Terminal pressure monitoring method and system

Abstract

The invention relates to the technical field of pressure monitoring and quality control, in particular to a terminal pressure monitoring method and system. The method comprises the following steps of: acquiring non-defective product pressure data, and generating a pressure curve according to the non-defective product pressure data to obtain non-defective product pressure curve data; in the production process of the terminal machine, pressure curve data in the crimping process are collected in real time, curve comparison is carried out according to the pressure curve data and the good product pressure curve data, and curve difference value data are obtained; performing abnormality judgment according to the curve difference data to obtain terminal pressure abnormal data; and performing terminal pressure monitoring alarm and shutdown auxiliary operation according to the terminal pressure abnormal data. According to the invention, the terminal crimping quality and stability in the production process are improved, and the fault and downtime in the production process are greatly reduced through an intelligent anomaly detection and automatic response mechanism, so that the overall production efficiency and production quality are improved.

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 processes, the crimping quality of terminal machines has a crucial impact on the performance and reliability of the final product, especially in areas with extremely high requirements for safety and long-term stability, such as automotive manufacturing, aerospace, and electronic product manufacturing. As a key process widely used in electrical connections, the quality of terminal crimping not only affects the electrical properties of the product (such as on-resistance and conductive stability), but also directly affects the mechanical properties of the product (such as tensile strength and vibration resistance). Therefore, effective monitoring and precise control of terminal crimping quality are the core links to improve product quality and ensure product safety. In the actual quality inspection process, traditional terminal crimping quality control methods seem to be inadequate, such as the fixed threshold method that determines whether the crimping is qualified by setting a pressure range. This method ignores the dynamic changes in material properties and process conditions, and is prone to false alarms (such as judging qualified crimping as abnormal) or missed alarms (such as ignoring actual abnormalities). 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: Step S1: acquiring good product pressure data, and generating a pressure curve according to the good product pressure data to obtain good product pressure curve data; Step S2: During the production process of the terminal machine, the pressure curve data of the crimping process is collected in real time, and a curve comparison is performed based on the pressure curve data and the pressure curve data of good products to obtain curve difference data; Step S3: Perform abnormality judgment based on the curve difference data to obtain terminal pressure abnormality data; Step S4: Perform terminal pressure monitoring alarm and shutdown auxiliary operations according to the abnormal terminal pressure data.

[0005] The terminal machine of 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 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.

[0006] Preferably, step S1 specifically includes: Step S11: obtaining good product pressure data; Step S12: extracting the crimping system conditions and the crimping method according to the good product pressure data, and obtaining the crimping system condition data and the crimping method data respectively, wherein the crimping system condition data includes the geometric dimension data of the terminal and the wiring harness, the terminal material characteristic data, the crimping die structure data and the force transmission path data, and the crimping method data includes the load application method data, the load loading path data and the loading rate data, the loading direction data and the loading duration data during the crimping process; Step S13: acquiring initial state data of good products, and performing initial state modeling according to the initial state data of good products to obtain an initial state model of good products; Step S14: performing material constitutive relationship processing according to terminal material characteristics to obtain material constitutive relationship data; Step S15: constructing a mechanical equation according to the connection pressure mode data to obtain connection pressure mechanical equation data; Step S16: constructing a contact model according to the contact pressure mode data to obtain a contact pressure contact model; Step S17: performing effect modeling according to material constitutive relationship data, contact pressure equation data, contact pressure contact model and good product initial state model to obtain a good product state model; Step S18: numerically discretizing the good product state model to obtain a discretized good product state model; Step S19: Calculate the pressure distribution of the discretized good product state model to obtain good product pressure distribution data; Step S110: performing curve fitting on the good product pressure distribution data to obtain good product pressure curve data.

[0007] In the present invention, 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.), all factors that affect the pressure change during the crimping process are deeply considered. By processing the material constitutive relationship and the crimping method respectively, 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 situation in actual production, rather than just standardized hypothetical data, thereby improving the accuracy and predictive ability of the overall model. Through the construction of mechanical equations and the processing of contact models, the transmission of force during the crimping process and the contact behavior between the terminals and wire harnesses are considered, thereby enhancing the physical reality of the model. Through numerical discretization 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 through curve fitting.

[0008] Preferably, the contact model is constructed as follows: Performing nonlinear elastic contact processing according to 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 the terminal material characteristic data and the connection and pressing 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 the terminal material characteristic data and the 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 according to 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.

[0009] In the present invention, various contact characteristics (such as nonlinear characteristics in the elastic stage, hardening behavior in the plastic stage, and damping effect in the viscous stage) are extracted and analyzed separately, so that the accuracy of the model is significantly improved. Specifically, the real stress-strain relationship in the elastic stage is captured, the over-simplification of the traditional linear elastic model is avoided, the hardening characteristics of the plastic region are modeled in detail, the yield behavior and plastic deformation of the material are accurately simulated, the energy loss and damping behavior in the viscous contact process are analyzed, and the response characteristics of the material under dynamic loading conditions are reflected. According to the terminal material characteristics and the connection and pressing method data, various contact characteristics are constructed respectively to ensure that the model can dynamically adapt to different material types (such as copper, aluminum alloy, etc.) and process conditions (such as loading rate, loading path). By extracting nonlinear elastic characteristic data (such as nonlinear stiffness, maximum strain) and elastoplastic characteristic data (such as yield point, hardening coefficient), the key features in the contact process can be clearly captured. Integrating different characteristic data (nonlinear elasticity, elastoplasticity, viscosity) into a unified contact model avoids the limitations of a single model and makes the analysis of contact mechanics more accurate. 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.

[0010] Preferably, the nonlinear elastic contact processing is specifically as follows: Perform preliminary contact area identification based on the contact pressure method 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 according to the nonlinear characteristic region data to obtain nonlinear stress-strain curve data; The nonlinear contact stiffness is calculated according to the nonlinear stress-strain curve data to obtain the nonlinear contact stiffness data; The nonlinear normal force is calculated according to 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.

[0011] 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 the contact process can be more effectively evaluated, the error 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, overly 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.

[0012] Preferably, the elastic-plastic contact treatment is specifically: 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 the 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; A hardening curve is generated according to the stress-strain curve data of the elastic stage and the plastic region data to obtain the hardening curve data of the plastic region; 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.

[0013] 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 elastic stage stress-strain curve and the plastic zone hardening curve, 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.

[0014] Preferably, the plastic region 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 according to the stress-strain curve data of the elastic stage, and 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 transformation 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.

[0015] In the present invention, by performing nonlinear elastic region identification on the stress-strain curve data in 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 elastic-plastic transition interval is accurately obtained using a hybrid regression calculation method, 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 variable conditions in actual production.

[0016] Preferably, the adhesive contact treatment is specifically: The viscosity coefficient is preliminarily estimated based on the terminal material characteristic data and the connection and pressing method data to obtain the 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; The time relaxation characteristics are calculated according to the rate-sensitive viscosity parameter data to obtain the relaxation characteristic data; Perform double-path integral calculation on the relaxation characteristic data to obtain the viscous hysteresis loop characteristic data; The dynamic contact force is calculated based on the relaxation characteristic data and the rate-sensitive viscosity parameter data to obtain the 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.

[0017] In the present invention, by combining the terminal material characteristic data and the connection and pressing method data, the viscosity coefficient is preliminarily estimated. In the case of different materials and different connection and pressing methods, effective preliminary parameter estimation can be provided, which is helpful to improve the calculation accuracy and reduce errors. Through rate weighted processing, the change of loading rate can be corrected, and the influence of loading rate on viscosity behavior is considered, so that the model can be adaptively adjusted under different production conditions, especially suitable for the situation where the loading rate fluctuates greatly during the crimping process, and the accuracy and stability of the simulation results are guaranteed. By calculating the time relaxation characteristics of the rate-sensitive viscosity parameters, the behavior of the material under long-term load or change 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, which can reflect the real process of material performance changing over time, and avoid 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 process, and then accurately simulates the influence of temperature and pressure changes on the material during the crimping process. The dynamic contact force is calculated based on the rate-sensitive viscosity parameters and relaxation characteristic data, 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.

[0018] Preferably, step S2 specifically includes: Step S21: During the production process of the terminal machine, real-time collection of pressure curve data of the crimping process; 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; Step S23: Calculate the coefficient of variation based on the production pressure difference data to obtain production fluctuation characteristic data; Step S24: weighting and adjusting the preset pressure threshold data according to the production fluctuation characteristic data to obtain first pressure threshold weighted data; Step S25: comparing the production pressure difference data according to the first pressure threshold weighted data to obtain first curve difference data; Step S26: extracting characteristic points of the pressure curve according to the pressure curve data and the good product pressure curve data, and obtaining the pressure curve characteristic point data and the good product pressure curve characteristic point data respectively; Step S27: Calculate characteristic difference values ​​based on the characteristic point data of the pressure curve and the characteristic point data of the pressure curve of good products to obtain characteristic difference data; Step S28: weighting and adjusting the preset pressure threshold data according to the characteristic difference data to obtain second pressure threshold weighted data; Step S29: comparing the production pressure difference data according to the second pressure threshold weighted data to obtain second curve difference data; Step S210: performing difference stability calculation according to the first curve difference data and the second curve difference data to obtain difference stability data; Step S211: Calculate the difference weight according to the difference stability data to obtain the difference weight data; 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.

[0019] In the present invention, by real-time acquisition of pressure curve data during the crimping process and point-by-point difference calculation with the good product pressure curve data, the pressure change of each crimping point in the production process can be monitored with high precision. By calculating the coefficient of variation of the production pressure difference data, the fluctuation characteristics of the pressure in the production process can be effectively analyzed. According to the pressure fluctuation characteristic data in 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 peak values, turning points, etc.) in the pressure curve and performing difference calculation with the feature points of the good product curve, the key part of the pressure change can be focused on, avoiding the computational burden brought by the full curve comparison, improving the computational efficiency, and improving the 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.

[0020] Preferably, step S3 specifically includes: Step S31: performing curve anomaly classification according to the curve difference data to obtain curve anomaly classification data; Step S32: extracting frequency features of the curve abnormality classification data to obtain curve abnormality frequency feature data; Step S33: mapping the abnormal terminal pressure event according to the abnormal curve classification data and the abnormal curve frequency characteristic data to obtain abnormal terminal pressure data.

[0021] In the present invention, abnormal classification is performed according to the curve difference data, and different types of abnormalities (such as pressure fluctuations, sudden pressure changes, continuous high or low pressure, etc.) can be distinguished. The classification can identify which specific abnormalities cause the pressure change, help users quickly locate the problem, and avoid simple overall false alarms. By extracting the frequency characteristics of curve abnormalities (such as abnormal fluctuation frequency, periodic change frequency, etc.), the sensitivity to specific abnormal patterns can be further enhanced. Different types of abnormalities may have different frequency characteristics, such as continuous pressure deviations may have lower frequencies, while short-term sudden pressure fluctuations may show higher frequency characteristics. Therefore, through frequency feature extraction, different types of abnormalities can be more accurately identified to avoid missing or false alarms due to simple pressure difference judgment. By extracting and analyzing the abnormal frequency, potential rules can be identified, and then future pressure abnormalities can be predicted. Terminal pressure abnormality events are mapped according to curve abnormality classification data and frequency feature data, and abnormal phenomena can be matched with actual production processes and equipment status.

[0022] Preferably, the present application also provides a terminal pressure monitoring system for executing the terminal pressure monitoring method as described above, the terminal pressure monitoring system comprising: A good product pressure curve generation module is used to obtain good product pressure data, and generate a pressure curve according to 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 compare the curves according to the pressure curve data and the pressure curve data of good products to obtain the 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 according to abnormal terminal pressure data.

[0023] The beneficial effect of the present invention is that a high-precision pressure reference model is established by acquiring good product pressure data and generating a good product pressure curve. This good product pressure curve can not only reflect the pressure change trend over time during normal crimping, but also cover the details of pressure fluctuations under different working conditions. By comparing with the real-time collected pressure curve data in the production process, the difference between the two can be accurately compared and the curve difference data can be obtained. In conventional pressure monitoring, abnormal judgment is usually based on simple threshold comparison, but this method is easily interfered by various factors in the production process, resulting in false alarms or missed alarms. By introducing point-by-point difference calculation and coefficient of variation calculation, combined with the abnormal judgment mechanism, the present invention can dynamically adapt to the pressure fluctuation characteristics in the production process. In different production stages or when the pressure fluctuation is large due to environmental changes, the system can automatically adjust the judgment standard to ensure the stability and accuracy of the monitoring system. Feature point extraction and frequency feature extraction can extract the key change mode (such as peak, mutation, fluctuation, etc.) of the pressure curve from the overall noise, enhancing the sensitivity to key abnormal modes. The extraction of frequency features further helps to identify the differences between periodic changes, short-term fluctuations and long-term trend changes, so that the system can more accurately identify different types of anomalies and reduce misjudgments caused by over-reliance on a single judgment standard. Through the early anomaly detection and warning mechanism based on curve difference data, the present invention can respond in a timely manner when terminal pressure deviation occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings: Figure 1 A flowchart of a terminal pressure monitoring method according to an embodiment is shown; Figure 2 A flowchart showing a method for generating a good product pressure curve according to an embodiment of the present invention is shown; Figure 3 A flow chart showing the steps of a curve comparison method according to an embodiment is shown; Figure 4 A flowchart of the steps of a method for determining abnormal terminal pressure according to an embodiment is shown. DETAILED DESCRIPTION

[0025] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] See also Figures 1 to 4 , the present application provides a terminal pressure monitoring method, comprising the following steps: Step S1: acquiring good product pressure data, and generating a pressure curve according to the good product pressure data to obtain good product pressure curve data; Specifically, the pressure data is collected through the crimping process of qualified terminals in actual production. The pressure changes during the contact between the terminal and the terminal machine are recorded in real time by sensors (such as pressure sensors) to obtain a series of pressure values. The pressure curve is generated using these data. The pressure data can be expressed as a time-pressure series ,in Indicates time, Indicates at a point in time The pressure value of the qualified terminal. Smoothing, denoising and fitting into a standard good product pressure curve .

[0029] Step S2: During the production process of the terminal machine, the pressure curve data of the crimping process is collected in real time, and a curve comparison is performed based on the pressure curve data and the pressure curve data of good products to obtain curve difference data; 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 Pressure curve of good products The difference is calculated by the following formula: ; The difference data obtained This is the curve comparison result. If the difference is too large, it means that there is an abnormality in the terminal crimping process.

[0030] Step S3: Perform abnormality judgment based on the curve difference data to obtain terminal pressure abnormality data; 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: ; If there is a difference exceeding the threshold value within a certain time window (for example, 10 consecutive time points), it can be determined that the terminal crimping pressure is abnormal.

[0031] Step S4: Perform terminal pressure monitoring alarm and shutdown auxiliary operations according to the abnormal terminal pressure data.

[0032] 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, and an alarm signal is automatically issued, or the machine is automatically shut down.

[0033] Preferably, step S1 specifically includes: Step S11: obtaining good product pressure data; 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. , ,…, Get pressure data at all times , ,…, The recorded pressure data sequence constitutes the pressure data set of good products .

[0034] Step S12: extracting the crimping system conditions and the crimping method according to the good product pressure data, and obtaining the crimping system condition data and the crimping method data respectively, wherein the crimping system condition data includes the geometric dimension data of the terminal and the wiring harness, the terminal material characteristic data, the crimping die structure data and the force transmission path data, and the crimping method data includes the load application method data, the load loading path data and the loading rate data, the loading direction data and the loading duration data during the crimping process; 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 Etc., obtained through standard material data sheets or experimental measurements. The crimping die structure data describes the geometry, stiffness, etc. of the die, and is extracted through CAD modeling or scanning data. The force transmission path data involves the transmission process of pressure from the crimping die to the terminal, and the geometry and pressure distribution path of the contact between the die and the terminal are determined through mechanical analysis.

[0035] The crimping method data includes information such as the load application method, path, loading rate, loading direction and duration during the crimping process: the load application method includes linear application or segmented application, etc., which is obtained through simulation experiments. The load loading path data describes the propagation path of the force during the load application process. The loading rate data is provided by the machine control system, that is, the rate of change of the force during the loading process. The loading direction data describes the direction of the force during the crimping process, whether it is forward or oblique. The loading duration data is provided through experiments or production records, that is, the duration of the loading process.

[0036] Step S13: acquiring initial state data of good products, and performing initial state modeling according to the initial state data of good products to obtain an initial state model of good products; Specifically, the initial state data of good products include geometric dimensions, material properties, and environmental conditions of the terminal in an unstressed state, etc. These data are used for modeling, such as by finite element modeling technology, to obtain the deformation and stress distribution of the terminal in the initial state.

[0037] Step S14: performing material constitutive relationship processing according to terminal material characteristics to obtain material constitutive relationship data; Specifically, the material constitutive relation is a mathematical model that describes the relationship between material stress and strain. If the material follows the elastic constitutive relation, based on Hooke's law, the stress-strain relationship is given as: ,in is stress, is the elastic modulus of the material, For strain.

[0038] Step S15: constructing a mechanical equation according to the connection pressure mode data to obtain connection pressure mechanical equation data; Specifically, the mechanical equations of the terminal crimping process are constructed based on the crimping method data. The contact force transmission process is based on the mechanical equations based on experience in the data query, such as static balance, stiffness distribution, etc. The equations of the crimping process are constructed through the mechanical balance equation, and 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: .

[0039] Step S16: constructing a contact model according to the contact pressure mode data to obtain a contact pressure contact model; Specifically, the construction of the contact model is based on the physical properties of material contact and deformation. A linear or nonlinear contact mechanics model is used to describe the pressure distribution when two objects are in 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.

[0040] 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 change of contact force. Friction model: 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 speed of the contact surface. The contact pressure distribution function is used to describe the pressure between the terminal and the mold. The pressure is set to follow a linear distribution and is symmetrical at the initial contact stage. The pressure follows a linear distribution, that is, in the contact area, the pressure gradually changes from the contact point to the edge along the contact surface, and the change law 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 contact surface center, 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 (e.g., 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, and elastic deformation are obtained by solving with 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.

[0041] Step S17: performing effect modeling according to material constitutive relationship data, contact pressure equation data, contact pressure contact model and good product initial state model to obtain a good product state model; Specifically, the constitutive relationship of the terminal material, the pressure equation of the crimping, the contact model and the initial state data of the good product are combined to perform effect modeling. The mechanical model is solved by numerical analysis methods (such as finite element analysis) to obtain the stress distribution and deformation of the terminal during the crimping process. Through some form of stress-strain calculation, the mathematical representation of the good product state model is obtained.

[0042] Step S18: numerically discretizing the good product state model to obtain a discretized good product state model; Specifically, in the solution process, the continuous model needs to be discretized. The discretization method is to divide the space and time into grids to form a finite element grid. The number of grids is N, and each grid node corresponds to a finite element solution. The shape function between the nodes is interpolated to obtain the discretized good product state model.

[0043] Step S19: Calculate the pressure distribution of the discretized good product state model to obtain good product pressure distribution data; Specifically, the pressure distribution data of the terminal during the crimping process is obtained by solving the discretized good product state model. The pressure distribution data of each node is obtained by solving the linear or nonlinear equations using numerical methods (such as direct solution or iterative method).

[0044] Step S110: performing curve fitting on the good product pressure distribution data to obtain good product pressure curve data.

[0045] 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 .

[0046] Preferably, the contact model is constructed as follows: Performing nonlinear elastic contact processing according to 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; Specifically, nonlinear elastic contact processing mainly considers the contact mechanics relationship between the terminal and the mold. During the contact process, the relationship between pressure and deformation is no longer a linear relationship, but has certain nonlinear characteristics. The deformation of the contact area follows the behavior of nonlinear elastic materials and is described using a 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 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 .

[0047] Performing elastic-plastic contact processing according to the terminal material characteristic data and the connection and pressing 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; Specifically, the elastic-plastic contact treatment considers that the material will yield during the contact process, and the local area will enter plastic deformation. The stress-strain relationship of the material needs to calculate both the elastic and plastic parts. The plastic region is described by using the following elastic-plastic constitutive model: ; ; 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. The characteristics 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.

[0048] Performing viscous contact processing according to the terminal material characteristic data and the 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; Specifically, the viscous contact treatment considers the friction and velocity-dependent viscous effects during the contact process. The mathematical model of the viscous contact behavior is related to the relative velocity between the contacting surfaces and is described by the following model: ; in, is the friction force, is the viscosity coefficient, is the relative speed of the contact point. For the contact process with non-constant speed, numerical solutions are performed according to different contact speeds and viscosity coefficients. The viscosity feature extraction process obtains the viscosity coefficient by analyzing the relationship between the friction force and the relative speed in the contact area. The characteristic data can be used to describe the energy loss and dynamic response during the contact process.

[0049] A preliminary contact model is constructed according to nonlinear elastic characteristic data, elastic-plastic characteristic data and viscous characteristic data to obtain a preliminary contact model; Specifically, the contribution of each part can be adjusted by the weighting coefficient, and the weight is , , , then the total contact force is: , For initial contact with the model, is the nonlinear elastic characteristic data, is the elastoplastic characteristic data, It is the viscosity characteristic data.

[0050] The contact behavior of the preliminary contact model is corrected to obtain the contact-pressure contact model.

[0051] Specifically, after the preliminary contact model is constructed, it is corrected to adjust for errors or non-ideal behaviors in 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 in the actual contact process. If there is a strong friction effect in the actual contact, the friction model is modified to make it more in line with the actual situation. For high strain or large deformation, the material parameters (such as elastic modulus, nonlinear index, etc.) in the nonlinear elastic model are adjusted according to experimental data. After these corrections, the crimping contact model is obtained, which can more accurately describe the contact behavior between the terminal and the mold.

[0052] Preferably, the nonlinear elastic contact processing is specifically as follows: Perform preliminary contact area identification based on the contact pressure method data to obtain preliminary contact area data; Specifically, the identification of the preliminary contact area is to determine the area where the terminal contacts the mold, which depends on the data of the crimping method, including the geometric characteristics of the contact area, the loading direction, the loading rate, etc. The contact area is determined by the relative position and relative speed between the contact surfaces. By simulating the contact surface between the terminal and the mold during the crimping process, the data for the preliminary identification of the contact area can be expressed as an area in a two-dimensional or three-dimensional space. For example, using the basic theory of contact mechanics, when the crimping force on the contact surface is greater than a certain threshold, it is defined as a contact area.

[0053] Perform a preliminary analysis of nonlinear characteristics based on preliminary contact area data to obtain nonlinear characteristic area data; 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 significant nonlinear characteristics. Nonlinear behavior is caused by the stress-strain relationship of the material, especially under large deformations 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 , and the nonlinear characteristic data in the region are obtained, marked as ,in Indicates that the point has nonlinear characteristics, and 0 indicates linear characteristics.

[0054] Perform stress-strain curve fitting according to the nonlinear characteristic region data to obtain nonlinear stress-strain curve data; Specifically, the data of the nonlinear characteristic region is used to fit the stress-strain curve. The relationship between stress and strain in the contact region follows the nonlinear material constitutive relationship (such as hyperbola, power law, etc.), and the stress-strain relationship is fitted through experimental data or numerical simulation. The nonlinear stress-strain relationship is represented by the following power law model: ; in is stress, For strain, is the proportionality constant of the material, is the power law index. The goal of fitting is to find the most suitable and The value is fitted using the least squares method. The fitted stress-strain curve is expressed as: ; This curve provides the stress response of the material in the contact area under different strains, which is the proportionality constant of the fitted material.

[0055] The nonlinear contact stiffness is calculated according to the nonlinear stress-strain curve data to obtain the nonlinear contact stiffness data; Specifically, nonlinear contact stiffness refers to the response of the deformation degree of the material in the contact area to the applied force, that is, the contact force required for unit deformation. According to the stress-strain relationship, the contact stiffness is calculated by the following formula: ; 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 of stiffness of the contact area under different strains. Through numerical solution, the contact stiffness data under different strains are obtained.

[0056] The nonlinear normal force is calculated according to the nonlinear contact stiffness data to obtain the nonlinear normal force curve data; Specifically, based on the nonlinear contact stiffness data, the normal force in the contact area can be further calculated. The normal force is the vertical force acting on the contact surface, which is determined by the contact stiffness and the contact deformation. During the calculation process, the relationship between the normal force and the contact deformation is nonlinear, which means 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, the nonlinear normal force curve data is obtained. These curves reflect how the normal force changes with deformation under different contact states, and can provide the distribution and change trend of the force during the contact process.

[0057] The nonlinear stress-strain curve data and the nonlinear normal force curve data are integrated to obtain the nonlinear elastic contact data.

[0058] 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 vertical 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 fully considers the mechanical properties of the contact interface. The integration process may involve methods such as data interpolation, smoothing or numerical optimization to ensure the 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.

[0059] Preferably, the elastic-plastic contact treatment is specifically: Extract yield behavior characteristics based on terminal material characteristic data and connection and pressure method data to obtain yield behavior characteristic data; Specifically, the yield behavior characteristics of the material are extracted based on the characteristic data of the terminal material and the connection and pressing method data. The yield behavior characteristics involve the stress and strain characteristics of the yield point (i.e., the point where the material begins to undergo plastic deformation) when the material is subjected to external force. Through experimental data or theoretical models, key parameters such as stress, strain, and yield strength of the material in the yield state are extracted.

[0060] Calculating the elastic stiffness characteristics of the yield behavior characteristic data to obtain the elastic stiffness characteristic data; Specifically, the elastic stiffness characteristics of the material are calculated based on the yield behavior characteristic data. Elastic stiffness describes the material's ability to resist deformation during the elastic deformation stage and is determined by the material's elastic modulus and geometric shape. By analyzing the yield behavior characteristic data, the stiffness characteristics of the material during the elastic deformation stage are calculated to ensure that during the crimping process, the deformation behavior of the material during the elastic stage is consistent with the actual situation. For example, the elastic stiffness characteristic refers to the force required for unit deformation during the elastic stage. In the elastic region, the material follows Hooke's law, and the force is linearly related to the displacement. The elastic stiffness is calculated using the material's elastic modulus and the geometric characteristics of the contact area.

[0061] Fitting the elastic stress-strain curve to the elastic stiffness characteristic data to obtain the elastic stage stress-strain curve data; Specifically, the elastic stage stress-strain curve is fitted according to the elastic stiffness characteristic data. The elastic stage refers to the stress-strain relationship of the material before yielding, which follows Hooke's law and is expressed as a linear relationship between stress and strain. For example, the stress-strain curve in the elastic stage follows Hooke's law, that is, stress and strain are linearly related. The linear relationship between stress and strain is fitted through experimental data. By fitting the elastic stress-strain curve, the stress-strain response of the material within the elastic deformation range is obtained, thereby providing important data support for contact mechanics analysis.

[0062] According to the stress-strain curve data of the elastic stage, the plastic region is divided to obtain the plastic region data; Specifically, the plastic region is divided based on the stress-strain curve data in the elastic stage. 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 exhibits plastic flow characteristics. By analyzing the stress-strain curve, the turning point from elastic deformation to plastic deformation is identified, and the plastic region of the material is delineated. Accurate division of the plastic region helps to generate hardening curves and accurately simulate material behavior.

[0063] A hardening curve is generated according to the stress-strain curve data of the elastic stage and the plastic region data to obtain the hardening curve data of the plastic region; Specifically, after the plastic region is identified, a hardening curve is generated based on the stress-strain curve in the elastic stage and the data in the plastic region. The hardening curve describes the relationship between stress and strain of the material in the plastic stage, reflecting the phenomenon that the strength of the material gradually increases as the strain increases after the material yields. The generation of the hardening curve takes into account the strain hardening behavior of the material, ensuring that during the crimping process, the strength and stiffness of the material under plastic deformation gradually increase as the deformation increases.

[0064] 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.

[0065] Specifically, the elastic stage stress-strain curve data is integrated with the plastic region hardening curve data to obtain complete elastic-plastic contact data. The integrated data can accurately reflect the behavior of the material during the entire deformation process, including the transition from elasticity to plasticity and then to hardening. Through this integration, the stress-strain behavior of the material during the crimping process can be fully described, thereby providing key physical data support for contact mechanics analysis and optimization during the crimping process.

[0066] Preferably, the plastic region 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; Specifically, by analyzing the stress-strain curve data in the elastic phase, the nonlinear behavior of the material in the elastic phase is identified. In this process, the emergence of nonlinear elastic behavior is manifested as the relationship between stress and strain begins 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 material from the purely elastic phase into the nonlinear elastic region. Through this identification, the boundary point data of the nonlinear elastic region can be obtained.

[0067] Specifically, in the elastic stage, stress and strain are linearly related. However, in some cases, the elastic response of the material may show 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. Find the point where the rate of change is no longer a constant as the boundary of the nonlinear elastic region. By setting a threshold (the amplitude of 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, the point is defined as a boundary point. These points form the nonlinear elastic boundary point data.

[0068] Perform elastic zone endpoint verification on nonlinear elastic boundary point data to obtain elastic zone endpoint data; Specifically, the endpoint of the nonlinear elastic boundary point is checked. By checking the evolution of the stress-strain curve in the nonlinear elastic region, it is verified whether the end point position of the elastic region is appropriate. The end point of the elastic region marks the boundary where the material changes from the elastic region to the plastic region. Through verification, the endpoint data is ensured to be accurate and consistent under different materials and loading conditions, thereby ensuring the accuracy of the subsequent plastic region division.

[0069] Specifically, the end point of the elastic zone refers to the point where the stress and strain relationship of the material changes nonlinearly, that is, the starting point of the material entering the plastic stage. The end point of the elastic zone is confirmed by performing a more detailed check near the nonlinear elastic boundary. The stress and strain data of the elastic stage are fitted by local regression analysis (such as the least squares method) to find the best fitting line, and then the end point is determined by residual analysis.

[0070] Determine the starting point of the plastic region according to the stress-strain curve data of the elastic stage, and obtain the starting point data of the plastic region; Specifically, after determining the end point of the elastic zone, the starting point of the plastic zone is determined by analyzing the stress-strain curve data of the elastic stage. The starting point of the plastic zone begins when the material reaches the yield point, which corresponds to the yield stress of the material. The starting point data of the plastic zone can accurately identify the critical stress and strain values ​​for the material to enter plastic deformation.

[0071] 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. Specifically, the elastic-plastic transition interval is calculated by using the data of the end point of the elastic zone and the starting point of the plastic zone, and a hybrid regression method is used. For example, the elastic zone and plastic zone data are regressed and fitted respectively to obtain two different stress-strain curves. For the elastic zone, linear fitting is used; for the plastic zone, 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 zone data, and the mathematical expression of the transition zone is obtained by minimizing the regression error. This transition interval represents the transition stage of the material from elastic deformation to plastic deformation. Regression calculation can obtain the stress-strain characteristics of the material in the transition interval through mathematical modeling combined with actual material data.

[0072] Perform strain hardening on the elastic-plastic transition interval data to obtain the elastic-plastic transition optimization data; Specifically, strain hardening is performed on the obtained elastic-plastic transition interval data. The hardening process describes the characteristic that the yield stress of a material gradually increases as the strain increases during the plastic deformation process. Through hardening, the stress-strain data in the transition interval are optimized to obtain more accurate elastic-plastic transition optimization data. Hardening helps predict the deformation capacity of a material and its ability to resist plastic deformation under large deformation or high load conditions.

[0073] The elastic-plastic transformation optimization data is divided into plastic dissipation intervals to obtain plastic dissipation interval data; Specifically, the plastic dissipation interval describes the energy dissipated by the material during the plastic deformation process. It occurs under large plastic deformation, and the energy dissipation is quantified by calculating the area of ​​the stress-strain curve. After completing the strain hardening process, the elastic-plastic transformation optimization data is divided into plastic dissipation intervals. The plastic dissipation interval reflects the energy loss caused by internal friction in the plastic stage. The division of the plastic dissipation interval is achieved through numerical integration (such as the trapezoidal method or the Simpson method), thereby capturing the nonlinear behavior of the material during large deformation and its energy consumption characteristics.

[0074] Rate correction is performed based on the plastic dissipation interval data and loading rate data to obtain the plastic zone data.

[0075] Specifically, rate correction is performed based on the plastic dissipation interval data and loading rate data. The loading rate has a significant effect on the plastic deformation of the material, especially under high-speed loading conditions, the yield behavior and hardening characteristics of the material are different, such as the faster the loading rate, the greater the strength of the material. Through rate correction, the deviation of the stress-strain relationship caused by the change in loading rate can be corrected to obtain the plastic zone data.

[0076] Specifically, the process of rate correction is to obtain more accurate plastic zone data by correcting the effect of loading rate on material plastic deformation. The effect of loading rate on material behavior is identified through experimental data or theoretical models; the stress-strain relationship is corrected using a rate-dependent constitutive model; and the rate-corrected plastic zone data is obtained to reflect the plastic deformation process of the material at different loading rates.

[0077] Preferably, the adhesive contact treatment is specifically: The viscosity coefficient is preliminarily estimated based on the terminal material characteristic data and the connection and pressing method data to obtain the preliminary viscosity parameter data; Specifically, the viscosity coefficient of the material is preliminarily estimated by obtaining the terminal material characteristic data (such as the viscosity, elasticity, and plasticity of the material) and the data of the crimping method such as the loading rate and loading path during the crimping process. During the estimation process, the preliminary viscosity coefficient is calculated using standard experimental data or theoretical models based on the known material properties and common loading conditions. This coefficient reflects the deformation resistance of the material due to the viscosity effect during the loading process. For example, under low-speed loading, the viscosity coefficient is inversely proportional to the temperature T of the material, or directly proportional to the hardness H of the material.

[0078] Perform loading rate weighting processing based on preliminary viscosity parameter data and loading rate data to obtain rate-sensitive viscosity parameter data; Specifically, in the actual crimping process, the loading rate has a significant effect on the viscous contact effect. According to the loading rate data during the crimping process, the preliminary viscosity coefficient is weighted. As the loading rate increases, the viscosity effect of the material becomes more significant. Therefore, by weighting the preliminary viscosity coefficient and the rate sensitivity, the rate-sensitive viscosity parameter is obtained. The rate weighting process is performed in the following way: ; 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, It is a positive value, indicating that the viscosity coefficient increases when the loading rate increases.

[0079] The time relaxation characteristics are calculated according to the rate-sensitive viscosity parameter data to obtain the relaxation characteristic data; 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 cause the hysteresis effect of the material. The time relaxation characteristic is used to describe the delayed response of the viscous material after loading. Relaxation time Inversely proportional to the material's internal friction coefficient and loading rate.

[0080] ; 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 from the viscosity and microstructure of the material.

[0081] Perform double-path integral calculation on the relaxation characteristic data to obtain the viscous hysteresis loop characteristic data; Specifically, in the actual crimping process, hysteresis occurs during loading and unloading. In order to quantitatively describe this phenomenon, the dual-path integral method is used to calculate the viscous hysteresis loop characteristics. This process obtains the shape and size of the hysteresis loop by analyzing the stress-strain difference during loading and unloading. By simulating the viscous hysteresis loop, the contact mechanical behavior of the terminal during repeated loading and unloading is evaluated, thereby providing data support for mechanical optimization. The dual-path integral is calculated using the following formula: ; in is the relaxation characteristic data, for The actual loading rate at the moment, is the total time. In this process, the integral path is divided into a loading path and an unloading path, and the corresponding mechanical work is calculated on each path. By calculating the integral of these two paths, the energy loss of the viscous hysteresis loop is obtained, which reflects the actual behavior of the viscous material.

[0082] The dynamic contact force is calculated based on the relaxation characteristic data and the rate-sensitive viscosity parameter data to obtain the dynamic viscous damping data; Specifically, the dynamic contact force during the terminal crimping process is calculated through the above relaxation characteristics and rate-sensitive viscosity parameters. In this process, the change of contact force over time reflects the elastic, plastic and viscosity characteristics of the material. The dynamic contact force calculation not only takes into account the deformation of the terminal material, but also involves the influence of the loading rate, which provides a theoretical basis for the precise control of the contact force. The dynamic contact force is calculated by the following formula: ; in is the dynamic viscous damping data, which shows the effect of loading rate and material viscosity on the contact force. is the rate-sensitive viscosity parameter data, is the instantaneous strain rate corresponding to the relaxation characteristic data.

[0083] The relaxation characteristic data, viscous hysteresis loop characteristic data and dynamic viscous damping data are partitioned and integrated to obtain the viscous contact data.

[0084] Specifically, all calculated viscosity characteristic data (including relaxation characteristics, hysteresis loop characteristics, dynamic contact force, etc.) are integrated. The contact data at different stages are partitioned and weighted to form a viscosity contact data model. This model can fully reflect the viscosity behavior of the contact between the terminal and the wire harness during the crimping process, thereby providing data support for the optimization and quality control of the crimping process.

[0085] Preferably, step S2 specifically includes: Step S21: During the production process of the terminal machine, real-time collection of pressure curve data of the crimping process; Specifically, during the production process of the terminal machine, the pressure data during the crimping process is collected in real time through sensors or mechanical measuring devices. The data uses time as the independent variable and pressure as the dependent variable to generate a real-time pressure curve.

[0086] 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; Specifically, the production pressure curve data collected in real time is compared point by point with the good product pressure curve data to calculate the pressure difference.

[0087] Step S23: Calculate the coefficient of variation based on the production pressure difference data to obtain production fluctuation characteristic data; Specifically, the coefficient of variation is an indicator that measures the fluctuation range of production pressure. Defined as the ratio of standard deviation to mean value, it reflects the relative volatility of production pressure data. Its calculation formula is: ; 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 in the production process and the higher the difficulty of quality control.

[0088] Step S24: weighting and adjusting the preset pressure threshold data according to the production fluctuation characteristic data to obtain first pressure threshold weighted data; Specifically, the preset pressure threshold is weighted and adjusted according to the calculated production fluctuation characteristics (i.e., coefficient of variation). , is the weight coefficient of production fluctuation characteristics. The purpose of threshold adjustment is to dynamically adjust the standard of pressure alarm according to the fluctuation characteristics of actual production, so as to improve the sensitivity of monitoring.

[0089] Step S25: comparing the production pressure difference data according to the first pressure threshold weighted data to obtain first curve difference data; Specifically, the production pressure difference data is compared using the weighted first pressure threshold to obtain the first curve difference data.

[0090] Step S26: extracting characteristic points of the pressure curve according to the pressure curve data and the good product pressure curve data, and obtaining the pressure curve characteristic point data and the good product pressure curve characteristic point data respectively; Specifically, the key behaviors in the crimping process are further analyzed by extracting characteristic points from the pressure curve. The characteristic points include the maximum value, minimum value, slope change point, etc. of the pressure curve. The specific characteristic point extraction method is as follows: extract the local maximum and minimum values ​​in the curve, corresponding to the important moments in the crimping process, such as pressure rise and fall, to obtain the characteristic points of the pressure curve. Similarly, extract the local extreme points in the good product pressure curve as the benchmark of the good product, and obtain the characteristic points of the good product pressure curve.

[0091] Step S27: Calculate characteristic difference values ​​based on the characteristic point data of the pressure curve and the characteristic point data of the pressure curve of good products to obtain characteristic difference data; Specifically, the characteristic point data in the real-time pressure curve and the characteristic point data in the good product pressure curve are subjected to difference calculation to obtain characteristic difference data.

[0092] Step S28: weighting and adjusting the preset pressure threshold data according to the characteristic difference data to obtain second pressure threshold weighted data; Specifically, the preset pressure threshold is weighted again according to the characteristic difference data, which is similar to step S24, but the weighting is performed based on the fluctuation of the characteristic difference data.

[0093] Step S29: comparing the production pressure difference data according to the second pressure threshold weighted data to obtain second curve difference data; Specifically, the production pressure difference data is compared according to 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.

[0094] Step S210: performing difference stability calculation according to the first curve difference data and the second curve difference data to obtain difference stability data; 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 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: , .

[0095] Step S211: Calculate the difference weight according to the difference stability data to obtain the difference weight data; 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, is the adjustment item for the difference data of the second curve. 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.

[0096] 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.

[0097] 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 are the weight coefficients of the first and second curves respectively, obtained by the reverse weighting method. The curve with a larger weight contributes more in the fusion process. If a set of data has poor stability (corresponding to a smaller weight), the influence of the data will be small; on the contrary, data with higher stability (larger weight) will dominate the fusion.

[0098] Preferably, step S3 specifically includes: Step S31: performing curve anomaly classification according to the curve difference data to obtain curve anomaly classification data; Specifically, according to 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 abnormality classification data set is obtained. There are multiple types of abnormal classification results, such as: too high pressure, too low pressure, abnormal pressure fluctuation, etc.

[0099] Step S32: extracting frequency features of the curve abnormality classification data to obtain curve abnormality frequency feature data; Specifically, after the curve abnormality classification is completed, the frequency characteristics of abnormal events are further extracted to evaluate the frequency and severity of abnormal events, so as to determine whether intervention is needed. The curve difference data is abnormally classified within a preset time, and the time points of all abnormal events are recorded. By calculating the frequency of these abnormal events, that is, the number of abnormal events per unit time, the abnormal fluctuation characteristics in the production process are evaluated.

[0100] Step S33: mapping the abnormal terminal pressure event according to the abnormal curve classification data and the abnormal curve frequency characteristic data to obtain abnormal terminal pressure data.

[0101] Specifically, once the curve abnormality 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 ​​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 abnormality classification data , and map it to the severity of the terminal pressure anomaly. For example:

[0102] in, It is The frequency of occurrence of abnormal types, is the weight coefficient associated with this type of anomaly. 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.

[0103] Preferably, the present application also provides a terminal pressure monitoring system for executing the terminal pressure monitoring method as described above, the terminal pressure monitoring system comprising: A good product pressure curve generation module is used to obtain good product pressure data, and generate a pressure curve according to 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 compare the curves according to the pressure curve data and the pressure curve data of good products to obtain the 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 according to abnormal terminal pressure data.

[0104] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and 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 falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0105] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A terminal pressure monitoring method, characterized in that: The following steps are involved: Step S1: acquiring good product pressure data, and generating a pressure curve according to the good product pressure data to obtain good product pressure curve data; Step S2: During the production process of the terminal machine, the pressure curve data of the crimping process is collected in real time, and a curve comparison is performed based on the pressure curve data and the pressure curve data of good products to obtain curve difference data; Step S3: Perform abnormality judgment based on the curve difference data to obtain terminal pressure abnormality data; Step S4: Perform terminal pressure monitoring alarm and shutdown auxiliary operations according to the abnormal terminal pressure data.

2. The method according to claim 1, characterized in that Step S1 is specifically as follows: Obtain good product pressure data; According to the good product pressure data, the crimping system condition extraction and the crimping method extraction are performed to obtain the crimping system condition data and the crimping method data respectively, wherein the crimping system condition data includes the geometric dimension data of the terminal and the wiring harness, the terminal material characteristic data, the crimping die structure data and the force transmission path data, and the crimping method data includes the load application method data, the load loading path data and the loading rate data, the loading direction data and the loading duration data during the crimping process; Acquire the initial state data of good products, and perform initial state modeling based on the initial state data of good products to obtain the 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; Effect modeling is performed based on material constitutive relationship data, contact pressure equation data, contact pressure contact model and good product initial state model to obtain a good product state model; The good product state model is numerically discretized 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; The good product pressure distribution data is subjected to curve fitting to obtain the 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 according to 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 the terminal material characteristic data and the connection and pressing 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 the terminal material characteristic data and the 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 according to 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 method 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 according to the nonlinear characteristic region data to obtain nonlinear stress-strain curve data; The nonlinear contact stiffness is calculated according to the nonlinear stress-strain curve data to obtain the nonlinear contact stiffness data; The nonlinear normal force is calculated according to 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 the 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; A hardening curve is generated according to the stress-strain curve data of the elastic stage and the plastic region data to obtain the hardening curve data of the plastic region; 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 according to the stress-strain curve data of the elastic stage, and 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 transformation 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 adhesive contact treatment is as follows: The viscosity coefficient is preliminarily estimated based on the terminal material characteristic data and the connection and pressing method data to obtain the 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; The time relaxation characteristics are calculated according to the rate-sensitive viscosity parameter data to obtain the relaxation characteristic data; Perform double-path integral calculation on the relaxation characteristic data to obtain the viscous hysteresis loop characteristic data; The dynamic contact force is calculated based on the relaxation characteristic data and the rate-sensitive viscosity parameter data to obtain the 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 S2 is specifically as follows: During the production process of the terminal machine, the pressure curve data of the crimping process is collected in real time; Perform point-by-point difference calculation based on the pressure curve data and the good product pressure curve data to obtain the production pressure difference data; Calculate the coefficient of variation based on the production pressure difference data to obtain the production fluctuation characteristic data; Performing weighted adjustment on preset pressure threshold data according to production fluctuation characteristic data to obtain first pressure threshold weighted data; Comparing the production pressure difference data according to the first pressure threshold weighted data to obtain the first curve difference data; Extract characteristic points of the pressure curve according to the pressure curve data and the good product pressure curve data, and obtain the characteristic point data of the pressure curve and the good product pressure curve characteristic point data respectively; Characteristic difference calculation is performed based on the characteristic point data of the pressure curve and the characteristic point data of the pressure curve of good products to obtain characteristic 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 the 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; The first curve difference data and the second curve difference data are weightedly fused according to the difference weight data to obtain curve difference data.

9. The method according to claim 1, characterized in that: Step S3 is specifically as follows: Perform curve anomaly classification according to 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; The abnormal terminal pressure event is mapped according to the abnormal curve classification data and the abnormal curve frequency characteristic data to obtain the abnormal terminal pressure data.

10. A terminal pressure monitoring system, characterized in that: Used to perform 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 according to 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 compare the curves according to the pressure curve data and the pressure curve data of good products to obtain the 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 according to abnormal terminal pressure data.

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