An automobile hub production line monitoring system based on internet of things

Abstract

The application discloses an automobile hub production line monitoring system based on Internet of Things, and relates to the technical field of production monitoring.The application comprises a step data analysis module, a production equipment monitoring module and a process parameter monitoring module.The performance of each raw material and the optimal process parameter of the automobile hub under each environmental parameter are analyzed by analyzing the equipment offset data of each production equipment, and then the available production equipment of the current batch of automobile hub is obtained through comprehensive analysis, so that the automobile hub quality problems caused by equipment parameter offset and raw material performance change are avoided, the pass rate of the automobile hub is ensured to the maximum extent, the process parameter is adjusted according to the environmental parameter change in the production process, the influence of environmental factors on the production quality of the automobile hub is eliminated, the pass rate and quality of the automobile hub are greatly improved, and the production cost caused by unqualified quality is reduced.

Description

Technical Field

[0001] This application relates to the field of production monitoring technology, specifically to an Internet of Things-based monitoring system for an automotive wheel hub production line. Background Technology

[0002] In the production process of automobile wheel hubs, environmental factors have a significant impact on the raw materials and production processes. When the ambient temperature is too low and the humidity is too high, it can lead to shrinkage and cracks in the casting process, as well as oxidation of the raw materials, resulting in a large number of impurities. Therefore, analyzing and monitoring the production process of automobile wheel hubs based on environmental factors is beneficial to ensuring the production efficiency and quality of automobile wheel hubs. Therefore, this application proposes an Internet of Things-based automobile wheel hub production line monitoring system.

[0003] Existing technology, such as the invention application patent with announcement number CN119024778A, discloses a real-time monitoring system for an automotive wheel hub production line based on digital twins. This system includes: a digital twin system construction module, a real-time data acquisition and transmission module, a monitoring module, and a simulation optimization module. The digital twin system construction module is used to construct a digital twin model to obtain the digital twin system. The real-time data acquisition and transmission module is used to acquire entity data in real time. The monitoring module is used to identify faults in the entity data, obtain fault identification results, and display the fault identification results and entity data. By constructing a digital twin system corresponding to the automotive wheel hub production line, data interaction is enhanced. Based on this, a fault prediction model is used to extract features from the production line equipment data and product surface images to perform fault identification, achieving accurate and timely fault prediction, and realizing efficient, real-time, and visual monitoring of the automotive wheel hub production line.

[0004] The above solution has the following technical problems: 1. The current technology only identifies faults on the automotive wheel and rail production line and does not take into account the impact of environmental factors on the raw materials of automotive wheel hubs. In areas such as plateaus or coastal areas, environmental factors will greatly affect the characteristics of raw materials. In addition, factors such as the service life and maintenance frequency of each production equipment will cause different degrees of parameter deviation, which is difficult to eliminate through parameter correction. At this time, the deviation of raw material characteristics and equipment parameters will seriously lead to quality problems of automotive wheel hubs.

[0005] 2. Current technology does not take into account the impact of environmental factors on the production process of automobile wheels. Using the same production process for automobile wheels in different environments will lead to inconsistent quality of automobile wheels, thereby increasing the probability of producing defective products. Summary of the Invention

[0006] The purpose of this application is to provide an Internet of Things-based monitoring system for automotive wheel hub production lines, which solves the problems existing in the background technology.

[0007] To solve the above technical problems, this application adopts the following technical solution: This application provides an Internet of Things-based monitoring system for automobile wheel hub production lines, including: a data analysis module: used to analyze the equipment parameter offset data of each production equipment in the automobile wheel hub production line and the process parameters of each production process of automobile wheel hub under various environmental parameters, and at the same time analyze and obtain the raw material performance thresholds of each production raw material of automobile wheel rail under various environmental conditions.

[0008] Production equipment monitoring module: It is used to comprehensively analyze the production equipment of the current batch of automobile wheel hubs based on the equipment parameter offset data of each production equipment, the process parameters of each production process of automobile wheel hubs under various environmental parameters, and the raw material performance thresholds of each production raw material of automobile wheel rails under various environmental conditions, and then monitor each production equipment.

[0009] Process parameter monitoring module: It is used to monitor various environmental parameters in the production workshop during the production process, and then adjust the process parameters of each production process in real time. At the same time, it monitors the equipment parameters of each production equipment, judges whether they meet the production requirements, and provides early warning prompts.

[0010] The beneficial effects of this application are as follows: 1. This application provides an Internet of Things-based automotive wheel hub production line monitoring system. By analyzing the equipment offset data of each production equipment, and simultaneously analyzing the performance of each raw material under various environmental parameters and the optimal process parameters of the automotive wheel hub, the system comprehensively analyzes and obtains the available production equipment for the current batch of automotive wheel hubs. This avoids quality problems of automotive wheel hubs caused by equipment parameter offsets and changes in raw material performance, and maximizes the pass rate of automotive wheel hubs. At the same time, the system adjusts the process parameters according to changes in environmental parameters during the production process, eliminating the impact of environmental factors on the production quality of automotive wheel hubs, greatly improving the pass rate and quality of automotive wheel hubs, and reducing production costs caused by substandard quality.

[0011] 2. This application analyzes the equipment offset data of each production equipment and the performance changes of each raw material under different environments. Based on the production environment of the current batch of automobile wheel hubs, it selects appropriate production equipment to avoid automobile wheel hub quality problems caused by equipment parameter offsets and changes in raw material characteristics, thus maximizing the pass rate of automobile wheel hubs.

[0012] 3. This application analyzes the process parameters of automobile wheel hubs under various environmental conditions, thereby achieving uniformity in product quality across regions. It eliminates the impact of environmental factors on automobile wheel hub quality from the source, greatly improving the quality of automobile wheel hubs. Therefore, this application effectively improves the production quality of automobile wheel hubs while ensuring their qualification, which is beneficial to the long-term development of the industry. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the system structure connection of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] Reference Figure 1 As shown, this application provides an Internet of Things-based monitoring system for an automotive wheel hub production line, including the following modules: a data analysis module: used to analyze the equipment parameter offset data of each production equipment in the automotive wheel hub production line and the process parameters of each production process of the automotive wheel hub under various environmental parameters, and at the same time to analyze and obtain the raw material performance thresholds of each production raw material of the automotive wheel rail under various environmental conditions.

[0017] It should be noted that the raw materials used to produce automobile wheels include aluminum alloys, steel, magnesium alloys, and carbon fiber composite materials.

[0018] It should be noted that the various production equipment in the automobile wheel hub production line includes gravity casting machines, low-pressure casting machines, lathes, drilling machines, painting equipment, and electroplating equipment, etc.

[0019] In a specific example, the environmental parameters include temperature, humidity, concentration of corrosive media in the air, and air pressure.

[0020] It should be noted that due to the different geographical locations of the factories and the significant seasonal differences in the environment, the environmental conditions for the production of each batch of car wheel hubs are different. Ambient temperature, humidity, airborne corrosion content, and air pressure will have a significant impact on the production process of car wheel hubs.

[0021] In a specific example, the analysis of the equipment parameter offset data of each production equipment in the automotive wheel hub production line is carried out as follows: The previous parameter correction time point of each production equipment is obtained from the management center, and then the parameter offset data of each production equipment after the previous parameter correction time point is retrieved from the parameter monitoring panel of each production equipment. The parameter offset data of each production equipment is used as the vertical axis, and the time point corresponding to the parameter offset data is used as the horizontal axis. The equipment parameter offset curve of each production equipment is constructed by nonlinear fitting, and the endpoint coordinates of the equipment parameter offset curve of each production equipment are obtained. Then, the endpoint slope of the equipment parameter offset curve of each production equipment is calculated, and the endpoint coordinates and endpoint slope are used as the equipment parameter offset data.

[0022] It should be noted that frequent parameter calibration of equipment consumes a large number of standard parts and consumables, resulting in significant material waste and economic costs. Therefore, when the parameter deviation data of each production equipment reaches the set parameter deviation threshold, the corresponding production equipment is then calibrated. The parameter deviation threshold is obtained by consulting the technical manual of the production equipment.

[0023] In a specific instance, the process parameters mentioned include forging process parameters, casting process parameters, and spinning process parameters.

[0024] In a specific example, the process parameters of each production process of automobile wheel hub under various environmental parameters are analyzed. The specific analysis process is as follows: S1. After determining the constraints of each environmental parameter, a full-size environmental chamber is used to simulate the environmental parameters during the production of automobile wheel hub. First, the full-size environmental chamber is adjusted to the standard environment. Then, any environmental parameter is adjusted according to the preset gradient, while keeping other environmental parameters unchanged. The performance of the produced product is then tested.

[0025] S2. Obtain the combination of various environmental parameters through orthogonal design experiments, and then adjust the combination of various environmental parameters according to each preset gradient to simulate complex environment and perform performance testing on the produced products.

[0026] S3. During the testing process, PLC and sensors are used to collect environmental parameters and process parameters of each production process in real time, and the corresponding output and quality detection results are recorded. The environmental parameters and process parameters of each production process are used as inputs, and the final product performance is used as the output. A dynamic adjustment model of process parameters is established based on deep learning algorithms, and the dynamic adjustment model of process parameters is deployed to the control terminal of each production equipment in the production line.

[0027] It should be noted that the preset gradients are set by the relevant personnel themselves, such as a preset temperature gradient of 5°C, humidity of 5%RH, corrosive medium content of 0.5ppm, and air pressure of 10kPa, etc., and no specific restrictions are imposed here.

[0028] It should be noted that the full-size environmental chamber integrates miniaturized production equipment such as die-casting machines and heating furnaces, which is one-fifth the size of the actual production equipment.

[0029] It should be noted that the standard environment is 25℃, 50%RH, 0ppm corrosive medium and 101kPa air pressure.

[0030] It should be noted that the maximum and minimum temperatures of the factory's location over the past year are obtained from the meteorological center and recorded as temperature constraints. Similarly, humidity constraints, air pressure constraints, and the content of corrosive media in the air are obtained.

[0031] It should be noted that the performance testing of the produced products includes strength testing, brittleness testing, and precision testing. Among these, strength testing and precision testing are existing technologies and will not be described in detail.

[0032] It should be noted that the orthogonal design experiment is existing technology, and therefore will not be described in detail.

[0033] In a specific example, the simultaneous analysis obtains the raw material performance thresholds of each production raw material for automobile wheel rails under various environmental conditions. The specific analysis process is as follows: A1. After determining the constraints of each environmental parameter, a full-size environmental chamber is used to simulate the environmental parameters during the production of automobile wheel hubs. First, the full-size environmental chamber is adjusted to the standard environment. Then, any environmental parameter is adjusted according to the preset gradient, while keeping other environmental parameters unchanged. The produced products are then subjected to quality inspection.

[0034] A2. Obtain the combination of various environmental parameters through orthogonal design experiments, and then adjust the combination of various environmental parameters according to each preset gradient to simulate a complex environment. Perform performance testing on each produced product, take the performance test results of each product and each environmental parameter as input, and take the performance threshold of each raw material as output, and obtain the functional relationship between each environmental parameter and each raw material performance threshold through response surface model analysis.

[0035] It should be noted that the preset gradients are set by the relevant personnel themselves, such as a preset temperature gradient of 5°C, humidity of 5%RH, corrosive medium content of 0.5ppm, and air pressure of 10kPa, etc., and no specific restrictions are imposed here.

[0036] It should be noted that, since increased temperature can lead to increased metal lattice spacing, decreased raw material strength and hardness, and excessive humidity or high levels of corrosive substances in the air can damage the oxide film on the material surface, analyzing the performance thresholds of raw materials can effectively prevent such problems.

[0037] It should be noted that the orthogonal design experiment and response surface model mentioned are existing technologies. By inputting the performance test results of each product and various environmental parameters into the orthogonal model expression, and solving for each parameter of the orthogonal model expression, the performance threshold expressions related to the environmental parameters of each product can be obtained. For example, the brittleness performance threshold expression of steel can be obtained by analysis as follows: The brittleness threshold of aluminum alloys is: wait.

[0038] Production equipment monitoring module: It is used to comprehensively analyze the production equipment of the current batch of automobile wheel hubs based on the equipment parameter offset data of each production equipment, the process parameters of each production process of automobile wheel hubs under various environmental parameters, and the raw material performance thresholds of each production raw material of automobile wheel rails under various environmental conditions, and then monitor each production equipment.

[0039] In a specific example, the production equipment for the current batch of automobile wheel hubs is obtained through comprehensive analysis based on the equipment parameter offset data of each production equipment, the process parameters of each production process of automobile wheel hubs under various environmental parameters, and the raw material performance thresholds of each production raw material of automobile wheel rails under various environmental conditions. The specific analysis process is as follows: Based on the performance test results of the products under various environmental combinations, the process parameters of each production process are used as indexes to divide the data and form a searchable process parameter table. At the same time, the production cycle of the current batch of automobile wheel hubs is obtained according to the production plan, and the average production environment parameters within the production cycle are obtained from the meteorological center and input into the process parameter dynamic adjustment model to obtain the preset process parameters of the current batch of automobile wheel hubs. The equipment parameters of each production equipment corresponding to each preset process parameter are obtained from the production table. Based on each equipment parameter and parameter offset data, the offset equipment parameters of each production equipment are calculated. The process parameters corresponding to the offset equipment parameters of each production equipment are obtained from the production table. The process parameters are matched with the performance parameter table to obtain the performance characteristic values ​​of each product.

[0040] It should be noted that the equipment parameters of each production equipment corresponding to each preset process parameter are obtained from the production table. For example, if the preset process parameter is a furnace temperature of 600℃, the corresponding equipment parameter is a furnace power of 1000KW, etc.

[0041] Each product's performance characteristic value is compared with the corresponding raw material performance threshold. If a product's performance data is less than the corresponding raw material performance characteristic threshold, the corresponding production equipment is marked as unusable production equipment; otherwise, it is marked as usable production equipment. Based on this, each usable production equipment is obtained.

[0042] It should be noted that the properties of each product and raw material are all positive properties, such as precision and strength, which are better the higher they are under the constraints. The constraints are the performance ranges of automobile wheel hubs recorded in the production table.

[0043] Process parameter monitoring module: It is used to monitor various environmental parameters in the production workshop during the production process, and then adjust the process parameters of each production process in real time. At the same time, it monitors the equipment parameters of each production equipment, judges whether they meet the production requirements, and provides early warning prompts.

[0044] In a specific example, the process of monitoring various environmental parameters in the production workshop and then adjusting the process parameters of each production process in real time is as follows: environmental parameters in the production workshop are collected by various environmental sensors at various preset time points, and the environmental parameters are uploaded to the process parameter dynamic adjustment model deployed on the terminal of each production equipment. The process parameter dynamic adjustment model outputs the optimal process parameters at each preset time point, and then the equipment parameters of each production equipment are adjusted based on the optimal process parameters.

[0045] It should be noted that the preset time points can be set to one per day or one every two days, and the specific settings are determined by the relevant personnel; the environmental sensors include temperature and humidity sensors, air quality sensors, and air pressure sensors, etc.

[0046] In a specific example, the process parameters of each production process and the equipment parameters of each production device are monitored simultaneously to determine whether they meet production requirements. The specific process is as follows: The equipment parameter monitoring panel of each production device is monitored in real time, and the equipment parameter offset data of each production device obtained by monitoring is compared with the set equipment parameter offset threshold of each production device. If the equipment parameter offset data of a certain production device changes and exceeds the set equipment parameter offset threshold of that production device, it is determined that the production device does not meet the production requirements and an early warning is required. Otherwise, it is determined that the production device meets the production requirements and no early warning is required.

[0047] It should be noted that when the equipment parameters of the production equipment deviate significantly, it will affect the process parameters of the production process, and thus affect the performance of the car wheel hub. For example, if the process parameters require the heating furnace temperature to be 600℃, the power of the heating furnace should be 80-100KW. If the power of the heating furnace deviates by 50KW, the process parameters will not meet the requirements.

[0048] In a specific instance, the early warning notification includes alerting staff to the production equipment number and specific equipment parameters that do not meet production requirements.

[0049] This application provides an IoT-based monitoring system for automotive wheel hub production lines. By analyzing the equipment offset data of each production device, and simultaneously analyzing the performance of each raw material under various environmental parameters and the optimal process parameters for automotive wheel hubs, the system comprehensively analyzes and determines the available production equipment for the current batch of automotive wheel hubs. This avoids quality problems caused by equipment parameter offsets and changes in raw material performance, maximizing the pass rate of automotive wheel hubs. Furthermore, by adjusting process parameters according to changes in environmental parameters during production, the system eliminates the impact of environmental factors on the production quality of automotive wheel hubs, significantly improving the pass rate and quality of automotive wheel hubs while reducing production costs due to quality defects.

[0050] The above content is merely an example and illustration of the concept of this application. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the inventive concept or exceed the scope defined in this application, they should all fall within the protection scope of this application.

Claims

1. A monitoring system for an automotive wheel hub production line based on the Internet of Things, characterized in that, include: Data Analysis Module: Used to analyze the equipment parameter offset data of each production equipment in the automobile wheel hub production line and the process parameters of each production process of automobile wheel hub under various environmental parameters. At the same time, it analyzes and obtains the raw material performance thresholds of each production raw material of automobile wheel rail under various environmental conditions. Production equipment monitoring module: It is used to comprehensively analyze the production equipment of the current batch of automobile wheel hubs based on the equipment parameter offset data of each production equipment, the process parameters of each production process of automobile wheel hubs under various environmental parameters, and the raw material performance thresholds of each production raw material of automobile wheel rails under various environmental conditions, and then monitor each production equipment. Process parameter monitoring module: It is used to monitor various environmental parameters in the production workshop during the production process, and then adjust the process parameters of each production process in real time. At the same time, it monitors the equipment parameters of each production equipment, judges whether they meet the production requirements, and provides early warning prompts.

2. The IoT-based automotive wheel hub production line monitoring system according to claim 1, characterized in that, The environmental parameters include temperature, humidity, content of corrosive media in the air, and air pressure.

3. The IoT-based automotive wheel hub production line monitoring system according to claim 2, characterized in that, The analysis of the equipment parameter offset data of each production equipment in the automotive wheel hub production line is as follows: The system obtains the previous parameter calibration time point for each production equipment from the management center, and then retrieves the parameter offset data of each production equipment after the previous parameter calibration time point from the parameter monitoring panel of each production equipment. The system uses the parameter offset data of each production equipment as the vertical axis and the time point corresponding to the parameter offset data as the horizontal axis. The system constructs the equipment parameter offset curve of each production equipment through nonlinear fitting, and obtains the endpoint coordinates of the equipment parameter offset curve of each production equipment. Then, the endpoint slope of the equipment parameter offset curve of each production equipment is calculated, and the endpoint coordinates and endpoint slope are used as the equipment parameter offset data.

4. The IoT-based automotive wheel hub production line monitoring system according to claim 1, characterized in that, The process parameters include forging process parameters, casting process parameters, and spinning process parameters.

5. The IoT-based automotive wheel hub production line monitoring system according to claim 4, characterized in that, The process parameters of each production process for automobile wheel hubs under various environmental parameters are described and analyzed. The specific analysis process is as follows: S1. After determining the constraints of each environmental parameter, use a full-size environmental chamber to simulate the environmental parameters during the production of automobile wheel hubs. First, adjust the full-size environmental chamber to the standard environment, adjust any environmental parameter according to the preset gradient, while keeping other environmental parameters unchanged, and perform performance testing on the produced products. S2. Obtain the combination of various environmental parameters through orthogonal design experiments, and then adjust the combination of various environmental parameters according to each preset gradient to simulate complex environment and perform performance testing on the produced products. S3. During the testing process, PLC and sensors are used to collect environmental parameters and process parameters of each production process in real time, and the corresponding output and quality detection results are recorded. The environmental parameters and process parameters of each production process are used as inputs, and the final product performance is used as the output. A dynamic adjustment model of process parameters is established based on deep learning algorithms, and the dynamic adjustment model of process parameters is deployed to the control terminal of each production equipment in the production line.

6. The IoT-based automotive wheel hub production line monitoring system according to claim 5, characterized in that, The simultaneous analysis yielded the raw material performance thresholds for each production raw material of automobile wheel rails under various environmental conditions. The specific analysis process is as follows: A1. After determining the constraints of each environmental parameter, use a full-size environmental chamber to simulate the environmental parameters during the production of automobile wheel hubs. First, adjust the full-size environmental chamber to the standard environment, adjust any environmental parameter according to the preset gradient, while keeping other environmental parameters unchanged, and conduct quality inspection on the produced products. A2. Obtain the combination of various environmental parameters through orthogonal design experiments, and then adjust the combination of various environmental parameters according to each preset gradient to simulate a complex environment. Perform performance testing on each produced product, take the performance test results of each product and each environmental parameter as input, and take the performance threshold of each raw material as output, and obtain the functional relationship between each environmental parameter and each raw material performance threshold through response surface model analysis.

7. The IoT-based automotive wheel hub production line monitoring system according to claim 6, characterized in that, The production equipment for the current batch of automotive wheel hubs is determined by a comprehensive analysis based on the equipment parameter offset data of each production equipment, the process parameters of each production process for automotive wheel hubs under various environmental parameters, and the raw material performance thresholds of each production raw material for automotive wheel rails under various environmental conditions. The specific analysis process is as follows: Based on the performance test results of products under various environmental combinations, the process parameters of each production process are used as indexes to divide the data and form a searchable process parameter table. At the same time, the production cycle of the current batch of automobile wheel hubs is obtained according to the production plan, and the average production environment parameters within the production cycle are obtained from the meteorological center and input into the dynamic adjustment model of process parameters to obtain the preset process parameters of the current batch of automobile wheel hubs. The equipment parameters of each production equipment corresponding to each preset process parameter are obtained from the production table. Based on each equipment parameter and parameter offset data, the offset equipment parameters of each production equipment are calculated. The process parameters corresponding to the offset equipment parameters of each production equipment are obtained from the production table. The process parameters are matched with the performance parameter table to obtain the performance characteristic values ​​of each product. Each product's performance characteristic value is compared with the corresponding raw material performance threshold. If a product's performance data is less than the corresponding raw material performance characteristic threshold, the corresponding production equipment is marked as unusable production equipment; otherwise, it is marked as usable production equipment. Based on this, each usable production equipment is obtained.

8. The IoT-based automotive wheel hub production line monitoring system according to claim 7, characterized in that, The process of monitoring various environmental parameters in the production workshop and then adjusting the process parameters of each production process in real time is as follows: Environmental parameters in the production workshop are collected by various environmental sensors at preset time points, and these parameters are uploaded to the dynamic adjustment model of process parameters deployed on the terminals of various production equipment. The dynamic adjustment model of process parameters outputs the optimal process parameters at each preset time point, and then adjusts the equipment parameters of each production equipment based on the optimal process parameters.

9. A monitoring system for an automotive wheel hub production line based on the Internet of Things according to claim 8, characterized in that, Simultaneously, the process parameters of each production process and the equipment parameters of each production equipment are monitored to determine whether they meet the production requirements. The specific process is as follows: The equipment parameter monitoring panel of each production equipment is monitored in real time, and the equipment parameter offset data of each production equipment is compared with the set equipment parameter offset threshold of each production equipment. If the equipment parameter offset data of a certain production equipment changes and exceeds the set equipment parameter offset threshold of that production equipment, it is determined that the production equipment does not meet the production requirements and an early warning is required. Otherwise, it is determined that the production equipment meets the production requirements and no early warning is required.

10. The IoT-based automotive wheel hub production line monitoring system according to claim 9, characterized in that, The early warning notification includes informing staff of the equipment numbers and specific parameters of production equipment that does not meet production requirements.

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