A control system for a fully automatic terminal crimping machine
By constructing a three-dimensional data set and a deviation isolation model, the buckling instability type of ultra-thin terminals is identified and calibrated, solving the problem of difficulty in distinguishing between instantaneous and gradual instability in existing technologies, and realizing efficient anti-buckling control and stability of mass production of ultra-thin terminals.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHAANXI LIBING LAMILA THERMAL ENERGY TECH CO LTD
- Filing Date
- 2026-02-28
- Publication Date
- 2026-07-21
AI Technical Summary
The existing fully automatic terminal crimping machine control system cannot effectively distinguish between instantaneous sudden instability and gradual instability of ultra-thin terminals. This results in an inability to respond quickly to the emergency risk of a sudden increase in strain energy, making it easy to miss the opportunity for intervention. Consequently, ultra-thin terminals will experience irreversible buckling deformation under both instability scenarios, making it difficult to improve the crimping pass rate.
By collecting pressure and displacement data in real time, a three-dimensional data set is constructed to identify buckling instability analysis points, distinguish between instantaneous and gradual instability, construct a deviation isolation model to eliminate occasional deviations, extract significant misalignment scenarios, perform calibration control based on strain energy characteristics, and output core control parameters to optimize the pressing process.
It enables precise instability analysis and buckling prevention control for ultra-thin terminals, reduces missed or false judgments, improves crimping pass rate and batch production stability, and adapts to the material differences of different batches of terminals.
Smart Images
Figure CN122431097A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of terminal crimping machine control technology, and more specifically to a control system for a fully automatic terminal crimping machine. Background Technology
[0002] With the miniaturization of electronic devices and the increasing precision of wiring harnesses in new energy vehicles, the demand for ultra-thin terminals has surged. However, their characteristics of low stiffness, sensitivity to stress concentration, and narrow buckling instability threshold place extremely high demands on the control precision of crimping machines. Existing fully automatic terminal crimping machine control systems face numerous technical bottlenecks when crimping ultra-thin terminals, making it difficult to meet buckling resistance requirements.
[0003] In the instability type identification and buckling risk assessment stages, existing technologies do not differentiate between instantaneous sudden instability and gradual instability, which are common in ultra-thin terminal crimping, and instead employ a uniform buckling risk criterion. For instantaneous sudden instability, the inability to quickly respond to the emergency risk of a sudden increase in strain energy easily leads to missed intervention opportunities; consequently, ultra-thin terminals are prone to irreversible buckling deformation in both instability scenarios, making it difficult to improve the crimping pass rate.
[0004] Therefore, the present invention provides a control system for a fully automatic terminal crimping machine. Summary of the Invention
[0005] The purpose of this invention is to provide a control system for a fully automatic terminal crimping machine to solve the aforementioned background problems.
[0006] The objective of this invention can be achieved through the following technical solutions: A control system for a fully automatic terminal crimping machine includes the following modules: Crimping data acquisition module: used to collect pressure and displacement data in real time during the crimping knife closing stage and construct a three-dimensional data set, and extract the crimping analysis points where the crimping force is unstable from the three-dimensional data set; Buckling identification module: Based on the crimping analysis point, the terminal is subjected to buckling instability analysis to obtain the terminal compression ratio feature group and determine whether there is a buckling risk in the terminal crimping. If so, the yield analysis point is extracted. Misalignment Analysis Module: Used to build a deviation isolation model, perform misalignment analysis on the extracted yield analysis points and historical instability points, determine whether there is misalignment in the instability determination of the pressing instability point, and if misalignment exists, locate the misalignment difference scene and extract the significant misalignment scene; Buckling control module: Extract strain energy features based on the segmented window of the significant misalignment scenario, define the controllable range based on the strain energy features, and construct a calibration control model if the strain energy features are within the controllable range. Output the core control parameters of the terminal and optimize the crimping process to achieve buckling prevention control of ultra-thin terminals.
[0007] Furthermore, the method for extracting the crimping analysis points is as follows: Collect the crimping force value and the displacement stroke value of the crimping knife at N time sampling points, and construct a three-dimensional data group containing the collection time, crimping force value, and displacement stroke value; Based on the three data sets, plot the time-pressure value variation curve and calculate the kurtosis coefficient of the pressure value in the three data sets; Based on the kurtosis coefficient, the peaks of the pressure change curve are screened. If a peak exists, the coefficient of variation of the pressure value within the corresponding sliding window is obtained. If the coefficient of variation of the crimping force value satisfies the crimping instability criterion, then the center point of the sliding window that satisfies the crimping instability criterion is taken as the crimping analysis point.
[0008] Furthermore, the yield analysis point is extracted as follows: Extract the dynamic compression ratio at the moment of instability and the dynamic compression ratio throughout the entire process from the terminal compression ratio feature set; Obtain the terminal material and structural parameters and extract the terminal critical compression amount; The reference compression rate at the moment of instability is set based on the terminal critical compression amount, as well as the reference compression rate throughout the entire process; A buckling risk criterion is established based on the benchmark compression rate to determine whether there is a buckling risk in the real-time sampled terminal crimping. If buckling risk exists, buckling risk location is performed, and yield analysis points are extracted.
[0009] Furthermore, the method for obtaining the terminal compression ratio feature group is as follows: Locate the crimping analysis point on the time-crimping force value change curve, and extract the half-peak width of the crimping analysis point peak; Based on the half-peak width, the instability type of the crimping analysis point is identified, and the segmentation window of the three data sets is determined; Based on the segmented window, the displacement stroke values in the three-dimensional data set are subjected to stroke instability compression analysis to obtain the dynamic compression ratio at the moment of instability and the dynamic compression ratio throughout the entire process. The dynamic compression ratio at the moment of instability and the dynamic compression ratio throughout the entire process are integrated to obtain the terminal compression ratio characteristic group.
[0010] Furthermore, the method for performing the aforementioned stroke instability compression analysis is as follows: Using the crimping analysis point as the segmentation point of the three-element data set, the three-element data set is cut off according to the segmentation window to obtain multiple sub-segment data sets of the three-element data set; The difference in displacement travel value from the start time sampling point to the end time sampling point in the sub-segment data group is obtained as the sub-segment compression displacement. Obtain the original height of the terminal, calculate the ratio of the deviation between the original height of the terminal and the compression displacement of the segment to obtain the dynamic compression ratio at the instability moment corresponding to the crimping analysis point; Calculate the difference in displacement travel value from the initial time sampling point to the final time sampling point in the three data elements, and use it as the total compression displacement of the entire segment; The deviation ratio between the original height of the terminal and the compression displacement of the entire segment is calculated as the dynamic compression ratio of the entire segment.
[0011] Furthermore, the method for locating the misalignment difference scene is as follows: If there is a misjudgment of instability, then obtain the mean value of the instability deviation coefficients corresponding to different instability types for the systematic deviation samples, as well as the proportion of systematic deviation samples for each instability type; The Mann-Whitney U test was performed on the instability deviation coefficients of gradual instability and instantaneous sudden instability to extract significance level values; If the proportion of systematic deviation samples with sudden, transient instability and the mean of the instability deviation coefficient are both higher than those with gradual instability, and the significance level value meets the significance range value, then sudden, transient instability is determined to be a significant misalignment scenario.
[0012] Furthermore, the way in which the instability determination is misaligned is as follows: Obtain the deviation data set, construct the deviation isolation model using the isolation forest algorithm, input the deviation data set into the deviation isolation model, and obtain the average path length of each deviation coefficient. Anomaly scores are extracted based on average path length, and a systematic bias criterion is established to distinguish between systematic bias and incidental bias. Based on systematic and occasional deviations, misalignment location analysis is performed to determine whether misalignment exists at the point of instability in the press-fitting instability assessment.
[0013] Furthermore, the method for obtaining the deviation data set is as follows: Obtain samples of terminals that failed the historical crimping process, extract the three-dimensional data set of the failed samples collected during the historical crimping knife closing stage, and extract the historical crimping analysis points and historically marked yield instability points. The sampling deviation rate is obtained by calculating the absolute deviation ratio between the historical pressing analysis points and the historical marked yield instability points at the time sampling points; The absolute deviation ratio between the historical crimping analysis points and the historical marked yield instability points is calculated to obtain the crimping deviation rate. The instability deviation coefficient is obtained by summing the sampling deviation rate and the pressing deviation rate. Obtain the instability deviation coefficients of the pressing analysis points and corresponding yield instability points of M samples from historical data, and establish a deviation data set.
[0014] Furthermore, the calibration control model is constructed as follows: Extract strain energy characteristics. If the strain energy characteristics are within a controllable range, integrate the characteristic parameters of the pressing acquisition module and the misalignment analysis module to construct an input variable set. A calibration control model is constructed using partial least squares regression. The input variable set is input into the calibration control model and the core control parameters for anti-buckling of ultra-thin terminals are output. The core control parameters are iteratively optimized using a cross-validation algorithm.
[0015] Furthermore, the strain energy characteristics are extracted in the following way: The volume of the pressing zone is obtained and the instantaneous strain energy density equation is constructed. Based on the displacement stroke difference and pressing force value of the identified significant misalignment scenarios, the instantaneous strain energy density is obtained by combining the volume of the pressing zone with the instantaneous strain energy density equation. Displacement stroke data are simultaneously collected at z characteristic locations in the pressing zone, and strain energy density is based on z characteristic locations in the pressing zone.
[0016] Establish the spatial gradient equation of strain energy, input the strain energy density at z characteristic locations into the spatial gradient equation of strain energy, and obtain the spatial gradient of strain energy. The spatial gradient of strain energy and the instantaneous strain energy density are used as characteristics of strain energy.
[0017] The beneficial effects of this invention are: (1) During the crimping knife closing stage, pressure, displacement, and time data are collected in real time to construct a three-dimensional data set. Then, the crimping analysis points are extracted through a dual judgment logic of sliding window division, peak filtering by kurtosis coefficient, and instability determination by coefficient of variation. This provides accurate and interference-free basic data for subsequent modules to analyze instability. Through multi-dimensional data integration and strict instability judgment criteria, the extracted crimping analysis points can reflect the state at the moment of crimping force instability, which helps to ensure the reliability and accuracy of the entire control system in analyzing the instability of ultra-thin terminal crimping from the data source.
[0018] (2) Based on the half-peak width of the crimping analysis point, instantaneous sudden instability is determined and the instability type is distinguished. A segmentation window is set to extract the three data group segments. The critical compression amount is calculated by combining the terminal material and structural parameters through the mechanical compression bar stability theory. Then, a differentiated buckling risk criterion is established, which can locate the buckling risk under different instability types and extract the yield analysis point. In view of the large differences in buckling mechanism of different instability types of ultra-thin terminals, the risk criterion is adapted to the scenario. It is conducive to the early identification of the hidden risks of insufficient compression in the early stage and overcompensation in the later stage of progressive instability, reducing the omission or misjudgment caused by the single criterion, and helping to prevent irreversible buckling deformation of ultra-thin terminals.
[0019] (3) By constructing a deviation isolation model using the isolated forest algorithm, significant misalignment scenarios can be extracted, which helps to eliminate occasional deviation interference and focus on high-risk scenarios caused by systemic misalignment. Through data-driven deviation isolation and statistical testing, the buckling control module can intervene only in significant misalignment scenarios that truly affect the pressing quality, reducing the waste of resources on occasional deviations and improving the targeting and efficiency of instability intervention.
[0020] (4) Extract the instantaneous strain energy density and strain energy spatial gradient from the segmented window of the significant misalignment scene. Obtain the trend deviation coefficient by the Euclidean distance with the characteristic sequence of qualified terminals in the same batch. When the coefficient is controllable, construct a calibration control model by partial least squares regression and cross-validate the optimized parameters for the next batch of crimping control. This can adapt to the buckling prevention requirements of ultra-thin terminals and realize the dynamic optimization of batch crimping. In view of the pain points of large batch differences in ultra-thin terminal materials and easy buckling with fixed parameters, the deviation degree is quantified by strain energy characteristics. Adaptive parameters are output in a model-based and iterative optimization manner to reduce buckling caused by strain energy exceeding the threshold or insufficient stress release. This is conducive to improving the crimping qualification rate of ultra-thin terminals and fully automatic control, and adapting to the batch production requirements of different batches of terminals. Attached Figure Description
[0021] The invention will now be further described with reference to the accompanying drawings.
[0022] Figure 1 This is a block diagram of the control system of a fully automatic terminal crimping machine according to the present invention; Figure 2 This is a flowchart of the process for triggering the deviation warning signal in this invention; Figure 3 This is a flowchart of a control method for a fully automatic terminal crimping machine according to the present invention. Detailed Implementation
[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0024] Example 1 Please see Figure 1 As shown, this embodiment is a control system for a fully automatic terminal crimping machine, including the following modules: A fully automatic crimping machine control method based on the prevention of buckling instability of ultra-thin terminals: Crimping data acquisition module: used to collect pressure and displacement data in real time during the crimping knife closing stage and construct a three-dimensional data set, and extract the crimping analysis points where the crimping force is unstable from the three-dimensional data set; The method for extracting the compression force instability analysis points from the three-dimensional data set is as follows: Preferably, during the crimping process of the wire by the fully automatic terminal crimping machine, within the effective time window from the contact of the crimping knife with the terminal to the completion of crimping, the crimping force value and the displacement stroke value of the crimping knife are collected at N time sampling points by a piezoelectric dynamic sensor and a laser displacement sensor; Based on the pressure values and displacement stroke values of the crimping knife at N time sampling points i, a system is constructed that includes the sampling time... crimping force value Displacement stroke value The three data sets; Data preprocessing is performed on the three-dimensional data set to remove noise and extract the effective data of the three-dimensional data set. At the same time, a time-pressure value change curve is plotted with the X-axis representing the sampling time and the Y-axis representing the pressure value. Divide the relay force value in the three data set into a sliding window and calculate the kurtosis coefficient of the relay force value in each sliding window. Based on the kurtosis coefficient, the peaks of the compression force variation curve are screened. If a peak exists, the coefficient of variation of the compression force value within the corresponding sliding window is obtained. If the coefficient of variation of the compression force value satisfies the compression instability criterion, the center point of the sliding window that satisfies the compression instability criterion is taken as the compression analysis point. , , ); Preferably, when the kurtosis coefficient is higher than 2.5, the crimping force is considered to have a peak, and when the coefficient of variation is higher than 8%, the coefficient of variation is determined to meet the crimping instability criteria.
[0025] Buckling identification module: Based on the crimping analysis point, the terminal is subjected to buckling instability analysis to obtain the terminal compression ratio feature group and determine whether there is a buckling risk in the terminal crimping. If so, the yield analysis point is extracted. The yield analysis is performed as follows: Locate the crimping analysis point on the time-crimping force variation curve, and extract the half-peak width of the crimping analysis point peak. ; Based on the half-peak width, the instability type of the crimping analysis point is identified, and the segmentation window of the three data sets is determined; Preferably, the instability type of the crimping analysis point is identified if If 2ms, the instability type is determined to be instantaneous sudden instability. If the time is 2ms, it is considered a gradual instability; The method for determining the segmentation window of the three data sets based on the instability type is as follows: If the instability is instantaneous and sudden, then the pressure should be applied before the analysis point. To the 2nd As a segmentation window; If it is a gradual instability, then the analysis point 2 before the pressure point will be pressed. After As a segmentation window; Using the crimping analysis point as the segmentation point of the three-element data set, the three-element data set is cut off according to the segmentation window to obtain multiple sub-segment data sets of the three-element data set; The difference in displacement travel value from the start time sampling point to the end time sampling point in the sub-segment data group is obtained as the sub-segment compression displacement. Obtain the original height of the terminal, calculate the deviation ratio between the original height of the terminal and the sub-segment compression displacement to obtain the dynamic compression ratio η1 at the instability moment corresponding to the crimping analysis point; Calculate the difference in displacement travel value from the initial time sampling point to the final time sampling point in the three data elements, and use it as the total compression displacement of the entire segment; The deviation ratio between the original height of the terminal and the compression displacement of the entire segment is calculated as the dynamic compression ratio η of the entire segment. 总 ; The dynamic compression ratio at the moment of instability and the dynamic compression ratio throughout the entire process are integrated to obtain the terminal compression ratio characteristic group; The method for determining buckling risk is as follows: Obtain the terminal material and structural parameters, including: yield strength, elastic modulus, effective length of the terminal crimp section, and terminal thickness; Based on the mechanical column stability theory and combined with the terminal material and structural parameters, the critical compression equation of the terminal is constructed as follows: Obtain the critical compression amount of the terminal ; in, , , These are the terminal's preset critical stress, elastic modulus, and effective length of the terminal crimping section, respectively. The reference compression ratio η at the moment of instability is set based on the terminal critical compression. 1临 and the baseline compression ratio η throughout the process 总临 ; A buckling risk criterion is established based on the benchmark compression rate to determine whether there is a buckling risk in the real-time sampled terminal crimping. Preferably, the method for determining whether there is a risk of buckling in the real-time sampling of terminal crimping is as follows: If the instability is sudden and instantaneous: when η1 > η 1临 , and η 总 >η 总临 When a buckling risk is identified, instantaneous instability requires both segment and overall compression rate anomalies to be met simultaneously, thus reducing misjudgments based on a single data point. If it is a gradual instability: when η1 < η 1临 , and η 总When the difference between η1 and η1 is greater than 5%, which reflects the characteristics of insufficient compression in the early stage and excessive compensation in the later stage of gradual instability, it is determined that there is a risk of buckling. If there is a risk of buckling, then the buckling risk is located and buckling analysis points are extracted; The method for determining buckling risk is as follows: Within the segmentation window, the sampling point where the displacement stroke value first shows a sudden change is taken as the starting yield analysis point. , , ); Within the segmentation window, the sampling point corresponding to the end of the half-peak width of the compression force value is obtained as the termination point of the yield analysis; It is understandable that determining the yield analysis point serves the purpose of; Function 1: It helps determine the moment when the terminal material transitions from elastic deformation to plastic deformation, providing a key node reference for the effective range of the crimping force; Secondly, as a core anchor point for evaluating the mechanical reliability of terminals after crimping, the connection strength, vibration resistance, and thermal cycling fatigue performance of the crimped structure can be quantitatively judged by parameters such as deformation, stress distribution, and strain energy density at the corresponding stage of the yield analysis point. Thirdly, it provides a measured benchmark for the iterative optimization of pressing process parameters and the accuracy calibration of finite element simulation models. By matching the measured mechanical response at the yield analysis point with the theoretical simulation results, the mold design, process procedure, or simulation parameters can be corrected in reverse, thereby improving the stability of the pressing process and the accuracy of simulation predictions.
[0026] Example 2 Please see Figure 1 As shown, this embodiment is a control system for a fully automatic terminal crimping machine, which also includes the following modules: Misalignment Analysis Module: Used to build a deviation isolation model, perform misalignment analysis on the extracted yield analysis points and historical instability points, determine whether there is misalignment in the instability determination of the pressing instability point, and if misalignment exists, locate the misalignment difference scene and extract the significant misalignment scene; Obtain samples of terminals that failed the historical crimping process, extract the three-dimensional data set of the failed samples collected during the historical crimping knife closing stage, and extract the historical crimping analysis points and historically marked yield instability points. It should be noted that the supplementary historical crimping failure sample judgment criteria are as follows: any of the following situations after terminal crimping is considered as failure; Judgment criterion 1: Buckling deformation (fold height, indentation depth) ≥ 0.08 mm (measured using an optical microscope (50x magnification)); Judgment criterion 2: The crimping strength is lower than the minimum value required by the industry standard (such as IPC / WHMA-A-620C); Judgment condition three: The contact resistance between the terminal and the wire is ≥5mΩ; The sampling deviation rate is obtained by calculating the absolute deviation ratio between the historical pressing analysis points and the historical marked yield instability points at the time sampling points; The absolute deviation ratio between the historical crimping analysis points and the historical marked yield instability points is calculated to obtain the crimping deviation rate. The instability deviation coefficient is obtained by summing the sampling deviation rate and the pressing deviation rate. Obtain the buckling deviation coefficients of the compression analysis points and corresponding yield buckling points for M samples in the historical data. Establish a deviation data group; It should be noted that i is the sample number, and j is the number of the instability deviation coefficient of each sample; The isolation path for extracting the instability deviation coefficient is as follows: S301. Construct a deviation isolation model using the Isolation Forest algorithm, input the deviation data set into the deviation isolation model, and obtain the coefficient of each deviation. Average path length ; Preferably, the unstable deviation coefficient sequence is converted into a single feature dataset; the isolated forest model is trained, and samples are randomly drawn to construct multiple isolation trees. Each tree recursively splits the data by randomly selecting split points until each sample is isolated individually. Calculate the average path length of each deviation coefficient across all trees (the shorter the path, the more likely the sample is to be an outlier). S302. Extract outlier scores based on average path length and establish a systematic deviation criterion to distinguish between systematic deviations and occasional deviations; Preferably, by formula: Obtain the anomaly score for each sample i. ; in, The average path length for a given sample size is obtained from the bias isolation model; It is understandable that if the outlier score is between (0.5, 1), the sample is an outlier that is easy to isolate, i.e., an occasional bias; if the outlier score is close to (0, 0.5), the sample is a normal point that is difficult to isolate, i.e., a systematic bias. S303. Based on systematic deviation and occasional deviation, perform misalignment location analysis to determine whether misalignment exists at the instability point of the press-fit instability. The ratio of the number of samples with outlier scores in the occasional bias to the total number of samples is obtained, and the ratio of the number of samples with outlier scores in the systematic bias to the total number of samples is obtained, and the systematic bias is obtained. If the proportion of incidental samples is significantly lower than the proportion of systematic samples, then there is a misjudgment in the determination of the instability point of the press-fit instability. Preferably, when the incidental sample ratio is 20 percentage points lower than the systematic sample ratio (e.g., when the systematic sample ratio is 60%, the incidental sample ratio is ≤40%), the incidental sample ratio is considered to be significantly lower than the systematic sample ratio. If there is a misalignment in the instability determination, the instability type of the yield analysis point is used to locate the misalignment difference scenario and extract the significant misalignment scenario; The method for locating misaligned difference scenarios is as follows: Obtain the mean value of the instability deviation coefficients for different instability types corresponding to the samples of systematic deviations, as well as the proportion of systematic deviation samples for each type of instability. The Mann-Whitney U test was performed on the instability deviation coefficients of gradual instability and instantaneous sudden instability to extract significance level values, which were used to determine whether there was a significant difference between the two sets of data distributions of the instability deviation coefficients of gradual instability and instantaneous sudden instability. Preferably, when the significance level is below 0.05, it is considered that there is a significant difference in the distribution of the two sets of data; If the proportion of systematic deviation samples with sudden instability and the mean of the instability deviation coefficient are both higher than those with gradual instability, and the significance level value meets the significance range value, then sudden instability is judged as a significant misalignment scenario. Understandably, the purpose of classifying instantaneous instability as a significant misalignment scenario is: Objective 1: To provide precise intervention targets for buckling control modules, enabling buckling prevention control of ultra-thin terminals. After identifying a significant misalignment scenario, the segmentation window of the scenario can be selectively extracted to extract core features such as instantaneous strain energy density and strain energy spatial gradient. This allows for the construction of a calibration control model adapted to the high-risk scenario, which is beneficial for achieving buckling control applied to key risk points. Objective 2: Eliminating occasional deviations and focusing on optimizing the root causes of systemic misalignments helps ensure the stability of batch crimping. Identifying significant misalignment scenarios can eliminate occasional deviations, reduce ineffective adjustments caused by misjudging occasional deviations, and ensure the quality consistency of different batches of ultra-thin terminals during batch crimping. Buckling control module: Extract strain energy features based on the segmentation window of the significant misalignment scenario, define the controllable range based on the strain energy features, and construct a calibration control model if the strain energy features are within the controllable range. Output the core control parameters of the terminal and optimize the crimping process to achieve buckling prevention control of ultra-thin terminals. The method for extracting strain energy characteristics of the compression zone based on significant misalignment scenarios is as follows: Based on the identified significant misalignment scenarios, the displacement difference between adjacent sampling points i and i-1 is calculated within the segmentation window of the significant misalignment scenario. ; Construct the instantaneous strain energy density equation: Obtaining instantaneous strain energy density ; in, Let V be the pressing force value at sampling point i, and V be the volume of the pressing area. The preferred elastic strain energy coefficient is... =0.5; It should be noted that the volume of the crimping area is calculated using the terminal material parameters and structural parameters; Based on a distributed laser sensor, displacement travel data Sz is simultaneously collected at z characteristic positions in the pressing area; Preferably, z=1, 2, 3, representing the left arm, right arm, and top of the crimping area, respectively; Based on the synchronous acquisition of displacement travel data Sz at z characteristic locations in the compression zone, the strain energy density at the corresponding sampling point i is obtained. ; By establishing the spatial gradient equation of strain energy: ; in, , ; Where, d x The distance between the sensors at positions z=1 and z=2. The distance between the sensors and the midpoints of the left and right arms is z=3; It is understandable that the physical meaning of instantaneous strain energy density is: the elastic strain energy accumulated per unit volume of the pressing area at a certain sampling moment during the pressing process. It is calculated by the pressing force value, the displacement difference between adjacent sampling points and the volume of the pressing area. It can directly reflect the energy bearing state of the material in the pressing area at that instant and determine whether it is close to the buckling instability threshold due to excessive energy accumulation. The physical meaning of the spatial gradient of strain energy is: the spatial rate of change of instantaneous strain energy density between different characteristic positions (left arm, right arm, top) of the pressing area. It is expressed in vector form (the horizontal direction is the direction of the distance between the left and right arms, and the vertical direction is the direction from the top to the midpoint of the left and right arms). It is calculated by the difference in strain energy density at different positions and the corresponding sensor distance. It can identify areas of local stress concentration or uneven energy distribution in the pressing area, and provide a physical basis for locating significant misalignment scenarios and reducing terminal buckling deformation caused by sudden local energy increases. The spatial gradient of strain energy and the instantaneous strain energy density are used as characteristics of strain energy; The method for defining the controllable range based on strain energy characteristics is as follows: Obtain the strain energy characteristics of qualified terminals in the same batch at all time sampling points, and construct the distribution sequence of strain energy gradient and instantaneous strain energy density over time; Feature subsequences of segmentation windows for significantly misaligned scenarios are extracted from the distribution sequences of strain energy gradient and instantaneous strain energy density; Establish strain energy characteristics for all segmented windows of a significantly misaligned scene and construct a scene feature sequence; The scene feature sequence and feature subsequence are aligned, and the Euclidean distance between the aligned scene feature sequence and feature subsequence is calculated as the trend deviation coefficient. like Figure 2 As shown, the trend deviation coefficient is compared with the preset controllable deviation range. If the trend deviation coefficient is within the controllable deviation range, that is, the strain energy characteristics are within the controllable deviation range, then the terminal calibration control model is constructed to dynamically control the terminal crimping parameters. If the trend deviation coefficient deviates from the controllable deviation range, a deviation warning signal will be triggered. The calibration control model is constructed as follows: S401. Integrate the characteristic parameters of the crimping acquisition module and the misalignment analysis module to construct an input variable set; Preferably, when constructing the input variable set by integrating the characteristic parameters of the crimping acquisition module and the misalignment analysis module within the crimping machine control system, core data is extracted from the crimping acquisition module: including the three-dimensional data set, instantaneous strain energy density and strain energy spatial gradient, and the volume V of the crimping zone; Next, feature parameters of significant misalignment scenarios are extracted from the misalignment analysis module, including the instability type identifier determined by the segmentation window (instantaneous sudden instability is marked as 1, and gradual instability is marked as 0) and the segmentation window duration; finally, the trend deviation coefficient D calculated in the buckling control module and the average trend deviation coefficient of qualified terminals in the same batch are added, and the above parameters are integrated into the input variable set. S402. Construct a calibration control model using partial least squares regression, input the set of input variables into the calibration control model and output the core control parameters for anti-buckling of ultra-thin terminals; Preferably, when constructing a calibration control model using partial least squares regression (PLSR) and outputting the core control parameters for buckling prevention of ultra-thin terminals (pressurization rate v, holding time t), the constructed set of input variables is first standardized to normalize parameters of different dimensions to a range suitable for the characteristics of ultra-thin terminals, thus eliminating dimensional differences. Using qualified terminal crimping data (sample size ≥ 30 groups) from the same batch confirmed by physical testing to be free of buckling as the training set, the input variable set was imported into the PLSR model. Principal components of the input variables were extracted through the model (selecting the first two principal components with a cumulative contribution rate ≥ 90%). Based on the principal components, regression equations for the core control parameters were fitted to obtain the fitted core control parameters, including: pressurization rate v. p Holding time t b ; S403. The core control parameters are iteratively optimized using the K-fold cross-validation algorithm; Preferably, when iteratively optimizing the core control parameters using the K-fold cross-validation algorithm (selecting K=5 to adapt to the batch sample size in the system), the training data of qualified terminals in the same batch are first randomly divided into 5 groups, with 6 samples in each group, and 5 validations are carried out in a round-robin manner of 4 groups for training and 1 group for testing. During each verification, the input variables of the test set are substituted into the calibration control model to obtain candidate core control parameters. , Then, the compression strain energy characteristic sequence of the ultra-thin terminal under the candidate parameters is collected by the compression acquisition module, and the Euclidean distance between the compression strain energy characteristic sequence and the strain energy characteristic sequence of qualified terminals in the same batch is calculated. If the distance is less than or equal to the preset Euclidean distance threshold, and the corresponding terminal shows no buckling phenomena such as wrinkles or dents after visual inspection, then the candidate parameter is valid; if invalid, the principal component count of the PLSR model is adjusted back (e.g., increased to a cumulative contribution rate ≥ 92%) or the regression equation coefficient, and the candidate parameters are regenerated and verified; after 5 verifications, the mean of all valid candidate parameters is taken as the final core control parameter after iterative optimization, so as to achieve parameter adaptation to the buckling prevention requirements of ultra-thin terminals; Based on the core control parameters that have been iteratively optimized, the crimping process of the next batch of the fully automatic terminal crimping machine is controlled and processed. Those skilled in the art will understand that when controlling the next batch of crimping based on the iteratively optimized core control parameters (pressurization rate v, holding time t), the material and structural parameters of the next batch of ultra-thin terminals are first retrieved and verified against the terminal parameters of the corresponding batch with the optimized parameters (deviation ≤ file allowable range). After verification, v and t are imported into the main control unit of the crimping machine, and the crimping actuator is linked to control the downward speed of the crimping knife according to v and maintain the holding pressure according to t to avoid strain energy exceeding the buckling threshold or insufficient stress release. During the process, the crimping acquisition module is called to collect the three data sets and strain energy characteristics in real time. If an anomaly occurs, the misalignment analysis module is used to determine whether there is a significant misalignment scenario, and v and t are slightly adjusted if necessary. After each terminal is crimped, it is confirmed by detection that there is no buckling and the crimping strength meets the standard, so as to realize the fully automatic anti-buckling control of the next batch.
[0027] Example 3 Please see Figure 3 As shown, this embodiment is a control method for a fully automatic terminal crimping machine, including the following steps: S1. During the crimping knife closing stage, pressure and displacement data are collected in real time and a three-dimensional data set is constructed. The crimping analysis points of crimping force instability in the three-dimensional data set are extracted. S2. Based on the crimping analysis points, perform buckling instability analysis on the terminals to calculate the dynamic compression rate of the terminals and determine whether there is a buckling risk in the terminal crimping. If so, extract the buckling analysis points. S3. Construct a deviation isolation model, perform misalignment analysis on the extracted yield analysis points and historical instability points, determine whether there is misalignment in the instability judgment of the pressing instability point, and if there is misalignment, locate the misalignment difference scene and extract the significant misalignment scene. S4. Extract strain energy features based on the segmented window of the significant misalignment scene, define the controllable range based on the strain energy features, and construct a calibration control model if the strain energy features are within the controllable range. Output the core control parameters of the terminal and optimize the crimping process to achieve anti-buckling control of ultra-thin terminals.
[0028] Example 4 This embodiment describes the operation process of a fully automatic terminal crimping machine, including the following steps: (1) Pre-power-on inspection Confirm that the power and air supply connections of the equipment are normal, and that the specifications of the terminal strips and wires match the crimping requirements; check that the crimping mold is free from wear and looseness, and that the equipment emergency stop button is in normal condition.
[0029] (2) Parameter settings Based on the terminal type and wire diameter, set parameters such as crimping height, crimping force, feeding length, and wire stripping length on the equipment control panel, and save the parameter scheme after setting.
[0030] (3) Loading and clamping Terminal loading: Load the terminal strip into the feeding mechanism, clamp and position it to ensure smooth and unobstructed conveying of the strip.
[0031] Wire clamping: Place the coiled wire into the wire feeding frame, pass the wire end through the guide wheel and wire stripping mechanism, adjust the position of the clamp to fix the wire, and ensure stable wire feeding.
[0032] (4) Pressure testing and commissioning Start the equipment in manual mode, trigger the crimping action once, remove the crimped terminal wire, and check whether the appearance and tensile strength of the crimped terminal meet the standards; if not, fine-tune the crimping parameters and repeat the crimping test until it is qualified.
[0033] (5) Automatic operation After successful debugging, switch the equipment to automatic mode, press the start button, and the equipment will automatically complete a series of actions such as wire stripping, terminal feeding, crimping, and cutting the finished product.
[0034] (6) Operation monitoring During production, the terminal crimping quality is checked regularly, and the equipment operation status is observed. If there are any issues such as jamming, abnormal noise, or poor crimping, the emergency stop button should be pressed immediately to stop the machine.
[0035] 7. Shutdown and finalization After the production task is completed, first turn off the automatic operation mode of the equipment, then cut off the power and air supply; clean the waste and dust on the surface of the equipment, lubricate and maintain the mold and transmission parts, and finally tidy up the remaining terminal strips and wires.
[0036] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.
Claims
1. A control system for a fully automatic terminal crimping machine, characterized in that: Includes the following modules: Crimping data acquisition module: used to collect pressure and displacement data in real time during the crimping knife closing stage and construct a three-dimensional data set, and extract the crimping analysis points where the crimping force is unstable from the three-dimensional data set; Buckling identification module: Based on the crimping analysis point, the terminal is subjected to buckling instability analysis to obtain the terminal compression ratio feature group and determine whether there is a buckling risk in the terminal crimping. If so, the yield analysis point is extracted. Misalignment Analysis Module: Used to build a deviation isolation model, perform misalignment analysis on the extracted yield analysis points and historical instability points, determine whether there is misalignment in the instability determination of the pressing instability point, and if misalignment exists, locate the misalignment difference scene and extract the significant misalignment scene; Buckling control module: Extract strain energy features based on the segmented window of the significant misalignment scenario, define the controllable range based on the strain energy features, and construct a calibration control model if the strain energy features are within the controllable range. Output the core control parameters of the terminal and optimize the crimping process to achieve buckling prevention control of ultra-thin terminals.
2. The control system of a fully automatic terminal crimping machine according to claim 1, characterized in that: The method for extracting the crimping analysis points is as follows: Collect the crimping force value and the displacement stroke value of the crimping knife at N time sampling points, and construct a three-dimensional data group containing the collection time, crimping force value, and displacement stroke value; Based on the three data sets, plot the time-pressure value variation curve and calculate the kurtosis coefficient of the pressure value in the three data sets; Based on the kurtosis coefficient, the peaks of the pressure change curve are screened. If a peak exists, the coefficient of variation of the pressure value within the corresponding sliding window is obtained. If the coefficient of variation of the crimping force value satisfies the crimping instability criterion, then the center point of the sliding window that satisfies the crimping instability criterion is taken as the crimping analysis point.
3. The control system of a fully automatic terminal crimping machine according to claim 1, characterized in that: The method for extracting the yield analysis points is as follows: Extract the dynamic compression ratio at the moment of instability and the dynamic compression ratio throughout the entire process from the terminal compression ratio feature set; Obtain the terminal material and structural parameters and extract the terminal critical compression amount; The reference compression rate at the moment of instability is set based on the terminal critical compression amount, as well as the reference compression rate throughout the entire process; A buckling risk criterion is established based on the benchmark compression rate to determine whether there is a buckling risk in the real-time sampled terminal crimping. If buckling risk exists, buckling risk location is performed, and yield analysis points are extracted.
4. The control system of a fully automatic terminal crimping machine according to claim 1, characterized in that: The method for obtaining the terminal compression ratio feature group is as follows: Locate the crimping analysis point on the time-crimping force value change curve, and extract the half-peak width of the crimping analysis point peak; Based on the half-peak width, the instability type of the crimping analysis point is identified, and the segmentation window of the three data sets is determined; Based on the segmented window, the displacement stroke values in the three-dimensional data set are subjected to stroke instability compression analysis to obtain the dynamic compression ratio at the moment of instability and the dynamic compression ratio throughout the entire process. The dynamic compression ratio at the moment of instability and the dynamic compression ratio throughout the entire process are integrated to obtain the terminal compression ratio characteristic group.
5. The control system of a fully automatic terminal crimping machine according to claim 4, characterized in that: The method for performing the aforementioned stroke instability compression analysis is as follows: Using the crimping analysis point as the segmentation point of the three-dimensional data set, the three-dimensional data set is cut off according to the segmentation window to obtain multiple sub-segment data sets of the three-dimensional data set; The difference in displacement travel value from the start time sampling point to the end time sampling point in the sub-segment data group is obtained as the sub-segment compression displacement. Obtain the original height of the terminal, calculate the ratio of the deviation between the original height of the terminal and the compression displacement of the segment to obtain the dynamic compression ratio at the instability moment corresponding to the crimping analysis point; Calculate the difference in displacement travel value from the initial time sampling point to the final time sampling point in the three data elements, and use it as the total compression displacement of the entire segment; The deviation ratio between the original height of the terminal and the compression displacement of the entire segment is calculated as the dynamic compression ratio of the entire segment.
6. The control system of a fully automatic terminal crimping machine according to claim 1, characterized in that: The method for locating the misalignment difference scene is as follows: If there is a misjudgment of instability, then obtain the mean value of the instability deviation coefficients corresponding to different instability types for the systematic deviation samples, as well as the proportion of systematic deviation samples for each instability type; The Mann-Whitney U test was performed on the instability deviation coefficients of gradual instability and instantaneous sudden instability to extract significance level values; If the proportion of systematic deviation samples with sudden, transient instability and the mean of the instability deviation coefficient are both higher than those with gradual instability, and the significance level value meets the significance range value, then sudden, transient instability is determined to be a significant misalignment scenario.
7. The control system of a fully automatic terminal crimping machine according to claim 6, characterized in that: The way instability is determined to be misaligned is as follows: Obtain the deviation data set, construct the deviation isolation model using the isolation forest algorithm, input the deviation data set into the deviation isolation model, and obtain the average path length of each deviation coefficient. Anomaly scores are extracted based on average path length, and a systematic bias criterion is established to distinguish between systematic bias and incidental bias. Based on systematic and occasional deviations, misalignment location analysis is performed to determine whether misalignment exists at the point of instability in the press-fitting instability assessment.
8. The control system of a fully automatic terminal crimping machine according to claim 7, characterized in that: The method for obtaining the deviation data set is as follows: Obtain samples of terminals that failed the historical crimping process, extract the three-dimensional data set of the failed samples collected during the historical crimping knife closing stage, and extract the historical crimping analysis points and historically marked yield instability points. The sampling deviation rate is obtained by calculating the absolute deviation ratio between the historical pressing analysis points and the historical marked yield instability points at the time sampling points; The absolute deviation ratio between the historical crimping analysis points and the historical marked yield instability points is calculated to obtain the crimping deviation rate. The instability deviation coefficient is obtained by summing the sampling deviation rate and the pressing deviation rate. Obtain the instability deviation coefficients of the pressing analysis points and corresponding yield instability points of M samples from historical data, and establish a deviation data set.
9. The control system of a fully automatic terminal crimping machine according to claim 1, characterized in that: The calibration control model is constructed as follows: Extract strain energy characteristics. If the strain energy characteristics are within a controllable range, integrate the characteristic parameters of the pressing acquisition module and the misalignment analysis module to construct an input variable set. A calibration control model is constructed using partial least squares regression. The input variable set is input into the calibration control model and the core control parameters for anti-buckling of ultra-thin terminals are output. The core control parameters are iteratively optimized using a cross-validation algorithm.
10. The control system of a fully automatic terminal crimping machine according to claim 9, characterized in that: The method for extracting the strain energy characteristics is as follows: The volume of the pressing zone is obtained and the instantaneous strain energy density equation is constructed. Based on the displacement stroke difference and pressing force value of the identified significant misalignment scenarios, the instantaneous strain energy density is obtained by combining the volume of the pressing zone with the instantaneous strain energy density equation. Displacement stroke data are simultaneously collected at z characteristic locations in the pressing zone, based on the strain energy density at z characteristic locations in the pressing zone; Establish the spatial gradient equation of strain energy, input the strain energy density at z characteristic locations into the spatial gradient equation of strain energy, and obtain the spatial gradient of strain energy. The spatial gradient of strain energy and the instantaneous strain energy density are used as characteristics of strain energy.