A method and system for analyzing process deviations of car tire building drum parts

Abstract

The present invention relates to the technical field of process deviation analysis, and in particular to a method and system for analyzing process deviations of passenger car tire building drum parts. The method comprises the following steps: by real-time monitoring of the process flow of passenger car tire building drum parts, analyzing abnormal fluctuations in bonding pressure / temperature, and further simulating imbalance in the distribution of cord bonding tension, pleat delamination, and incremental force slippage of the pleated cord, thereby identifying and logically learning the gradient of tire structural performance loss, and forming a logical gradient of tire structural performance loss. Subsequently, the logical gradient and incremental force slippage data of the pleated cord are used to trace the weak structural points of the parts, identify process deviations, and ultimately feed back the deviation data to the terminal. The present invention makes the process deviation analysis technology more perfect through the optimization processing of the process deviation analysis technology.

Description

Technical Field

[0001] The present invention relates to the technical field of process deviation analysis, and in particular to a process deviation analysis method and system for car tire building drum parts. Background Art

[0002] During tire manufacturing, the drum assembly is a key component, and its process quality plays a crucial role in the structural performance of the entire tire. The production process involves multiple complex steps, including raw material selection, ply lamination, temperature control, and pressure distribution. Deviations in any of these steps can lead to a loss of tire performance, impacting the tire's service life and safety. Therefore, accurately monitoring and analyzing the production process of drum assembly components, promptly identifying potential process deviations, and implementing effective adjustments are crucial for ensuring tire quality and improving production efficiency. However, a traditional method for analyzing process deviations in passenger car tire drum components suffers from inaccurate analysis of tire performance structural losses caused by the ply, resulting in large errors in process deviation analysis. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for analyzing process deviations of car tire building drum parts to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for analyzing process deviation of car tire building drum parts is provided, the method comprising the following steps:

[0005] Step S1: Real-time monitoring of the process flow of the passenger car tire building drum parts is performed to obtain the real-time monitoring process flow of the building drum parts process; abnormal lamination pressure / temperature fluctuation analysis of the car tire ply is performed on the real-time monitoring process flow of the building drum parts process to obtain filling data of abnormal lamination pressure / temperature fluctuation;

[0006] Step S2: performing a simulation of the imbalanced distribution of the cord laminating tension based on the filling data of the abnormal laminating pressure / temperature fluctuation to obtain the data of the imbalanced distribution of the cord laminating tension; performing a simulation analysis of the cord ply wrinkle and delamination based on the data of the imbalanced distribution of the cord laminating tension to obtain the data of the cord ply wrinkle and delamination; performing an incremental integration of the force slip of the cord in the wrinkle area on the cord ply wrinkle and delamination data to obtain the incremental force slip of the wrinkled cord;

[0007] Step S3: Identifying the tire structural performance loss gradient based on the wrinkle cord force slippage increment data to obtain the tire structural performance loss gradient; performing logical learning on the tire structural performance loss gradient to obtain the tire structural performance loss logical gradient;

[0008] Step S4: Tracing the principle of the weak structural point of the tire building drum parts according to the logical gradient of the tire structural performance loss and the incremental data of the force slip of the pleated cord to obtain the principle tracing data of the weak structural point of the parts; identifying the process deviation based on the principle tracing data of the weak structural point of the parts to obtain the process deviation data of the weak structural point, and feeding back the process deviation data of the weak structural point to the terminal.

[0009] Preferably, step S1 includes the following steps:

[0010] Step S11: Real-time monitoring of the process flow of the car tire building drum parts to obtain a real-time monitoring process flow of the building drum parts process;

[0011] Step S12: extracting the ply process flow from the real-time monitoring process of the forming drum part process to obtain the ply process flow data;

[0012] Step S13: analyzing abnormal lamination pressure / temperature fluctuation of the ply on the ply process flow data to obtain abnormal lamination pressure / temperature fluctuation data;

[0013] Step S14: Fill missing values in the bonding pressure / temperature abnormal fluctuation data to obtain bonding pressure / temperature abnormal fluctuation filled data.

[0014] Preferably, step S2 includes the following steps:

[0015] Step S21: performing a simulation of unbalanced tension distribution of the cord bonding according to the filling data of abnormal bonding pressure / temperature fluctuation, and obtaining unbalanced tension distribution data of the cord bonding;

[0016] Step S22: performing non-normal distribution structure analysis on the cord lamination tension distribution imbalance data to obtain tension imbalance non-normal distribution structure data;

[0017] Step S23: performing a simulation analysis of the cord ply wrinkle layering based on the tension imbalance non-normal distribution structure data to obtain cord ply wrinkle layering data;

[0018] Step S24: integrating the force-slip increment of the cord in the wrinkle region of the cord ply wrinkle layer data to obtain the force-slip increment data of the wrinkle cord.

[0019] Preferably, step S23 includes the following steps:

[0020] Step S231: performing spectrum conversion on the tension imbalance non-normal distribution structure data to obtain a tension imbalance non-normal distribution spectrum diagram;

[0021] Step S232: performing imbalance outlier multi-peak intensity asymmetry analysis on the tension imbalance non-normal distribution spectrum to obtain tension imbalance multi-peak intensity asymmetry data;

[0022] Step S233: Calculating the tension difference between adjacent regions based on the tension imbalance multi-peak intensity asymmetric data to obtain the tension difference between adjacent regions;

[0023] Step S234: Calculating the cyclic stretch kinetic energy difference of the carcass layer according to the tension difference between adjacent regions to obtain the cyclic stretch kinetic energy difference of the carcass layer;

[0024] Step S235: analyzing the loss of adhesion between the plies based on the kinetic energy difference of the cyclic stretching of the plies to obtain data on the loss of adhesion between the plies;

[0025] Step S236: performing a simulation analysis of the ply wrinkle delamination based on the ply cyclic stretching kinetic energy difference and the inter-ply adhesion loss data to obtain ply wrinkle delamination data.

[0026] Preferably, step S24 includes the following steps:

[0027] Step S241: analyzing the cord radian tilt variation in the wrinkle region of the cord ply wrinkle layer data to obtain cord radian tilt variation data;

[0028] Step S242: performing cord spacing interleaving density increment analysis on the cord ply wrinkle layer data according to the cord radian tilt change data to obtain cord spacing interleaving density increment data;

[0029] Step S243: calculating the cord misalignment force loss interval based on the cord spacing interlacing density increment data to obtain the cord misalignment force loss interval;

[0030] Step S244: performing tire lateral rotation force imbalance simulation calculation based on the cord misalignment force loss interval to obtain lateral rotation force imbalance data;

[0031] Step S245: integrating the force slip increment of the cord in the wrinkle area based on the cord misalignment force loss interval and the lateral rotation force imbalance data to obtain the force slip increment data of the wrinkle cord.

[0032] Preferably, step S244 includes the following steps:

[0033] The force amplitude change rate of the tire lateral rotation cord is extracted according to the cord misalignment force loss interval to obtain the force amplitude change rate of the lateral rotation cord;

[0034] Based on the lateral rotation cord force amplitude change rate, the cord spacing interlaced density increment data is used to calculate the cumulative difference of the cord intersection angle force offset to obtain the cumulative difference data of the angle force offset;

[0035] Performing directional resultant force vector superposition processing on the angle force offset cumulative difference data to obtain angle force offset resultant force superposition data;

[0036] Based on the superposition data of the angle force offset resultant force, the tire lateral rotation force imbalance simulation calculation is performed to obtain the lateral rotation force imbalance data.

[0037] Preferably, step S3 includes the following steps:

[0038] Step S31: normalizing the incremental force slip data of the pleated cord to obtain normalized incremental force slip data of the pleated cord;

[0039] Step S32: performing tire dynamic balance imbalance simulation evaluation based on the normalized data of the wrinkled cord force slip increment to obtain tire dynamic balance imbalance evaluation data;

[0040] Step S33: identifying the tire structural performance loss gradient based on the tire dynamic balance imbalance assessment data and the normalized data of the pleated cord force slip increment to obtain the tire structural performance loss gradient;

[0041] Step S34: performing logical learning on the tire structure performance loss gradient to obtain the tire structure performance loss logical gradient.

[0042] Preferably, step S33 includes the following steps:

[0043] Step S331: performing high-speed rotation centrifugal force loss simulation equivalent evaluation on the tire dynamic balance imbalance evaluation data to obtain high-speed rotation centrifugal force loss equivalent data;

[0044] Step S332: performing radial force / lateral force fluctuation transient mean difference regression analysis on the tire dynamic balance imbalance assessment data based on the high-speed rotation centrifugal force loss equivalent data to obtain radial force / lateral force fluctuation transient mean difference regression data;

[0045] Step S333: performing numerical quantization of the shear stress accumulation increment on the normalized data of the force slip increment of the pleated cord according to the transient mean difference regression data of the radial force / lateral force fluctuation to obtain a quantized value of the shear stress accumulation increment;

[0046] Step S334: Identify the tire structural performance loss gradient based on the shear stress accumulation increment quantization value and the radial force / lateral force fluctuation transient mean difference regression data to obtain the tire structural performance loss gradient.

[0047] Preferably, step S4 includes the following steps:

[0048] Step S41: tracing the principle of the weak structural point of the tire building drum parts according to the logical gradient of the tire structural performance loss and the incremental data of the force slip of the pleated cord to obtain the principle tracing data of the weak structural point of the parts;

[0049] Step S42: performing iterative tracing processing on the principle traceability data of the weak structural point of the part to obtain iterative tracing data of the principle traceability of the weak structural point;

[0050] Step S43: Based on the weak structure point principle tracing iterative data, process deviation identification is performed to obtain weak structure point process deviation data, and the weak structure point process deviation data is fed back to the terminal.

[0051] Preferably, the present invention further provides a passenger car tire building drum parts process deviation analysis system for executing the passenger car tire building drum parts process deviation analysis method described above, the passenger car tire building drum parts process deviation analysis system comprising:

[0052] The cord layer lamination monitoring module is used to monitor the process flow of the passenger car tire building drum parts in real time and obtain the real-time monitoring process of the building drum parts process. It also analyzes the abnormal fluctuation of the cord layer lamination pressure / temperature in the real-time monitoring process of the building drum parts process and obtains the filling data of the abnormal lamination pressure / temperature fluctuation.

[0053] The cord force slip analysis module is used to simulate the imbalance of the cord lamination tension distribution based on the filling data of abnormal lamination pressure / temperature fluctuations to obtain the data of the imbalance of the cord lamination tension distribution; simulate and analyze the cord layer wrinkle and delamination based on the data of the imbalance of the cord lamination tension distribution to obtain the data of the cord layer wrinkle and delamination; and integrate the incremental force slip of the cord in the wrinkle area on the cord layer wrinkle and delamination data to obtain the incremental force slip data of the wrinkled cord;

[0054] The structural performance loss analysis module is used to identify the tire structural performance loss gradient based on the pleated cord force slip increment data to obtain the tire structural performance loss gradient; perform logical learning on the tire structural performance loss gradient to obtain the tire structural performance loss logical gradient;

[0055] The process deviation traceability module is used to trace the principle of weak structural points of tire building drum parts based on the logical gradient of tire structural performance loss and the force slip increment data of the pleated cord, and obtain the principle traceability data of weak structural points of parts; based on the principle traceability data of weak structural points of parts, process deviation identification is performed to obtain process deviation data of weak structural points, and the process deviation data of weak structural points is fed back to the terminal.

[0056] The present invention provides a beneficial effect by enabling real-time monitoring of the process flow for tire drum components, enabling real-time acquisition of key process parameters related to drum components, such as temperature and pressure. By monitoring this data, abnormal fluctuations in the process, such as laminating pressure and temperature, can be promptly detected. Analysis of these fluctuations can help pinpoint process issues and provide data support for further optimization of subsequent steps. This monitoring process allows for a better understanding of the causes of process fluctuations, providing real-time feedback for production and reducing production failures due to process issues. By analyzing data from abnormal laminating pressure and temperature fluctuations, imbalances in tire cord laminating tension distribution can be simulated, identifying uneven distribution of tire cord during the forming process. Further layer-by-layer simulation and analysis of wrinkles in the tire cord plies reveals the specific layers of wrinkles and their causes. This layer-by-layer analysis allows for precise identification of structural defects that occur during the forming process. Furthermore, by integrating the force-slip increments of the cord in the wrinkle region, the mechanical response of the local region can be understood, providing a basis for subsequent structural optimization. When identifying tire structural performance loss gradients, analysis of the incremental force slip data of the pleated cords can identify areas of structural performance degradation during the tire building process and calculate the gradient of the loss. This step quantifies the structural performance loss of each component during the tire building process and further analyzes which areas are most affected. This step not only helps optimize process parameters but also provides a scientific basis for improving tire design, enhancing product stability and quality. By learning the logical gradient of tire structural performance loss, weak points in tire building drum components can be traced and analyzed. This analysis helps reveal structural weaknesses in the drum components and pinpoints the root causes from a process perspective. Based on this, process deviation identification can identify deviations in the production process caused by weak points and provide feedback, enabling adjustments to the production process to ensure product quality meets expectations. This feedback mechanism can effectively improve production efficiency, reduce defect rates, and enhance overall quality control. Therefore, the present invention is an optimization treatment of a traditional method for analyzing process deviations of car tire building drum parts, which solves the problem that the traditional method for analyzing process deviations of car tire building drum parts has inaccurate analysis of tire performance and structural losses caused by the cord layer, thereby causing large errors in process deviation analysis. It improves the accuracy of the analysis of tire performance and structural losses caused by the cord layer and reduces the error in process deviation analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic flow chart of the steps of a process deviation analysis method for a car tire building drum part;

[0058] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0059] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. DETAILED DESCRIPTION

[0060] See also Figures 1 to 3 A method for analyzing process deviation of a car tire building drum part comprises the following steps:

[0061] Step S1: Real-time monitoring of the process flow of the passenger car tire building drum parts is performed to obtain the real-time monitoring process flow of the building drum parts process; abnormal lamination pressure / temperature fluctuation analysis of the car tire ply is performed on the real-time monitoring process flow of the building drum parts process to obtain filling data of abnormal lamination pressure / temperature fluctuation;

[0062] Step S2: performing a simulation of the imbalanced distribution of the cord laminating tension based on the filling data of the abnormal laminating pressure / temperature fluctuation to obtain the data of the imbalanced distribution of the cord laminating tension; performing a simulation analysis of the cord ply wrinkle and delamination based on the data of the imbalanced distribution of the cord laminating tension to obtain the data of the cord ply wrinkle and delamination; performing an incremental integration of the force slip of the cord in the wrinkle area on the cord ply wrinkle and delamination data to obtain the incremental force slip of the wrinkled cord;

[0063] Step S3: Identifying the tire structural performance loss gradient based on the wrinkle cord force slippage increment data to obtain the tire structural performance loss gradient; performing logical learning on the tire structural performance loss gradient to obtain the tire structural performance loss logical gradient;

[0064] Step S4: Tracing the principle of the weak structural point of the tire building drum parts according to the logical gradient of the tire structural performance loss and the incremental data of the force slip of the pleated cord to obtain the principle tracing data of the weak structural point of the parts; identifying the process deviation based on the principle tracing data of the weak structural point of the parts to obtain the process deviation data of the weak structural point, and feeding back the process deviation data of the weak structural point to the terminal.

[0065] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for analyzing process deviations of car tire building drum parts according to the present invention. In this example, the method for analyzing process deviations of car tire building drum parts includes the following steps:

[0066] Step S1: Real-time monitoring of the process flow of the passenger car tire building drum parts is performed to obtain the real-time monitoring process flow of the building drum parts process; abnormal lamination pressure / temperature fluctuation analysis of the car tire ply is performed on the real-time monitoring process flow of the building drum parts process to obtain filling data of abnormal lamination pressure / temperature fluctuation;

[0067] In this embodiment of the present invention, multiple sets of industrial-grade temperature and pressure sensors installed on the forming drum equipment collect data on the contact surface temperature during the cord lamination process (with a sampling frequency of 200Hz and a sensor accuracy of ±0.1°C) and the contact pressure between the cord and drum surface (also with a sampling frequency of 200Hz, a sensor range of 0-5MPa, and an accuracy of ±0.01MPa). The collected data is transmitted in real time to a back-end edge computing device via an industrial fieldbus (such as CANopen or Modbus-TCP). During this stage, the real-time monitoring process identifies abnormal fluctuations by setting abnormality threshold judgment rules. The upper and lower limits for temperature fluctuations are set to ±1.5°C / second, and the pressure fluctuation threshold is set to ±0.2MPa / second. If the system detects that the data exceeds the threshold for three consecutive sampling periods, it is considered an abnormal fluctuation. The spline interpolation method is implemented on the abnormal segment data to fill missing values and smooth fluctuations. The cubic B-spline is used for fitting, and the filling accuracy is improved through node adjustment. The final generated fitting pressure / temperature abnormal fluctuation filling data is saved in a structured database. The format is stored in a two-dimensional matrix, where each row represents a time point and each column is the value of the corresponding sensor channel.

[0068] Step S2: performing a simulation of the imbalanced distribution of the cord laminating tension based on the filling data of the abnormal laminating pressure / temperature fluctuation to obtain the data of the imbalanced distribution of the cord laminating tension; performing a simulation analysis of the cord ply wrinkle and delamination based on the data of the imbalanced distribution of the cord laminating tension to obtain the data of the cord ply wrinkle and delamination; performing an incremental integration of the force slip of the cord in the wrinkle area on the cord ply wrinkle and delamination data to obtain the incremental force slip of the wrinkled cord;

[0069] In this embodiment of the present invention, a simulation of unbalanced tension distribution during cord lamination is performed. First, a cord laying tension conversion function is constructed based on the pressure / temperature gradient distribution. A two-dimensional finite difference method is used to calculate the radial and circumferential stress distributions generated during the lamination process. Each lamination cell on the drum surface is divided into 2mm×2mm grid nodes. The tension per unit area is calculated at each node, and the tension imbalance distribution is visualized using a color map. Next, a non-normal distribution structure analysis is performed. The Shapiro-Wilk test is used to determine whether the tension distribution conforms to a normal distribution. If not, the skewness and kurtosis coefficients are calculated. A K-means clustering algorithm is then used to classify regions with extreme tension values and mark them as high-imbalance areas. Subsequently, based on the tension imbalance non-normal distribution structure data, the wrinkling and delamination behavior of the cord plies is simulated. A multi-layer discrete elastic model is constructed to analyze the relative displacement between different plies. During the simulation, the inter-ply friction coefficient is set to 0.18 and the tensile modulus is set to 500 MPa. The lateral fluctuations caused by the tension difference are iteratively calculated within each imbalanced region. Finally, the delamination depth and location data corresponding to the wrinkled regions are obtained. Next, the resulting cord ply wrinkle delamination data is subjected to a slip increment integration operation. First, the slip distance between cord nodes in the wrinkle region is calculated. The slip vectors are calculated using a discrete point linear fit method. The inter-node slip increment is recorded for each time segment, with an integration window of 10ms. All slip vectors are then integrated along the time axis to accumulate the total amount of cord slip over different time periods. The cord force slip increments are recorded as a heat map and converted into a tensor data structure for input into subsequent steps.

[0070] Step S3: Identifying the tire structural performance loss gradient based on the wrinkle cord force slippage increment data to obtain the tire structural performance loss gradient; performing logical learning on the tire structural performance loss gradient to obtain the tire structural performance loss logical gradient;

[0071] In an embodiment of the present invention, the tire structural performance loss gradient is identified based on the incremental slip data of the pleated cords. Specifically, a grid model is constructed with typical stress concentration areas of the tire as nodes. Based on the incremental cord slip input tensor at each node, linear regression and weighted difference analysis methods are used to calculate the gradient of the performance degradation degree of each structural section of the tire. The gradient range is divided into five levels: no damage, mild damage, moderate damage, severe damage, and extreme damage. After that, a logical learning operation is performed to establish a causal mapping relationship between the above gradient division and the slip increment. A decision path is established through a decision tree classification algorithm, using ID3 information gain as the basis for division. The decision path for each type of damage gradient records key parameters in the node, including indicators such as the slip starting point, maximum increment value, and duration. Finally, a logical gradient result is generated to provide a basis for subsequent positioning.

[0072] Step S4: Tracing the principle of the weak structural point of the tire building drum parts according to the logical gradient of the tire structural performance loss and the incremental data of the force slip of the pleated cord to obtain the principle tracing data of the weak structural point of the parts; identifying the process deviation based on the principle tracing data of the weak structural point of the parts to obtain the process deviation data of the weak structural point, and feeding back the process deviation data of the weak structural point to the terminal.

[0073] In this embodiment of the present invention, the principle traceability of weak structural points in tire building drum components is performed based on the logical gradient of tire structural performance loss and the incremental force slip data of the pleated cord. This traceability process utilizes a backpropagation mechanism, tracing the path of abnormality formation along the tension and structural stress distribution trajectory. Based on known drum surface structural parameters, such as a drum surface material modulus of 210 GPa and a drum surface wall thickness of 10 mm, and using a cord slip exceeding two standard deviations of the mean as the traceability trigger, the traceability path is deduced layer by layer along the traceability path, revealing root causes such as lamination time delays and abnormal lamination temperatures during the initial process stages. These root causes are then mapped to weak structural point principle traceability data in the form of structural node, abnormality type, and abnormality parameters. Finally, process deviation identification is performed on this principle traceability data. For abnormal parameters involved in the traceability data, such as lamination speed and pressure gradient, a discrimination domain is defined (e.g., pressure deviation greater than 0.3 MPa, lamination temperature fluctuation exceeding 2°C), and abnormal process sections are marked using a threshold judgment method. By combining production logs and equipment data, a deviation timeline map is constructed, correlating and comparing deviation data with specific process steps and operation batches, ultimately generating process deviation data for weak structural points. This data is output to the MES system interface in JSON format, enabling real-time feedback and alarm prompts for process anomalies.

[0074] Step S1 includes the following steps:

[0075] Step S11: Real-time monitoring of the process flow of the car tire building drum parts to obtain a real-time monitoring process flow of the building drum parts process;

[0076] Step S12: extracting the ply process flow from the real-time monitoring process of the forming drum part process to obtain the ply process flow data;

[0077] Step S13: analyzing abnormal lamination pressure / temperature fluctuation of the ply on the ply process flow data to obtain abnormal lamination pressure / temperature fluctuation data;

[0078] Step S14: Fill missing values in the bonding pressure / temperature abnormal fluctuation data to obtain bonding pressure / temperature abnormal fluctuation filled data.

[0079] In an embodiment of the present invention, the process flow of passenger car tire building drum components is monitored in real time. The monitoring system employed includes multi-point data acquisition modules deployed on the surface of the tire building drum equipment and in the process control process. The monitoring module consists of high-precision pressure sensors and surface thermocouples (temperature range 300°C, accuracy ±0.1°C), communicating in real time with a central controller via Industrial Ethernet (using the PROFINET protocol). The system uses a PLC (Programmable Logic Controller) as the primary control unit. The acquisition trigger condition is set to record data after the drum surface completes a full rotation cycle, collecting no fewer than 1000 sets of pressure and temperature data during each cycle. The monitoring process utilizes a periodic rolling window analysis mechanism, generating separate data frames for each data acquisition, which are stored in chronological order as a one-dimensional time series file. The real-time monitoring process is stored as a structured log file on a local edge server. The data fields include timestamp, sensor channel number, temperature value, pressure value, drum surface rotation speed, and the coordinates of the tire cord lamination position, fully recording the key process changes during the tire cord lamination phase of the building drum. The cord ply process flow is extracted from real-time monitoring data of the building drum part process. Specifically, logical state discrimination rules are constructed to select process segments corresponding to the bonding process. First, characteristic segments of the cord bonding phase are extracted from the acquired data. The rapid pressure rise upon entering the bonding zone is used as the starting point for judgment, with a slope threshold of ≥0.5 MPa / s. Combined with the workstation positioning information from the synchronized camera system, a time synchronization algorithm is used to align the visual frame and the pressure-time axis. All process parameters within this time period are then extracted. Within-frame feature stability analysis is performed using a sliding window (10 seconds) to eliminate non-steady-state segments during initial bonding and final unloading. The resulting cord ply process flow data is structured as a three-dimensional array, with dimensions representing time point, sensor number, and value type (temperature / pressure). Each data point corresponds to a unique drum surface coordinate point. This data is used for subsequent abnormal fluctuation analysis during the bonding process. Analysis of abnormal bonding pressure / temperature fluctuations in the extracted cord ply process flow data is performed using multi-segment time series segmented sliding analysis and a baseline fitting algorithm. First, the temperature and pressure data were segmented into 10-second analysis units. Within each segment, the background fluctuation trend was fitted, and a local weighted regression method (Lowess regression) was used to extract the baseline curve. A differential algorithm was then used to calculate the local rate of change at each time point to determine whether there were any abnormal fluctuation points. The criterion for determining abnormal fluctuation points was set at a local first-order difference exceeding three standard deviations of the average of the previous 20 time points. Short-term pulse errors were eliminated by combining the additional condition that the continuous fluctuation duration was greater than one second. Furthermore, abnormal fluctuation points in both temperature and pressure dimensions were merged to form a joint anomaly window, which was visualized using a binary heat map.The final output is a table of abnormal fluctuation data. Each row records the timestamp, temperature and pressure values of the abnormal point, the coordinates of the fitting location, and the abnormal amplitude to support the subsequent data repair process. Missing values are filled in for the identified abnormal fitting pressure / temperature fluctuation data using a composite method combining double interpolation and trend smoothing. First, linear interpolation is performed on the data segments marked as missing or abnormal. The upper and lower limits of the interpolation are set to the five valid points before and after the abnormal segment. A weighted average method is used to account for historical trend weights, with a weight distribution ratio of 3 for the first segment: 2 for the second segment. After the initial linear interpolation filling, the entire segment is smoothed using cubic spline interpolation, with natural spline boundary conditions and node spacing within 2 seconds to ensure continuity of numerical changes. For data segments with continuous missing values exceeding 10 seconds, a similar segment matching method based on adjacent batch data is used to fill in the gaps. The Euclidean distance is used to measure similarity, and the process segment with the smallest distance is extracted from other batch data under the same process conditions as a reference. After filling is complete, the filled values are smoothed using first-order differences. The corrected data is then compared with the original data for fitting residual analysis. The residual control threshold is set at ±2%. Data that does not meet the requirements will enter a secondary repair process. The final output is filled data that fits the abnormal pressure / temperature fluctuations. The format is a time series structure, including a repair flag field and a fill value source type field to ensure traceability during subsequent processing.

[0080] Step S2 includes the following steps:

[0081] Step S21: performing a simulation of unbalanced tension distribution of the cord bonding according to the filling data of abnormal bonding pressure / temperature fluctuation, and obtaining unbalanced tension distribution data of the cord bonding;

[0082] Step S22: performing non-normal distribution structure analysis on the cord lamination tension distribution imbalance data to obtain tension imbalance non-normal distribution structure data;

[0083] Step S23: performing a simulation analysis of the cord ply wrinkle layering based on the tension imbalance non-normal distribution structure data to obtain cord ply wrinkle layering data;

[0084] Step S24: integrating the force-slip increment of the cord in the wrinkle region of the cord ply wrinkle layer data to obtain the force-slip increment data of the wrinkle cord.

[0085] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0086] Step S21: performing a simulation of unbalanced tension distribution of the cord bonding according to the filling data of abnormal bonding pressure / temperature fluctuation, and obtaining unbalanced tension distribution data of the cord bonding;

[0087] In an embodiment of the present invention, a simulation of the imbalanced distribution of the cord bonding tension is performed based on the filling data of abnormal bonding pressure / temperature fluctuations. During the simulation process, the data of different angles and bonding segment lengths at the bonding station are used as input to establish a tension discrete segment analysis mechanism. The unit line segment of each cord between the forming drum surface and the bonding wheel is used as the analysis unit. Based on the continuous medium tension distribution theory, the bonding pressure and bonding temperature changes within each unit length are integrated and calculated to calculate the length of the tension transfer path and the rate of change of its slope. The spatial distribution of the bonding pressure data uses two-dimensional interpolation to generate a tension surface, and spline functions are used for surface fitting. Under different bonding speeds, the drum surface rotation period range is set to 0.6~0.8 seconds, and the frequency of the unit cord segment tension data is set to sample once every 10ms. The tension data of each bonding point in each time period is subjected to a sliding window range analysis to extract the point with the maximum tension gradient. Based on the tension-strain imbalance phenomenon caused by different tension transmission paths, a tension discreteness index matrix of the bonding section is constructed, and the imbalance data of the cord bonding tension distribution is output. The data structure includes the bonding section length, local tension difference, bonding angle and local temperature difference influencing factors, which serves as the basis for subsequent non-normal structure analysis.

[0088] Step S22: performing non-normal distribution structure analysis on the cord lamination tension distribution imbalance data to obtain tension imbalance non-normal distribution structure data;

[0089] In the embodiment of the present invention, the non-normal distribution structure analysis of the imbalanced distribution data of the cord lamination tension is performed. First, the distribution test of the tension data of each lamination section is performed, and the Shapiro-Wilk test and the Kolmogorov-Smirnov test are used for double judgment, and the significance level is set. =0.01. For data segments that do not meet the normal distribution conditions, their skewness coefficient, kurtosis, standard deviation and mean are extracted to construct the tension imbalance structure feature vector. Then the sliding quantile difference method is used for data segmentation processing. The tension curve of each fitting section is divided into 5 equal segments, and the sum of squares of deviations of its local peak points is calculated, and the direction of variation is determined by the variance distribution diagram. For the identified high-skewness areas, their multi-peak structural points are further extracted, and the local extreme point counts are used to determine whether there are multiple tension overlaps in the curtain fabric in the fitting section. The above analysis results are formed into a tension imbalance non-normal distribution structure data table, the fields of which include fitting section coordinates, skewness value, kurtosis, range ratio, and number of local extreme values, providing data support for subsequent wrinkle simulation analysis.

[0090] Step S23: performing a simulation analysis of the cord ply wrinkle layering based on the tension imbalance non-normal distribution structure data to obtain cord ply wrinkle layering data;

[0091] In this embodiment of the present invention, a simulation analysis of ply wrinkle delamination is performed based on tension-imbalanced non-normally distributed structural data. The simulation identifies areas with the highest tension gradient as high-risk areas for wrinkle generation, and a simplified interlayer perturbation model is constructed. A two-dimensional finite-difference method is used to calculate perturbation transmission within the ply. The interlayer spacing of each laminated cord is set to 0.45 mm, the initial modulus of the material is set to 250 MPa, the Poisson's ratio is set to 0.35, and the interlayer damping coefficient is set to 0.02. The perturbation input is the tension peak transfer value identified in the previous step, in the form of a boundary perturbation displacement gradient. The action time is set to 10 ms, with a time step of 0.5 ms. During the simulation, each computational unit applies a tension perturbation load to its upper and lower layers, and the change in displacement response is calculated. The presence of wrinkles is determined by the layered difference in displacement response. A wrinkle point is identified if the displacement difference between the upper and lower cords within a unit layer exceeds 0.1 mm. After the simulation is completed, the density and distribution area of the wrinkle points are counted to generate the cord layer wrinkle stratification data, including key fields such as the wrinkle area boundary, the number of wrinkle points, and the average inter-layer displacement difference.

[0092] Step S24: integrating the force-slip increment of the cord in the wrinkle region of the cord ply wrinkle layer data to obtain the force-slip increment data of the wrinkle cord.

[0093] In this embodiment of the present invention, the wrinkle layer data of the cord ply is subjected to incremental integration of the force and slip of the cord in the wrinkle region. During this operation, a stress-slip integration model is established based on the unit node of the cord. The initial distance between cord nodes is set to 1 mm. The tension direction depends on the drum surface contact angle, and the unit tension input is the measured tension data from the previous tension distribution data. The wrinkle region boundary is used as the integration boundary, and the slip displacement of each node in the tension direction within the time period is used as the integration variable. Using the method of summing the slip increments between discrete nodes, a forward Euler method is used for stepwise integration, with a time step of 0.001 seconds and a total integration duration of 80% of the contact period. Incremental integration is performed for each cord node, and the cumulative slip is extracted. The angle of change in the force direction is also recorded. If the angle changes by more than 15 degrees, it is determined to be a slip nonlinearity point. The integration results are finally converted into a data table of incremental force and slip of the wrinkle cord, with fields including cord number, cumulative slip, force gradient between nodes, and nonlinear point location.

[0094] Step S23 includes the following steps:

[0095] Step S231: performing spectrum conversion on the tension imbalance non-normal distribution structure data to obtain a tension imbalance non-normal distribution spectrum diagram;

[0096] Step S232: performing imbalance outlier multi-peak intensity asymmetry analysis on the tension imbalance non-normal distribution spectrum to obtain tension imbalance multi-peak intensity asymmetry data;

[0097] Step S233: Calculating the tension difference between adjacent regions based on the tension imbalance multi-peak intensity asymmetric data to obtain the tension difference between adjacent regions;

[0098] Step S234: Calculating the cyclic stretch kinetic energy difference of the carcass layer according to the tension difference between adjacent regions to obtain the cyclic stretch kinetic energy difference of the carcass layer;

[0099] Step S235: analyzing the loss of adhesion between the plies based on the kinetic energy difference of the cyclic stretching of the plies to obtain data on the loss of adhesion between the plies;

[0100] Step S236: performing a simulation analysis of the ply wrinkle delamination based on the ply cyclic stretching kinetic energy difference and the inter-ply adhesion loss data to obtain ply wrinkle delamination data.

[0101] In this embodiment of the present invention, spectral conversion is performed on tension imbalance non-normal distribution data. First, the tension time series of each cord bonding section is used as the input, with a sampling frequency set to 100 Hz. Each tension data segment is windowed into segments with a period of 5 seconds. Fast Fourier Transform (FFT) is used to convert the tension time series signal from the time domain to the frequency domain, retaining the amplitude spectrum within the frequency range of 0–50 Hz. To reduce the impact of boundary effects on the spectrum, a Hanning window function is applied within each time domain window for weighting. The spectrum results generated for different bonding areas are analyzed by analyzing the dominant frequency distribution of the amplitude spectrum and the degree of frequency domain energy concentration. The number of spectral peaks, the location of the dominant peak, and their relative intensities are extracted to form the key characteristic parameters of the spectrum. The conversion results are visualized with frequency (Hz) on the horizontal axis and amplitude on the vertical axis to generate a spectrum of tension imbalance non-normal distribution. The peak amplitude in each frequency segment represents the region of concentrated tension fluctuation energy at that frequency component. This spectrum serves as the input for subsequent tension multi-peak asymmetry analysis. The non-normal distribution spectrum of tension imbalance was analyzed for outlier multi-peak intensity asymmetry, and the amplitude threshold method was used for multi-peak extraction. The basic judgment criterion was set as a frequency domain peak amplitude greater than 1.8 times the average amplitude. If this condition was met, the frequency point was identified as a main peak. To further eliminate pseudo-peaks caused by local noise disturbances, the symmetry deviation coefficient of the frequency range on both sides of each peak point was calculated. When the difference between the left and right decline rates was greater than 30%, the peak point was identified as an asymmetric main peak. The intensity asymmetry distribution structure was constructed by counting the number of main peaks and the symmetry deviation in each spectrum, generating tension imbalance multi-peak intensity asymmetry data. The data fields included main peak frequency, main peak amplitude, left and right decline slopes, asymmetry index, and spectrum center offset. To further analyze the outlier characteristics, the spectral intensity anomaly interval was calibrated using the triple standard deviation method. Outlier frequency points exceeding this threshold were extracted from the spectrum, and their positions and intensity increments were recorded. The resulting outliers are combined with the multimodal distribution structure for analysis, marking areas of unusually concentrated frequency energy in the spectrum. This provides a basis for subsequent regional tension difference calculations. Based on the asymmetric data of the tension imbalance multimodal intensity, the tension difference between adjacent regions is calculated. The tension difference between two adjacent bonded regions is defined as the difference in tension amplitude corresponding to the main tension peak within that region. Each bonded unit segment within the same cord bonded process is analyzed, and the main peak amplitude of the spectrum within each bonded segment is used as the regional tension intensity indicator. The main peak frequency and main peak amplitude differences between adjacent regions are calculated, and the main peak amplitude difference is normalized by the bonded segment length in N / m. To accurately assess the directionality of the difference, the center of gravity offset of the spectral energy of adjacent regions is calculated. A significant tension difference is considered if the main peak frequencies of adjacent spectra are within 5 Hz but the main peak amplitude difference exceeds 20%. The tension difference is calculated for all bonded regions sequentially along the bonded path, and a tension difference distribution sequence is generated.The data structure includes region number, main peak amplitude, frequency difference, amplitude difference, energy offset value, and tension gradient. The resulting tension difference between adjacent regions is used as input for determining the risk of cord wrinkles, providing a foundation for setting tension boundaries in cord wrinkle delamination simulation analysis.

[0102] The cyclic stretch kinetic energy difference of the cord layer is calculated based on the tension difference between adjacent areas. First, the time series data of the tension change of each cord bonding section during the cord bonding process is collected. Taking each bonding section as a unit, the repeated fluctuation amplitude of the main peak tension value in the area per unit time is integrated to construct the periodic tension change function of the cord layer. Taking tension as the external force, the corresponding cord unit length mass density is 1.24 g / cm. According to the dynamic discretization method with a time step of 0.01 seconds, the unit mass kinetic energy change of the cord under the action of periodic tension is calculated. On this basis, the unit mass kinetic energy difference of the cord layer caused by the tension difference between different bonding sections is calculated as the core parameter of the cyclic stretch kinetic energy difference of the cord layer. In the process of kinetic energy difference calculation, the finite difference method with time weighted function is used to deal with the change of periodic tension in the time domain. The kinetic energy difference per unit area is obtained by integrating the product of cord mass density, tension strength and periodic frequency. The unit is . The two areas before and after each bonding section were selected for pairwise comparison, and their kinetic energy difference matrix was calculated, and a two-dimensional distribution map of the kinetic energy difference of the cord layer was generated to provide quantitative support for the strain concentration trend between the cord layers. Based on the kinetic energy difference of the cyclic stretching of the cord layers, the adhesion loss analysis between the cord layers was carried out. The known adhesive strength-strain curve was selected as the theoretical support to establish the corresponding relationship between the kinetic energy input and the shear strain of the bonding interface. By mapping the kinetic energy difference between the cord layers to the adhesive interface, under the conditions of the adhesive layer thickness of 0.15mm and the shear modulus of 1.6MPa, the corresponding shear strain increment was deduced based on the principle of equivalent energy conversion of shear strain. The shear strain value borne by the adhesive layer of each bonding section was compared with its own initial bonding strength threshold. If the shear strain exceeds 1.5%, it is considered that there is adhesion degradation. The amount of adhesion loss was calculated using the exponential decay relationship between adhesion strength and strain. The decrease in adhesion strength of each bonding section was measured separately, and the location where adhesion decay occurred was marked. All the results are arrayed in the form of the axial coordinates of the cord fitting and the loss of adhesion to generate the adhesion loss data between the cord layers. The data fields include the fitting segment number, shear strain increment, initial value of adhesion strength, residual value of adhesion strength and percentage of strength reduction. The cord layer wrinkle delamination simulation analysis is carried out based on the kinetic energy difference of the cord layer cyclic stretching and the adhesion loss data between the cord layers. First, the two types of data are mapped to the spatial coordinate system on the cord fitting path, and a two-dimensional tensor distribution field is constructed in the fitting direction and the drum surface expansion direction. Combining the kinetic energy difference distribution map with the adhesion loss distribution map, the superposition judgment criterion is used for spatial coupling, that is, when the kinetic energy difference is greater than 15 When the adhesion loss in the corresponding area exceeds 35%, the area is identified as a high-risk wrinkle delamination point. Based on this, a node displacement evolution algorithm driven by a finite-difference tensor field is used to simulate the slip trend between the plies in this area. During the simulation, an initial ply position matrix is set, and the ply position evolves node by node based on the directional displacement imposed by the slip force field. With a total fitting path length of 180 cm, a 300-step loop simulation is performed for each 1cm grid cell, and the displacement change value for each step is recorded. The final output is the ply wrinkle delamination results, including parameters such as the wrinkle starting position, expansion direction, wrinkle angle, maximum displacement value, delamination length, and delamination width. The ply delamination distribution is visualized in the form of a heat map, forming a complete result set of ply wrinkle delamination data.

[0103] Step S24 includes the following steps:

[0104] Step S241: analyzing the cord radian tilt variation in the wrinkle region of the cord ply wrinkle layer data to obtain cord radian tilt variation data;

[0105] Step S242: performing cord spacing interleaving density increment analysis on the cord ply wrinkle layer data according to the cord radian tilt change data to obtain cord spacing interleaving density increment data;

[0106] Step S243: calculating the cord misalignment force loss interval based on the cord spacing interlacing density increment data to obtain the cord misalignment force loss interval;

[0107] Step S244: performing tire lateral rotation force imbalance simulation calculation based on the cord misalignment force loss interval to obtain lateral rotation force imbalance data;

[0108] Step S245: integrating the force slip increment of the cord in the wrinkle area based on the cord misalignment force loss interval and the lateral rotation force imbalance data to obtain the force slip increment data of the wrinkle cord.

[0109] In this embodiment of the present invention, the cord described herein is a steel cord. Cord camber tilt variation in the pleat region is analyzed using the pleat layer data. First, based on parameters such as the pleat starting position, expansion direction, pleat angle, maximum displacement, layer length, and layer width, the angle between the pleat edge and the drum surface baseline is extracted. In a coordinate system aligned with the drum surface, bidirectional differential interpolation is used to continuously interpolate and reconstruct the cord offset angle in the pleat region, creating a spatial tilt angle grid map for nodes within the cord pleat boundary. In the coordinate point distribution, the camber variation is based on the center point normal direction, with a camber variation threshold of 3°. A time derivative of the angular offset of each cord unit node is processed to determine the temporal trend of the camber tilt variation. This tilt trend value is used to quantify the degree of conformity between the cord material and the drum surface during pleat boundary formation and to map the tension disturbance changes during the transition of the cord material's lateral displacement to the radial direction. Finally, the cord camber tilt variation data is output. Based on the cord arc tilt change data, the cord layer wrinkle layer data is analyzed for the cord spacing interlaced density increment in the wrinkle area. The tilt angle change field is mapped to the cord arrangement axis, and a multi-dimensional interlaced density difference function is constructed with an inclination gradient interval of 0.1°. In areas where the tilt causes the cords to slip and dislocate in the radial or tangential direction, the arrangement benchmark of the original uniform cord spacing of 0.45mm is analyzed. If the actual spacing deviation between adjacent cords caused by the tilt exceeds 0.12mm, the area is judged to be an interlaced density increment area. Using the tension direction density iteration method of two-dimensional discrete grid points, the density increment function per unit length of the cord is established for each interlaced area. The output includes data such as the interlaced segment number, initial density, incremental density, maximum cord spacing, minimum cord spacing, and average offset distance, forming a cord spacing interlaced density increment data table. The cord dislocation force loss interval is estimated by the incremental data of cord spacing interlaced density. Based on the nonlinear perturbation relationship between cord arrangement imbalance and tension transmission path, the cord force transmission function is established by using the segmented weighted calculation method of strength transmission interruption probability. In this function, the incremental density is higher than 0.18. The area is identified as a high-risk area for tension interruption. A tension conduction loss coefficient of approximately 13% is introduced for every 0.1mm spacing offset. A tension loss superposition coefficient matrix is constructed based on the number of regional cords and the difference in local cord spacing. During the calculation process, the drum surface expansion diagram is divided into 180×180 node intervals, and the degree of cord tension flow in each interval is calculated. Ultimately, the cord misalignment force loss interval data is obtained, including technical parameters such as tension conduction loss percentage, force interruption span, failure area, and main direction cord tension offset. Based on the cord misalignment force loss interval, a simulation of the tire's lateral rotational force imbalance is performed. The tire's lateral force coordinate system is constructed based on the drum surface expansion diagram and drum surface contour line. The cord misalignment loss interval is mapped to the tire cross-sectional load area, and a Gaussian weight factor is used to process the weighted effect of different cord forces on the lateral force balance. A perturbation superposition calculation was performed on the tire sidewall force matrix. A tire configuration with an inner diameter of 406mm, an outer diameter of 635mm, and five plies was selected. A perturbation tension of 100N was applied to each imbalance zone and superimposed at 240 equally divided points around the tire circumference. The force deviation along the Z axis (perpendicular to the tire's rolling direction) was calculated for each point. The simulation employed a static force balance integration method to calculate the tension eccentricity distribution over a 360-degree radius around the entire tire circumference. Lateral rotational imbalance data was output, including key metrics such as maximum torque offset, lateral force distribution curve, and balance point offset angle. Based on the cord misalignment force loss interval and lateral rotational force imbalance data, the cord force slip increments in the wrinkle region were integrated to construct a cord force slip function. Using the force direction, cord force starting point, and tension direction deviation angle as inputs, a line integral was performed on each cord's path on the drum surface unfolded diagram. The integration step size was set to 1 mm, and the slip of each cord node was calculated segmentally according to the force direction. A linear mapping relationship was used, with a force difference-displacement response conversion coefficient of 0.022 N / mm. The integration result was the cumulative slip displacement experienced by each cord from the starting point of the wrinkle area to the end point of the lateral deflection. Combined with the slip path lengths of multiple cords, a cord slip distribution density map was obtained for the area. Parameters such as the maximum value, average value, standard deviation, and distribution direction angle of the slip increment were statistically analyzed to form the force slip increment data of the wrinkle cord.

[0110] Step S244 includes the following steps:

[0111] The force amplitude change rate of the tire lateral rotation cord is extracted according to the cord misalignment force loss interval to obtain the force amplitude change rate of the lateral rotation cord;

[0112] Based on the lateral rotation cord force amplitude change rate, the cord spacing interlaced density increment data is used to calculate the cumulative difference of the cord intersection angle force offset to obtain the cumulative difference data of the angle force offset;

[0113] Performing directional resultant force vector superposition processing on the angle force offset cumulative difference data to obtain angle force offset resultant force superposition data;

[0114] Based on the superposition data of the angle force offset resultant force, the tire lateral rotation force imbalance simulation calculation is performed to obtain the lateral rotation force imbalance data.

[0115] In one embodiment of the present invention, the force amplitude change rate of the tire's lateral rotational cord is extracted based on the cord misalignment force loss interval. The cord force loss interval is first expanded 360 degrees along the tire's circumference, and each degree interval is divided into 1mm linear segments. All cord nodes are numbered, and the original cord force value of 180N is used as a reference. Within each segment, the radial force variation trend of the cord is extracted. The force values of two adjacent cord nodes are compared using the differential slope method to calculate the force amplitude change rate. This process records the tension loss difference between adjacent nodes and further calculates the average gradient change. A continuous sliding window algorithm is applied to each cord between the start and end nodes of the force loss segment. The local extreme value difference is calculated for every three nodes in a group, forming a complete tire lateral cord tension amplitude variation curve. The output data fields include the node number, the original cord tension value, the current tension value, the tension difference, the tension gradient change rate, and the location of the peak value of the change rate. Based on the rate of change in cord force amplitude during lateral rotation, the cumulative difference calculation of cord intersection angle force offset is performed on the incremental data of cord spacing stagger density. First, the cord intersection locations within all stagger density regions on the tire drum are identified. The cord arrangement angles at all staggered points are extracted, and the intersection angle θ is calculated using the vector angle formula. Using an initial design angle of 15° as a benchmark, the change in angle is recorded for areas where the angle offset exceeds ±2°. For each intersection angle, the rate of change in cord tension on either side is projected onto the angle direction and the normal direction, respectively. The offset in the direction of the resultant tension at the angle is calculated using trigonometric expansion. Based on this, a cumulative difference analysis path is established from the start to the end point. Point-by-point integration of the offset along the path is performed to generate a cumulative difference dataset of angular force offset. This dataset includes the path start and end numbers, intersection angle offset values, tension offset vector components, cumulative path length, and total cumulative offset value. The cumulative difference data of angular force offset are processed by directional resultant force vector superposition using a two-dimensional planar resultant force superposition method. First, a Cartesian coordinate system is established using the cord fitting reference surface. The tension offset vectors at all angle positions are transformed and standardized. The path of each offset vector is used as a direction line, and each vector is projected onto the tire's lateral rotation reference axis (transverse axis) and radial axis according to its direction. The resultant force components within each 10mm segment are accumulated using a sliding window method, ultimately forming a force vector superposition diagram generated by the tire's lateral torque. For areas with a high density of angle intersections, a resultant force weight correction process is performed to prevent distortion of local tension superposition. The output data fields include detailed values such as the resultant force direction, resultant force magnitude, main force vector direction angle, mean resultant force per unit area, superposition path range, and extreme value position, which are used for subsequent torque calculation input.Tire lateral rotational force imbalance simulation is performed based on the superposition of angular force offset resultant force data. Using the rotational axis unbalance torque calculation method, the resultant force vectors are superimposed on the left and right hemispheres, based on the tire structure circumference. The difference in resultant force along the lateral direction (X-axis) between the two hemispheres is calculated. Under a static load of 3000N, the total tire force is analyzed for local force variations in different angular regions. Using the center of the circle as the moment reference point, the moment arm distance between each force vector and the line connecting the tire center is calculated and multiplied by the corresponding force value to determine the total lateral torque. First-order derivative and extreme point analysis are performed on the torque function graph to identify lateral imbalance extremes and asymmetric torque trends. Corresponding imbalance areas are then matched with the carcass area distribution map. Ultimately, lateral rotational force imbalance data is generated, including the magnitude of the imbalance torque, the angular location of the torque extreme point, the offset distance of the force center, the number of the area with the highest force concentration, and the percentage of force symmetry deviation, providing a basis for subsequent structural modifications.

[0116] Step S3 includes the following steps:

[0117] Step S31: normalizing the incremental force slip data of the pleated cord to obtain normalized incremental force slip data of the pleated cord;

[0118] Step S32: performing tire dynamic balance imbalance simulation evaluation based on the normalized data of the wrinkled cord force slip increment to obtain tire dynamic balance imbalance evaluation data;

[0119] Step S33: identifying the tire structural performance loss gradient based on the tire dynamic balance imbalance assessment data and the normalized data of the pleated cord force slip increment to obtain the tire structural performance loss gradient;

[0120] Step S34: performing logical learning on the tire structure performance loss gradient to obtain the tire structure performance loss logical gradient.

[0121] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0122] Step S31: normalizing the incremental force slip data of the pleated cord to obtain normalized incremental force slip data of the pleated cord;

[0123] In one embodiment of the present invention, the incremental force slip data of the corrugated cords is normalized. First, the incremental force slip data of the corrugated area obtained in the previous step is collected. This data contains the slip vector of each cord in the corrugated area due to misaligned force, measured in micrometers, and recorded separately in both the radial and circumferential directions of the tire. The tire circumference is uniformly divided into 360 angular units, each corresponding to 1°. Each unit is further subdivided into multiple points based on the number of cords. A global slip increment distribution statistics table is established by extracting the maximum, minimum, and mean slip increments at each point. A linear minimum-maximum normalization method is then used to normalize all slip increment data to a range between 0 and 1. Specifically, the minimum slip increment of the entire cord is used as the reference point for 0, and the maximum slip increment is used as the normalization endpoint for 1. Normalization calculations are performed on each slip value within this range, and the normalized cord slip increment normalized data fields are output, including the angle number, point number, raw slip value, and normalized slip value.

[0124] Step S32: performing tire dynamic balance imbalance simulation evaluation based on the normalized data of the wrinkled cord force slip increment to obtain tire dynamic balance imbalance evaluation data;

[0125] In this embodiment of the present invention, tire dynamic balance assessment is performed based on normalized data from the incremental force and slip of the corrugated tire cords. Initially, a tire rotational angular velocity of 1800 rpm is used as the primary input parameter, and a circumferentially non-uniform mass distribution model is constructed in combination with the normalized slip value. In the specific operation, each normalized value is treated as a unit density change factor and superimposed on the local mass distribution at the location of the tire ply, simulating the slight density shifts caused by slip during the drum manufacturing process. Rigid body rotational dynamics formulas are used to integrate the centrifugal imbalance of the entire wheel at high rotational speeds, constructing a dynamic imbalance torque vector map. This process calculates the radial mass offset for each angle, and the moment arm vector between the radial mass offset and the tire's geometric center is calculated. This is then multiplied by the square of the angular velocity to obtain the centrifugal force. The resulting tire dynamic balance assessment data includes quantitative parameters such as the imbalance force value (in g·cm), the location of the maximum eccentricity angle, the imbalance amplitude distribution curve, and the difference in mass offset between the left and right half-turns.

[0126] Step S33: identifying the tire structural performance loss gradient based on the tire dynamic balance imbalance assessment data and the normalized data of the pleated cord force slip increment to obtain the tire structural performance loss gradient;

[0127] In this embodiment of the present invention, tire structural performance loss gradients are identified based on tire dynamic balance and imbalance assessment data and normalized data on the incremental force and slip of the corrugated cords. First, the normalized slip data is regrouped by angular coordinates and mapped one-to-one with the imbalance force data to establish a triple angle-slip-imbalance correspondence matrix. The correlation coefficients and curve fitting residuals between the normalized slip values and the imbalance data are analyzed within this matrix to identify the primary slip regions causing imbalance peaks. Subsequently, the cord numbers are mapped according to the ply orientation, and the distribution gradient of slip values within the imbalance torque region is extracted. The performance degradation trend caused by cord slip at each point is determined using a combination of first-order differences and local averaging. The data is then aggregated along the gradient direction to generate a structural performance loss gradient map. The gradient values are expressed as the local eccentricity deviation (in g·cm / mm) caused by unit slip, forming a structural performance loss gradient map that includes fields such as angular range, slip growth rate, loss sensitivity coefficient, and ply number.

[0128] Step S34: performing logical learning on the tire structure performance loss gradient to obtain the tire structure performance loss logical gradient.

[0129] In this embodiment of the present invention, logical learning is performed on tire structural performance loss gradients, and a hierarchical logical induction method is used to classify patterns within each gradient interval. First, the loss gradient values are logically divided, and three partition thresholds are set: mild loss (gradient value 0–0.3), moderate loss (0.3–0.7), and severe loss (>0.7). Corresponding angle segments are labeled, and multi-dimensional factor features, such as slip behavior, cord arrangement density, and inter-cord angular offset, are extracted for each segment. These features are logically grouped by ply group, and Boolean logic association is used to analyze the relationship between each factor combination and loss level. High-frequency loss factor combination patterns are identified. For example, a cord density exceeding 12 cords per 25mm width, an angle offset exceeding 3 degrees, and a normalized slip value greater than 0.6 are highly correlated with severe performance loss. Finally, logical gradient data for tire structural performance loss is output, including gradient level number, influencing factor combination, normalized slip threshold, cord arrangement density standard, and logical judgment results. A logical path diagram is then generated for subsequent feedback and correction of the building drum process.

[0130] Step S33 includes the following steps:

[0131] Step S331: performing high-speed rotation centrifugal force loss simulation equivalent evaluation on the tire dynamic balance imbalance evaluation data to obtain high-speed rotation centrifugal force loss equivalent data;

[0132] Step S332: performing radial force / lateral force fluctuation transient mean difference regression analysis on the tire dynamic balance imbalance assessment data based on the high-speed rotation centrifugal force loss equivalent data to obtain radial force / lateral force fluctuation transient mean difference regression data;

[0133] Step S333: performing numerical quantization of the shear stress accumulation increment on the normalized data of the force slip increment of the pleated cord according to the transient mean difference regression data of the radial force / lateral force fluctuation to obtain a quantized value of the shear stress accumulation increment;

[0134] Step S334: Identify the tire structural performance loss gradient based on the shear stress accumulation increment quantization value and the radial force / lateral force fluctuation transient mean difference regression data to obtain the tire structural performance loss gradient.

[0135] In this embodiment of the present invention, a high-speed rotation centrifugal force loss simulation is performed on tire dynamic balance and imbalance assessment data. The data generated in the previous step is first extracted, including the mass offset value (in g) at different tire angular positions, the tire radius (in mm), and the operating speed (1800 rpm). Using this data as input, an equivalent centrifugal force loss simulation environment is constructed. The tire circumference is divided into 360 cells, and the centrifugal force generated within each cell is calculated based on the mass offset value. By setting a unified centrifugal loss threshold (e.g., a maximum centrifugal force difference allowed for each cell of ±12 g·cm), a point-by-point evaluation method is used to calculate the actual centrifugal loss for each cell. Cells exceeding the threshold are marked as equivalent loss cells. The centrifugal force errors within all equivalent loss cells are vector-integrated to determine the equivalent centrifugal force imbalance value for the entire tire under high-speed rotation. The final output data includes fields such as angle number, mass offset value, centrifugal force value, equivalent loss segment number, and total loss value. Based on the equivalent data of high-speed rotation centrifugal force loss, a transient mean difference regression analysis of radial force / lateral force fluctuations is performed on the tire dynamic imbalance assessment data. First, the radial centrifugal force and lateral eccentric force at each angular position in the tire dynamic imbalance assessment data are extracted and a dynamic fluctuation time series curve is established. The equivalent loss segment number output in step S331 is used as a classification label. The radial and lateral forces at the angle points in the same segment are averaged, and the transient mean difference between the radial and lateral forces and the overall tire average is calculated. A first-order regression residual analysis method is used to fit the transient mean difference at each angle point, and the residual value and mean squared error index are calculated to quantify the contribution of the equivalent loss to force fluctuations. The regression output fields include parameters such as angular position, radial mean difference, lateral mean difference, residual sum of squares, and goodness of fit. A fluctuation mean difference heat map is plotted in polar coordinates to visually display the distribution of sensitive areas of stress fluctuation. The shear stress accumulation increment is numerically quantified based on the normalized slip increment data of the pleated cords using transient mean difference regression data of radial and lateral force fluctuations. The normalized slip data is first expanded into a two-dimensional slip matrix along the radial direction of the tire cross section, with each column representing an angular position and each row representing a cord position point. The radial and lateral force fluctuation mean difference data obtained in step S332 are mapped onto the slip matrix by angular point, and the resultant matrix is superimposed to form a stress perturbation superposition matrix. Local convolution differential analysis is performed on the radial and circumferential slip gradients of each slip point, extracting the variability of the slip slope concentrated at the cord intersection. This is then multiplied by the mean difference perturbation weight to calculate the shear stress accumulation increment at the corresponding point. This value, expressed in MPa, reflects the shear deformation concentration trend. Output fields include angle number, cord number, shear increment value, disturbance source mean difference index, and local slip gradient value. Tire structural performance loss gradients are identified based on the quantified shear stress accumulation increment and the transient mean difference regression data of radial and lateral force fluctuations.First, critical shear stress concentration ranges are defined: mild concentration is below 0.3 MPa, moderate concentration is 0.3–0.7 MPa, and severe concentration is above 0.7 MPa. Thresholds are then set based on the mean difference regression residuals (e.g., a residual > 0.08 indicates a high-risk fluctuation range). Bivariate cross-matching analysis is used to identify points on the slip matrix where both the shear increment and the mean difference of fluctuation exceed moderate levels. Next, cluster identification is performed along the cord arrangement and angle directions to identify continuous regions of high loss gradients. The failure risk index is calculated based on the integral length of the gradient value. The tire structural performance loss gradient results are output, labeled as high, medium, and low, along with technical parameters such as a slip-mean difference interaction plot, a gradient length distribution table, stress overlap ratios for each gradient segment, and distribution angle range. These serve as key indicators for subsequent local adjustments to the building drum and cord placement.

[0136] Step S4 includes the following steps:

[0137] Step S41: tracing the principle of the weak structural point of the tire building drum parts according to the logical gradient of the tire structural performance loss and the incremental data of the force slip of the pleated cord to obtain the principle tracing data of the weak structural point of the parts;

[0138] Step S42: performing iterative tracing processing on the principle traceability data of the weak structural point of the part to obtain iterative tracing data of the principle traceability of the weak structural point;

[0139] Step S43: Based on the weak structure point principle tracing iterative data, process deviation identification is performed to obtain weak structure point process deviation data, and the weak structure point process deviation data is fed back to the terminal.

[0140] In this embodiment of the present invention, the source of weak structural points in tire building drum components is traced based on the tire structural performance loss logic gradient and the pleated cord force slip increment data. First, the cord ply regions marked as high-level loss in the tire structural performance loss logic gradient data are extracted, and their corresponding angular coordinates, radial depth, and shear accumulation energy index (unit: MPa·mm) are obtained. Simultaneously, the slip increment value, slip gradient direction, and gradient change rate (unit: mm / deg) of the corresponding region are extracted from the pleated cord force slip increment data. Starting from the point of structural failure, the system traces back in the direction of the maximum slip gradient, calculating the superposition of the cord slip energy vector and the loss logic gradient along each unit path. When the slip energy superposition threshold falls below a set standard (e.g., 0.35 MPa·mm), the region is identified as the source of the impact of the building drum component. By marking the slip direction change rate, shear overlap strength mutation locations, and sudden drop points in the tire ply thickness on the tracing path, a mapping function is established that correlates these parameters with the tire building drum component structural parameters. The path endpoint is then compared with a database of drum component structural parameters (including the main drum profile, building groove gap, and drum platform eccentricity), yielding the structural weakness principle traceability results. The output data fields include the starting point coordinates, tracing path vector, path length, structural failure source location number, drum component structural parameter anomaly type, and anomaly numerical offset. The traceability data for the component weak structural point principle is iteratively processed, and a cyclic gradient backtracking method is used to simulate structural parameter perturbations at each identified weakness source location. Each iteration perturbs the drum component's structural parameters, such as groove width, groove depth, and step transition radius, by ±5%, ±10%, and ±15%. The tire ply building simulation is then repeated using these perturbed parameters to generate new tire ply slip increments and shear stress fields. The new stress data is overlapped and verified against the original traceability path. If the loss gradient is significantly reduced (e.g., by more than 0.25 MPa·mm) under the new parameter settings, the perturbation parameter is recorded as a key influencing factor, and the next round of fine-grained perturbations (±1%, ±2%) is initiated. The entire process is completed within 10 rounds, ultimately selecting the parameter perturbation combination that shows the most stable loss gradient decline trend under consecutive iterations. This is then identified as the weak geometric segment of the part structure most likely to cause slip imbalance. The output fields include the iteration round number, parameter perturbation combination number, gradient decline value for each round, key influencing parameter name, perturbation value, and corresponding loss gradient variation map. Based on the weak structural point principle, iterative data is traced back to identify process deviations. First, the drum part geometric parameters highly correlated with the loss gradient in the iterative traceability results are compared item by item with the measured parameters of the molding process (such as measured groove depth, drum runout, molding temperature, etc.) to identify all parameters that exceed the tolerance range. Matching analysis was conducted using three typical failure causal templates: "abnormal groove depth causing over-tensioning of the fabric", "eccentric drum causing slippage and dislocation", and "insufficient transition chamfer causing shear accumulation".The contribution of each abnormal item to the slip increment is calculated (for example, a 0.2mm undercut in groove depth results in a 0.08mm increase in average slip increment). The weighted cumulative errors from multiple parameters are then combined to form a process deviation impact weight matrix. This matrix is then mapped point by point with the structural logic gradient map to output the final process deviation data for the weak structural points. The feedback data includes the deviation item name, deviation value, deviation unit, location number of the structural weakening point caused by the deviation, impact gradient value, deviation traceability path diagram, and a causal chain table of structural partition losses, comprehensively reflecting the direct impact of each process step on the tire's structural performance.

[0141] Preferably, the present invention further provides a passenger car tire building drum parts process deviation analysis system for executing the passenger car tire building drum parts process deviation analysis method described above, the passenger car tire building drum parts process deviation analysis system comprising:

[0142] The cord layer lamination monitoring module is used to monitor the process flow of the passenger car tire building drum parts in real time and obtain the real-time monitoring process of the building drum parts process. It also analyzes the abnormal fluctuation of the cord layer lamination pressure / temperature in the real-time monitoring process of the building drum parts process and obtains the filling data of the abnormal lamination pressure / temperature fluctuation.

[0143] The cord force slip analysis module is used to simulate the imbalance of the cord lamination tension distribution based on the filling data of abnormal lamination pressure / temperature fluctuations to obtain the data of the imbalance of the cord lamination tension distribution; simulate and analyze the cord layer wrinkle and delamination based on the data of the imbalance of the cord lamination tension distribution to obtain the data of the cord layer wrinkle and delamination; and integrate the incremental force slip of the cord in the wrinkle area on the cord layer wrinkle and delamination data to obtain the incremental force slip data of the wrinkled cord;

[0144] The structural performance loss analysis module is used to identify the tire structural performance loss gradient based on the pleated cord force slip increment data to obtain the tire structural performance loss gradient; perform logical learning on the tire structural performance loss gradient to obtain the tire structural performance loss logical gradient;

[0145] The process deviation traceability module is used to trace the principle of weak structural points of tire building drum parts based on the logical gradient of tire structural performance loss and the force slip increment data of the pleated cord, and obtain the principle traceability data of weak structural points of parts; based on the principle traceability data of weak structural points of parts, process deviation identification is performed to obtain process deviation data of weak structural points, and the process deviation data of weak structural points is fed back to the terminal.

[0146] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing process deviation of car tire building drum parts, characterized in that: The following steps are involved: Step S1: Real-time monitoring of the process flow of the car tire building drum parts to obtain a real-time monitoring process flow of the building drum parts; Analyze abnormal lamination pressure / temperature fluctuations of the cord layer in the real-time monitoring process of the forming drum parts process to obtain filling data of abnormal lamination pressure / temperature fluctuations; Step S2: simulating the imbalance distribution of tension in the cord fabric lamination according to the filling data of abnormal lamination pressure / temperature fluctuation to obtain the imbalance distribution data of tension in the cord fabric lamination; Carry out simulation analysis of ply wrinkle and delamination based on the imbalance data of ply lamination tension distribution to obtain ply wrinkle and delamination data; Integrate the force-slip increment of the cord in the wrinkle area on the wrinkle layer data to obtain the force-slip increment data of the wrinkle cord; Step S3: Identifying the tire structural performance loss gradient based on the wrinkle cord force slippage increment data to obtain the tire structural performance loss gradient; performing logical learning on the tire structural performance loss gradient to obtain the tire structural performance loss logical gradient; Step S4: tracing the principle of the weak structural point of the tire building drum parts according to the logical gradient of the tire structural performance loss and the incremental data of the force slip of the pleated cord to obtain the principle tracing data of the weak structural point of the parts; Based on the traceability data of the weak structural points of parts, process deviations are identified to obtain the process deviation data of the weak structural points, and the process deviation data of the weak structural points are fed back to the terminal.

2. The method for analyzing process deviation of car tire building drum parts according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Real-time monitoring of the process flow of the car tire building drum parts to obtain a real-time monitoring process flow of the building drum parts process; Step S12: extracting the ply process flow from the real-time monitoring process of the forming drum part process to obtain the ply process flow data; Step S13: analyzing abnormal lamination pressure / temperature fluctuation of the ply on the ply process flow data to obtain abnormal lamination pressure / temperature fluctuation data; Step S14: Fill missing values in the bonding pressure / temperature abnormal fluctuation data to obtain bonding pressure / temperature abnormal fluctuation filled data.

3. The method for analyzing process deviation of car tire building drum parts according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: simulating the imbalance distribution of tension in the cord bonding process according to the filling data of abnormal bonding pressure / temperature fluctuations to obtain the imbalance distribution data of tension in the cord bonding process; Step S22: performing non-normal distribution structure analysis on the cord lamination tension distribution imbalance data to obtain tension imbalance non-normal distribution structure data; Step S23: performing a simulation analysis of the cord ply wrinkle layering based on the tension imbalance non-normal distribution structure data to obtain cord ply wrinkle layering data; Step S24: integrating the force-slip increment of the cord in the wrinkle area of the cord ply wrinkle layer data to obtain the force-slip increment data of the wrinkle cord.

4. The method for analyzing process deviation of car tire building drum parts according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing spectrum conversion on the tension imbalance non-normal distribution structure data to obtain a tension imbalance non-normal distribution spectrum diagram; Step S232: performing imbalance outlier multi-peak intensity asymmetry analysis on the tension imbalance non-normal distribution spectrum to obtain tension imbalance multi-peak intensity asymmetry data; Step S233: Calculating the tension difference between adjacent regions based on the tension imbalance multi-peak intensity asymmetric data to obtain the tension difference between adjacent regions; Step S234: Calculating the cyclic stretch kinetic energy difference of the carcass layer according to the tension difference between adjacent regions to obtain the cyclic stretch kinetic energy difference of the carcass layer; Step S235: analyzing the loss of adhesion between the plies based on the kinetic energy difference of the cyclic stretching of the plies to obtain data on the loss of adhesion between the plies; Step S236: performing a simulation analysis of the ply wrinkle delamination based on the ply cyclic stretching kinetic energy difference and the inter-ply adhesion loss data to obtain ply wrinkle delamination data.

5. The method for analyzing process deviation of car tire building drum parts according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: analyzing the cord radian tilt variation in the wrinkle region of the cord ply wrinkle layer data to obtain cord radian tilt variation data; Step S242: performing cord spacing interleaving density increment analysis on the cord ply wrinkle layer data according to the cord radian tilt change data to obtain cord spacing interleaving density increment data; Step S243: calculating the cord misalignment force loss interval based on the cord spacing interlacing density increment data to obtain the cord misalignment force loss interval; Step S244: performing tire lateral rotation force imbalance simulation calculation based on the cord misalignment force loss interval to obtain lateral rotation force imbalance data; Step S245: integrating the force slip increment of the cord in the wrinkle area based on the cord misalignment force loss interval and the lateral rotation force imbalance data to obtain the force slip increment data of the wrinkle cord.

6. The method for analyzing process deviation of car tire building drum parts according to claim 5, characterized in that: Step S244 includes the following steps: The force amplitude change rate of the tire lateral rotation cord is extracted according to the cord misalignment force loss interval to obtain the force amplitude change rate of the lateral rotation cord; Based on the lateral rotation cord force amplitude change rate, the cord spacing interlaced density increment data is used to calculate the cumulative difference of the cord intersection angle force offset to obtain the cumulative difference data of the angle force offset; Performing directional resultant force vector superposition processing on the angle force offset cumulative difference data to obtain angle force offset resultant force superposition data; Based on the superposition data of the angle force offset resultant force, the tire lateral rotation force imbalance simulation calculation is performed to obtain the lateral rotation force imbalance data.

7. The method for analyzing process deviation of car tire building drum parts according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: normalizing the incremental force slip data of the pleated cord to obtain normalized incremental force slip data of the pleated cord; Step S32: performing tire dynamic balance imbalance simulation evaluation based on the normalized data of the wrinkled cord force slip increment to obtain tire dynamic balance imbalance evaluation data; Step S33: identifying the tire structural performance loss gradient based on the tire dynamic balance imbalance assessment data and the normalized data of the pleated cord force slip increment to obtain the tire structural performance loss gradient; Step S34: performing logical learning on the tire structure performance loss gradient to obtain the tire structure performance loss logical gradient.

8. The method for analyzing process deviation of car tire building drum parts according to claim 7, characterized in that: Step S33 includes the following steps: Step S331: performing high-speed rotation centrifugal force loss simulation equivalent evaluation on the tire dynamic balance imbalance evaluation data to obtain high-speed rotation centrifugal force loss equivalent data; Step S332: performing radial force / lateral force fluctuation transient mean difference regression analysis on the tire dynamic balance imbalance assessment data based on the high-speed rotation centrifugal force loss equivalent data to obtain radial force / lateral force fluctuation transient mean difference regression data; Step S333: performing numerical quantization of the shear stress accumulation increment on the normalized data of the force slip increment of the pleated cord according to the transient mean difference regression data of the radial force / lateral force fluctuation to obtain a quantized value of the shear stress accumulation increment; Step S334: Identify the tire structural performance loss gradient based on the shear stress accumulation increment quantization value and the radial force / lateral force fluctuation transient mean difference regression data to obtain the tire structural performance loss gradient.

9. The method for analyzing process deviation of car tire building drum parts according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: tracing the principle of the weak structural point of the tire building drum parts according to the logical gradient of the tire structural performance loss and the incremental data of the force slip of the pleated cord to obtain the principle tracing data of the weak structural point of the parts; Step S42: performing iterative tracing processing on the principle traceability data of the weak structural point of the part to obtain iterative tracing data of the principle traceability of the weak structural point; Step S43: Based on the weak structure point principle tracing iterative data, process deviation identification is performed to obtain weak structure point process deviation data, and the weak structure point process deviation data is fed back to the terminal.

10. A car tire building drum parts process deviation analysis system, characterized in that: Used to execute the car tire building drum parts process deviation analysis method according to claim 1, the car tire building drum parts process deviation analysis system comprises: The cord layer lamination monitoring module is used to monitor the process flow of the passenger car tire building drum parts in real time and obtain the real-time monitoring process of the building drum parts process. It also analyzes the abnormal fluctuation of the cord layer lamination pressure / temperature in the real-time monitoring process of the building drum parts process and obtains the filling data of the abnormal lamination pressure / temperature fluctuation. The cord force slip analysis module is used to simulate the imbalance of the cord lamination tension distribution based on the filling data of abnormal lamination pressure / temperature fluctuations to obtain the data of the imbalance of the cord lamination tension distribution; simulate and analyze the cord layer wrinkle and delamination based on the data of the imbalance of the cord lamination tension distribution to obtain the data of the cord layer wrinkle and delamination; and integrate the incremental force slip of the cord in the wrinkle area on the cord layer wrinkle and delamination data to obtain the incremental force slip data of the wrinkled cord; The structural performance loss analysis module is used to identify the tire structural performance loss gradient based on the pleated cord force slip increment data to obtain the tire structural performance loss gradient; perform logical learning on the tire structural performance loss gradient to obtain the tire structural performance loss logical gradient; The process deviation traceability module is used to trace the principle of weak structural points of tire building drum parts based on the logical gradient of tire structural performance loss and the force slip increment data of the pleated cord, and obtain the principle traceability data of weak structural points of parts; based on the principle traceability data of weak structural points of parts, process deviation identification is performed to obtain process deviation data of weak structural points, and the process deviation data of weak structural points is fed back to the terminal.

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