in is stress, For strain, is the proportionality constant of the material, is the power law exponent. The goal of fitting is to find the most suitable and The stress-strain curve after fitting is expressed as:

[0155] ;

[0156] is the proportionality constant of the fitted material, and this curve provides the stress response of the material in the contact area under different strains.

[0157] The nonlinear contact stiffness is calculated based on the nonlinear stress-strain curve data to obtain the nonlinear contact stiffness data;

[0158] Specifically, nonlinear contact stiffness refers to the response of the material's deformation within the contact region to the applied force, that is, the contact force required per unit deformation. Based on the stress-strain relationship, the contact stiffness is calculated using the following formula:

[0159] ;

[0160] in is the nonlinear contact stiffness data, is the applied force, which represents the external force acting on the contact interface, usually the normal force perpendicular to the contact surface. is the contact deformation, is stress, For strain, is the contact area, which represents the actual contact area on the contact interface. is the characteristic size of the contact area, which is the effective length or diameter of the contact area, depending on the geometry of the contact surface. By differentiating the stress-strain curve, the nonlinear contact stiffness is obtained. , which represents the change in stiffness of the contact area under different strains. Through numerical solution, the contact stiffness data under different strains are obtained.

[0161] The nonlinear normal force is calculated based on the nonlinear contact stiffness data to obtain the nonlinear normal force curve data;

[0162] Specifically, based on the nonlinear contact stiffness data, the normal force in the contact area can be further calculated. The normal force is the perpendicular force acting on the contact surface and is determined by both the contact stiffness and the contact deformation. During the calculation process, the relationship between the normal force and the contact deformation is nonlinear, meaning that the contact force changes as the deformation of the contact surface increases. By integrating the nonlinear contact stiffness data or performing other numerical processing methods, nonlinear normal force curve data is obtained. These curves reflect how the normal force changes with deformation under different contact states, providing information on the force distribution and changing trends during the contact process.

[0163] The nonlinear stress-strain curve data and the nonlinear normal force curve data are integrated to obtain the nonlinear elastic contact data.

[0164] Specifically, the nonlinear stress-strain curve data is integrated with the nonlinear normal force curve data. The stress-strain curve describes the deformation behavior of the material at different stress levels, while the normal force curve provides the normal force response at the contact interface. By combining these two types of data, a more comprehensive contact model can be obtained, which not only considers the mechanical properties of the material but also the mechanical characteristics of the contact interface. The integration process may involve methods such as data interpolation, smoothing, or numerical optimization to ensure consistency and complementarity between the two types of data. The integrated nonlinear elastic contact data can provide a more accurate reference for contact mechanics analysis.

[0165] Preferably, the elastic-plastic contact treatment is specifically:

[0166] Extract yield behavior characteristics based on terminal material characteristic data and connection and pressure method data to obtain yield behavior characteristic data;

[0167] Specifically, the material's yield behavior characteristics are extracted based on the terminal material's characteristic data and the connection method data. Yield behavior characteristics involve the stress and strain characteristics at the material's yield point (the point at which plastic deformation begins) when subjected to an external force. Key parameters such as stress, strain, and yield strength are extracted from experimental data or theoretical models.

[0168] Calculating the elastic stiffness characteristics of the yield behavior characteristic data to obtain elastic stiffness characteristic data;

[0169] Specifically, the material's elastic stiffness characteristics are calculated based on the yield behavior characteristic data. Elastic stiffness describes a material's ability to resist deformation during the elastic deformation phase and is determined by the material's elastic modulus and geometry. By analyzing the yield behavior characteristic data, the material's stiffness characteristics during the elastic deformation phase are calculated to ensure that the deformation behavior of the material during the elastic phase conforms to actual conditions during the crimping process. For example, the elastic stiffness characteristic refers to the force required for unit deformation during the elastic phase. In the elastic region, the material follows Hooke's law, and the force and displacement are linearly related. The elastic stiffness is calculated using the material's elastic modulus and the geometric characteristics of the contact area.

[0170] Fitting the elastic stress-strain curve to the elastic stiffness characteristic data to obtain the elastic stage stress-strain curve data;

[0171] Specifically, the elastic phase stress-strain curve is fitted based on the elastic stiffness characteristic data. The elastic phase refers to the stress-strain relationship of the material before yielding, which follows Hooke's law and exhibits a linear relationship between stress and strain. For example, the stress-strain curve in the elastic phase follows Hooke's law, meaning that stress and strain have a linear relationship. This linear relationship is then fitted using experimental data. By fitting the elastic stress-strain curve, the stress-strain response of the material within the elastic deformation range is obtained, providing important data support for contact mechanics analysis.

[0172] According to the stress-strain curve data of the elastic stage, the plastic region is divided to obtain the plastic region data;

[0173] Specifically, the plastic region is delineated based on the stress-strain curve data from the elastic phase. The plastic region refers to the stage where the material enters a permanent deformation state after yielding. The material no longer follows Hooke's law but instead exhibits plastic flow characteristics. By analyzing the stress-strain curve, the turning point from elastic to plastic deformation is identified, and the plastic region of the material is delineated. Accurate delineation of the plastic region facilitates the generation of hardening curves and accurate simulation of material behavior.

[0174] Generate a hardening curve based on the stress-strain curve data of the elastic stage and the plastic region data to obtain the plastic region hardening curve data;

[0175] Specifically, after identifying the plastic region, a hardening curve is generated based on the elastic phase stress-strain curve and the plastic phase data. The hardening curve describes the relationship between stress and strain in the material during the plastic phase, reflecting the gradual increase in strength as strain increases after the material yields. The generation of the hardening curve takes into account the material's strain-hardening behavior, ensuring that during the crimping process, the material's strength and stiffness gradually increase as the amount of deformation increases under plastic deformation.

[0176] The elastic-plastic contact data are obtained by integrating the stress-strain curve data in the elastic stage and the hardening curve data in the plastic region.

[0177] Specifically, the elastic-stage stress-strain curve data is integrated with the plastic-region hardening curve data to obtain complete elastic-plastic contact data. This integrated data accurately reflects the material's behavior throughout the deformation process, including the transition from elasticity to plasticity and then to hardening. This integration allows for a comprehensive description of the material's stress-strain behavior during the crimping process, providing critical physical data support for contact mechanics analysis and optimization during the crimping process.

[0178] Preferably, the plastic region is divided into:

[0179] According to the stress-strain curve data of the elastic stage, the nonlinear elastic region is identified to obtain the nonlinear elastic boundary point data;

[0180] Specifically, by analyzing the stress-strain curve data during the elastic phase, the nonlinear behavior of the material during this phase can be identified. During this process, the onset of nonlinear elastic behavior manifests itself as the relationship between stress and strain beginning to deviate from the ideal linear pattern. The key to identifying the nonlinear elastic region is to determine the turning point in the stress-strain curve, marking the transition from a purely elastic phase to the nonlinear elastic region. This identification allows for the acquisition of boundary data for the nonlinear elastic region.

[0181] Specifically, in the elastic phase, stress and strain have a linear relationship. However, in some cases, the elastic response of the material may exhibit slightly nonlinear characteristics. In order to accurately demarcate the plastic region, it is necessary to identify the boundary points of the nonlinear elastic region. The nonlinear elastic region occurs when the stress approaches the yield strength. This point is identified by calculating the rate of change of stress and strain. The point where the rate of change is no longer constant is found as the boundary of the nonlinear elastic region. By setting a threshold (the amplitude of the change in the rate of change of stress and strain), the boundary of the nonlinear elastic region can be determined. For example, when the rate of change of stress suddenly deviates from a constant value, this point is defined as a boundary point. These points form the nonlinear elastic boundary point data.

[0182] Perform elastic zone endpoint verification on nonlinear elastic boundary point data to obtain elastic zone endpoint data;

[0183] Specifically, endpoint verification is performed on the nonlinear elastic boundary points. By examining the evolution of the stress-strain curve within the nonlinear elastic region, the appropriate endpoint of the elastic region is verified. The elastic region endpoint marks the boundary where the material transitions from the elastic to the plastic region. This verification ensures that the endpoint data is accurate and consistent across different materials and loading conditions, thereby ensuring the accuracy of subsequent plastic region delineation.

[0184] Specifically, the end point of the elastic region is the point where the material's stress-strain relationship undergoes a nonlinear change, i.e., the starting point of the material's plastic phase. The end point of the elastic region is confirmed by performing more detailed verification near the nonlinear elastic boundary. Local regression analysis (such as the least squares method) is used to fit the stress-strain data of the elastic phase to find the best fitting line, and residual analysis is then used to determine the end point.

[0185] Determine the starting point of the plastic region based on the stress-strain curve data of the elastic stage to obtain the starting point data of the plastic region;

[0186] Specifically, after determining the end point of the elastic region, the starting point of the plastic region is determined by analyzing the stress-strain curve data from the elastic phase. The starting point of the plastic region begins when the material reaches the yield point, which corresponds to the material's yield stress. This starting point data for the plastic region accurately identifies the critical stress and strain values ​​at which the material enters plastic deformation.

[0187] The elastic-plastic transition interval data is obtained by performing mixed regression calculation based on the elastic zone end point data and the plastic zone start point data.

[0188] Specifically, a hybrid regression method is used to calculate the elastic-plastic transition interval using data from the endpoint of the elastic region and the starting point of the plastic region. For example, regression fitting is performed on the elastic and plastic region data separately to obtain two different stress-strain curves. For the elastic region, a linear fit is used; for the plastic region, a hardening model (such as a linear hardening model or a power-law hardening model) is used. Through hybrid regression (hybrid function), a fusion model is generated for the transition region data. By minimizing the regression error, a mathematical expression for the transition region is obtained. This transition interval represents the transition stage of the material from elastic deformation to plastic deformation. Regression calculations can use mathematical modeling combined with actual material data to obtain the stress and strain characteristics of the material within the transition interval.

[0189] Perform strain hardening on the elastic-plastic transition interval data to obtain the elastic-plastic transition optimization data;

[0190] Specifically, strain hardening is performed on the obtained elastic-plastic transition interval data. The hardening process describes the characteristic of a material's yield stress gradually increasing with increasing strain during plastic deformation. Through hardening, the stress-strain data within the transition interval is optimized to obtain more accurate elastic-plastic transition optimization data. Hardening helps predict a material's deformation capacity and resistance to plastic deformation under large deformation or high load conditions.

[0191] The elastic-plastic transition optimization data is divided into plastic dissipation intervals to obtain plastic dissipation interval data;

[0192] Specifically, the plastic dissipation interval describes the energy dissipated by the material during plastic deformation. This occurs under large plastic deformations, and the energy dissipation is quantified by calculating the area between the stress and strain curves. After strain hardening, the elastic-plastic transition optimization data is divided into plastic dissipation intervals. This plastic dissipation interval reflects the energy loss due to internal friction in the material during the plastic stage. This division of the plastic dissipation interval is achieved through numerical integration (such as the trapezoidal method or Simpson's method), thereby capturing the nonlinear behavior of the material during large deformations and its energy consumption characteristics.

[0193] Rate correction is performed based on the plastic dissipation interval data and loading rate data to obtain the plastic zone data.

[0194] Specifically, rate correction is performed based on the plastic dissipation interval data and loading rate data. Loading rate has a significant impact on the plastic deformation of the material, especially under high-speed loading conditions, where the yield behavior and hardening characteristics of the material differ. For example, faster loading rates increase the material's strength. Rate correction can correct for deviations in the stress-strain relationship caused by changes in loading rate, thereby obtaining plastic zone data.

[0195] Specifically, the rate correction process corrects the effect of loading rate on the material's plastic deformation to obtain more accurate plastic zone data. The effect of loading rate on material behavior is identified through experimental data or theoretical models. The stress-strain relationship is modified using a rate-dependent constitutive model. Rate-corrected plastic zone data is obtained to reflect the material's plastic deformation process at different loading rates.

[0196] Preferably, the adhesive contact treatment is specifically:

[0197] A preliminary estimate of the viscosity coefficient is made based on the terminal material characteristic data and the connection and pressure method data to obtain preliminary viscosity parameter data;

[0198] Specifically, the material viscosity coefficient is initially estimated by obtaining data on the terminal material properties (such as viscosity, elasticity, and plasticity) as well as data on the crimping process, such as the loading rate and loading path. During this estimation process, a preliminary viscosity coefficient is calculated using standard experimental data or theoretical models based on known material properties and common loading conditions. This coefficient reflects the material's resistance to deformation due to viscosity during loading. For example, under low-speed loading, the viscosity coefficient is inversely proportional to the material's temperature, T, or directly proportional to the material's hardness, H.

[0199] Perform loading rate weighting processing based on preliminary viscosity parameter data and loading rate data to obtain rate-sensitive viscosity parameter data;

[0200] Specifically, during the actual crimping process, the loading rate significantly influences the viscous contact effect. Based on the loading rate data during the crimping process, a weighted calculation is performed on the preliminary viscosity coefficient. As the loading rate increases, the material's viscous effect becomes more pronounced. Therefore, a rate-sensitive viscosity parameter is derived by weighting the preliminary viscosity coefficient with the rate sensitivity. This rate-weighted calculation is performed as follows:

[0201] ;

[0202] in is the rate-sensitive viscosity parameter data, At standard rate The initial viscosity coefficient under is the rate sensitivity index, is the actual loading rate. The formula represents the relationship between the viscosity coefficient and the loading rate, A positive value indicates that the viscosity coefficient increases when the loading rate increases.

[0203] Calculate the time relaxation characteristics based on the rate-sensitive viscosity parameter data to obtain relaxation characteristic data;

[0204] Specifically, the characteristics of the viscous contact process are not only related to the loading rate, but also to time. Especially under long-term loading, the viscous effect will lead to the hysteresis effect of the material. The time relaxation characteristic is used to describe the delayed response of the viscous material after loading. Inversely proportional to the material's internal friction coefficient and loading rate.

[0205] ;

[0206] in For time The viscosity coefficient at , i.e. the relaxation characteristic data, is the rate-sensitive viscosity parameter data, is an exponential function, is the time parameter, is the relaxation time constant. Determined experimentally or estimated based on the viscosity and microstructure of the material.

[0207] Perform double-path integral calculation on the relaxation characteristic data to obtain the viscous hysteresis loop characteristic data;

[0208] Specifically, during the actual crimping process, hysteresis occurs during loading and unloading. To quantitatively describe this phenomenon, a dual-path integral method is used to calculate the viscous hysteresis loop characteristics. This process analyzes the stress-strain differences during loading and unloading to determine the shape and size of the hysteresis loop. By simulating the viscous hysteresis loop, the contact mechanical behavior of the terminal during repeated loading and unloading is evaluated, providing data support for mechanical optimization. The dual-path integral is calculated using the following formula:

[0209] ;

[0210] in is the relaxation characteristic data, for The actual loading rate at the moment, is the total time. During this process, the integration path is divided into a loading path and an unloading path, and the corresponding mechanical work is calculated along each path. By integrating these two paths, the energy loss of the viscous hysteresis loop is obtained, reflecting the actual behavior of the viscous material.

[0211] Dynamic contact force is calculated based on relaxation characteristic data and rate-sensitive viscosity parameter data to obtain dynamic viscous damping data;

[0212] Specifically, the dynamic contact force during the terminal crimping process is calculated using the aforementioned relaxation characteristics and rate-sensitive viscosity parameters. During this process, the change in contact force over time reflects the elastic, plastic, and viscous properties of the material. The dynamic contact force calculation not only considers the deformation of the terminal material but also the influence of the loading rate, providing a theoretical basis for precise control of the contact force. The dynamic contact force is calculated using the following formula:

[0213] ;

[0214] in is the dynamic viscous damping data, which shows the effect of loading rate and material viscosity on contact force. is the rate-sensitive viscosity parameter data, is the instantaneous strain rate corresponding to the relaxation characteristic data.

[0215] The relaxation characteristic data, viscous hysteresis loop characteristic data and dynamic viscous damping data are partitioned and integrated to obtain the viscous contact data.

[0216] Specifically, all calculated viscous characteristic data (including relaxation characteristics, hysteresis loop characteristics, dynamic contact force, etc.) are comprehensively integrated. By partitioning the contact data at different stages and integrating them weightedly, a viscous contact data model is formed. This model comprehensively reflects the viscous behavior of the terminal-wire contact during the crimping process, providing data support for crimping process optimization and quality control.

[0217] Preferably, step S2 is specifically:

[0218] Step S21: During the production process of the terminal machine, real-time collection of pressure curve data of the crimping process;

[0219] Specifically, during the production process of the terminal machine, sensors or mechanical measurement devices collect real-time pressure data during the crimping process. The data uses time as the independent variable and pressure as the dependent variable to generate a real-time pressure curve.

[0220] Step S22: performing point-by-point difference calculation based on the pressure curve data and the good product pressure curve data to obtain production pressure difference data;

[0221] Specifically, the real-time collected production pressure curve data is compared point by point with the good product pressure curve data to calculate the pressure difference.

[0222] Step S23: Calculating the coefficient of variation based on the production pressure difference data to obtain production fluctuation characteristic data;

[0223] Specifically, the coefficient of variation is an indicator that measures the fluctuation range of production pressure. It is defined as the ratio of standard deviation to mean value, reflecting the relative volatility of production pressure data. Its calculation formula is:

[0224] ;

[0225] in To produce fluctuation characteristic data, is the standard deviation of the pressure difference data, It is the average value of the pressure difference data. The larger the coefficient of variation, the greater the pressure fluctuation during the production process and the higher the difficulty of quality control.

[0226] Step S24: performing weighted adjustment on the preset pressure threshold data according to the production fluctuation characteristic data to obtain first pressure threshold weighted data;

[0227] Specifically, the preset pressure threshold is weighted and adjusted according to the calculated production fluctuation characteristics (i.e., coefficient of variation). , The purpose of threshold adjustment is to dynamically adjust the pressure alarm standard according to the actual production fluctuation characteristics, thereby improving the monitoring sensitivity.

[0228] Step S25: comparing the production pressure difference data according to the first pressure threshold weighted data to obtain first curve difference data;

[0229] Specifically, the production pressure difference data is compared using the weighted first pressure threshold to obtain the first curve difference data.

[0230] Step S26: extracting characteristic points of the pressure curve according to the pressure curve data and the qualified product pressure curve data, and obtaining pressure curve characteristic point data and qualified product pressure curve characteristic point data respectively;

[0231] Specifically, key behaviors during the crimping process are further analyzed by extracting characteristic points from the pressure curve. These include the maximum and minimum values ​​of the pressure curve, as well as points where the slope changes. The specific characteristic point extraction method is as follows: Local maxima and minima in the curve are extracted, corresponding to important moments in the crimping process, such as pressure increases and decreases, to obtain characteristic points of the pressure curve. Similarly, local extreme points in the pressure curve of good products are extracted as benchmarks for good products, resulting in characteristic points of the pressure curve for good products.

[0232] Step S27: Calculating characteristic differences based on the pressure curve characteristic point data and the good product pressure curve characteristic point data to obtain characteristic difference data;

[0233] Specifically, a difference calculation is performed between the characteristic point data in the real-time pressure curve and the characteristic point data in the good product pressure curve to obtain characteristic difference data.

[0234] Step S28: performing weighted adjustment on the preset pressure threshold data according to the characteristic difference data to obtain second pressure threshold weighted data;

[0235] Specifically, the preset pressure threshold is weighted and adjusted again according to the characteristic difference data, similar to step S24, but this time the weighting is performed based on the fluctuation of the characteristic difference data.

[0236] Step S29: comparing the production pressure difference data according to the second pressure threshold weighted data to obtain second curve difference data;

[0237] Specifically, the production pressure difference data is compared with the weighted second pressure threshold to obtain the second curve difference data. Similar to the comparison method of the first curve difference data, if the difference exceeds the adjusted threshold, it is abnormal.

[0238] Step S210: performing difference stability calculation based on the first curve difference data and the second curve difference data to obtain difference stability data;

[0239] Specifically, obtain the first curve difference data and the second curve difference data , calculate the standard deviation , ,in is the standard deviation of the difference data of the first curve, is the number of difference data of the first curve, is the order term of the first curve difference data, is the first curve difference data data points, is the average value of the difference data of the first curve, is the standard deviation of the difference data of the second curve, is the number of difference data of the second curve, is the order term of the second curve difference data, is the first difference value in the second curve data points, is the average value of the second curve difference data. Calculate the coefficient of variation of the two curve differences, where for the first curve difference data and the second curve difference data , coefficient of variation as well as Calculated as , Using the coefficient of variation and , calculate the stability of each set of data as well as An adjustment coefficient GO (0.5 or 0.7) is used to control the influence of the coefficient of variation in the stability calculation. The calculation formula is as follows: , .

[0240] Step S211: performing difference weight calculation based on the difference stability data to obtain difference weight data;

[0241] Specifically, the difference weight is calculated based on the difference stability data. The weight is obtained by the reverse weighting method. When the stability is higher, a larger weight is given, indicating that the data is more reliable. Difference weight data: , , is the first curve difference data adjustment item, For the second curve difference data adjustment item. For data with large fluctuations (such as the first curve), If set to a larger value, the data of the first curve will be "flattened" when calculating the weight, reducing its interference with the result. For stable and consistent data (such as the second curve), Set to a smaller value to increase the influence of the second curve in the weight calculation.

[0242] Step S212: performing weighted fusion on the first curve difference data and the second curve difference data according to the difference weight data to obtain curve difference data.

[0243] Specifically, obtain the first curve difference data and the second curve difference data , obtain the difference weight data calculated by the difference stability and Curve difference data , and The weight coefficients for the first and second curves, respectively, are obtained through an inverse weighting method. Curves with larger weights contribute more to the fusion process. If a set of data is less stable (corresponding to a smaller weight), its influence will be smaller; conversely, data with higher stability (larger weight) will dominate the fusion process.

[0244] Preferably, step S3 is specifically:

[0245] Step S31: performing curve anomaly classification based on the curve difference data to obtain curve anomaly classification data;

[0246] Specifically, based on the curve difference data, the pressure difference in the production process is classified as abnormal through clustering calculation, vector machine algorithm or threshold judgment. The goal of abnormal classification is to identify unqualified data points in the production process, that is, to determine whether the difference between the production pressure curve and the good pressure curve belongs to the normal fluctuation range or abnormal fluctuation. The size and change trend of the pressure difference are used to determine whether it is abnormal. A preliminary threshold range is set, and points with pressure differences greater than the threshold are classified as abnormal types. If the difference exceeds the set threshold, the pressure state at the current time point is considered abnormal. For each time point, if an abnormal point is detected, it is classified as an abnormal event. By classifying the pressure difference data of the entire crimping process, a curve anomaly classification data set is obtained. There are multiple types of abnormal classification results, such as: pressure is too high, pressure is too low, pressure fluctuation is abnormal, etc.

[0247] Step S32: extracting frequency features from the curve abnormality classification data to obtain curve abnormality frequency feature data;

[0248] Specifically, after completing curve anomaly classification, the frequency characteristics of abnormal events are further extracted. This is used to assess the frequency and severity of abnormal events and determine whether intervention is necessary. Anomaly classification is performed on the curve difference data within a preset timeframe, and the time points of all abnormal events are recorded. By calculating the frequency of these abnormal events—the number of times an abnormal event occurs per unit time—the abnormal fluctuation characteristics of the production process are assessed.

[0249] Step S33: performing terminal pressure abnormality event mapping according to the curve abnormality classification data and the curve abnormality frequency characteristic data to obtain terminal pressure abnormality data.

[0250] Specifically, once the curve anomaly classification data and frequency characteristic data are obtained, this information is mapped to the terminal pressure abnormality event. According to the frequency, type and severity of the abnormal event, it is evaluated whether the terminal pressure of the entire production process exceeds the reasonable range, and an alarm is triggered or production is stopped. The core idea of ​​the terminal pressure abnormality event mapping is to combine different types of abnormal data with frequency characteristics to generate abnormal evaluation data for terminal pressure. According to the frequency of occurrence of each abnormal type in the curve anomaly classification data , and map it to the severity of the terminal pressure anomaly. For example:

[0251]

[0252] in, It is The frequency of occurrence of abnormal types, Is the weight coefficient associated with this type of anomaly. Weight coefficient It is set according to actual production requirements and experience. Different types of abnormalities have different effects on the severity of pressure abnormalities. Combining these weighted frequency characteristic data, the terminal pressure abnormality data is obtained. This value indicates whether the terminal pressure exceeds the normal range. If it exceeds the set warning value, it means that a serious pressure abnormality has occurred.

[0253] Preferably, the present application further provides a terminal pressure monitoring system for executing the terminal pressure monitoring method described above, the terminal pressure monitoring system comprising:

[0254] A good product pressure curve generation module is used to obtain good product pressure data and generate a pressure curve based on the good product pressure data to obtain good product pressure curve data;

[0255] The curve comparison module is used to collect the pressure curve data of the crimping process in real time during the production process of the terminal machine, and perform curve comparison based on the pressure curve data and the pressure curve data of good products to obtain curve difference data;

[0256] The terminal pressure abnormality judgment module is used to make abnormality judgment based on the curve difference data to obtain the terminal pressure abnormality data;

[0257] The terminal pressure monitoring alarm and shutdown auxiliary operation module is used to perform terminal pressure monitoring alarm and shutdown auxiliary operations based on abnormal terminal pressure data.

[0258] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes that fall within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0259] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A terminal pressure monitoring method, characterized in that: The following steps are involved: Step S1: obtaining good product pressure data, and generating a pressure curve based on the good product pressure data to obtain good product pressure curve data; Step S2: During the production process of the terminal machine, pressure curve data of the crimping process is collected in real time; point-by-point difference calculation is performed based on the pressure curve data and the good product pressure curve data to obtain production pressure difference data; a coefficient of variation is calculated based on the production pressure difference data to obtain production fluctuation characteristic data; a weighted adjustment is performed on the preset pressure threshold data based on the production fluctuation characteristic data to obtain first pressure threshold weighted data; the production pressure difference data is compared based on the first pressure threshold weighted data to obtain first curve difference data; pressure curve feature points are extracted based on the pressure curve data and the good product pressure curve data to obtain pressure curve feature point data and good product pressure curve feature point data respectively; feature difference calculation is performed based on the pressure curve feature point data and the good product pressure curve feature point data to obtain feature difference data; Performing weighted adjustment on the preset pressure threshold data according to the characteristic difference data to obtain second pressure threshold weighted data; comparing the production pressure difference data according to the second pressure threshold weighted data to obtain second curve difference data; Performing a difference stability calculation based on the first curve difference data and the second curve difference data to obtain difference stability data; Calculate the difference weight according to the difference stability data to obtain the difference weight data; Performing weighted fusion on the first curve difference data and the second curve difference data according to the difference weight data to obtain curve difference data; Step S3: performing abnormality judgment based on the curve difference data to obtain abnormal terminal pressure data; Step S4: Perform terminal pressure monitoring alarm and shutdown auxiliary operations based on the abnormal terminal pressure data.

2. The method according to claim 1, characterized in that Step S1 is specifically as follows: Obtaining good product pressure data; Extract the crimping system conditions and crimping method based on the good product pressure data, and obtain the crimping system condition data and crimping method data, respectively. The crimping system condition data includes the geometric dimension data of the terminal and the wiring harness, the terminal material property data, the crimping die structure data, and the force transmission path data. The crimping method data includes the load application method data, the load loading path data, the loading rate data, the loading direction data, and the loading duration data during the crimping process. Obtaining initial state data of good products, and performing initial state modeling based on the initial state data of good products to obtain an initial state model of good products; Process the material constitutive relationship according to the terminal material characteristics to obtain the material constitutive relationship data; According to the connection pressure mode data, the mechanical equation is constructed to obtain the connection pressure mechanical equation data; The contact model is constructed according to the contact pressure mode data to obtain the contact pressure contact model; The good product state model is obtained by effect modeling based on the material constitutive relationship data, the contact pressure equation data, the contact pressure contact model and the good product initial state model; Numerically discretize the good product state model to obtain a discretized good product state model; Calculate the pressure distribution of the discretized good product state model to obtain good product pressure distribution data; Perform curve fitting on the good product pressure distribution data to obtain good product pressure curve data.

3. The method according to claim 2, characterized in that The contact model is constructed as follows: Performing nonlinear elastic contact processing on the contact pressure mode data to obtain nonlinear elastic contact data, and performing nonlinear elastic feature extraction on the nonlinear elastic contact data to obtain nonlinear elastic feature data; Performing elastic-plastic contact processing according to terminal material characteristic data and contact pressure method data to obtain elastic-plastic contact data, and performing elastic-plastic feature extraction on the elastic-plastic contact data to obtain elastic-plastic feature data; Performing viscous contact processing according to terminal material characteristic data and connection and pressing method data to obtain viscous contact data, and performing viscous feature extraction on the viscous contact data to obtain viscous feature data; A preliminary contact model is constructed based on nonlinear elastic characteristic data, elastic-plastic characteristic data and viscous characteristic data to obtain a preliminary contact model; The contact behavior of the preliminary contact model is corrected to obtain the contact-pressure contact model.

4. The method according to claim 3, characterized in that The specific nonlinear elastic contact treatment is as follows: Perform preliminary contact area identification based on the contact pressure mode data to obtain preliminary contact area data; Perform a preliminary analysis of nonlinear characteristics based on preliminary contact area data to obtain nonlinear characteristic area data; Perform stress-strain curve fitting based on the nonlinear characteristic region data to obtain nonlinear stress-strain curve data; The nonlinear contact stiffness is calculated based on the nonlinear stress-strain curve data to obtain the nonlinear contact stiffness data; The nonlinear normal force is calculated based on the nonlinear contact stiffness data to obtain the nonlinear normal force curve data; The nonlinear stress-strain curve data and the nonlinear normal force curve data are integrated to obtain the nonlinear elastic contact data.

5. The method according to claim 3, characterized in that The specific elastic-plastic contact treatment is as follows: Extract yield behavior characteristics based on terminal material characteristic data and connection and pressure method data to obtain yield behavior characteristic data; Calculating the elastic stiffness characteristics of the yield behavior characteristic data to obtain elastic stiffness characteristic data; Fitting the elastic stress-strain curve to the elastic stiffness characteristic data to obtain the elastic stage stress-strain curve data; According to the stress-strain curve data of the elastic stage, the plastic region is divided to obtain the plastic region data; Generate a hardening curve based on the stress-strain curve data of the elastic stage and the plastic region data to obtain the plastic region hardening curve data; The elastic-plastic contact data are obtained by integrating the stress-strain curve data in the elastic stage and the hardening curve data in the plastic region.

6. The method according to claim 5, characterized in that The plastic zone is divided into: According to the stress-strain curve data of the elastic stage, the nonlinear elastic region is identified to obtain the nonlinear elastic boundary point data; Perform elastic zone endpoint verification on nonlinear elastic boundary point data to obtain elastic zone endpoint data; Determine the starting point of the plastic region based on the stress-strain curve data of the elastic stage to obtain the starting point data of the plastic region; The elastic-plastic transition interval data is obtained by performing mixed regression calculation based on the elastic zone end point data and the plastic zone start point data. Perform strain hardening on the elastic-plastic transition interval data to obtain the elastic-plastic transition optimization data; The elastic-plastic transition optimization data is divided into plastic dissipation intervals to obtain plastic dissipation interval data; Rate correction is performed based on the plastic dissipation interval data and loading rate data to obtain the plastic zone data.

7. The method according to claim 3, characterized in that The specific sticky contact treatment is as follows: A preliminary estimate of the viscosity coefficient is made based on the terminal material characteristic data and the connection and pressure method data to obtain preliminary viscosity parameter data; Perform loading rate weighting processing based on preliminary viscosity parameter data and loading rate data to obtain rate-sensitive viscosity parameter data; Calculate the time relaxation characteristics based on the rate-sensitive viscosity parameter data to obtain relaxation characteristic data; Perform double-path integral calculation on the relaxation characteristic data to obtain the viscous hysteresis loop characteristic data; Dynamic contact force is calculated based on relaxation characteristic data and rate-sensitive viscosity parameter data to obtain dynamic viscous damping data; The relaxation characteristic data, viscous hysteresis loop characteristic data and dynamic viscous damping data are partitioned and integrated to obtain the viscous contact data.

8. The method according to claim 1, characterized in that Step S3 is specifically as follows: Perform curve anomaly classification based on curve difference data to obtain curve anomaly classification data; Perform frequency feature extraction on curve abnormality classification data to obtain curve abnormality frequency feature data; Terminal pressure abnormality events are mapped based on curve abnormality classification data and curve abnormality frequency characteristic data to obtain terminal pressure abnormality data.

9. A terminal pressure monitoring system, characterized in that: For executing the terminal pressure monitoring method according to claim 1, the terminal pressure monitoring system comprises: A good product pressure curve generation module is used to obtain good product pressure data and generate a pressure curve based on the good product pressure data to obtain good product pressure curve data; The curve comparison module is used to collect the pressure curve data of the crimping process in real time during the production process of the terminal machine, and perform curve comparison based on the pressure curve data and the pressure curve data of good products to obtain curve difference data; The terminal pressure abnormality judgment module is used to make abnormality judgment based on the curve difference data to obtain the terminal pressure abnormality data; The terminal pressure monitoring alarm and shutdown auxiliary operation module is used to perform terminal pressure monitoring alarm and shutdown auxiliary operations based on abnormal terminal pressure data.

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