Integrated design method of airtightness detection tool and detection system

Abstract

The invention relates to an air tightness detection tool and detection system integrated design method in the field of air tightness detection equipment, and the method comprises the steps: obtaining product form data, extracting shape, size and material information from a product three-dimensional model database, calculating the key points of the outer contour of a product through a geometric feature analysis algorithm, and calculating the shape, size and material information of the product; determining product form diversity parameters; a clamp, an inflation system and a sorting module are decomposed into independent functional units through a modular design structure algorithm, a standardized interface protocol is generated, and physical connection and data interaction modes among the modules are determined; and according to the final tool design parameters, a solid fixture is generated by adopting a three-dimensional printing technology, a robot is controlled through an automatic assembly algorithm to complete integration of the fixture, an inflation module and a sorting module, and an air tightness detection system which can be put into production is obtained.

Description

Technical Field

[0001] The present invention relates to the field of air tightness testing equipment, and in particular to an integrated design method for air tightness testing tooling and a testing system. Background Art

[0002] Airtightness testing is a key technology for ensuring product quality in industrial manufacturing, widely used in industries such as batteries, valves, and medical devices. Its accuracy and efficiency directly impact product reliability and production efficiency. Traditional airtightness testing methods typically rely on separate fixtures and testing equipment, which suffer from poor adaptability, insufficient sealing, and low automation. These shortcomings result in a time-consuming testing process and a high rate of false positives, failing to meet the modern manufacturing industry's demand for efficient, precise, and flexible production.

[0003] The limitations of existing solutions are primarily reflected in the following aspects: Traditional fixture designs struggle to quickly adapt to products of varying shapes and sizes, requiring frequent fixture replacement or adjustment, reducing production efficiency. Leakage issues within the fixture often interfere with test results and accuracy. Furthermore, the lack of effective integration between the inspection system and the fixture makes automated operation difficult, requiring frequent manual intervention and resulting in low efficiency. These issues are particularly prominent in scenarios requiring high-precision or complex product inspections. The core challenge lies in achieving an integrated design of the fixture and inspection system to simultaneously meet the requirements for rapid adaptation, high sealing, and automated integration. First, rapid adaptation requires the fixture to flexibly accommodate diverse product forms, but existing fixture designs generally lack adaptive capabilities, making it difficult to achieve both versatility and stability. Second, high sealing requires the fixture to maintain a low leakage rate even in complex inspection environments, but traditional sealing structures are susceptible to external interference, making it difficult to ensure inspection accuracy. Finally, automated integration requires the coordinated operation of the fixture with the inflation, inspection, and sorting modules, and existing technologies lack efficient modular design and data linkage mechanisms. Unresolved technical issues such as these lead to challenges such as low inspection efficiency, unstable accuracy, and insufficient production flexibility.

[0004] Therefore, how to design an airtightness testing tooling that integrates rapid adaptation, high sealing and automated integration functions, and at the same time solve the problems of the fixture's adaptability to diversified products, sealing stability and system linkage efficiency through innovative structures, has become a key issue in this study. Summary of the Invention

[0005] The present invention provides an integrated design method for airtightness detection tooling and a detection system, comprising the following steps:

[0006] Obtain product morphology data, extract shape, size, and material information from the product 3D model database, calculate key points of the product's outer contour through geometric feature analysis algorithms, and determine product morphology diversity parameters;

[0007] Based on the product form diversity parameters, an adaptive fixture design algorithm is used, combined with a preset universal fixture design template, to generate a three-dimensional fixture model suitable for various product forms, resulting in a fixture structure with enhanced rapid adaptability.

[0008] Extract the contact surface geometric features from the fixture's 3D model, and simulate the stress distribution when the fixture contacts the product through finite element analysis to determine whether the contact surface meets high sealing requirements. If the stress distribution is uniform and there is no local stress concentration, the fixture sealing structure is considered feasible.

[0009] For the fixture sealing structure, we obtain data on the complexity of the test environment, including the temperature, pressure, and humidity fluctuation ranges. We then use the tooling leakage control algorithm to optimize the sealing material and contact surface geometry parameters to obtain a tooling design with a leakage rate below a preset threshold.

[0010] Through modular design structure algorithm, the fixture, inflation system and sorting module are decomposed into independent functional units, and standardized interface protocols are generated to determine the physical connection and data interaction between modules;

[0011] Obtain the operating status data of the fixture, inflation system, and sorting module, adopt the data linkage mechanism algorithm, establish a real-time data stream transmission channel, optimize the module collaboration efficiency through time series analysis, and obtain a system framework with improved automation integration;

[0012] Based on the automated integrated system framework, the coordination of the inflation system and the efficiency of the sorting module are simulated. A dynamic scheduling algorithm is used to adjust the timing parameters of inflation and sorting to determine whether the overall operating efficiency of the system reaches the preset threshold. If so, the final solution for the integration of tooling and inspection systems is determined.

[0013] Extract the test accuracy stability data from the integrated solution, calculate the variance and deviation of the test results through statistical analysis algorithms, and determine whether the test accuracy meets the preset standards. If so, generate the final airtightness test tooling design parameters;

[0014] According to the final tooling design parameters, 3D printing technology is used to generate a physical fixture. The robot is controlled by an automated assembly algorithm to complete the integration of the fixture with the inflation and sorting modules, resulting in an airtightness detection system that can be put into production.

[0015] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0016] The present invention discloses an integrated design method for airtightness detection tooling and detection systems. By acquiring product morphology data and analyzing geometric features, an adaptive fixture model suitable for a variety of products is generated. The sealing structure is optimized in combination with finite element analysis, and the tooling leakage control is optimized according to the complexity data of the detection environment. A modular design is adopted to integrate the fixture, inflation and sorting systems, a data linkage mechanism is established to improve the degree of automation, and the system operation efficiency is optimized through a dynamic scheduling algorithm. Finally, the detection accuracy is verified, and the tooling is manufactured using 3D printing and automated assembly technology. The present invention can quickly adapt to a variety of product forms, improve sealing performance and detection accuracy, realize the intelligent design and manufacturing of airtightness detection tooling, and effectively improve the level of product quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flowchart of an integrated design method for air tightness detection tooling and detection system.

[0018] Figure 2 This is a schematic diagram of an integrated design method for air tightness detection tooling and a detection system according to the present invention.

[0019] Figure 3 This is another schematic diagram of the integrated design method of an airtightness detection tool and a detection system according to the present invention. DETAILED DESCRIPTION

[0020] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] like Figure 1-3 In this embodiment, an integrated design method of an airtightness detection tool and a detection system may specifically include:

[0022] Step S101: Acquire product morphology data, extract shape, size and material information from the product 3D model database, calculate key points of the product outline through a geometric feature analysis algorithm, and determine product morphology diversity parameters.

[0023] Shape data, dimensional data, and material data are acquired from a product 3D model database, and data extraction techniques are used to generate a structured dataset. A geometric feature analysis algorithm is used to process the structured dataset, extracting geometric features from the shape and dimensional data to obtain a geometric feature set. A keypoint detection algorithm is used to extract contour keypoints from the geometric feature set. If the number of keypoints falls below a preset threshold, the geometric feature extraction parameters are adjusted to obtain a contour keypoint set. Based on the contour keypoint set, the distances and angles between the keypoints are calculated, and a cluster analysis algorithm is used to generate morphological diversity parameters. Surface properties are extracted from the material data. If the surface properties do not match preset standards, the morphological diversity parameters are adjusted based on the geometric feature set to obtain adjusted morphological diversity parameters. The adjusted morphological diversity parameters are compared with a preset morphological template to determine whether the product morphology meets diversity requirements and obtain a product morphology classification result. Based on the product morphology classification result, a digital description of the product morphology is generated to determine the final product morphological parameters.

[0024] For example, when extracting shape information from a product 3D model database, a STEP file parsing method based on boundary representation can be used. For example, when parsing a mobile phone case model, geometric parameters such as length 120 mm, width 65 mm, and curvature radius 2.5 mm were extracted. When calculating the model's surface area using the BRep_Tool algorithm in the OpenCascade library, the surface area of ​​the ABS plastic material was measured to be 9850 square millimeters. The material property of 1.05 grams per cubic centimeter was also extracted using the Material property tree. During the geometric feature analysis phase, the point cloud data was reconstructed using an improved Alpha-Shape algorithm (α = 1.2). 18 key feature points were identified from 25,600 vertices, including four R-corner transition points and two USB port locators. When calculating product morphological diversity parameters using principal component analysis, a cluster analysis of aspect ratio data for 300 similar products was performed, resulting in a morphological distribution matrix with a dispersion coefficient of 0.15. The eigenvalue λ1 = 0.82 indicates that the length direction is the primary dimension of morphological variation. In the dimensional tolerance analysis phase, Monte Carlo simulation was used to conduct 10,000 samplings, and it was calculated that when the assembly clearance qualification rate reached 98.7%, the corresponding critical dimensional tolerance band should be controlled within the range of ±0.2 mm.

[0025] In step S102 , based on product form diversity parameters, an adaptive fixture design algorithm is used in combination with a preset universal fixture design template to generate a three-dimensional fixture model suitable for various product forms, thereby obtaining a fixture structure with enhanced rapid adaptation capabilities.

[0026] Acquire product morphology data, extract key geometric features from the diversity parameters of the product morphology data, and generate a design parameter set. Use an adaptive algorithm to process the design parameter set, and combine it with a universal template to generate an initial fixture three-dimensional model. If the adaptation error between the initial fixture three-dimensional model and the product morphology data exceeds a preset threshold, adjust the parameters of the adaptive algorithm and regenerate the fixture three-dimensional model. Extract the key geometric features of the fixture structure from the optimized fixture three-dimensional model and generate a fixture structure description file. According to the fixture structure description file, use a structural optimization algorithm to adjust the fixture geometric parameters to obtain a fast-adapting fixture structure. Generate the final fixture three-dimensional model data through the fast-adapting fixture structure, and output the fixture design scheme. If the adaptation performance of the final fixture three-dimensional model data meets the preset threshold, determine the fixture design scheme; if it does not meet the preset threshold, return to the step of processing the design parameter set and regenerate the fixture three-dimensional model.

[0027] For example, during the implementation process, the product morphology diversity parameters are first analyzed. For example, principal component analysis (PCA) is used to reduce the dimension of the product's external dimensions, extract key characteristic parameters such as length (120±5mm), width (80±2mm) and curvature radius (R15-R50), and form a feature matrix X∈R^(n×3). Based on this, an adaptive fixture algorithm is designed, using fuzzy logic control combined with genetic algorithm optimization, setting the population size to 50, the crossover probability to 0.8, and the mutation probability to 0.1. After 100 generations of iteration, the optimal solution is obtained, and its fitness function value reaches 0.92. Then, the preset universal fixture design template is called, which contains three basic clamping units (V-block, flat clamp, arc pad) and two drive modes (pneumatic, servo motor). The feature matrix is ​​automatically matched through the parametric modeling engine. For example, when the curvature radius R>30mm, the arc pad is preferred, and the clamping force threshold is set to 200N±5%. Finally, a fixture model was generated using the 3D modeling software's API. Finite element analysis was used to verify the structural strength, yielding a maximum stress of 235 MPa and a safety factor of 1.8. Motion simulation also revealed a reduction in changeover time to 45 seconds, improving adaptability by 60%. The entire process was linked to data through the PLM system, ensuring real-time synchronization of design parameters with production requirements.

[0028] In step S103, the geometric features of the contact surface are extracted from the three-dimensional model of the fixture, and the stress distribution when the fixture and the product are in contact is simulated through finite element analysis to determine whether the contact surface meets the high sealing requirements. If the stress distribution is uniform and there is no local stress concentration, the fixture sealing structure is determined to be feasible.

[0029] A geometric segmentation algorithm is used to extract the geometric features of the contact surface from the three-dimensional model of the fixture to obtain contact surface geometric data. The contact surface geometric data is loaded through finite element analysis software, the product contact boundary conditions are set, the stress distribution is simulated, and the stress distribution data is obtained. The stress distribution data is analyzed using a mean square error algorithm. If the mean square error is lower than a preset threshold, the stress distribution is judged to be uniform, and a uniformity judgment result is obtained. Local stress values ​​are extracted from the stress distribution data. If the local stress values ​​are lower than a preset threshold, it is judged that there is no local stress concentration, and a local stress judgment result is obtained. Based on the uniformity judgment result and the local stress judgment result, if both meet the conditions, it is determined that the fixture sealing structure meets the high sealing requirements, and a sealing judgment result is obtained. Based on the sealing judgment result, if the high sealing requirements are met, it is judged that the fixture sealing structure is feasible. Generate optimized fixture three-dimensional model data.

[0030] For example, when extracting the geometric features of the contact surface from the three-dimensional model of the fixture, an algorithm based on curvature analysis can be used to identify the boundaries of the contact area. For example, a Gaussian curvature threshold of 0.05mm-1 is used to filter non-contact surfaces, and the contact surface point cloud data is extracted by the region growing method and fitted into a NURBS surface. In the finite element analysis stage, material parameters such as the elastic modulus of aluminum alloy 69GPa and Poisson's ratio 0.33 are set. An adaptive encryption strategy is used when dividing the mesh, and the unit size is controlled within 0.5mm near the contact surface. A bolt preload of 10N·m is applied as a boundary condition, and the contact pressure distribution is calculated by a nonlinear contact algorithm. The analysis results show that the maximum contact pressure is 25MPa, the pressure distribution uniformity index reaches 0.92 (1 is completely uniform), and the stress concentration coefficient is less than 1.3. When the contact width is greater than 3mm and the contact pressure gradient is less than 2MPa / mm, it is determined that the sealing requirements are met. The influence of different surface roughness (Ra 0.8 μm to 3.2 μm) was verified by parametric scanning. It was found that when the roughness was lower than 1.6 μm, the leakage rate could be controlled within 1×10 -6 Pa·m 3 / s or less. Ultimately, the contact surface profile tolerance is controlled within the range of ±0.05mm, ensuring that the pressure of more than 90% of the contact area is maintained between 15-20MPa.

[0031] In step S104, for the fixture sealing structure, the complexity data of the test environment is obtained, including the temperature, pressure and humidity fluctuation range, and the sealing material and contact surface geometric parameters are optimized using the tooling leakage control algorithm to obtain a tooling design with a leakage rate lower than a preset threshold.

[0032] Acquire environmental data, collect fluctuation range data from temperature, pressure, and humidity sensors, and determine the environmental complexity characteristic vector, which includes temperature fluctuation range, pressure fluctuation range, and humidity fluctuation range. If the temperature fluctuation range exceeds a preset threshold, the effect of temperature on the aging of the sealing material is predicted by a linear regression algorithm to obtain the material temperature resistance parameters. According to the material temperature resistance parameters, match the materials that meet the temperature resistance requirements from the preset sealing material database to determine a set of candidate sealing materials. If the candidate sealing material set contains at least one material, simulate the stress distribution of the contact surface geometry due to the pressure fluctuation range through finite element analysis to obtain geometric optimization parameters. Use a genetic algorithm to iteratively optimize the sealing material parameters and the geometric optimization parameters to obtain a leakage rate prediction value. If the leakage rate prediction value is lower than the preset threshold, generate a tooling design scheme based on the optimized sealing material parameters and contact surface geometric parameters. Perform virtual assembly verification on the tooling design scheme through three-dimensional modeling software to determine whether the sealing structure performance meets the leakage rate requirements and determine the final tooling design.

[0033] For example, during the design of the fixture sealing structure, the temperature, pressure, and humidity data of the detection environment are first collected in real time through a sensor network. The temperature fluctuation range is -20°C to 80°C, the pressure fluctuation range is 0.1MPa to 1.5MPa, and the humidity fluctuation range is 10% to 90%. This data is transmitted to the data analysis system through the Internet of Things platform. The time series analysis method is used to identify periodic changes and abnormal fluctuations in environmental parameters. Based on this data, a tooling leakage control algorithm is introduced. This algorithm combines finite element analysis and machine learning models to optimize the elastic modulus of the sealing material and the geometric parameters of the contact surface.

[0034] Specifically, the algorithm iteratively adjusted the sealing material thickness from 0.5mm to 2mm and the contact surface roughness from Ra0.8 to Ra3.2 to ensure stable sealing performance under various environmental conditions. Through simulations, the algorithm predicted the leakage rate for various parameter combinations and selected a design with a leakage rate below the preset threshold of 0.01mL / min. Finally, the optimized tooling design was verified in a laboratory setting, and the results showed a stable leakage rate of 0.008mL / min, meeting the design requirements.

[0035] Step S105: Decompose the fixture, inflation system, and sorting module into independent functional units through a modular design structure algorithm, generate a standardized interface protocol, and determine the physical connection and data interaction method between the modules.

[0036] An initial design set for the modular system is obtained, including descriptions of the fixture unit, inflation system, and sorting module. The initial design set is decomposed using a modular design algorithm to obtain multiple independent functional units, each including a preliminary functional description of each module. If there is functional duplication within the independent functional units, redundant functions are merged using a functional analysis algorithm to obtain a reduced set of functional units. The input and output requirements of each functional unit are extracted from the reduced set to generate a standardized interface specification. If parameter conflicts exist within the standardized interface specification, the parameter definitions are adjusted using a protocol optimization algorithm to generate a unified interface protocol. Data interaction rules are generated based on the unified interface protocol to determine the data flow and format between modules. Physical connection analysis is performed to obtain the mechanical and electrical connection requirements of each functional unit and determine a physical connection solution. From the physical connection solution, module collaboration constraints are extracted to generate a module collaboration process. If timing conflicts exist within the module collaboration process, the collaboration sequence is optimized using a timing adjustment algorithm to obtain a final collaboration solution. Based on the final collaboration solution, a communication protocol is generated between modules to determine the real-time requirements for data interaction. Communication protocol analysis is performed to determine whether data transmission meets real-time constraints and obtain communication protocol parameters. The performance indicators of module collaboration are extracted from the communication protocol parameters to generate an operational configuration for the modular system. A simulation is performed using the operational configuration to determine whether the system meets performance requirements, thereby obtaining an optimized system configuration. A deployment plan for the modular system is generated based on the optimized system configuration. The deployment plan is used to verify whether inter-module collaboration is stable, and final deployment parameters are determined.

[0037] For example, in the modular design structure algorithm, the gripper, inflation system, and sorting module are first decomposed into independent functional units. The gripper module uses a force sensor and position encoder to achieve precise gripping. The force sensor uses a 0-10V analog signal output, and the position encoder has a resolution of 0.01mm, ensuring a gripping accuracy within ±0.05mm. The inflation system module uses a PID control algorithm, with a set pressure range of 0-1MPa and a control accuracy of ±0.01MPa. The pressure sensor provides real-time pressure feedback to adjust the solenoid valve opening to ensure a stable inflation process. The sorting module uses an image recognition algorithm to classify items. Using a convolutional neural network (CNN) model, with an input image resolution of 1920×1080, the recognition accuracy reaches 99.5%, and the sorting speed can reach 200 items / minute. Physical connections and data exchange between modules are achieved through a standardized interface protocol. The RS485 communication protocol is used with a baud rate of 115200bps and a data frame format of 8 data bits, 1 stop bit, and no parity bit, ensuring reliable and real-time data transmission. The physical connection uses an M12 connector, ensuring a secure connection and easy maintenance. Data exchange utilizes the Modbus protocol and a master-slave architecture, with the master station polling each slave station to ensure data synchronization and coordinated system operation. This design enables each module to operate independently while also collaborating efficiently, improving overall system stability and efficiency.

[0038] In step S106, the operating status data of the fixture, inflation system, and sorting module are obtained, and a data linkage mechanism algorithm is used to establish a real-time data stream transmission channel. The module collaboration efficiency is optimized through time series analysis to obtain a system framework with improved automation integration.

[0039] The operating status data of the fixture module, inflation system and sorting module are obtained, and the operating parameters of each module are collected through sensors and stored in a preset database to obtain a structured operating status data set. The operating status data set is processed using an association rule mining algorithm, the operating status correlation between each module is analyzed, a data linkage model between modules is generated, and real-time data flow generation rules are determined. According to the real-time data flow generation rules, a data flow channel is established, dynamic data is extracted from the operating status data set, and the message queue technology is used to transmit it to the analysis node to obtain a real-time data flow. If the transmission delay of the real-time data flow exceeds the preset threshold, the priority of the message queue is adjusted, and the bandwidth of the data flow channel is reallocated to obtain a stable data flow channel. The real-time data flow is analyzed by time series analysis using the ARIMA model to predict the operating trend of each module. The coordination parameters between modules are adjusted according to the prediction results to obtain optimized module coordination efficiency. According to the optimized module coordination efficiency, the control logic of the system framework is adjusted, and the operating instructions of the fixture module, inflation system and sorting module are integrated using a distributed computing architecture to obtain an automated integrated optimization system framework. The dynamic change data of module collaboration efficiency is extracted through the operation log of the system framework, and the efficiency change trend is analyzed using the K-means clustering algorithm to determine the optimization efficiency of the system framework.

[0040] For example, a pressure sensor deployed on the fixture collects clamping force data with a sampling frequency of 100Hz. A sliding window algorithm is used to calculate the standard deviation of force values ​​within 10 seconds. When the standard deviation exceeds 5N, an abnormality alarm is triggered. Real-time data is simultaneously pushed to a Kafka message queue via the MQTT protocol. The inflation system uses a PID control algorithm to regulate air pressure, with a set target pressure of 0.6MPa. A Kalman filter is used to address pressure sensor noise with a control cycle of 50ms. The solenoid valve opening is automatically adjusted when the actual pressure fluctuation exceeds ±0.02MPa. The sorting module uses the YOLOv5 model to identify workpieces on the conveyor belt in real time, with a detection frame rate of 30fps. It combines the Hungarian algorithm for multi-target tracking and triggers a model retraining mechanism when the sorting accuracy falls below 98%. An Apache Flink-based stream processing platform is established with a time window set to 1 minute. The dynamic time warping (DTW) algorithm is used to calculate the temporal correlation between the three modules. The task scheduling strategy is automatically adjusted when the coordination delay exceeds 200ms. The ARIMA model is used to predict the load trend of each module. When the predicted queue backlog exceeds 50 items, elastic expansion is initiated in advance, ultimately increasing the overall system throughput by 35% and shortening the fault response time to less than 500ms.

[0041] Step S107: Based on the automated integrated system framework, simulate the coordination of the inflation system and the efficiency of the sorting module, use a dynamic scheduling algorithm to adjust the timing parameters of inflation and sorting, and determine whether the overall operating efficiency of the system reaches the preset threshold. If so, determine the final tooling and inspection system integration solution.

[0042] Real-time operating data from the inflation system and sorting module is acquired to construct a dynamic dataset containing timing parameters. A particle swarm optimization algorithm is used to adjust the task scheduling sequence and obtain an optimized timing parameter configuration. Based on this optimized timing parameter configuration, the coordinated operation of the inflation system and sorting module is simulated, and the collaborative efficiency index is calculated to obtain the overall system operating efficiency. If the overall system operating efficiency reaches a preset threshold, a preliminary configuration plan for the tooling system is generated based on the optimized timing parameters, resulting in a tooling system parameter set. The tooling system parameter set undergoes quality verification through a testing system, and verification results are obtained to determine whether it meets the requirements of the integrated solution. A tooling parameter set that passes the test is then iteratively optimized using a genetic algorithm to obtain the final tooling parameter set. By fusing the final tooling parameter set with the testing system data, an integrated solution for the automated integration system is generated, and the final configuration of the system framework is determined. Based on this final configuration, the timing parameters of the inflation system and sorting module are dynamically updated. A rule-based judgment method is used to verify the system operating efficiency, resulting in a confirmed result for the integrated solution's operating efficiency.

[0043] For example, in the framework of the automated integrated system, when simulating the collaborative efficiency of the inflation system and the sorting module, the inflation pressure data is first collected in real time through the sensor (such as setting the target pressure value to 0.5MPa, with an allowable error of ±0.02MPa), and the throughput data of the sorting module (such as processing 1200 pieces per hour) is input into the dynamic scheduling algorithm. The algorithm uses an improved genetic algorithm (population size 50, number of iterations 100, crossover probability 0.8, mutation probability 0.1) to optimize the timing parameters, and the objective function is to minimize the cycle time T = inflation time t1 (default 2 seconds) plus sorting delay t2 (default 1.5 seconds). Through simulation analysis, when the algorithm converges to T≤3.2 seconds (preset threshold), the system efficiency meets the standard. At this time, the inflation valve opening is adjusted based on fuzzy PID control (proportional coefficient Kp=1.2, integral time Ti=0.5 seconds), and the sorting module uses a priority queue (high priority item response time ≤0.3 seconds) to ensure collaborative stability. The reliability of the integrated solution was ultimately verified through Monte Carlo simulation (with a sample size of 10,000). The fixture positioning accuracy was required to be ≤0.1mm, and the detection system error rate was required to be <0.05%. Kalman filtering (process noise Q = 0.01, observation noise R = 0.005) was used for data fusion to synchronize multi-sensor data. When all parameters met the constraints, the system automatically generated the CAD parameters for the fixture (e.g., clamping force 20N ± 5%, travel 50mm) and visual inspection path planning (scanning speed 200mm / s, repeatability ±0.05mm).

[0044] Step S108: extract the detection accuracy stability data from the integrated solution, calculate the variance and deviation of the detection results through a statistical analysis algorithm, and determine whether the detection accuracy meets the preset standards. If so, generate the final airtightness detection tooling design parameters.

[0045] The detection accuracy stability data is obtained from the integrated solution, and the outliers are removed using data cleaning technology to obtain the first data set. The first data set is processed using a statistical analysis algorithm to calculate the variance and deviation of the detection results to obtain the accuracy statistical index. If the variance and deviation in the accuracy statistical index are both lower than the preset standard, the detection accuracy is determined to meet the requirements, and an accuracy determination result is generated. Based on the accuracy determination result, key feature parameters are extracted from the detection data that meets the requirements to obtain an airtightness feature set. The airtightness feature set is modeled using a regression analysis algorithm to generate tooling design parameters to obtain a preliminary design parameter set. The preliminary design parameter set is adjusted using a geometric optimization algorithm to obtain the final airtightness detection tooling design parameters. Core parameters are extracted from the final airtightness detection tooling design parameters to generate a digital model of the airtightness detection tooling.

[0046] For example, in the integrated solution, 100 sets of test accuracy data were first extracted from the air tightness test system. These data covered the test results under different pressure conditions. The variance and deviation of these data were calculated using the statistical analysis algorithm. Specifically, the variance formula σ 2 =Σ(xi-μ) 2 / N, where xi is each test value, μ is the average value, and N is the total number of data, and the calculated variance is 0.05. At the same time, using the deviation formula Bias = Σ(xi-T) / N, where T is the preset standard value, the calculated deviation is 0.02. According to the preset standard, the variance should be less than 0.1 and the deviation should be less than 0.05, so the detection accuracy meets the requirements. Based on this, the final airtightness detection tooling design parameters were generated, including a pressure range of 0.5 to 1.5 MPa, a detection accuracy of ±0.02 MPa, a tooling material of stainless steel, and a sealing ring material of fluororubber to ensure the stability and reliability of the detection.

[0047] In step S109, a physical fixture is generated using 3D printing technology according to the final tooling design parameters, and the robot is controlled by an automated assembly algorithm to complete the integration of the fixture with the inflation and sorting modules to obtain an airtightness detection system that can be put into production.

[0048] Obtain tooling design parameters, use 3D printing technology to generate a fixture entity, and obtain a high-precision fixture. Based on the high-precision fixture, apply the automated assembly algorithm to generate robot control instructions and determine the assembly path. Use the robot control instructions to drive the robot to perform the integration of the fixture and the inflation module to obtain a preliminary integrated component. If the preliminary integrated component meets the preset inflation module position deviation threshold, the sorting module integration is performed by the robot to obtain a complete integrated component. For the complete integrated component, obtain airtightness test data, and judge the functional integrity of the system by comparing it with the preset airtightness standard. According to the airtightness test data, adjust the integrated control logic, optimize the robot assembly parameters, and obtain a production-ready airtightness test system. Perform modular design verification through the production-ready airtightness test system.

[0049] For example, according to the final tooling design parameters, a physical fixture is generated using 3D printing technology. First, the 3D model of the fixture is designed using CAD software. The model size is 200mm×150mm×100mm, the wall thickness is 3mm, and the material is nylon 12 to ensure sufficient strength and lightweight. After the design is completed, SLS (selective laser sintering) technology is used for printing. The laser power is 50W, the layer thickness is 0.1mm, and the printing speed is 20mm / s. During the printing process, a real-time monitoring system is used to ensure that the printing accuracy is within the range of ±0.05mm. After printing is completed, post-processing is performed, including removing support structures and surface polishing to improve the surface finish of the fixture. Next, the robot is controlled by an automated assembly algorithm to complete the integration of the fixture with the inflation and sorting modules. The algorithm uses path planning based on visual recognition. The robot arm length is 800mm, the load is 5kg, and the motion accuracy is ±0.02mm. The vision system captures the position of the fixture and module using a camera with a resolution of 1280×1024 pixels and a frame rate of 30fps. Image processing algorithms identify the precise position of the fixture and module, with an error control within ±0.1mm. The robot performs assembly according to the planned path. During the assembly process, force sensors monitor the assembly force in real time, ensuring it remains between 10N and 20N to avoid overload or underload. After assembly, an airtightness test is performed. The test system uses a differential pressure method with a test pressure of 0.5MPa and a test time of 10s. Pressure changes are monitored in real time using pressure sensors, and pressure fluctuations are controlled within ±0.01MPa, ensuring the system's airtightness meets production requirements. The result is a production-ready airtightness test system with overall dimensions of 1000mm×800mm×600mm, a weight of 50kg, and a test accuracy of ±0.02MPa, meeting the efficiency and high-precision requirements of the production line.

[0050] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An integrated design method for airtightness detection tooling and detection system, characterized in that: The method comprises the following steps: S101. Acquire product morphology data, extract shape, size, and material information from a product 3D model database, calculate key points of the product's outer contour using a geometric feature analysis algorithm, and determine product morphology diversity parameters; S102. Based on product form diversity parameters, an adaptive fixture design algorithm is used in combination with a preset universal fixture design template to generate a three-dimensional fixture model suitable for various product forms, thereby obtaining a fixture structure with enhanced rapid adaptability. S103. Extract the contact surface geometric features from the three-dimensional model of the fixture, simulate the stress distribution when the fixture and the product are in contact through finite element analysis, and determine whether the contact surface meets the high sealing requirements. If the stress distribution is uniform and there is no local stress concentration, the fixture sealing structure is determined to be feasible. S104. Obtaining data on the complexity of the test environment, including temperature, pressure, and humidity fluctuation ranges, for the fixture sealing structure. Using a tooling leakage control algorithm, optimize the sealing material and contact surface geometry parameters to obtain a tooling design with a leakage rate below a preset threshold. S105. Decompose the fixture, inflation system, and sorting module into independent functional units through a modular design structure algorithm, generate a standardized interface protocol, and determine the physical connection and data interaction method between the modules; S106. Obtain operating status data of the fixture, inflation system, and sorting module, adopt a data linkage mechanism algorithm, establish a real-time data stream transmission channel, optimize module collaboration efficiency through time series analysis, and obtain a system framework with improved automation integration; S107. Simulate the coordination of the inflation system and the efficiency of the sorting module within the automated integrated system framework. Use a dynamic scheduling algorithm to adjust the timing parameters of inflation and sorting to determine whether the overall system operating efficiency reaches a preset threshold. If so, determine the final solution for integrating the tooling and inspection systems. S108. Extracting the test accuracy stability data from the integrated solution, calculating the variance and deviation of the test results using a statistical analysis algorithm, and determining whether the test accuracy meets the preset standard. If so, generating the final airtightness test tooling design parameters; S109. Based on the final tooling design parameters, a physical fixture is generated using 3D printing technology. The robot is controlled by an automated assembly algorithm to complete the integration of the fixture with the inflation and sorting modules, thereby obtaining an airtightness detection system that can be put into production.

2. The integrated design method of an airtightness detection tool and a detection system according to claim 1, characterized in that: The S101 includes: Obtain shape data, size data, and material data from the product 3D model database and use data extraction technology to generate a structured data set; Processing the structured data set by a geometric feature analysis algorithm to extract geometric features of the shape data and the size data to obtain a geometric feature set; Extracting outer contour key points from the geometric feature set using a key point detection algorithm, and if the number of key points is lower than a preset threshold, adjusting the geometric feature extraction parameters to obtain an outer contour key point set; According to the outer contour key point set, the distance and angle characteristics between the key points are calculated, and the morphological diversity parameters are generated by using a cluster analysis algorithm; extracting surface attributes from the material data, and if the surface attributes do not match a preset standard, adjusting the morphological diversity parameter according to the geometric feature set to obtain an adjusted morphological diversity parameter; By comparing the adjusted morphology diversity parameters with the preset morphology template, determining whether the product morphology meets the diversity requirements, and obtaining a product morphology classification result; According to the product form classification result, a digital description of the product form is generated, and the final product form parameters are determined.

3. The integrated design method of an airtightness detection tool and a detection system according to claim 1, characterized in that: The S102 includes: Acquiring product morphology data, extracting key geometric features from diverse parameters of the product morphology data, and generating a design parameter set; An adaptive algorithm is used to process the design parameter set and, in combination with a universal template, an initial fixture three-dimensional model is generated; If the adaptation error between the initial fixture three-dimensional model and the product shape data exceeds a preset threshold, adjusting the parameters of the adaptive algorithm to regenerate the fixture three-dimensional model; Extract key geometric features of the fixture structure from the optimized three-dimensional model of the fixture to generate a fixture structure description file; According to the fixture structure description file, a structure optimization algorithm is used to adjust the fixture geometric parameters to obtain a fast-adapting fixture structure; Generate final fixture three-dimensional model data through the fast-adapted fixture structure and output the fixture design plan; If the adaptability of the final fixture three-dimensional model data meets a preset threshold, determining the fixture design solution; If the preset threshold is not met, the process returns to the step of processing the design parameter set and regenerates the fixture three-dimensional model.

4. The integrated design method of an airtightness detection tool and a detection system according to any one of claims 1 to 3, characterized in that: The S103 includes: The geometric features of the contact surface are extracted from the three-dimensional model of the fixture using a geometric segmentation algorithm to obtain the contact surface geometric data; Loading the contact surface geometry data through finite element analysis software, setting product contact boundary conditions, simulating stress distribution, and obtaining stress distribution data; The stress distribution data is analyzed using a mean square error algorithm. If the mean square error is lower than a preset threshold, the stress distribution is judged to be uniform, thereby obtaining a uniformity judgment result. Extracting a local stress value from the stress distribution data, and if the local stress value is lower than a preset threshold, determining that there is no local stress concentration, and obtaining a local stress judgment result; According to the uniformity judgment result and the local stress judgment result, if both conditions are met, it is determined that the clamp sealing structure meets the high sealing requirement, and a sealing judgment result is obtained; According to the sealing judgment result, if the high sealing requirement is met, the clamp sealing structure is judged to be feasible; Generate optimized fixture 3D model data.

5. The integrated design method of airtightness detection tooling and detection system according to any one of claims 1 to 3, characterized in that: The S104 includes: Acquire environmental data, collect fluctuation range data from temperature, pressure, and humidity sensors, and determine an environmental complexity feature vector, wherein the environmental complexity feature vector includes a temperature fluctuation range, a pressure fluctuation range, and a humidity fluctuation range; If the temperature fluctuation range exceeds a preset threshold, the influence of temperature on the aging of the sealing material is predicted by a linear regression algorithm to obtain the material temperature resistance parameter; According to the material temperature resistance parameters, matching materials that meet the temperature resistance requirements from a preset sealing material database to determine a set of candidate sealing materials; If the candidate sealing material set includes at least one material, simulating the stress distribution of the contact surface geometry under the pressure fluctuation range by finite element analysis to obtain geometric optimization parameters; Iteratively optimizing the sealing material parameters and the geometric optimization parameters using a genetic algorithm to obtain a leakage rate prediction value; If the leakage rate prediction value is lower than a preset threshold, generating a tooling design solution based on the optimized sealing material parameters and contact surface geometric parameters; The tooling design scheme is virtually assembled and verified using 3D modeling software to determine whether the sealing structure performance meets the leakage rate requirements and determine the final tooling design.

6. The integrated design method of an airtightness detection tool and a detection system according to any one of claims 1 to 3, characterized in that: The S105 includes: Obtaining an initial design set of the modular system, the initial design set including description information of a clamp unit, an inflation system, and a sorting module; Decomposing the initial design set by a modular design algorithm to obtain a plurality of independent functional units, each of which includes a preliminary functional description of each module; If there is duplication of functions in the independent functional units, the redundant functions are merged through a functional analysis algorithm to obtain a streamlined functional unit set; Extracting input and output requirements of each functional unit from the reduced functional unit set and generating a standardized interface specification; If there are parameter conflicts in the standardized interface specifications, the parameter definitions are adjusted using a protocol optimization algorithm to obtain a unified interface protocol; Generate data interaction rules based on the unified interface protocol to determine the data flow and format between modules; Obtain the mechanical and electrical connection requirements of each functional unit through physical connection analysis and determine the physical connection solution; Extracting module collaboration constraints from the physical connection scheme to generate a module collaboration process; If there is a timing conflict in the module collaboration process, the collaboration sequence is optimized through a timing adjustment algorithm to obtain a final collaboration solution; Generate a communication protocol between modules based on the final collaboration plan and determine the real-time requirements for data interaction; By analyzing the communication protocol, we can determine whether the data transmission meets the real-time constraints and obtain the communication protocol parameters. Extracting module collaboration performance indicators from the communication protocol parameters to generate a running configuration for the modular system; Perform simulations based on the operating configuration to determine whether the system meets performance requirements and obtain an optimized system configuration; generating a deployment plan for the modular system according to the optimized system configuration; The deployment plan is used to verify whether the collaboration between modules is stable and determine the final deployment parameters.

7. The integrated design method of an airtightness testing tool and a testing system according to any one of claims 1 to 3, characterized in that: The S106 includes: Obtain the operating status data of the fixture module, inflation system and sorting module, collect the operating parameters of each module through sensors, store them in a preset database, and obtain a structured operating status data set; Using association rule mining algorithms to process the operating status data set, analyzing the operating status correlation between modules, generating a data linkage model between modules, and determining real-time data flow generation rules; By using the real-time data flow generation rules, a data flow channel is established, dynamic data is extracted from the running status data set, and the data is transmitted to the analysis node using the message queue technology to obtain the real-time data flow; If the transmission delay of the real-time data stream exceeds a preset threshold, the priority of the message queue is adjusted and the bandwidth of the data stream channel is reallocated to obtain a stable data stream channel; An ARIMA model is used to perform time series analysis on the real-time data stream to predict the operating trend of each module, and the coordination parameters between modules are adjusted according to the prediction results to obtain optimized module coordination efficiency; According to the optimized module collaboration efficiency, the control logic of the system framework is adjusted, and the operating instructions of the fixture module, the inflation system and the sorting module are integrated using a distributed computing architecture to obtain an automated integrated optimization system framework; The dynamic change data of module collaboration efficiency is extracted through the operation log of the system framework, and the efficiency change trend is analyzed using the K-means clustering algorithm to determine the optimization efficiency of the system framework.

8. The integrated design method of airtightness testing tool and testing system according to any one of claims 1 to 3, characterized in that: The S107 includes: Obtain real-time operating data of the inflation system and sorting module and construct a dynamic data set containing time series parameters; The particle swarm optimization algorithm is used to adjust the task scheduling order and obtain the optimized timing parameter configuration; Based on the optimized timing parameter configuration, the coordinated operation process of the inflation system and the sorting module is simulated, the coordinated efficiency index is calculated, and the overall operating efficiency value of the system is obtained; If the overall operating efficiency value of the system reaches the preset threshold, a preliminary configuration plan of the tooling system is generated based on the optimized timing parameters to obtain the tooling system parameter set; Perform quality verification on the tooling system parameter set through the detection system, obtain the verification result data, determine whether it meets the requirements of the integrated solution, and obtain the tooling parameter set that has passed the inspection; Genetic algorithm is used to iteratively optimize the tooling parameter set that has passed the inspection to obtain the final tooling parameter set; By integrating the final tooling parameter set with the inspection system data, an integrated solution for the automated integration system is generated, and the final configuration of the system framework is determined; According to the final configuration, the timing parameters of the inflation system and sorting module are dynamically updated, and the system operation efficiency is verified using a rule-based judgment method to obtain the operation efficiency confirmation result of the integrated solution.

9. The integrated design method of airtightness testing tool and testing system according to any one of claims 1 to 3, characterized in that: The S108 includes: Obtain detection accuracy stability data from the integrated solution and use data cleaning technology to remove outliers to obtain the first data set; Processing the first data set using a statistical analysis algorithm, calculating the variance and deviation of the detection results, and obtaining accuracy statistical indicators; If the variance and deviation in the accuracy statistical indicators are both lower than the preset standards, the detection accuracy is determined to meet the requirements and an accuracy determination result is generated; According to the accuracy determination results, key characteristic parameters are extracted from the test data that meets the requirements to obtain the airtightness feature set; A regression analysis algorithm is used to model the airtightness feature set, generate tooling design parameters, and obtain a preliminary design parameter set; The preliminary design parameter set is adjusted by a geometric optimization algorithm to obtain the final airtightness test tooling design parameters; Core parameters are extracted from the final air tightness testing tool design parameters to generate a digital model of the air tightness testing tool.

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