Production quality management method for aluminum ornament locking assembly for passenger car

Abstract

The invention discloses a passenger car aluminum ornament locking assembly production quality management method, and belongs to the technical field of production quality management, and the method specifically comprises the steps: collecting locking force performance data of historical production batches and synchronous production line operation beat data; mapping and binding on a time axis to generate a time sequence data set containing quality state and production intensity associated information; a dynamic response curved surface of quality to production intensity is constructed based on the data set, and a quality mutation inflection point is identified to determine a critical production rhythm threshold value; generating a floating process control boundary and a dynamic translation locking force early warning interval according to a threshold value; the locking force detection value and the floating boundary are compared in real time, and a compensation instruction is output to finely adjust the forming pressure when a warning interval is approached; and finally, acquiring and returning compensated quality response data, and iteratively correcting a dynamic response curved surface and a floating control boundary.

Description

Technical Field

[0001] This invention relates to the field of production quality management technology, specifically to a production quality management method for aluminum trim locking components used in passenger vehicles. Background Technology

[0002] As a key connecting component in the vehicle's interior system, the locking force performance of aluminum trim locking assemblies directly determines the reliability of the connection and the safety of the entire vehicle. Therefore, related production quality management has become a core focus of the industry. Currently, for the production quality management of locking assemblies, existing technologies generally employ basic control methods such as performance data collection, quality standard setting, real-time detection and comparison, and process parameter adjustment. By establishing fixed quality control thresholds and early warning ranges, preliminary monitoring of locking force performance during the production process can be achieved, providing some technical support for ensuring basic product quality and production efficiency.

[0003] However, current quality management methods in locking component production fail to fully consider the overall trend of quality performance dynamically changing with production pace and organizational methods, resulting in a lack of flexibility and adaptability in quality control. Existing methods are often based on static or fixed quality control standards, which cannot capture quality fluctuations caused by production adjustments, resource allocation, or process changes during manufacturing in real time, making it difficult to form a stable and continuous quality management mechanism. This limitation makes quality control susceptible to changes in the production environment, failing to guarantee long-term quality stability. Especially in multi-batch production, quality maintenance may be interrupted, affecting product reliability and the long-term quality assurance foundation for the entire vehicle's use. Summary of the Invention

[0004] The purpose of this invention is to provide a production quality management method for aluminum trim locking components used in passenger vehicles, thereby solving the problems in the background art: The objective of this invention can be achieved through the following technical solutions: A method for quality management in the production of locking assemblies for aluminum trim parts in passenger vehicles, comprising the following steps: S1: Collect locking force performance data of historical production batches of aluminum trim locking components for passenger vehicles and synchronous production line cycle data; map and bind the locking force performance data and cycle data on the time axis to generate a time-series dataset containing information related to quality status and production intensity. S2: Construct a dynamic response surface of the quality performance of locking components relative to production intensity using time-series datasets; identify inflection points where quality indicators on the dynamic response surface change abruptly, and determine the critical production rhythm threshold for maintaining quality stability under the current production organization method; S3: Generates a floating process control boundary that changes in real time with the production line's operating rhythm based on the critical production rhythm threshold; during production acceleration or deceleration, automatically shifts the upper and lower limits of the locking force warning range to construct a dynamic quality monitoring range that matches the current production load capacity. S4: Compare the real-time detected locking force value of the locking component with the floating process control boundary; when the detected value approaches the warning range of the floating boundary, output the process parameter compensation command to the production equipment control unit, and offset the tendency of quality fluctuation caused by changes in production rhythm by fine-tuning the molding pressure. S5: Collect the quality response data of the locking component after process parameter compensation and send it back to the dynamic response surface model; use the response data to correct the dynamic response surface and subsequent floating process control boundaries, and complete the iterative update of the relationship between production rhythm and quality performance.

[0005] As a further aspect of the present invention: in step S1, the process of mapping and binding the locking force performance data and the operating cycle data on the time axis to generate a time-series dataset containing information related to quality status and production intensity is as follows: The production line main controller and locking force detection unit read the production line cycle time data and locking force performance data respectively; the high-precision network timing protocol of industrial Ethernet is used to synchronize the clocks of the two independent data sources, and each piece of collected data is marked with a nanosecond-level timestamp. Using the timestamp sequence of production line cycle data as a reference coordinate, the locking force performance data is mapped to the corresponding cycle based on the proximity principle of timestamps; by comparing the consistency of timestamps, discrete locking force detection values ​​are logically associated and bound to the continuously changing production line cycle data at that moment. The logically related cycle time values ​​and locking force performance data are merged and stored; a multidimensional structured data table with timestamp as the unique index key is constructed, and a time-series dataset containing the correspondence between production intensity input and quality status output at each moment is output.

[0006] As a further aspect of the present invention: In step S2, the process of constructing a dynamic response surface of the quality performance of the locking component relative to production intensity using a time-series dataset; identifying inflection points where quality indicators on the dynamic response surface undergo abrupt changes; and determining the critical production rhythm threshold for maintaining quality stability under the current production organization method is as follows: Production intensity data was extracted from the time series dataset as the independent variable and locking component quality performance data as the dependent variable. The least squares method was used to perform nonlinear fitting on the independent and dependent variables to establish a dynamic response surface mathematical model describing the continuous change of quality indicators with production intensity. The second derivative of the established dynamic response surface model with respect to the production intensity variable is obtained; the curvature values ​​of each discrete point on the surface are calculated, and a curvature distribution curve reflecting the sensitivity of the quality performance of the locking component to changes in production intensity is generated. Retrieve the coordinates of the curvature distribution curve where the curvature value exhibits a local maximum; mark the coordinates as the inflection point where the quality index undergoes a sudden change, and use them as the critical dividing point to distinguish between a stable production state and a nonlinear quality fluctuation state; The identified inflection point coordinates are orthogonally projected onto the production intensity coordinate axis of the dynamic response surface; the specific production intensity value corresponding to the projection point is read and locked as the critical production rhythm threshold for maintaining quality stability under the current production organization mode.

[0007] As a further aspect of the present invention: the process of obtaining the second derivative of the established dynamic response surface model with respect to the production intensity variable; calculating the curvature values ​​at each discrete point on the surface; and generating a curvature distribution curve reflecting the sensitivity of the locking component's quality performance to changes in production intensity is as follows: The numerical computation unit is invoked to parse the function polynomial expression of the dynamic response surface mathematical model; second-order differential operations are performed on the production intensity variable in the expression to derive the analytical expression of the second-order derivative function describing the gradient change rate of the quality index; Extract the production intensity coordinate values ​​of each discrete point on the dynamic response surface model and substitute them into the analytical expression of the second derivative function; combine the first derivative values ​​of the corresponding points and the differential geometric curvature calculation formula to calculate the geometric curvature value at each discrete point. An analytical coordinate system is established with production intensity as the horizontal axis and geometric curvature values ​​as the vertical axis. The geometric curvature values ​​are mapped to this coordinate system and a smooth interpolation fitting algorithm is used to connect the points to generate a curvature distribution curve that reflects the continuous change of sensitivity.

[0008] As a further aspect of the present invention: in step S3, the process of generating a floating process control boundary that changes in real time with the production line's operating rhythm based on the critical production rhythm threshold is as follows: Collect the real-time production line operating cycle time and calculate the load difference ratio between it and the critical production rhythm threshold; substitute this load difference ratio into the preset boundary compensation algorithm to calculate the boundary shift correction value adapted to the current production load state. Retrieve the basic static control range data of the locking component; use the boundary translation correction value to perform a weighted offset calculation on the upper and lower limits of the basic static control range to obtain the instantaneous coordinate values ​​of the dynamic range; The calculated dynamic range real-time coordinate values ​​are continuously output to the process monitoring module in a time series; by continuously updating the real-time coordinate values, a floating process control boundary is formed that is linked in real time with the production line's operating rhythm in the time domain.

[0009] As a further aspect of the present invention: in step S3, the process of automatically shifting the upper and lower limits of the locking force warning range during production acceleration or deceleration to construct a dynamic quality monitoring range that matches the current production load capacity is as follows: Calculate the real-time differential value of the production line's operating cycle time to obtain the cycle time change rate; determine the current production acceleration or deceleration state based on the positive or negative sign of the cycle time change rate, and simultaneously quantify the specific intensity of the change. The quantified intensity of change is substituted into a preset rhythm-deviation compensation function model for calculation; the interval translation compensation value that maintains a linear mapping relationship with the current acceleration or deceleration trend is obtained analytically. The interval translation compensation value is superimposed and corrected to the current upper and lower limits of the floating process control boundary; the numerical interval after superposition calculation is locked as the final dynamic quality monitoring range adapted to the current production load capacity.

[0010] As a further aspect of the present invention: in step S4, the process of outputting process parameter compensation instructions to the production equipment control unit to offset the tendency of quality fluctuations caused by changes in production rhythm by fine-tuning the molding pressure is as follows: Calculate the deviation of the real-time locking force value relative to the center line of the floating process control boundary; combine the deformation modulus of the locking component material to reverse-calculate the molding pressure compensation value required to correct the current deviation; The molding pressure compensation value is encoded into a parameter correction instruction specific to the production equipment control unit; this instruction is sent first to the underlying programmable logic controller responsible for executing the pressing action via a high-speed industrial bus network. The programmable logic controller dynamically adjusts the output power of the pressing actuator according to instructions; in subsequent pressing strokes, it directly outputs the changed forming pressure to physically correct the mass data deviation of the locking component.

[0011] As a further aspect of the present invention: in step S5, the process of using response data to correct the dynamic response surface and subsequent floating process control boundaries to complete the iterative update of the correlation between production rhythm and quality performance is as follows: Import the quality response data of the locking components after process parameter compensation and the corresponding production line cycle time data into the original time-series dataset; assign the latest data in this set a higher calculation weight than the historical data to generate a weighted update dataset that focuses on reflecting the recent equipment status; We refit the nonlinear relationship between production intensity and quality performance using a weighted updated dataset to generate a corrected dynamic response surface; we recalculate the curvature extrema to locate the inflection point coordinates where quality indicators reflecting current equipment capabilities undergo abrupt changes. The critical production rhythm threshold is refreshed based on the new inflection point coordinates, and the subsequent floating process control boundary is reconstructed. The updated boundary parameters are then sent to the process monitoring module to cover the old standards in order to complete the iterative update of the relationship between production rhythm and quality performance.

[0012] The beneficial effects of this invention are: Based on the above technical solution, the beneficial effects of the present invention are as follows: This invention overcomes the limitations of traditional static quality control standards, which cannot adapt to flexible production rhythms, by constructing a dynamic response surface of quality performance relative to production intensity. Utilizing time-series datasets, it accurately identifies inflection points where quality indicators undergo abrupt changes, scientifically defining critical production rhythm thresholds under current equipment capacity and process conditions. It upgrades the quality monitoring range from a rigid, fixed numerical range to a floating process control boundary that is linked in real-time with the production line's operating rhythm. This mechanism automatically adapts to changes in inertial load during production acceleration, deceleration, or steady-state high-speed operation, effectively solving the problem in existing technologies where fluctuations in the locking force of aluminum components caused by adjustments in production organization methods and changes in cycle speed are difficult to capture by traditional standards. This ensures the accuracy and physical applicability of quality judgment standards under different production loads, achieving a dynamic balance between improving production efficiency and maintaining quality stability.

[0013] This invention establishes a closed-loop proactive compensation mechanism based on real-time detection and model iteration, significantly improving the long-term stability and system adaptability of quality management. By proactively outputting process parameter compensation commands to the equipment when the detected values ​​approach the warning range of the floating boundary, the quality deviation tendency caused by rhythm changes can be offset by fine-tuning the molding pressure, transforming passive post-screening into proactive pre-intervention. More importantly, by continuously correcting the dynamic response surface and control boundaries using the response data after process compensation, this management method possesses self-learning and self-evolution capabilities, automatically absorbing the accumulated errors caused by equipment aging, mold wear, or environmental drift. This iterative update mechanism not only ensures the high consistency of locking components in long-cycle, multi-batch production but also provides a solid underlying process guarantee for the reliability of aluminum trim connections in the vehicle's usage phase. Attached Figure Description

[0014] The invention will now be further described with reference to the accompanying drawings.

[0015] Figure 1 This is a flowchart illustrating a production quality management method for aluminum trim locking components for passenger vehicles according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 As shown, this invention relates to a production quality management method for aluminum trim locking assemblies used in passenger vehicles, comprising the following steps: S1: Collect locking force performance data of historical production batches of aluminum trim locking components for passenger vehicles and synchronous production line cycle data; map and bind the locking force performance data and cycle data on the time axis to generate a time-series dataset containing information related to quality status and production intensity. S2: Construct a dynamic response surface of the quality performance of locking components relative to production intensity using time-series datasets; identify inflection points where quality indicators on the dynamic response surface change abruptly, and determine the critical production rhythm threshold for maintaining quality stability under the current production organization method; S3: Generates a floating process control boundary that changes in real time with the production line's operating rhythm based on the critical production rhythm threshold; during production acceleration or deceleration, automatically shifts the upper and lower limits of the locking force warning range to construct a dynamic quality monitoring range that matches the current production load capacity. S4: Compare the real-time detected locking force value of the locking component with the floating process control boundary; when the detected value approaches the warning range of the floating boundary, output the process parameter compensation command to the production equipment control unit, and offset the tendency of quality fluctuation caused by changes in production rhythm by fine-tuning the molding pressure. S5: Collect the quality response data of the locking component after process parameter compensation and send it back to the dynamic response surface model; use the response data to correct the dynamic response surface and subsequent floating process control boundaries, and complete the iterative update of the relationship between production rhythm and quality performance.

[0018] In one embodiment of the present invention, the process of mapping and binding the locking force performance data and the operating cycle data on the time axis in step S1 to generate a time-series dataset containing information related to quality status and production intensity is as follows: First, basic physical data collection was conducted for the locking assemblies of aluminum trim components used in passenger vehicles. In the context of large-scale continuous manufacturing of these components, key performance indicator data covering the entire historical production batches was acquired as an analytical benchmark. The specific operational process involved selecting equipment operation records from the past 3 to 6 months as the sample source, focusing on the locking force performance data of the locking components after pressing and the real-time operating cycle data of the production line at that time. Locking force performance data was directly read by high-precision mechanical sensors installed at the end of the production line, recording the actual holding force value of each individual product in Newtons, reflecting the final delivery quality of the product. Production line operating cycle data was obtained through encoders or counters, reflecting the current production efficiency and equipment operating speed, typically measured in seconds or minutes. During the data collection process, data integrity was ensured, with samples covering all periods from low-speed trial production to high-speed full-load operation, thus providing sufficient sample space for establishing a correlation model between quality and speed. These historical data should include not only parameters for qualified products, but also data for some marginal or non-qualified products, in order to comprehensively reflect the actual impact of changes in production intensity on product quality and lay a solid data foundation for establishing an accurate control model.

[0019] The main controller of the production line and the locking force detection unit at the end are connected via an industrial Ethernet bus to capture data streams from these two independent sources in real time. To address potential discrepancies between the internal clock oscillators of different devices, the IEEE 1588 Precision Clock Synchronization Protocol (PTP) is used to provide unified time synchronization for all nodes in the network. The master clock node sends synchronization messages to the slave clock nodes, calculates and automatically compensates for network transmission delays and clock drift, thus strictly controlling the clock deviation between the main controller and the detection unit to the sub-microsecond level. Based on this high-precision synchronization, each piece of operating cycle data read from the main controller and each piece of locking force performance data read from the detection instrument is stamped with an absolute timestamp with nanosecond precision. This high-precision timestamp accurately records the physical moment of each signal generation, completely eliminating the risk of timing errors caused by signal transmission delays or processing queue backlogs, ensuring strict alignment of multi-source heterogeneous data in the time dimension, and providing a unique physical reference for accurate data correlation.

[0020] Since production line cycle time data is typically a continuously changing analog quantity or high-frequency sampling sequence that varies with equipment acceleration and deceleration, while locking force performance data is based on discrete event data from individual product output, a high-frequency cycle time data timestamp sequence is needed as a reference coordinate axis. The processing unit iterates through the nanosecond-level timestamps of each locking force data point, using a binary search or sliding window algorithm on the cycle time axis to find the closest time stamp. By comparing the consistency of the two timestamps, a point-to-point logical match is performed between the discrete locking force detection value and the instantaneous operating speed of the production line at that specific moment. If the difference between the two timestamps is within a preset minimum tolerance range, they are determined to belong to the same production cycle, thus establishing a one-to-one logical association. This binding method can accurately restore the actual production intensity experienced by the product at the moment of forming, effectively eliminating the errors caused by relying solely on average speed calculations, achieving precise linkage between quality data and process rhythm at the micro level, and ensuring that the speed background at the time of each quality data point can be found.

[0021] The processing unit allocates dedicated storage space in the database to merge the successfully paired operating cycle time values ​​and locking force performance data. A multidimensional structured data table is constructed, using nanosecond-level timestamps as unique index keys. This table includes auxiliary fields such as serial number, absolute time, instantaneous production cycle time, measured locking force, and ambient temperature. Each row of data strictly corresponds to a specific production moment, clearly showing the production intensity input conditions and corresponding quality status output results at that moment. After cleaning and formatting, the data table ultimately forms a standardized time-series dataset, which can be directly called by the mathematical modeling unit to analyze the specific impact of production rhythm on quality stability. In this way, the originally chaotic and independent equipment parameters are transformed into high-value information assets with strong correlation characteristics. This data can intuitively reflect the dynamic evolution trend of product performance under different load conditions, providing reliable data support for intelligent process parameter compensation and also providing a quantitative basis for long-term equipment capability assessment.

[0022] In one embodiment of the present invention, step S2 involves constructing a dynamic response surface of the locking component's quality performance relative to production intensity using a time-series dataset; identifying inflection points where quality indicators on the dynamic response surface undergo abrupt changes; and determining the critical production rhythm threshold for maintaining quality stability under the current production organization method. First, the data processing unit extracts and defines variables for production intensity data and locking component quality performance data. It directly accesses the previously constructed time-series dataset, precisely selecting the production line cycle time values ​​as the independent variable data sequence, and simultaneously extracting the locking force detection values ​​corresponding to the same timestamp as the dependent variable data sequence. During this process, the processing unit preprocesses the raw data to remove invalid null values ​​caused by sensor malfunctions or network packet loss, ensuring that each data pair input to the model has complete physical meaning. The independent variables represent the workload and operating speed of the production equipment, which are active input parameters in process control, while the dependent variable objectively reflects the final physical performance of the product under those load conditions, representing the passive output of the process. By aligning these two sets of data in mathematical space, the processing unit prepares the data for establishing the analytical relationship between them. This aims to uncover the inherent laws governing the impact of equipment operating speed on product quality through a large number of discrete production records, thereby transforming the complex physical production process into a numerical analysis object capable of mathematical calculations.

[0023] The core step in constructing the mathematical model is to perform nonlinear fitting of the extracted independent and dependent variables using the least squares method. The computation module selects a polynomial function of appropriate order as the basic model framework and determines the optimal solution for each coefficient through iterative calculations. During the calculation process, the module calculates the sum of squared residuals between each actual observation point and the fitted surface, and minimizes this sum of squared residuals by adjusting the function coefficients, thereby obtaining a continuous curve or surface that best approximates the distribution trend of historical data. This fitting process effectively smooths out random quality fluctuation noise caused by minor material differences or environmental vibrations, and extracts the main evolutionary trend between production intensity and locking quality. The established dynamic response surface mathematical model is no longer a set of discrete numbers, but a continuously differentiable functional expression, capable of predicting the corresponding theoretical locking force value at any given production cycle point.

[0024] The second derivative of the established dynamic response surface model with respect to the production intensity variable is obtained; the curvature values ​​at each discrete point on the surface are calculated to generate a curvature distribution curve reflecting the sensitivity of the locking component's quality performance to changes in production intensity; the specific process is as follows: The numerical computation unit first initiates a deep analytical program for the dynamic response surface mathematical model. This unit reads the pre-established polynomial function expression from the storage module, accurately identifying the sign of the core independent variable representing production intensity and the corresponding constant coefficients. Based on the fundamental principles of calculus, the computation unit performs rigorous second-order differential operations on the production intensity variable in the function expression. This process first performs a first derivative operation on the original function to obtain the first derivative expression describing the rate of change of the quality index with production intensity. Then, it performs a second derivative operation on this first derivative, ultimately deriving the analytical expression of the second derivative function describing the gradient rate of change of the quality index. This analytical expression, mathematically, represents the concavity and convexity of the function curve; physically, it profoundly reveals the acceleration characteristics of the fluctuations in the quality performance of the locking component as the production rhythm changes. Through this analytical operation, the originally static surface model is transformed into a mathematical tool capable of dynamically describing the degree of change, providing underlying mathematical logic support for accurately capturing the characteristics of sudden quality changes and ensuring that the analysis of process sensitivity is based on rigorous mathematical derivation.

[0025] The computation module then performs large-scale numerical substitution and geometric operations to obtain the curvature characteristics of discrete points. It traverses every discrete sampling point within the domain of the dynamic response surface model, extracting the corresponding production intensity coordinates as input parameters. These values ​​are then substituted into the previously derived analytical expressions for the second and first derivative functions to accurately obtain the second and first derivative values ​​at that specific point. Based on the standard plane curve curvature calculation formula in differential geometry, the computation module constructs an operational relationship using the obtained first and second derivatives. The denominator of this relationship is set to the cube of the sum of the squares of the first and second derivatives, and the numerator is the absolute value of the second derivative. Through this complex algebraic operation, the computation module calculates the geometric curvature value at each discrete point. This value not only reflects the magnitude of the second derivative, but also takes into account the influence of the tangent slope, thus obtaining a normalized geometric quantity. It can quantify the severity of bending deformation of the locking component under corresponding production intensity with extremely high precision, providing quantitative data indicators for identifying potential process risk points.

[0026] A two-dimensional Cartesian coordinate system specifically for sensitivity analysis is constructed to visualize the data results. This coordinate system sets the horizontal axis to represent the production intensity variable and the vertical axis to represent the geometric curvature value. All discrete geometric curvature values ​​calculated in the previous step are mapped and projected onto this analysis coordinate system according to their corresponding production intensity coordinates, forming a series of data points scattered across the coordinate plane. To obtain a continuous and intuitive view of the changing trends, cubic spline interpolation or Gaussian smoothing fitting algorithms are used to connect these discrete points. This algorithm can fill in the missing information between points while ensuring data fidelity, ultimately generating a smooth and continuous curvature distribution curve. This curve intuitively reflects the distribution of the sensitivity of the locking component's quality performance to changes in production intensity throughout the entire production speed range. The peak areas on the curve clearly indicate the speed range where the process is extremely unstable, while the flat areas correspond to the relatively robust safe range, thus transforming complex differential calculation results into a graphical decision-making basis that engineers can easily understand.

[0027] Retrieving the coordinates of local maxima in the curvature distribution curve is a crucial step in identifying process state transitions. A full-domain scan of the generated curvature distribution curve is performed, and an extreme value search algorithm is used to find the coordinates of the curve peaks. These local maxima physically correspond to inflection points, signifying a fundamental shift in the way quality indicators change with production intensity. To the left of the inflection point, production is in a relatively stable linear control zone with minimal quality fluctuations, while to the right, production may enter a nonlinear oscillation zone, where quality deteriorates sharply with increasing speed. The points with the largest curvature values ​​are marked as the most critical abrupt inflection points and defined as the critical boundary between stable production and nonlinear quality fluctuations. This determination process is entirely based on objective mathematical calculations, avoiding subjective errors caused by human experience and accurately capturing the critical moments when equipment performance undergoes qualitative changes.

[0028] In the final stage, a geometric projection operation is used to determine the critical production rhythm threshold under the current production organization method. The processing unit orthogonally projects the coordinates of the identified quality index abrupt change inflection points onto the horizontal axis representing production intensity in mathematical space. The intersection of this projection line and the production intensity coordinate axis is the specific production rhythm value corresponding to that inflection point. The processing unit reads this value and locks it as the critical production rhythm threshold. This threshold represents the maximum safe production speed limit under the combined effects of the current equipment status, mold wear, and material properties. Once the actual production speed exceeds this value, product quality will face an extremely high risk of getting out of control. This threshold is stored in the process parameter database as a rigid constraint to guide future production scheduling and speed settings. In this way, the originally abstract mathematical model analysis results are transformed into specific process parameters that workshop managers and equipment controllers can directly identify and execute, realizing a closed-loop implementation from data analysis to engineering application.

[0029] In one embodiment of the present invention, the process of generating a floating process control boundary that changes in real time with the production line cycle time based on the critical production rhythm threshold in step S3 is as follows: First, real-time production data is collected and load status is quantitatively calculated. The data processing unit reads the actual operating cycle time of the production line at high frequency through a high-speed communication interface directly connected to the production line controller. This value accurately reflects the instantaneous processing speed and mechanical load status of the equipment. The processing unit calls the critical production rhythm threshold for maintaining quality stability, which was determined in the previous stage, from memory and compares the collected real-time operating cycle time value with this critical threshold. Through specific division and difference operation logic, the processing unit calculates the degree of approximation of the current cycle time to the critical threshold, thus obtaining a dimensionless load difference ratio. This ratio intuitively quantifies how much safety buffer space is left before the current production state is at risk of a sudden quality change. Next, the processing unit uses this load difference ratio as a key input parameter into a preset boundary compensation algorithm model. This algorithm integrates a conversion function based on material mechanical properties and equipment dynamics, which can map and convert the abstract load ratio value into a specific physical and mechanical compensation quantity. After a series of floating-point operations, the algorithm finally outputs a boundary shift correction value that adapts to the current production load state. This correction value is in Newtons and represents the theoretical offset of the control range required to maintain process stability at the current speed.

[0030] Next, a dynamic correction calculation is performed on the basic static control range of the locking assembly. The calculation module retrieves the basic static control range data of this model of locking assembly, measured under standard laboratory conditions, from the process database. This data includes the upper and lower limits of the static force value that meet the quality acceptance standards. Using the boundary translation correction value obtained in the previous step, the calculation module performs a weighted offset calculation in the same direction on these two basic static thresholds. This means that if the correction value is positive, both the upper and lower limits will shift in the direction of increasing value, and vice versa, thus maintaining a relatively constant bandwidth of the control range or fine-tuning it according to specific weights. During the calculation, different weighting coefficients are assigned to the upper and lower limits according to the process characteristics to reflect the different degrees of influence of speed changes on the extreme boundary. Through this real-time algebraic superposition operation, the calculation module transforms the originally fixed static standard into a set of instantaneous coordinate values ​​for a dynamic range that can adapt to the current working conditions. This set of values ​​is no longer a rigid inspection line, but has physical meaning that matches the current operating state of the equipment, accurately defining the reasonable mechanical range for determining whether the product is qualified at the current instant.

[0031] In the final stage, a dynamically changing process monitoring envelope is constructed through continuous data stream output. The processing unit continuously sends the calculated dynamic interval instantaneous coordinate values ​​to the process monitoring module or the visual human-machine interface via a high-speed bus, according to a strict time series format. With each millisecond change in the production line's operating rhythm, this set of upper and lower limit coordinate values ​​is constantly refreshed and reconstructed. In the time domain coordinate system, this series of continuously updated instantaneous coordinate values ​​connect to form a floating process control boundary, similar to a ribbon channel. This boundary is like skin breathing closely along the production rhythm; when the production line accelerates, the boundary automatically shifts upward or downward to accommodate reasonable fluctuations caused by inertia; when the production line decelerates, the boundary quickly falls back to tighten quality control standards.

[0032] In one embodiment of the present invention, in step S3, the process of automatically shifting the upper and lower limits of the locking force warning range during production acceleration or deceleration to construct a dynamic quality monitoring range that matches the current production load capacity is as follows: First, differential calculations and trend identification are performed to analyze changes in the production line's operating status. The calculation module continuously reads the real-time numerical stream of the production line's operating cycle time at millisecond intervals, and uses a numerical differential algorithm to perform real-time calculations on this continuously changing data sequence. This calculation process calculates the difference between the current cycle time value and the cycle time value at the previous sampling time, and divides it by an extremely short time interval increment, thereby accurately obtaining the real-time differential value of the production line's operating cycle time with respect to time. This value, in a physical sense, is the rate of change of the cycle time. The calculation module determines the current dynamic state of the equipment based on the sign of this differential value. A positive differential value may represent an extended cycle time (deceleration), while a negative differential value represents a shortened cycle time (acceleration), thus accurately identifying the current production acceleration or deceleration state. At the same time, the calculation module takes the absolute value of this differential value, thereby stripping away the directional information of the change and quantifying only the specific intensity of the change.

[0033] Next, the calculation converts the intensity of speed change into a mechanical compensation value using a pre-set function model. The processing unit calls a pre-built rhythm and deviation compensation function model from memory. This model is a linear or nonlinear relationship fitted based on a large amount of historical mechanical experimental data, specifically used to describe the specific impact of the inertial force generated by the acceleration and deceleration of the equipment on the forming quality of the locking component. The processing unit inputs the quantified intensity value of the change as an independent variable into the function model. By performing algebraic operations such as multiplication and addition, combined with the inherent stiffness coefficient and damping coefficient in the model, it analytically derives an interval translation compensation value that maintains a strict linear mapping relationship with the current acceleration or deceleration trend. This compensation value is expressed in Newtons or megapascals, and its magnitude is directly proportional to the rate of change of the equipment speed. For example, under rapid acceleration, the model will calculate a large positive compensation value to offset the pressure loss caused by machine vibration, while under slow deceleration, it will calculate a small negative compensation value.

[0034] The final stage involves the dynamic correction and final locking of the floating process control boundary. The control module acquires the calculated interval translation compensation value and applies it instantly to the floating process control boundary at the current moment. Specifically, this compensation value is superimposed and corrected onto the current upper and lower limits of the floating process control boundary, performing an algebraic weighted calculation with the same frequency and amplitude. Through this superposition correction, the floating boundary, which originally only changed with the absolute value of velocity, undergoes a secondary shift based on acceleration, giving the monitoring interval a highly flexible dynamic adaptation characteristic on the time axis. The control module then locks the calculated numerical interval, establishing it as the final dynamic quality monitoring range adapted to the current production load capacity. This range is a dedicated standard for this instantaneous millisecond, perfectly encompassing reasonable quality data fluctuations caused by equipment acceleration and deceleration inertia, while also sensitively intercepting genuine abnormal defects.

[0035] In one embodiment of the present invention, step S4, in which a process parameter compensation command is output to the production equipment control unit to offset the tendency of quality fluctuations caused by changes in production rhythm by fine-tuning the molding pressure, is as follows: First, mathematical calculations and inverse physical quantity calculations are performed to address the real-time deviation. The calculation module first obtains the real-time value of the locking force detected at the current moment, and simultaneously reads the upper and lower coordinates of the floating process control boundary at the current moment. The calculation module constructs a virtual process centerline by calculating the arithmetic mean of the upper and lower limits. This centerline represents the ideal quality target value under the current production cycle. Then, the real-time locking force value is subtracted from this process centerline to obtain a deviation value with a positive or negative sign. This value precisely quantifies the extent to which the current product quality deviates from the ideal state. To convert this quality-level deviation into a controllable quantity that the equipment can execute, the calculation module introduces the deformation modulus of the locking component material as a key conversion parameter. Based on the stress-strain relationship in mechanics of materials and the generalized form of Hooke's Law, the calculation module establishes an inverse calculation model. This model takes the deviation value as input and, combined with the elastic modulus and plastic deformation characteristic parameters of the material, inversely derives the additional molding pressure required to eliminate this deviation. After this series of floating-point operations, the module finally outputs the molding pressure compensation value required to correct the current deviation. This value is accurate to two decimal places, providing the actuator with a clear mechanical adjustment target.

[0036] Next, the task of encoding and transmitting the physical compensation values ​​into industrial control commands is executed. After receiving the calculated molding pressure compensation values, the communication processing module first digitizes them according to the standard format of the underlying equipment communication protocol. The module converts the analog pressure values ​​into hexadecimal control words, adds a frame header, frame trailer, and cyclic redundancy check code, and encapsulates it into a parameter correction command data packet specific to the production equipment control unit. To ensure timely response to this command, the communication module establishes a dedicated transmission channel using a high-speed industrial bus network. In this network protocol stack, the communication module marks the parameter correction command as the highest priority real-time data frame, allowing it to preempt bandwidth and surpass the transmission queue of other non-critical status data. Through this real-time communication mechanism, the command is preferentially sent to the underlying programmable logic controller responsible for executing the pressing action.

[0037] In the final stage, the programmable logic controller (PLC) performs physical-level power regulation and quality correction. Upon receiving a high-priority parameter correction command, the underlying PLC immediately triggers its internal interrupt service routine to parse and verify the command. Based on the pressure compensation value contained in the command, the controller dynamically adjusts the output power signal of the proportional valve or servo driver connected to the press-fitting actuator via a digital-to-analog converter interface. This adjustment is not merely simple on / off control, but a fine-tuning of the output voltage or current. At the instant the subsequent press-fitting stroke begins, the press-fitting actuator outputs the corrected forming pressure based on the changed power parameters. This new pressure value acts directly on the new aluminum trim blank, physically altering the material's compression deformation. This real-time physical intervention effectively counteracts the quality fluctuations caused by previous production rhythm changes, forcing the locking force of the newly produced locking components back to the center of the floating process control boundary, thus achieving piece-by-piece closed-loop quality correction in continuous production.

[0038] In one embodiment of the present invention, step S5, which involves using response data to correct the dynamic response surface and subsequent floating process control boundaries to complete the iterative update of the relationship between production rhythm and quality performance, is as follows: First, a weighted import and dataset update operation is performed based on the latest production feedback data. Once the process parameter compensation command is executed and the new locking assembly is installed, the detection instrument immediately reads the final quality response data and pairs it with the corresponding production line cycle time data. The processing unit imports this data stream containing the latest equipment status characteristics into the existing time-series dataset in the storage module in real time. To accurately reflect the current wear status of the equipment and the impact of environmental thermal drift, the calculation algorithm introduces a time-decay-based weight allocation mechanism when merging data. This mechanism assigns a very high calculation weight to the newly generated data, typically set to a baseline value of 1.0, while the weight coefficient for historical data from several days or weeks ago is gradually reduced according to the time interval. Through this differentiated data processing strategy, the processing unit generates a weighted updated dataset that focuses on reflecting the recent equipment status. This dataset gradually forgets outdated equipment behavior patterns in terms of statistical characteristics, focusing instead on current physical characteristics, thus providing the most timely sample basis for subsequent mathematical modeling.

[0039] The computation module utilizes the constructed weighted updated dataset to perform refitting and model correction tasks for nonlinear relationships. The module calls the weighted least squares algorithm, using the weighted production intensity data as the independent variable and quality performance data as the dependent variable, to recalculate the polynomial coefficients in the dynamic response surface function. This iterative calculation process fine-tunes the geometry of the dynamic response surface, thus more closely approximating the actual performance curve of the equipment during operation. Next, the module performs second-order differentiation on the corrected dynamic response surface again and, combined with the first derivative, recalculates the curvature value at each discrete point on the surface. By scanning the updated curvature distribution across the entire domain, the module can accurately capture the latest coordinates where the curvature value exhibits a local maximum. This new coordinate point objectively reveals the specific critical point where quality indicators undergo abrupt changes with production intensity under current equipment capacity conditions, thus achieving dynamic positioning of the inflection point coordinates and ensuring that the mathematical model can sensitively perceive minute drifts in equipment performance.

[0040] In the final stage, the core control parameters are refreshed and a closed-loop update is completed based on the repositioned inflection point coordinates. The processing unit reads the projection value of the new inflection point coordinates onto the production intensity axis and establishes it as the latest critical production rhythm threshold. Based on this updated threshold standard, the processing unit calls the boundary generation algorithm to reconstruct the subsequent floating process control boundary and recalculates the upper and lower limits of the safe pressure range under different operating cycles. These corrected boundary parameters are immediately packaged into update instruction packages and sent to the process monitoring module in the field via the industrial communication network. Upon receiving the instruction, the process monitoring module immediately performs a parameter hot-swap operation, using the new control boundary to cover the old monitoring standard. This operation ensures that in the next production decision, the quality management logic is entirely based on the latest equipment capabilities and process correlation, realizing a fully automated closed-loop iteration from data acquisition to model correction to standard update, eliminating the risk of misjudgment caused by the control standard lagging behind changes in equipment status.

[0041] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A production quality management method for an aluminum trim lock assembly for a passenger vehicle, characterized by, The method comprises the following steps: S1: Collecting the locking force performance data of the historical production batch of the aluminum trim part locking assembly for vehicle use and the synchronous production line running rhythm data; mapping and binding the locking force performance data and the running rhythm data on the time axis to generate a time series data set containing the quality state and production intensity association information; S2: Using the time series data set to construct a dynamic response surface of the quality performance of the locking assembly relative to the production intensity; identifying the inflection point of the sudden change of the quality index on the dynamic response surface to determine the critical production rhythm threshold for maintaining quality stability under the current production organization mode; S3: Generating a floating process control boundary that changes in real time with the running rhythm of the production line according to the critical production rhythm threshold; during the acceleration or deceleration of production, automatically translating the upper limit value and the lower limit value of the locking force warning interval to construct a dynamic quality monitoring range that matches the current production load capacity; S4: Comparing the real-time detected locking force value of the locking assembly with the floating process control boundary; when the detected value approaches the warning interval of the floating boundary, outputting a process parameter compensation instruction to the production equipment control unit to offset the quality fluctuation tendency caused by the change of production rhythm by fine-tuning the molding pressure; S5: Collecting the quality response data of the locking assembly after process parameter compensation and returning it to the dynamic response surface model; using the response data to correct the dynamic response surface and the subsequent floating process control boundary to complete the iterative update of the correlation between production rhythm and quality performance.

2. The production quality management method for an aluminum trim locking assembly for a passenger vehicle according to claim 1, characterized by, In the step S1, the process of mapping and binding the locking force performance data and the running rhythm data on the time axis to generate a time series data set containing the quality state and production intensity association information is as follows: Reading the production line running rhythm data and the locking force performance data from the production line main controller and the locking force detection unit respectively; using the high-precision network time protocol of industrial Ethernet to synchronize the clocks of the two independent data sources, and marking each collected data with a nanosecond-level time stamp; Taking the time stamp sequence of the production line running rhythm data as the reference coordinate, mapping the locking force performance data into the corresponding rhythm period according to the time stamp proximity principle; by comparing the consistency of the time mark, logically associating and binding the discrete locking force detection values with the continuously changing production line running rhythm data at that time point; Merging and storing the running rhythm values and the locking force performance data that have completed logical association; constructing a multi-dimensional structured data table with time stamp as the unique index key value, and outputting a time series data set containing the corresponding relationship between the production intensity input and the quality state output at each time.

3. The production quality management method for an aluminum trim locking assembly for a passenger vehicle according to claim 1, characterized by, In the step S2, the process of using the time series data set to construct a dynamic response surface of the quality performance of the locking assembly relative to the production intensity; identifying the inflection point of the sudden change of the quality index on the dynamic response surface to determine the critical production rhythm threshold for maintaining quality stability under the current production organization mode is as follows: Extracting production intensity data from the time series data set as the independent variable and the locking assembly quality performance data as the dependent variable; using the least squares method to nonlinearly fit the independent variable and the dependent variable, and establishing a dynamic response surface mathematical model describing the continuous change of the quality index with the production intensity; Taking the second derivative of the established dynamic response surface model with respect to the production intensity variable; calculating the curvature values of each discrete point on the surface to generate a curvature distribution curve reflecting the sensitivity of the locking assembly quality performance to changes in production intensity; Retrieving the coordinate position where the curvature value in the curvature distribution curve presents a local maximum value; marking this coordinate position as the inflection point where the quality index undergoes a sudden change, as the critical dividing point between stable production state and quality nonlinear fluctuation state; Orthogonal projecting the identified inflection point coordinates to the production intensity coordinate axis of the dynamic response surface; reading the specific production intensity value corresponding to the projection point and locking it as the critical production rhythm threshold for maintaining quality stability under the current production organization mode.

4. The production quality management method for an aluminum trim part locking assembly for a passenger vehicle according to claim 3, characterized by, The process of taking the second derivative of the established dynamic response surface model with respect to the production intensity variable; calculating the curvature values of each discrete point on the surface to generate a curvature distribution curve reflecting the sensitivity of the locking assembly quality performance to changes in production intensity is as follows: Calling the numerical calculation unit to analyze the function polynomial expression of the dynamic response surface mathematical model; performing second-order differentiation operation on the production intensity variable in the expression to derive the second-order derivative function analytical expression describing the gradient change rate of the quality index; Extracting the production intensity coordinate values of each discrete point on the dynamic response surface model and substituting them into the second-order derivative function analytical expression; Combining the first-order derivative value of the corresponding point and the differential geometry curvature calculation formula, the geometric curvature value at each discrete point is calculated; Establishing an analysis coordinate system with production intensity as the horizontal axis and geometric curvature value as the vertical axis; mapping the geometric curvature value to this coordinate system and connecting the points using a smooth interpolation fitting algorithm to generate a curvature distribution curve reflecting the continuous change of sensitivity.

5. The production quality management method for an aluminum trim part locking assembly for a passenger vehicle according to claim 1, characterized by, In step S3, the process of generating a floating process control boundary that changes in real time with the production line running tempo based on the critical production rhythm threshold is as follows: Collecting the real-time production line running tempo and calculating the load difference ratio between it and the critical production rhythm threshold; substituting this load difference ratio into the pre-set boundary compensation algorithm to calculate the boundary translation correction value that adapts to the current production load state; Retrieving the basic static control interval data of the locking assembly; performing same-direction weighted offset calculation on the upper and lower limit values of the basic static control interval using the boundary translation correction value to obtain the instantaneous coordinate values of the dynamic interval; Continuously outputting the calculated dynamic interval instantaneous coordinate values in time series to the process monitoring module; forming a floating process control boundary that is linked in real time with the production line running tempo in the time domain by continuously updating the instantaneous coordinate values.

6. The production quality management method for an aluminum trim part locking assembly for a passenger vehicle according to claim 5, characterized by, In step S3, the process of automatically translating the upper and lower limit values of the locking force warning interval during production acceleration or deceleration to build a dynamic quality monitoring range that matches the current production load capacity is as follows: The real-time differential value of the production line running tempo with respect to time is calculated to obtain a tempo change rate; the current production acceleration state or production deceleration state is determined according to the positive or negative sign of the tempo change rate, and the specific change intensity is quantified synchronously; The quantified change intensity is substituted into a preset tempo-deviation compensation function model for calculation; and an interval translation compensation value in linear mapping relationship with the current acceleration or deceleration trend is obtained by analysis; The interval translation compensation value is superimposed and corrected to the current upper limit value and the current lower limit value of the floating process control boundary; and the value interval after superimposition and calculation is locked as the final dynamic quality monitoring range adapted to the current production load capacity.

7. The production quality management method for an aluminum trim part locking assembly for a passenger vehicle according to claim 1, characterized by, In the step S4, the process of outputting a process parameter compensation instruction to the production equipment control unit to offset the quality fluctuation tendency caused by the production tempo change by fine-tuning the molding pressure is as follows: The deviation value of the real-time locking force value from the center line of the floating process control boundary is calculated; and the deviation value is inversely calculated into a molding pressure compensation value required for correcting the current deviation in combination with the deformation modulus of the locking assembly material; The molding pressure compensation value is encoded into a parameter correction instruction special for the production equipment control unit; and the instruction is preferentially sent to the bottom programmable logic controller responsible for performing the press-fitting action through a high-speed industrial bus network; The programmable logic controller dynamically adjusts the output power of the press-fitting actuator according to the instruction; and the changed molding pressure is directly output in the subsequent press-fitting stroke to physically correct the quality data deviation of the locking assembly.

8. The production quality management method for an aluminum trim part locking assembly for a passenger vehicle according to claim 1, characterized by, In the step S5, the process of completing the iterative update of the correlation between the production tempo and the quality performance by using the response data to correct the dynamic response surface and the subsequent floating process control boundary is as follows: The quality response data of the locking assembly after process parameter compensation and the corresponding production line running tempo data are imported into the original time sequence data set; the set of latest data is given a higher calculation weight than the historical data to generate a weighted update data set focusing on reflecting the recent equipment state; The nonlinear relationship between the production intensity and the quality performance is fitted again by using the weighted update data set to generate a corrected dynamic response surface; and the curvature extreme point is recalculated to locate the inflection point coordinates reflecting the sudden change of the quality index of the current equipment capacity; The critical production tempo threshold is refreshed according to the new inflection point coordinates, and the subsequent floating process control boundary is reconstructed; and the updated boundary parameters are sent to the process monitoring module to cover the old standard, thereby completing the iterative update of the correlation between the production tempo and the quality performance.

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