Air spring automatic production line and control system thereof

Abstract

The invention provides an air spring automatic production line and a control system thereof, and relates to the technical field of air spring manufacturing, and the air spring automatic production line comprises a raw material module used for automatically blending raw materials through a dynamic planning algorithm and conveying the raw materials to a processing forming module; the processing and forming module is used for cutting and shaping the raw materials and assembling the raw materials into the air spring; the quality detection module is used for carrying out quality detection, including size detection, air tightness detection and strength detection, on the processed and formed air spring so as to obtain a detection report; and the finished product packaging module is used for packaging the qualified air spring and conveying the packaged air spring to a designated position according to the final conveying path. Through integrated module design and intelligent algorithm control, high automation of the production process and resource optimization utilization are realized, and the production efficiency and the product quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air spring manufacturing, and particularly to an air spring automated production line and its control system. Background Art

[0002] Traditional air spring production lines rely on a large amount of manual operations, such as material handling, assembly, inspection, etc. These manual operations not only take a long time but also are difficult to maintain an efficient and stable working rhythm. For example, during the assembly process of air springs, workers need to manually align and fix each component, which is not only inefficient but also prone to assembly errors.

[0003] In addition, the degree of automation of traditional production lines is relatively low, lacking advanced automated equipment and intelligent control systems, and unable to achieve rapid response and flexible adjustment during the production process. For example, when a failure occurs in the production line or the production specifications need to be adjusted, it takes a long downtime for manual adjustment or repair. In the prior art, the invention patent with the publication number of CN118664323A discloses an assembly and detection production line for both ends of a gas spring, whose classification number is B23P. It includes a dust-proof seal assembly unit, which includes a first conveying unit, a dust cover assembly mechanism and a dust cover detection mechanism arranged in sequence behind the first conveying unit, a bullet head loading mechanism, a dust sleeve pre-assembly mechanism, a bullet head unloading mechanism and a dust sleeve assembly mechanism arranged in sequence in front of the first conveying unit, and a bullet head return mechanism is arranged between the bullet head loading mechanism and the bullet head unloading mechanism; a joint assembly unit, which includes a second conveying unit, a joint tightening station and a joint angle adjustment station arranged in sequence along the second conveying unit, a first joint assembly mechanism and a second joint assembly mechanism arranged opposite to each other in the front and back at the joint tightening station, and a joint angle adjustment mechanism located at the joint angle adjustment station. It can realize the automatic assembly of dust-proof seals at both ends of the cylinder, the automatic assembly and detection of large and small joints at both ends, and greatly improve the production efficiency. Secondly, the invention patent with the publication number of CN118752198A discloses an assembly device and assembly method for air spring production, whose classification number is B23P. It includes a first conveying device for conveying a cylinder body; a second conveying device for conveying a rod; a bracket located between the first conveying device and the second conveying device; a nut arranged on the bracket; a screw rod rotatably arranged on the bracket, the screw rod is threadedly connected with the nut, the screw rod has a first end and a second end. After the screw rod rotates, the first end moves away from the first conveying device and the second end approaches the second conveying device, or the first end approaches the first conveying device and the second end moves away from the second conveying device; a first cleaning member is rotatably arranged relative to the first end, the rotation axis of the first cleaning member coincides with the axis of the screw rod, and the first cleaning member is used for cleaning the inner wall of the cylinder body; a second cleaning member is rotatably arranged relative to the second end, and the second cleaning member is used for cleaning the outer wall of the rod. Through the above technical solutions, the problem that impurities on the rod and the cylinder body affect the service life of the air spring in the prior art is solved.

[0004] However, the energy consumption control of traditional air spring production lines is relatively crude, lacking precise energy consumption monitoring and management means. It is often difficult to grasp the energy consumption in real time during the production process, resulting in the inability to detect and solve the problem of excessive energy consumption in a timely manner. For example, some aging production equipment may have low energy efficiency, but due to the lack of effective energy consumption monitoring means, these problems are often overlooked.

[0005] At the same time, the production processes and procedures of traditional production lines may not be optimized enough, resulting in unnecessary energy consumption waste during the production process. For example, during the manufacturing process of air springs, multiple heating and cooling processes may be required. If the temperature and time control of these processes are not precise enough, it will lead to an increase in energy consumption. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide an automated production line for air springs and its control system, which improves the efficiency of the air spring production line and reduces energy consumption.

[0007] To solve the above technical problems, the technical solution of the present invention is as follows:

[0008] In the first aspect, an automated production line for air springs includes:

[0009] A raw material module for automatically allocating raw materials through a dynamic programming algorithm and transporting them to the processing and forming module;

[0010] A processing and forming module for cutting, shaping, and assembling the raw materials into air springs;

[0011] A quality inspection module for inspecting the quality of the processed air springs, including size, airtightness, and strength inspections, to obtain an inspection report;

[0012] A finished product packaging module for packaging the qualified air springs and transporting the packaged air springs to the designated location according to the final transportation path;

[0013] An automated transportation module for achieving seamless material transportation between the raw material transportation module, the processing and forming module, the quality inspection module, and the finished product packaging module through a conveyor belt and a positioning sensor.

[0014] Furthermore, automatically allocating raw materials through a dynamic programming algorithm and transporting them to the processing and forming module includes:

[0015] Identifying the types, specifications, quantities, and production requirements of raw materials through sensors;

[0016] Classifying and storing the raw materials in the designated storage area and assigning a unique index to each type of raw material;

[0017] Initialize the data structure of the dynamic programming algorithm, including the state array, and determine the state transition equation of the dynamic programming algorithm;

[0018] Set the initial conditions of the dynamic programming algorithm, and gradually fill the state array according to the state transition equation and the initial conditions until the final solution is obtained;

[0019] According to the final solution, starting from the last state of the state array, gradually trace back the types and quantities of the selected raw materials forward to determine the final raw material selection and blending plan;

[0020] Send instructions to the automated conveying equipment to select the corresponding raw materials from the storage area according to the instructions, and mix or blend the final raw material selection and blending plan to obtain the blended raw materials;

[0021] The blended raw materials are conveyed to the entrance of the processing and forming module through a conveyor belt.

[0022] Furthermore, conduct quality inspections on the processed air springs, including size, airtightness, and strength inspections, to obtain inspection reports, including:

[0023] Determine a certain number of air springs on the production line in sequence as inspection samples, and use an automated dimension measuring instrument to measure the outer diameter, inner diameter, and height of the inspection samples;

[0024] Record the dimension data of each sample, compare it with the set standard, and determine whether the dimension of each sample is qualified to obtain the dimension determination result;

[0025] According to the dimension determination result, place the samples with qualified dimensions in the airtightness test equipment, and fill the samples with qualified dimensions with a certain pressure of gas and keep it for a period of time;

[0026] Use a pressure sensor to monitor the pressure change of the samples with qualified dimensions, and determine whether the airtightness of the samples with qualified dimensions is qualified according to the pressure change situation to obtain the airtightness determination result;

[0027] According to the airtightness determination result, install the samples with qualified airtightness on the strength testing machine, and gradually apply pressure to the samples with qualified airtightness according to the set standard until the maximum design pressure is reached;

[0028] A displacement sensor monitors the deformation of the samples with qualified airtightness under the action of pressure in real time, and determines whether the strength of the samples with qualified airtightness is qualified according to the deformation situation to obtain the strength determination result;

[0029] Integrate the dimension determination result, airtightness determination result, and strength determination result to generate an inspection report, including the inspection data, determination result, and classification mark of each sample.

[0030] Furthermore, package the qualified air springs and transport the packaged air springs to the designated location according to the final transport path, including:

[0031] Calculate the final packaging plan using a genetic algorithm based on the dimensions, shape, weight of the air spring, and the characteristics of available packaging materials. The packaging plan includes the type, specification, and packaging method of the packaging materials;

[0032] According to the final packaging plan, control the packaging equipment to determine the corresponding packaging materials and packaging methods, and package the qualified air springs;

[0033] After packaging, calculate the final transport path using a path planning algorithm based on the layout of the production line, the status of the transport equipment, the target storage location, and the transport time, and transport the packaged air springs to the designated storage location according to the final transport path.

[0034] Furthermore, calculate the final packaging plan using a genetic algorithm based on the dimensions, shape, weight of the air spring, and the characteristics of available packaging materials. The packaging plan includes the type, specification, and packaging method of the packaging materials, including:

[0035] Sensors on the production line automatically detect and obtain the physical parameters of the current air spring, such as dimensions, shape, and weight, and collect packaging material characteristic data, including type, thickness, strength, weight, cost, and environmental protection index;

[0036] Integrate the air spring physical parameter data and packaging material characteristic data to form a data set, and set the basic parameters of the genetic algorithm, including population size, number of iterations, crossover rate, and mutation rate;

[0037] Define the optimization objective, and randomly generate a set of initial packaging plans according to the characteristics of the packaging materials and the physical parameters of the air spring. Each plan includes the type, specification, and packaging method of the packaging materials;

[0038] Each individual, that is, the initial packaging plan, is represented as a chromosome in the population, and the genes on the chromosome represent the various parameters of the packaging plan;

[0039] Evaluate the fitness of each individual, that is, calculate the objective function value corresponding to each individual, and determine the candidate individuals according to the objective function value of the individuals;

[0040] Perform crossover operations on the candidate individuals to generate new individuals by exchanging some genes, and perform mutation operations on the new individuals to update the individual population;

[0041] Repeat the crossover and mutation operations until the preset number of iterations is reached. Determine the final solution from the final population according to the objective function values of the individuals, that is, the final packaging scheme.

[0042] Furthermore, the calculation formula for the objective function value corresponding to each individual is:

[0043]

[0044] where f(x) represents the objective function value corresponding to each individual; α and β represent weight coefficients; n represents the number of types of packaging materials; i represents the index of the type of packaging material; p i represents the unit price of the i-th type of packaging material; q i (x) represents the usage amount of the i-th type of packaging material in the packaging scheme x; γ i represents the loss coefficient of the i-th type of packaging material; δ i represents the discount coefficient of the i-th type of packaging material; V s represents the volume of the air spring; ∈ represents the packaging efficiency coefficient; V p represents the total volume of the packaging materials; θ represents the packaging material compression coefficient; λ represents the weight coefficient of the transportation cost; c represents the transportation unit price; d(x,e) represents the actual transportation distance of the goods from the starting point to the end point under the given packaging scheme x and transportation route e.

[0045] Furthermore, after packaging, according to the layout of the production line, the state of the conveying equipment, the target storage location, and the transportation time, use the path planning algorithm to calculate the final conveying path, and according to the final conveying path, convey the packaged air springs to the specified storage location, including:

[0046] Obtain the layout diagram of the production line, including equipment positions, connection relationships, and aisle widths, and regard the production line layout as a graph structure, where nodes represent storage locations and edges represent conveying paths;

[0047] Assign a weight to each edge and initialize an open list and a closed list;

[0048] Add the starting position to the open list, and loop to execute the steps of taking out the current node with the lowest cost, checking the storage location, traversing adjacent nodes, and updating the list until the open list is empty, to obtain the final path composed of a series of nodes from the starting position to the storage location;

[0049] According to the final path, control the conveying equipment to convey the packaged air springs to each node on the path in turn.

[0050] In the second aspect, a control system for an air spring automatic production line realizes the following functions:

[0051] Collect data from various sensors on the production line, including air compressor pressure, variable pressure reducing valve opening, solenoid valve status, air spring deformation height, displacement sensor signal, and pressure sensor signal;

[0052] According to the predefined air spring production process flow and quality control requirements, formulate a control strategy, which includes inflation and deflation control, displacement control, and pressure control of the air spring;

[0053] According to the control strategy, send control instructions to the actuators on the production line through the control unit, and monitor the operating status of the production line in real time and receive feedback signals;

[0054] Transmit the real-time data and operating status to the remote terminal to monitor the operating data and status of the production line in real time, and dynamically adjust the control strategy according to the feedback signal;

[0055] When an abnormality or fault occurs on the production line, automatically send an alarm signal and transmit the alarm information to the remote terminal.

[0056] The above solution of the present invention has at least the following beneficial effects:

[0057] The raw material module automatically allocates raw materials through the dynamic programming algorithm, reducing manual intervention and accelerating the preparation and transportation speed of raw materials. The processing and forming module realizes the automation of cutting, shaping, and assembling of raw materials, improves the processing efficiency of the production line, and shortens the production cycle. The automated conveying module realizes seamless material transfer between modules through conveyor belts and positioning sensors, reduces material waiting time, and further improves the overall production efficiency. The quality inspection module conducts a comprehensive quality inspection on the processed and formed air springs, including aspects such as dimensions, airtightness, and strength, ensuring the quality and reliability of the products. The automated production line reduces errors caused by manual operations and improves the consistency and stability of products.

[0058] The automated production line reduces unnecessary energy consumption waste by optimizing the production process and flow. The application of the dynamic programming algorithm in raw material allocation makes the use of raw materials more reasonable, avoiding energy consumption caused by over-purchasing and inventory backlogs. The entire production line adopts advanced automated technologies and equipment, such as conveyor belts and positioning sensors, to realize the automation and intelligence of the production process. The improvement of the automation level not only improves production efficiency but also reduces manual labor intensity and improves the working environment. The design of the automated production line has strong flexibility and scalability, and can be quickly adjusted and optimized according to changes in market demand and production scale. Description of the Drawings

[0059] Figure 1It is a schematic flow diagram of an air spring automated production line provided by an embodiment of the present invention.

[0060] Figure 2 It is a schematic flow diagram of the control system of an air spring automated production line provided by an embodiment of the present invention. Detailed implementation manners

[0061] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0062] As Figure 1 shown, an embodiment of the present invention provides an air spring automated production line, including:

[0063] A raw material module 11, configured to automatically allocate raw materials through a dynamic programming algorithm and convey them to the processing and forming module;

[0064] A processing and forming module 12, configured to cut, shape and assemble the raw materials into an air spring;

[0065] A quality inspection module 13, configured to perform quality inspection on the processed and formed air springs, including size, airtightness and strength inspection, to obtain an inspection report;

[0066] A finished product packaging module 14, configured to package the qualified air springs and convey the packaged air springs to a designated position according to the final conveying path;

[0067] An automated conveying module 15, configured to achieve seamless material transfer between the raw material conveying module, the processing and forming module, the quality inspection module and the finished product packaging module through a conveyor belt and a positioning sensor.

[0068] In the embodiment of the present invention, the raw materials are automatically allocated through a dynamic programming algorithm, realizing efficient and accurate management of the raw materials, and reducing errors and delays in manual allocation. Automatically allocating and conveying the raw materials to the processing and forming module speeds up the production process and improves the overall efficiency of the production line.

[0069] Cutting, shaping and assembling the raw materials into an air spring realizes the automation of the production process, improves the production efficiency and product quality. Automated processing reduces manual operations, reduces labor intensity, and improves production safety at the same time.

[0070] Comprehensive quality inspections are carried out on the processed air springs, including aspects such as dimensions, airtightness, and strength, ensuring the qualification rate and reliability of the products. The inspection reports provide a basis for subsequent product traceability and quality control, and contribute to improving the enterprise's quality management level.

[0071] The qualified air springs are packaged to protect the products from damage during transportation and storage. According to the final delivery route, the packaged air springs are transported to the designated location, realizing the automated management and distribution of the finished products and improving the logistics efficiency.

[0072] Seamless material transfer between modules is achieved through conveyor belts and positioning sensors, reducing material waiting time and manual handling costs. Automated conveying improves the coherence and fluency of the production line, making the entire production process more efficient and orderly.

[0073] In a preferred embodiment of the present invention, the automatic allocation of raw materials by the dynamic programming algorithm and the transportation to the processing and forming module may include:

[0074] Identify the types, specifications, quantities, and production requirements of raw materials through sensors;

[0075] Classify and store the raw materials in the designated storage areas, and assign a unique index to each type of raw material;

[0076] Initialize the data structure of the dynamic programming algorithm, including the state array, and determine the state transition equation of the dynamic programming algorithm;

[0077] Set the initial conditions of the dynamic programming algorithm, and gradually fill the state array according to the state transition equation and the initial conditions until the final solution is obtained;

[0078] According to the final solution, starting from the last state of the state array, gradually trace back the types and quantities of the selected raw materials forward to determine the final raw material selection and allocation plan;

[0079] Send instructions to the automated conveying equipment, select the corresponding raw materials from the storage area according to the instructions, and mix or allocate the final raw material selection and allocation plan to obtain the allocated raw materials;

[0080] The allocated raw materials are transported to the entrance of the processing and forming module through a conveyor belt.

[0081] In the embodiments of the present invention, multiple types of sensors (such as RFID sensors, weight sensors, visual recognition sensors) are installed at the raw material inlet. The sensors read the label information on the raw materials (such as RFID tags) or identify the types and specifications of the raw materials through visual recognition technology, and the weight sensors measure the quantity of the raw materials. The identified information (types, specifications, quantities) and production requirements (such as order information) are transmitted to the central control system. According to the types and specifications of the raw materials, different storage areas are designed and divided. A unique index (such as a number or a string) is assigned to each raw material to uniquely identify the raw material in the system. When the raw material is identified, it is stored in the corresponding storage area according to the index. The inventory information of the storage area is updated to record the quantity and location of each raw material.

[0082] According to the production requirements and the types and specifications of the raw materials, define the state variables and state arrays of the dynamic programming algorithm. The state variables include the types, quantities, production progress, etc. of the raw materials that have been used. According to the constraints of the production process (such as the usage order, ratio, etc. of the raw materials), determine the state transition equation dp[m′][p′] = max / min(dp[m][p] + value a dded(m,p,m′,p′)); where dp[m][p] is the state array; dp[m′][p′] is the benefit under the given quantity m′ of the raw material and production progress p′; value a dded(m,p,m′,p′) is the increased benefit when transferring from the state (m,p) to the state (m′,p′); max / min(dp[m][p] + value a dded(m,p,m′,p′)) means starting from the current state [m][p], considering all possible new states (m′,p′), calculating the total value after each possible transfer, and selecting the final transfer path that maximizes the total value (or minimizes the total cost); max means selecting the path with the maximum total value among all possible transfer paths; min means selecting the path with the minimum total cost among all possible transfer paths.

[0083] Suppose there is a backpack with a capacity of p, and there are n items, each with a weight and a value. The goal is to select items to maximize the total value.

[0084] m represents the number of the item currently being considered (from the 1st to the nth), and p represents the remaining capacity of the current backpack.

[0085] Determine the state transition formula according to whether to select the current item m:

[0086] If the mth item is not selected, then the remaining capacity and total value of the backpack remain unchanged, that is, dp[m][p] = dp[m - 1][p].

[0087] If the m-th item is selected (provided that the remaining capacity p of the backpack is greater than or equal to the weight weight t[m] of the item), then the remaining capacity of the backpack will be reduced by weight t[m], and at the same time the total value will be increased by value[m], that is, dp[m][p] = dp[m - 1][p - weight[m]] + value[m].

[0088] Combining the above two cases, the comprehensive formula for state transition can be obtained: dp[m][p] = max(dp[m - 1][p], dp[m - 1][p - weight[m]] + value[m]).

[0089] In this formula, value a dded(m, p, m', p') corresponds to value[m], that is, the newly added value when transferring from the state (m - 1, p - weight[m]) to (m, p). By comparing the total values of selecting and not selecting the current item, it is possible to determine which item should be selected in each state, thereby obtaining the final solution to the backpack problem.

[0090] Initialize the state array, set the initial state (such as not using any raw materials and the production progress is 0), and set the initial conditions of the dynamic programming algorithm, such as the initial state, target state, etc. According to the state transition equation, start from the initial state and gradually calculate and fill the state array. During the filling process, record the final solution of each state (such as the type, quantity of raw materials, production progress, etc.). When the target state is reached, stop filling to obtain the final solution.

[0091] Start tracing back gradually from the last state (i.e., the target state) of the state array. According to the state transition equation, determine the previous state of each state, and the type and quantity of raw materials used from the previous state to the current state. Record the traced type and quantity of raw materials to form the final raw material selection and allocation plan. According to the final raw material selection and allocation plan, generate instructions for the automated conveying equipment. The instructions include which raw material to select from which storage area, how much quantity to select, etc. Send the instructions to the automated conveying equipment to control its selection and conveying operations. During the conveying process, it is necessary to mix or allocate the raw materials (such as through equipment like mixers, blenders, etc.) to obtain the allocated raw materials. Place the allocated raw materials on the conveyor belt, and the conveyor belt transports the raw materials to the entrance of the processing and forming module. During the conveying process, it is necessary to control the speed and direction of the conveyor belt to ensure that the raw materials can enter the processing and forming module accurately and stably.

[0092] Suppose it is necessary to produce a composite material composed of three raw materials (A, B, C). Each raw material has different specifications and quantity requirements, and the production requirement is to produce 1000 pieces of composite materials per day.

[0093] The sensors identify that the quantities of raw materials A (specification 1), B (specification 2), and C (specification 3) are 500 kg, 300 kg, and 200 kg respectively, and the production requirement is to produce 1000 pieces of composite materials per day. Raw material A is stored in storage area 1 with the index "A1"; raw material B is stored in storage area 2 with the index "B2"; raw material C is stored in storage area 3 with the index "C3".

[0094] Define the state variables as (quantity of A used, quantity of B used, quantity of C used, production progress); initialize the state array and set the initial state as (0, 0, 0, 0);

[0095] Determine the state transition equation. For example, if the current state is (x, y, z, p), and the quantities of raw material A used are a, B used are b, and C used are c, then the next state is (x + a, y + b, z + c, p + min(a / A_per_piece, b / B_per_piece, c / C_per_piece)), where A_per_piece, B_per_piece, and C_per_piece are the quantities of A, B, and C required for each piece of composite material respectively.

[0096] According to the state transition equation and the initial conditions, gradually fill the state array. During the filling process, record the final solution of each state. Starting from the target state (such as (400, 300, 200, 1000)), trace back step by step to determine the previous state of each state, as well as the types and quantities of raw materials used from the previous state to the current state, and obtain the final raw material selection and allocation plan. For example, use 400 kg of A, 300 kg of B, and 200 kg of C to produce 1000 pieces of composite materials.

[0097] Send instructions to the automated conveying equipment to select 400 kg of A from storage area 1, 300 kg of B from storage area 2, and 200 kg of C from storage area 3. Mix or allocate the selected raw materials to obtain the allocated raw materials. Place the allocated raw materials on the conveyor belt, and the conveyor belt transports the raw materials to the entrance of the processing and forming module.

[0098] Automatically allocating raw materials through a dynamic programming algorithm can optimize the usage sequence and proportion of raw materials, reduce waiting time and waste during the production process, and thus improve production efficiency. The dynamic programming algorithm can automatically select the optimal raw material selection and allocation plan based on production requirements and raw material inventory conditions, thereby reducing production costs. Through precise raw material selection and allocation, the quality stability and consistency of each composite material can be ensured, and product quality can be improved. When production requirements or raw material inventory change, the dynamic programming algorithm can quickly adjust the raw material selection and allocation plan, enhancing the flexibility of the production system. Through the combination of sensors, automated conveying equipment, and the dynamic programming algorithm, automatic identification, classified storage, automatic allocation, and conveyance of raw materials can be achieved, thus realizing automated production.

[0099] In a preferred embodiment of the present invention, quality inspection of the processed and formed air spring is performed, including dimension, airtightness, and strength inspections, to obtain an inspection report, which may include:

[0100] Determine a certain number of air springs from the production line in sequence as inspection samples, and use an automated dimension measuring instrument to measure the outer diameter, inner diameter, and height of the inspection samples;

[0101] Record the dimension data of each sample, compare it with the set standard, and determine whether the dimension of each sample is qualified to obtain a dimension determination result;

[0102] According to the dimension determination result, place the samples with qualified dimensions in an airtightness testing device, and fill the samples with qualified dimensions with a certain pressure of gas and maintain it for a period of time;

[0103] Use a pressure sensor to monitor the pressure change of the samples with qualified dimensions, and according to the pressure change situation, determine whether the airtightness of the samples with qualified dimensions is qualified to obtain an airtightness determination result;

[0104] According to the airtightness determination result, install the samples with qualified airtightness on a strength testing machine, and gradually apply pressure to the samples with qualified airtightness according to the set standard until the maximum design pressure is reached;

[0105] A displacement sensor monitors the deformation of the samples with qualified airtightness under the action of pressure in real time, and according to the deformation situation, determines whether the strength of the samples with qualified airtightness is qualified to obtain a strength determination result;

[0106] Integrate the dimension determination result, airtightness determination result, and strength determination result to generate an inspection report, including the inspection data, determination result, and classification mark of each sample.

[0107] In the embodiments of the present invention, sensors on the production line (such as RFID or barcode scanners) are used to identify each air spring. According to the production plan, a certain number of air springs are selected as test samples, and the conveyor belt transports the selected samples to the test area.

[0108] The samples are placed on an automated dimensional measuring instrument, which uses technologies such as laser ranging and optical sensors to automatically measure the outer diameter, inner diameter, and height, and transmits the measurement data to the data processing system in real time.

[0109] The data processing system compares the measurement data with the preset standard dimensions, and the determination algorithm determines whether the dimensions of each sample are qualified according to the deviation range, and records the dimension determination result.

[0110] The conveyor belt moves the samples with qualified dimensions to the airtightness test equipment. The airtightness test equipment automatically fills the samples with gas at a set pressure and maintains it for a set time to ensure stable pressure. A high-precision pressure sensor is used to monitor the pressure change inside the samples in real time. According to the pressure change range, it is determined whether the airtightness is qualified, and the airtightness determination result is recorded.

[0111] The samples with qualified airtightness are installed on the strength testing machine. The testing machine gradually applies pressure according to the set standard until the maximum design pressure is reached. The displacement sensor monitors the deformation of the samples under the action of pressure in real time. According to the degree of deformation, it is determined whether the strength is qualified, and the strength determination result is recorded.

[0112] The data processing system integrates the dimension determination result, airtightness determination result, and strength determination result, and automatically generates a test report, including the test data, determination result, and classification mark (such as qualified or unqualified) of each sample. The report can be saved in formats such as PDF and Excel, and transmitted to the production management system or quality control system.

[0113] Suppose there are 100 air springs on the production line, and the system randomly selects 10 as the inspection samples, identifying and selecting the air springs numbered 1, 5, 10, 15, 20, 25, 30, 35, 40, 45. The measuring instrument measures the outer diameter of sample 1 as 150.0 mm, the inner diameter as 100.0 mm, and the height as 200.0 mm. Repeat this process to measure the dimensions of all selected samples. The dimensions of sample 1 are compared with the standard (outer diameter 150.0 ± 0.5 mm, inner diameter 100.0 ± 0.5 mm, height 200.0 ± 1.0 mm) and determined to be qualified. Repeat this process to determine the dimensions of all samples. Sample 1 is moved to the airtightness test equipment, filled with 1.5 MPa of gas, and maintained for 1 minute. Repeat this process to conduct airtightness tests on all samples with qualified dimensions. The pressure change of sample 1 is within the allowable range and is determined to be qualified. Repeat this process to determine the airtightness of all samples. Sample 1 is installed on the strength testing machine, and the pressure is gradually applied to 2.0 MPa. The displacement sensor monitors the deformation situation and is determined to be qualified. Repeat this process to conduct strength tests on all samples with qualified airtightness. Integrate all the determination results to generate an inspection report, showing the detailed data and determination results of each sample.

[0114] The automated inspection process reduces manual intervention and significantly improves the inspection speed and efficiency. Using high-precision sensors and equipment ensures the accuracy and reliability of the inspection data. Strict inspection standards and comprehensive inspection items help to detect and eliminate unqualified products and improve product quality. The detailed inspection report records the inspection data of each sample, facilitating subsequent quality traceability and analysis. Real-time feedback of the inspection results helps to promptly adjust production parameters, optimize the production process, and the automated inspection reduces the labor demand and lowers the labor cost.

[0115] In a preferred embodiment of the present invention, packaging the qualified air springs and transporting the packaged air springs to the designated location according to the final transportation path may include:

[0116] According to the size, shape, weight of the air spring and the characteristics of the available packaging materials, use the genetic algorithm to calculate the final packaging plan, and the packaging plan includes the type, specification and packaging method of the packaging materials;

[0117] According to the final packaging plan, control the packaging equipment to determine the corresponding packaging materials and packaging methods, and package the qualified air springs;

[0118] After packaging, according to the layout of the production line, the status of the transportation equipment, the target storage location and the transportation time, use the path planning algorithm to calculate the final transportation path, and according to the final transportation path, transport the packaged air springs to the designated storage location.

[0119] In the embodiments of the present invention, data such as the size (outer diameter, inner diameter, height), shape, and weight of the air spring are collected, and characteristics such as the type, specification, strength, and cost of available packaging materials are collected. A set of initial packaging schemes is randomly generated, and each scheme includes the type, specification, and packaging method of the packaging materials. The fitness function of the genetic algorithm is set, and this function evaluates the schemes according to the feasibility of the packaging schemes (such as whether they meet the requirements of protecting the product, cost-effectiveness, etc.). The selection operation is carried out, and the better packaging schemes are selected according to the fitness function for reproduction. The crossover operation is carried out to combine the genes (i.e., the type, specification, and packaging method of the packaging materials) of two or more packaging schemes to generate new packaging schemes. The mutation operation is carried out to randomly change some genes of the new packaging schemes to increase the diversity of the schemes. The selection, crossover, and mutation operations are repeated until the preset number of iterations is reached, and the packaging scheme with the highest fitness is used as the final scheme, including the type, specification, and packaging method of the packaging materials.

[0120] According to the final packaging scheme, the corresponding packaging materials and packaging methods are selected, and the parameters of the packaging equipment, such as packaging speed, pressure, etc., are adjusted to meet the requirements of the packaging scheme. The qualified air springs are placed on the packaging equipment, and the packaging equipment packages the air springs according to the preset procedures and parameters. During the packaging process, the working state of the packaging equipment is monitored to ensure packaging quality and safety.

[0121] The layout information of the production line is collected, including the positions of the equipment on the production line, the width of the passageways, etc., and the state information of the conveying equipment, such as the speed and direction of the conveyor belt, etc., is collected to determine the target storage location and transportation time requirements. According to the production line layout and the state of the conveying equipment, a set of initial conveying paths is generated. The fitness function of the path planning algorithm is set, and this function evaluates the paths according to the feasibility of the paths (such as whether they avoid collisions, whether they meet the transportation time requirements, etc.). The selection operation is carried out, and the better conveying paths are selected according to the fitness function for reproduction. The crossover operation is carried out to combine the genes (i.e., the nodes and order on the paths) of two or more conveying paths to generate new conveying paths. The mutation operation is carried out to randomly change some genes of the new conveying paths to increase the diversity of the paths. The selection, crossover, and mutation operations are repeated until the preset number of iterations is reached, and the conveying path with the highest fitness is used as the final path, including the nodes and order on the path. According to the final conveying path, the conveying equipment is controlled to transport the packaged air springs to the specified storage location. During the transportation process, the working state of the conveying equipment is monitored to ensure transportation safety and punctuality.

[0122] Suppose there are 10 qualified air springs that need to be packaged and transported to the designated storage location. Collect data such as the size, shape, weight of the air springs, and the characteristics of available packaging materials. Use the genetic algorithm to calculate the final packaging plan. For example, use foam plastic as the packaging material with a specification of 200mm×200mm×300mm. The packaging method is to place the air springs in a foam plastic box and fix them with tape. According to the final packaging plan, select the foam plastic box and tape as the packaging materials, and adjust the parameters of the packaging equipment, such as packaging speed, pressure, etc. Place the qualified air springs on the packaging equipment, and the packaging equipment packages them according to the preset procedures and parameters. Collect the layout information of the production line, the status information of the conveying equipment, as well as the requirements for the target storage location and transportation time. Use the path planning algorithm to calculate the final conveying path. For example, starting from the packaging equipment, passing through conveyor belt A, turning machine B, conveyor belt C, and finally reaching the storage location D. Control the conveying equipment to transport the packaged air springs to the designated storage location according to the final conveying path.

[0123] The automated packaging and conveying process reduces manual intervention and significantly improves the packaging and conveying speed and efficiency. Using the genetic algorithm to calculate the packaging plan ensures the rationality of the selection of packaging materials and the packaging method, improving the packaging quality. Using the path planning algorithm to calculate the conveying path avoids collisions and congestion, optimizes the conveying process, and improves the conveying efficiency. Automated packaging and conveying reduce the labor demand and lower the labor cost. During the automated packaging and conveying process, monitor the working status of the equipment, promptly detect and handle abnormal situations, and improve production safety. The efficient packaging and conveying process ensure the on-time delivery and intactness of the products, improving the customer satisfaction and trust in the products.

[0124] In another preferred embodiment of the present invention, according to the size, shape, weight of the air spring and the characteristics of available packaging materials, use the genetic algorithm to calculate the final packaging plan. The packaging plan includes the type, specification and packaging method of the packaging materials, and may include:

[0125] Sensors on the production line automatically detect and obtain the physical parameters of the current air spring's size, shape and weight, and collect packaging material characteristic data, including type, thickness, strength, weight, cost, environmental protection index;

[0126] Integrate the air spring physical parameter and packaging material characteristic data to form a data set, and set the basic parameters of the genetic algorithm, including population size, number of iterations, crossover rate, mutation rate;

[0127] Define the optimization objective, and randomly generate a set of initial packaging plans according to the characteristics of the packaging materials and the physical parameters of the air springs. Each plan includes the type, specification and packaging method of the packaging materials;

[0128] Each individual, that is, the initial packaging scheme is represented as a chromosome in the population, and the genes on the chromosome represent the various parameters of the packaging scheme;

[0129] Perform fitness evaluation on each individual, that is, calculate the objective function value corresponding to each individual, and determine candidate individuals according to the objective function value of the individual;

[0130] Perform crossover operations on the candidate individuals, generate new individuals by exchanging some genes, and perform mutation operations on the new individuals to update the individual population;

[0131] Repeat the crossover and mutation operations until the preset number of iterations is reached, and determine the final solution from the final population according to the objective function value of the individual, that is, the final packaging scheme.

[0132] In the embodiment of the present invention, the air springs on the production line are detected in real time through a preset sensor network (such as a laser rangefinder, a visual recognition system, a load cell, etc.). The sensors transmit the collected data (dimensions, shapes, weights) to the data processing unit. At the same time, the characteristic data of the packaging material (type, thickness, strength, weight, cost, environmental protection index) is obtained through a database or an external input device (such as an RFID scanner) and integrated into the same data processing unit. The data processing unit matches and integrates the physical parameters of the air spring and the characteristic data of the packaging material to form a data set containing all necessary information. According to the complexity of the problem and the computing resources, set the basic parameters of the genetic algorithm, such as the population size (indicating the number of packaging schemes considered simultaneously), the number of iterations (the number of loops for the algorithm to run), the crossover rate (the probability of gene exchange), and the mutation rate (the probability of random gene change).

[0133] Define the optimization objective according to the actual requirements (such as cost minimization, environmental protection index maximization, protection performance optimization, etc.). A predefined rule set randomly generates a set of initial packaging schemes according to the characteristics of the packaging material and the physical parameters of the air spring. Encode each initial packaging scheme as a chromosome, and the genes on the chromosome (such as binary bits, real numbers, etc.) represent the various parameters of the packaging scheme (such as packaging material type, specifications, packaging method, etc.). Use the predefined objective function to evaluate the fitness of each individual. Sort the individuals according to the fitness value (i.e., the objective function value), and select the individuals with high fitness as candidate individuals.

[0134] Select two candidate individuals for crossover operations, and generate new individuals by exchanging some of their genes (such as single-point crossover, multi-point crossover, etc.). Perform mutation operations on the newly generated individuals (such as randomly changing the value of a certain gene) to increase the diversity of the population. Repeat the crossover and mutation operations until the preset number of iterations is reached. In the final population, select the individual with the highest fitness as the final solution, that is, the final packaging scheme.

[0135] Suppose we are dealing with the packaging problem of an air spring. The air spring has a size of 50 cm in length, 30 cm in width, 20 cm in height, weighs 5 kg, and has a rectangular shape. At the same time, there are three available packaging materials: foam plastic (thickness 5 cm, medium strength, weight 0.2 kg, cost 1 yuan / kg, environmental protection index 3), corrugated paper (thickness 2 cm, high strength, weight 0.5 kg, cost 0.5 yuan / kg, environmental protection index 5), and bubble film (thickness 0.5 cm, low strength, weight 0.1 kg, cost 2 yuan / kg, environmental protection index 2).

[0136] Integrate the physical parameters of the air spring and the characteristic data of the packaging materials into a dataset, and randomly generate the following three initial packaging schemes:

[0137] Scheme 1: Use foam plastic with specifications of 55 cm in length, 35 cm in width, and 25 cm in height, and the packaging method is full wrapping.

[0138] Scheme 2: Use corrugated paper with specifications of 52 cm in length, 32 cm in width, and 22 cm in height, and the packaging method is to wrap the bottom and the four sides.

[0139] Scheme 3: Use bubble film with specifications of 51 cm in length, 31 cm in width, and 21 cm in height, and the packaging method is to wrap the top and the four sides.

[0140] Each scheme is encoded as a chromosome. For example, the chromosome of Scheme 1 may be [1, 55, 35, 25, 1] (1 represents foam plastic, the next four numbers represent the specifications, and the last 1 represents full wrapping). Calculate the fitness value of each scheme, considering factors such as cost, environmental protection index, and protection performance. Perform crossover and mutation operations on the schemes with higher fitness values to generate new schemes. Repeat the crossover and mutation operations until the preset number of iterations is reached, and select the scheme with the highest fitness value from the final population as the final solution. For example, it may be the scheme of using corrugated paper to wrap the bottom and the four sides.

[0141] Automatically optimizing the packaging scheme through genetic algorithms can significantly reduce the time of manual trial and error and adjustment, and improve packaging efficiency. Genetic algorithms can comprehensively consider multiple factors such as cost, environmental protection index, and protection performance, and find the packaging scheme with the lowest cost. By optimizing the use of packaging materials with higher environmental protection indices, genetic algorithms help reduce the impact of packaging waste on the environment. Genetic algorithms can find the best packaging method according to the physical parameters of the air spring and the characteristics of the packaging materials, and improve the protection performance of the product. The combination of genetic algorithms with devices such as sensors and data processing units realizes the automation and intelligence of the packaging scheme, providing strong support for intelligent production.

[0142] In another preferred embodiment of the present invention, the calculation formula for the objective function value corresponding to each individual is as follows:

[0143]

[0144] where f(x) represents the objective function value corresponding to each individual; α and β represent weight coefficients; n represents the number of types of packaging materials; i represents the index of the type of packaging material; p i represents the unit price of the i-th type of packaging material; q i (x) represents the usage amount of the i-th type of packaging material in the packaging scheme x; γ i represents the loss coefficient of the i-th type of packaging material; δ i represents the discount coefficient of the i-th type of packaging material; V s represents the volume of the air spring; ∈ represents the packaging efficiency coefficient; V p represents the total volume of the packaging materials; θ represents the packaging material compression coefficient; λ represents the weight coefficient of the transportation cost; c represents the transportation unit price; d(x, e) represents the actual transportation distance of the goods from the starting point to the ending point under the given packaging scheme x and transportation route e.

[0145] In the embodiment of the present invention, all relevant parameters are collected, including the weight α = 0.6 of the packaging material cost, the weight β = 0.3 of the packaging efficiency adjustment item, and the weight λ = 0.1 of the transportation cost; the packaging material parameters include the number of types of packaging materials n, the unit price p i of the i-th type of packaging material, the usage amount q i (x) of the i-th type of packaging material in the packaging scheme x, the loss coefficient γ i = 1.1 of the i-th type of packaging material, and the discount coefficient δ i = 0.95 of the i-th type of packaging material; the volume V s of the air spring, the packaging efficiency coefficient ∈ = 0.85, the total volume of the packaging materials, and the packaging material compression coefficient θ = 0.75; the transportation unit price c, and the actual transportation distance d(x, e) of the goods from the starting point to the ending point under the given packaging scheme x and transportation route e.

[0146] Calculate the packaging cost part For each type of packaging material i, multiply its unit price, usage amount, loss coefficient, and discount coefficient, then sum them up, and multiply the result by the weight coefficient α to adjust the proportion of the packaging cost in the overall objective function.

[0147] Calculate the packaging efficiency and material utilization part First, calculate the ratio of the volume of the air spring to the total volume of the packaging material, multiply it by the reciprocal of the packaging efficiency coefficient and the compression coefficient, and then multiply the result by the weight coefficient -β to adjust the proportion of packaging efficiency and material utilization in the overall objective function and make it negative so as to minimize this part during the optimization process.

[0148] Calculate the transportation cost part λ×c×d(x,e). First, multiply the transportation unit price by the actual transportation distance and then multiply by the weight coefficient λ. This part represents the contribution of transportation cost to the overall objective function. Add the above-mentioned packaging cost part, packaging efficiency and material utilization part, and transportation cost part to obtain the objective function value f(x) corresponding to each individual.

[0149] By comprehensively considering the packaging material cost, packaging efficiency, and transportation cost, this function can help enterprises find the option with the lowest cost among different packaging and transportation plans, improving the overall economic efficiency. The loss coefficient and discount coefficient make the calculation closer to reality, helping enterprises optimize material procurement and usage and reduce waste. By calculating the transportation distance and transportation cost, enterprises can choose more economical transportation routes and methods to reduce logistics costs. This objective function provides an enterprise with a quantitative evaluation tool to help decision-makers compare different plans and make more scientific and reasonable decisions.

[0150] In another preferred embodiment of the present invention, after packaging, according to the layout of the production line, the status of the conveying equipment, the target storage location, and the transportation time, use the path planning algorithm to calculate the final conveying path, and according to the final conveying path, convey the packaged air spring to the designated storage location, which may include:

[0151] Obtain the layout diagram of the production line, including equipment positions, connection relationships, and aisle widths, and regard the production line layout as a graph structure, where nodes represent storage locations and edges represent conveying paths;

[0152] Assign a weight to each edge and initialize an open list and a closed list;

[0153] Add the starting position to the open list, and loop to execute the steps of taking out the current node with the lowest cost, checking the storage location, traversing adjacent nodes, and updating the list until the open list is empty to obtain the final path composed of a series of nodes from the starting position to the storage location;

[0154] According to the final path, control the conveying equipment to convey the packaged air spring to each node on the path in sequence.

[0155] In the embodiment of the present invention, obtain the layout information of the production line through sensors or preset data interfaces.

[0156] Extract key information such as device location, connection relationship, and channel width for constructing a graph structure. Based on the obtained layout information, construct a graph structure where the nodes in the graph represent various possible storage locations on the production line, and the edges in the graph represent the conveying paths from one storage location to another. Assign a weight to each edge according to factors such as the length of the conveying path, channel width, and device status, and the weight can represent the conveying cost or time consumption. Create an open list for storing nodes to be processed and a closed list for storing nodes that have been processed.

[0157] Add the starting position after packaging (i.e., the initial position of the air spring) to the open list. Take out the node with the lowest current cost from the open list and check if this node is at the target storage location; if so, the path planning is completed; if not, traverse all adjacent nodes of this node; for each adjacent node, calculate the total cost from the starting position to this node, and update its weight and parent node information. If the adjacent node is not in the open list, add it. If the adjacent node is already in the open list and the new total cost is lower, update its weight and parent node information. Add the current node to the closed list to prevent repeated processing. Repeat the above steps until the open list is empty or the target storage location is found, and based on the parent node information, backtrack to obtain the final path composed of a series of nodes from the starting position to the storage location.

[0158] Send the final path to the control system of the conveying device, and the control system controls the conveying device to convey the air spring to each node on the path in sequence according to the path information.

[0159] Suppose the production line layout is as follows:

[0160] Node A: Starting position (position of the air spring after packaging);

[0161] Node B: Intermediate storage location 1;

[0162] Node C: Intermediate storage location 2;

[0163] Node D: Target storage location.

[0164] The edges and weights are as follows:

[0165] A to B: Weight 2 (representing conveying cost or time);

[0166] A to C: Weight 4;

[0167] B to C: Weight 1;

[0168] B to D: Weight 3;

[0169] C to D: Weight 2.

[0170] The steps to execute the path planning algorithm are as follows:

[0171] Initialize the open list (containing node A) and the closed list (empty). Take out node A (with the lowest current cost) from the open list and check that it is not the target storage location. Traverse the adjacent nodes B and C of node A, calculate the total cost and update the weight and parent node information, and add nodes B and C to the open list. Take out node B (with the lowest current cost) from the open list and check that it is not the target storage location. Traverse the adjacent nodes C and D of node B, calculate the total cost and update the weight and parent node information. Add node D (if it was not in the open list before) to the open list or update its weight, and add node B to the closed list. Repeat the above steps until the target storage location D is found or the open list is empty. Backtrack to obtain the final path: A -> B -> D. According to the final path, control the conveying device to convey the air springs to node B and node D in sequence.

[0172] Through the path planning algorithm, the shortest path or the lowest-cost path from the starting position to the target storage location can be found, which helps to reduce the conveying time and improve the overall efficiency of the production line. By optimizing the path, the energy consumption, equipment wear, and labor costs during the conveying process can be reduced, which helps to lower the operating costs of the enterprise and improve economic benefits. The path planning algorithm can adapt to the dynamic changes of the production line, such as equipment failures and channel blockages, which helps to enhance the flexibility and adaptability of the production line and improve the ability to handle emergencies. Through automated path planning and conveying control, manual intervention can be reduced and the automation level of the production line can be improved, which helps to lower labor costs and improve the stability and consistency of the production line. By reasonably planning the conveying path, the resources and space of the production line can be fully utilized, which helps to reduce resource waste and improve resource utilization efficiency.

[0173] As Figure 2 shown, an embodiment of the present invention also provides a control system for an air spring automated production line, including:

[0174] Collect data from various sensors on the production line, including air compressor pressure, variable pressure reducing valve opening, solenoid valve status, air spring deformation height, displacement sensor signal, and pressure sensor signal;

[0175] According to the predefined air spring production process flow and quality control requirements, formulate a control strategy, and the control strategy includes air spring inflation and deflation control, displacement control, and pressure control;

[0176] According to the control strategy, send control instructions to the actuators on the production line through the control unit, and monitor the operating status of the production line in real time and receive feedback signals;

[0177] Transmit real-time data and operating status to the remote terminal to monitor the operating data and status of the production line in real time, and dynamically adjust the control strategy according to the feedback signal;

[0178] When an abnormality or failure occurs in the production line, an alarm signal is automatically sent, and the alarm information is transmitted to the remote terminal.

[0179] It should be noted that this method corresponds to the above-mentioned air spring automated production line. All implementation methods in the above-mentioned embodiments of the air spring automated production line are applicable to this embodiment and can achieve the same technical effects.

[0180] The above are the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An air spring automated production line, characterized in that, Including: A raw material module, which is used to automatically allocate raw materials through a dynamic programming algorithm and convey them to the processing and forming module; A processing and forming module, which is used to cut, shape and assemble the raw materials into an air spring; A quality inspection module, which is used to conduct quality inspections on the processed and formed air springs, including size, airtightness and strength inspections, to obtain an inspection report; A finished product packaging module, which is used to package the qualified air springs and convey the packaged air springs to the designated location according to the final conveying path; An automated conveying module, which is used to achieve seamless material transfer between the raw material conveying module, the processing and forming module, the quality inspection module and the finished product packaging module through conveyor belts and positioning sensors.

2. The air spring automated production line according to claim 1, wherein, Automatically allocate raw materials through a dynamic programming algorithm and convey them to the processing and forming module, including: Identify the types, specifications, quantities and production requirements of raw materials through sensors; Classify and store the raw materials in the designated storage area and assign a unique index to each type of raw material; Initialize the data structure of the dynamic programming algorithm, including the state array, and determine the state transition equation of the dynamic programming algorithm; Set the initial conditions of the dynamic programming algorithm, and gradually fill the state array according to the state transition equation and the initial conditions until the final solution is obtained; According to the final solution, starting from the last state of the state array, gradually trace back the types and quantities of raw materials selected forward to determine the final raw material selection and allocation plan; Send instructions to the automated conveying equipment, select the corresponding raw materials from the storage area according to the instructions, and mix or allocate the final raw material selection and allocation plan to obtain the allocated raw materials; The allocated raw materials are conveyed to the entrance of the processing and forming module through a conveyor belt.

3. The air spring automated production line according to claim 2, characterized in that Conduct quality inspections on the processed and formed air springs, including size, airtightness and strength inspections, to obtain an inspection report, including: Determine a certain number of air springs on the production line in sequence as inspection samples, and use an automated dimension measuring instrument to measure the outer diameter, inner diameter and height of the inspection samples; Record the dimension data of each sample and compare it with the set standard to determine whether the dimension of each sample is qualified to obtain a dimension determination result; According to the dimension determination result, place the samples with qualified dimensions in the airtightness test equipment, and fill the samples with qualified dimensions with a certain pressure of gas and maintain it for a period of time; Use a pressure sensor to monitor the pressure change of the samples with qualified dimensions, and determine whether the airtightness of the samples with qualified dimensions is qualified according to the pressure change situation to obtain an airtightness determination result; According to the airtightness determination result, install the samples with qualified airtightness on the strength testing machine, and gradually apply pressure to the samples with qualified airtightness according to the set standard until the maximum design pressure is reached; A displacement sensor monitors the deformation of the samples with qualified airtightness under the action of pressure in real time, and determines whether the strength of the samples with qualified airtightness is qualified according to the deformation situation to obtain a strength determination result; Integrate the dimension determination result, the airtightness determination result and the strength determination result to generate an inspection report, including the inspection data, determination result and classification mark of each sample.

4. The air spring automated production line according to claim 3, characterized in that, Package the qualified air springs and transport the packaged air springs to the designated location according to the final transportation path, including: Calculate the final packaging plan using a genetic algorithm based on the size, shape, weight of the air spring and the characteristics of the available packaging materials. The packaging plan includes the type, specification and packaging method of the packaging materials; According to the final packaging plan, control the packaging equipment to determine the corresponding packaging materials and packaging methods, and package the qualified air springs; After packaging, calculate the final transportation path using a path planning algorithm according to the layout of the production line, the status of the transportation equipment, the target storage location and the transportation time, and transport the packaged air springs to the designated storage location according to the final transportation path.

5. The air spring automated production line according to claim 4, wherein Calculate the final packaging plan using a genetic algorithm based on the size, shape, weight of the air spring and the characteristics of the available packaging materials. The packaging plan includes the type, specification and packaging method of the packaging materials, including: Sensors on the production line automatically detect and obtain the physical parameters of the current air spring, such as size, shape and weight, and collect the data of the packaging material characteristics, including type, thickness, strength, weight, cost, environmental protection index; Integrate the physical parameters of the air spring and the data of the packaging material characteristics to form a data set, and set the basic parameters of the genetic algorithm, including population size, number of iterations, crossover rate, mutation rate; Define the optimization objective, and randomly generate a set of initial packaging plans according to the characteristics of the packaging materials and the physical parameters of the air spring. Each plan includes the type, specification and packaging method of the packaging materials; Each individual, that is, the initial packaging plan is represented as a chromosome in the population, and the genes on the chromosome represent the various parameters of the packaging plan; Evaluate the fitness of each individual, that is, calculate the objective function value corresponding to each individual, and determine the candidate individuals according to the objective function value of the individual; Perform crossover operations on the candidate individuals, generate new individuals by exchanging some genes, and perform mutation operations on the new individuals to update the individual population; Repeat the crossover and mutation operations until the preset number of iterations is reached, and determine the final solution, that is, the final packaging plan, from the final population according to the objective function value of the individual.

6. The automated production line of an air spring according to claim 5, wherein, The calculation formula for the objective function value corresponding to each individual is: Among them, f(x) represents the objective function value corresponding to each individual; α and β represent weight coefficients; n represents the number of types of packaging materials; i represents the index of the type of packaging material; p i represents the unit price of the i-th type of packaging material; q i (x) represents the usage amount of the i-th type of packaging material in the packaging scheme x; γ i represents the loss coefficient of the i-th type of packaging material; δ i represents the discount coefficient of the i-th type of packaging material; V s represents the volume of the air spring; ∈ represents the packaging efficiency coefficient; V p represents the total volume of the packaging materials; θ represents the packaging material compression coefficient; λ represents the weight coefficient of the transportation cost; c represents the transportation unit price; d(x,e) represents the actual transportation distance of the goods from the starting point to the ending point under the conditions of the given packaging scheme x and the transportation route e.

7. The air spring automated production line according to claim 6, characterized in that, After packaging, calculate the final transportation path using a path planning algorithm according to the layout of the production line, the status of the transportation equipment, the target storage location and the transportation time, and transport the packaged air springs to the designated storage location according to the final transportation path, including: Obtain the layout diagram of the production line, including equipment positions, connection relationships, and aisle widths, and regard the production line layout as a graph structure, where the nodes represent storage locations and the edges represent transportation paths; Assign a weight to each edge, and initialize an open list and a closed list; Add the starting position to the open list, and loop through the steps of taking out the current node with the lowest cost, checking the storage location, traversing adjacent nodes and updating the list until the open list is empty, to obtain the final path composed of a series of nodes from the starting position to the storage location. According to the final path, control the conveying equipment to sequentially convey the packaged air springs to each node on the path.

8. A control system for an air spring automated production line according to any one of claims 1 to 7, characterized in that, The control system realizes the following functions: Collect data from various sensors on the production line, including air compressor pressure, variable pressure reducing valve opening, solenoid valve status, air spring deformation height, displacement sensor signal, and pressure sensor signal; According to the predefined air spring production process flow and quality control requirements, formulate control strategies, including air spring inflation and deflation control, displacement control, and pressure control; According to the control strategy, send control instructions to the actuators on the production line through the control unit, and monitor the operating status of the production line in real time and receive feedback signals; Transmit the real-time data and operating status to the remote terminal to monitor the operating data and status of the production line in real time, and dynamically adjust the control strategy according to the feedback signals; When an abnormality or failure occurs on the production line, automatically send an alarm signal and transmit the alarm information to the remote terminal.

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