Aluminum alloy wheel forging process based on automatic control and digital simulation

Abstract

The present invention discloses an aluminum alloy wheel forging process based on automated control and digital simulation, belonging to the field of automotive parts forging technology. The present invention solves the problems of low digitalization level of existing processes, single function of workshop information systems, and small amount of integrated information. By introducing digital simulation technology and automated control technology, the present invention fully replicates the aluminum alloy wheel forging process flow and forging results using digital simulation technology, so that workers can predict the aluminum alloy wheel forging effect based on current equipment parameters and various monitoring data in the scene, thereby facilitating workers to adjust the parameters of the forging equipment and scene parameters in advance, thereby improving the accuracy of aluminum alloy wheel forging; secondly, by deploying a variety of sensing devices in various forging scenes, relying on the sensing devices to monitor various data in the forging scenes, thereby ensuring that the forging environment is maximally suitable for the needs of aluminum alloy wheel forging, further improving the accuracy and effect of aluminum alloy wheel forging.

Description

Technical Field

[0001] The invention relates to the technical field of automobile parts forging, in particular to an aluminum alloy wheel forging process based on automatic control and digital simulation. Background Art

[0002] The continuous improvement of automotive parts forging technology has driven the rapid development of automotive engineering. With the increasing demand for vehicle performance and safety, the quality and performance of all automotive components must continue to improve to better meet the needs of current social development. Previously, steel wheels were susceptible to external factors and could develop quality issues during use. However, aluminum alloy wheels offer superior quality and performance, are recyclable, and offer significant advantages in automotive wheel manufacturing. Aluminum alloy forging technology is a key manufacturing method for high-performance aluminum alloy products. In recent years, aluminum alloy wheel forging production technology has made rapid progress, forming a relatively complete aluminum alloy forging production system.

[0003] Since the production and processing technology of aluminum alloy wheels is relatively complex, in order to complete the aluminum alloy wheel manufacturing work quickly and efficiently, we should focus on the use of advanced technical means to improve the efficiency of wheel production and manufacturing; however, in the existing technology, there is a certain gap in the overall intelligence level of the aluminum alloy wheel forging industry. The main problems are: the digitalization level of aluminum alloy forging equipment is not high, the workshop information system has a single function, and the amount of integrated information is small. These problems lead to uncontrollable production processes, unstable product quality, and high operating and maintenance costs in aluminum alloy wheel forging companies.

[0004] Therefore, the existing needs are not met, so we propose an aluminum alloy wheel forging process based on automatic control and digital simulation. Summary of the Invention

[0005] The purpose of the present invention is to provide an aluminum alloy wheel forging process based on automated control and digital simulation. By introducing digital simulation technology and automated control technology, the digital simulation technology is used to fully replicate the aluminum alloy wheel forging process flow and forging results, so that workers can predict the aluminum alloy wheel forging effect based on current equipment parameters and various monitoring data in the scene, thereby facilitating workers to adjust the parameters of the forging equipment and scene parameters in advance; by deploying a variety of sensing devices in various forging scenes, relying on the sensing devices to monitor various data in the forging scenes, thereby ensuring that the forging environment is maximally suitable for the needs of aluminum alloy wheel forging, improving the accuracy and effect of aluminum alloy wheel forging, and solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The aluminum alloy wheel forging process based on automated control and digital simulation includes the following steps:

[0008] Step 1: Digital Simulation: Obtain historical aluminum alloy wheel forging process data and simulate the temperature field distribution, mold filling, and solidification processes during the aluminum alloy wheel forging process using the finite element method (FEM). Compare the simulated forging results with the actual forging results of the historical aluminum alloy wheels to obtain simulation results of the temperature field, solidification field, and the distribution of shrinkage and shrinkage defects that are prone to form during the aluminum alloy wheel forging process. Based on the simulation results, adjust the parameters of the current forging equipment and scenario.

[0009] Step 2: Automatic control: Multiple sensor devices are deployed in the forging process equipment to obtain various monitoring data, and the operating status of the forging process equipment is determined based on the monitoring data. Based on the operating status, each forging equipment is automatically controlled;

[0010] Step 3: Testing process: Conduct comprehensive testing on the chemical composition, crystal structure and mechanical properties of each raw material to ensure the quality of the raw materials;

[0011] Step 4. Forging process: Introducing digital simulation and automatic control technology into the current forging process, the aluminum rods in the raw material are cut, heated, forged, cold spun and heat treated to complete the forging process of the aluminum alloy wheels.

[0012] Furthermore, in step 1, the temperature field distribution, filling and solidification processes of the forging process are simulated by the finite element method (FEM), which specifically includes the following steps:

[0013] Modeling and Meshing: Use UGNX software to model the aluminum alloy wheel hub. Import the 3D model of the aluminum alloy wheel hub created by UGNX into ProCAST software for 2D and 3D meshing.

[0014] Setting parameters: Determine the selection of wheel hub and mold materials, obtain the material's thermophysical and high-temperature mechanical properties, and set relevant process parameters, including pouring temperature, cooling system, and filling pressure, to ensure the simulation results and the quality of the final casting;

[0015] Finite Element Analysis: The temperature field of the forging model is analyzed using the finite element analysis method, including analyzing the temperature of each small unit to obtain the temperature distribution of the entire forging model;

[0016] Digital Simulation: ProCAST software is used to digitally simulate the low-pressure casting process for aluminum alloy wheels. The simulation examines the temperature field, solidification field, and the distribution of defects such as shrinkage and shrinkage cavities that are prone to forming during the aluminum alloy wheel forging process. By adjusting process parameters such as pouring temperature, cooling system, and filling pressure, the casting process is optimized, casting defects are reduced, and casting quality is improved.

[0017] Verify results: Compare simulation results with experimental data to verify the accuracy of the simulation, including comparing the obtained simulation results with the variation patterns in the actual forging process, analyzing the quality issues of the castings based on the simulation results, and adjusting the process parameters based on the analysis results to optimize the process to achieve the goal of improving casting quality and production efficiency, and ensure the reliability of the simulation.

[0018] Furthermore, in step 2, multiple sensing devices are arranged in the forging process equipment, specifically including the following steps:

[0019] Data Collection: Displacement sensors, pressure sensors, temperature sensors, vision systems, and safety light curtains installed in various forging scenes collect real-time position data, pressure data, temperature data, and forging production images during the forging process.

[0020] Data processing: Receives data from each forging scenario, compares and analyzes each data point with the threshold, obtains the operating status of the process equipment in each forging scenario, and feeds it back to the industrial computer;

[0021] Equipment control: The industrial computer receives the operating status of the process equipment in each forging scene and sends corresponding control signals to each process equipment to drive and control the operation of each process equipment.

[0022] Furthermore, the data receiving operation status of the position data, pressure data and temperature data is monitored in real time, and when the data receiving operation is abnormal, an abnormal operation alarm is issued, including:

[0023] Real-time monitoring of data reception operating parameters of position data, pressure data, and temperature data; wherein the data reception operating parameters include data reception delay ratio, data reception bit error rate, and data reception interruption rate;

[0024] Obtaining a first data reception indicator parameter using the data reception delay ratio and the data reception bit error rate;

[0025] The first data reception indicator parameter is obtained by the following formula:

[0026]

[0027] Among them, G 01 Indicates the first data reception indicator parameter; n indicates the number of data receptions; R i represents the data reception delay rate corresponding to the i-th data reception; W i R represents the data reception bit error rate corresponding to the i-th data reception; b W represents the standard deviation of the data reception delay rate corresponding to n data receptions; bC represents the standard deviation of the data reception bit error rate corresponding to n data receptions; rw Indicates the maximum difference between the data reception delay rate and the data reception bit error rate corresponding to n data receptions; C 01 represents a preset first difference reference value;

[0028] Comparing the first data reception indicator parameter with a preset first indicator parameter threshold;

[0029] When the first data reception index parameter exceeds a preset first index parameter threshold, the data reception interruption rate is retrieved and combined with the first data reception index parameter to perform an abnormality determination on the data reception operation state.

[0030] Furthermore, when the first data reception indicator parameter exceeds a preset first indicator parameter threshold, the data reception interruption rate is retrieved and combined with the first data reception indicator parameter to perform abnormal determination on the data reception operation state, including:

[0031] When the first data reception indicator parameter exceeds a preset first indicator parameter threshold, retrieving a data reception interruption rate;

[0032] Obtaining a second data reception indicator parameter using the first data reception indicator parameter and the data reception interruption rate;

[0033] The second data reception indicator parameter is obtained by the following formula:

[0034]

[0035] Among them, G 02 Indicates the second data reception indicator parameter; n indicates the number of data reception times; Z i represents the data reception interruption rate corresponding to the i-th data reception; R i represents the data reception delay rate corresponding to the i-th data reception; W i represents the data reception bit error rate corresponding to the i-th data reception; C rz Indicates the maximum value of the difference between the data reception delay rate and the data reception interruption rate corresponding to n data receptions; C wz Indicates the maximum difference between the data reception interruption rate and the data reception bit error rate corresponding to n data receptions; C 02 Indicates the preset second difference reference value; C 03 Indicates a preset third difference reference value;

[0036] comparing the second data reception indicator parameter with a preset second indicator parameter threshold;

[0037] When the second data reception index parameter exceeds the preset second index parameter threshold, it is determined that the data reception operation state is abnormal, and an abnormality alarm is issued.

[0038] Furthermore, in step 4, the aluminum rod in the raw material is cut, heated, forged, cold-spun and heat-treated, which specifically includes the following steps:

[0039] Cutting and weighing: The aluminum bars are automatically cut by a fully automatic cutting machine, and the weight of the cut aluminum bars is tested;

[0040] Heating process: The cut aluminum bars are heated in a heating furnace to reach the optimal forgeable temperature, preparing for the subsequent forging process.

[0041] Forging process: High-tonnage hydraulic presses complete the initial forging, forming forging, and expansion processes, achieving a denser structure and improving product performance under high pressure.

[0042] Powerful cold spinning: Using high-performance spinning machines to complete the cold spinning process, ensuring that the product's size, precision and appearance quality meet qualified standards;

[0043] Enhanced T6 heat treatment: Through an integrated heat treatment system of high-temperature solid solution, quenching, and low-temperature aging, the mechanical strength and hardness of the wheel hub are improved.

[0044] Furthermore, in modeling and meshing, when modeling the aluminum alloy wheel hub of an automobile, the modeling factors include: the diameter and width of the aluminum alloy wheel hub of the automobile, as well as the chamfer and groove structural details on the hub hub surface, to ensure the accuracy of the model and the completeness of the details.

[0045] Furthermore, during modeling and meshing, when meshing the 3D model of the aluminum alloy wheel hub, different mesh densities need to be used according to different parts of the model to ensure effective and fine meshing of contour curves and areas with smaller curvature radii.

[0046] Furthermore, in the verification results, the process parameters were adjusted according to the analysis results, including: comparing the location and degree of shrinkage holes and shrinkage defects of the aluminum alloy wheels in the simulation results with the actual forging results.

[0047] Furthermore, in the forging process, the aluminum rod is taken out after reaching the set temperature, and the preheated aluminum rod is forged into a blank using a forging machine; the blank is then spin-formed using a spinning machine to form the basic shape of the wheel hub; the aluminum wheel hub is heat-treated after spinning to improve the performance of the material; the heat-treated aluminum wheel hub is lathe-processed for further precise shaping; the lathe-processed wheel hub is then polished again to make its surface smooth; the wheel hub is punched according to structural requirements until it is finally formed.

[0048] Furthermore, during cutting and weighing, it is necessary to ensure that the weight of each product is strictly controlled within ±0.1kg, and unqualified products are eliminated.

[0049] Furthermore, the detection process in step three includes but is not limited to the following equipment: a spectrum analyzer, a metallographic analyzer, a metallographic microscope, a Brinell hardness tester and a tensile testing machine.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention introduces digital simulation technology and automated control technology into the existing forging process, and fully replicates the aluminum alloy wheel forging process flow and forging results using digital simulation technology, so that workers can predict the aluminum alloy wheel forging effect based on current equipment parameters and various monitoring data in the scene, thereby facilitating workers to adjust the parameters of the forging equipment and scene parameters in advance, thereby improving the accuracy of aluminum alloy wheel forging; secondly, by deploying a variety of sensor devices in various forging scenes, relying on the sensor devices to monitor various data in the forging scenes, it is ensured that the forging environment is maximally suitable for the needs of aluminum alloy wheel forging, thereby further improving the accuracy and effect of aluminum alloy wheel forging. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The figure is a process flow chart of aluminum alloy wheel forging based on automatic control and digital simulation according to the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] In order to solve the current gap in the overall intelligent level of the aluminum alloy wheel forging industry, the main problems are: the digital level of aluminum alloy forging equipment is not high, the workshop information system has a single function, and the amount of integrated information is small. These problems lead to technical problems such as uncontrollable production processes, unstable product quality, and high operation and maintenance costs in aluminum alloy wheel forging enterprises. Figure 1 , this embodiment provides the following technical solutions:

[0055] The aluminum alloy wheel forging process based on automated control and digital simulation includes the following steps:

[0056] Step 1: Digital Simulation: Obtain historical aluminum alloy wheel forging process data, simulate the temperature field distribution, filling, and solidification processes during the aluminum alloy wheel forging process using the finite element method (FEM). Compare the simulated forging results of the aluminum alloy wheel with the actual forging results of the historical aluminum alloy wheel to obtain simulation results of the temperature field, solidification field, and the distribution of shrinkage and shrinkage defects that are prone to form in the aluminum alloy wheel forging process. Adjust the parameters of the current forging equipment and scenario based on the simulation results. Step 1 uses the finite element method (FEM) to simulate the temperature field distribution, filling, and solidification processes of the forging process, which specifically includes the following steps:

[0057] Modeling and Meshing: Use UGNX software to create a geometric model of the forging model for the aluminum alloy wheel hub. When modeling the aluminum alloy wheel hub, the modeling factors include: the actual diameter and width of the aluminum alloy wheel hub, as well as the chamfer and groove structure details on the wheel hub surface, to ensure the accuracy of the model and the integrity of the details. After the modeling is completed, the 3D model of the aluminum alloy wheel hub created by UGNX is imported into ProCAST software for 2D and 3D meshing. When meshing the 3D model of the aluminum alloy wheel hub, different mesh densities should be used according to different parts of the model to ensure that the contour curve and the parts with smaller curvature radius are meshed. Carry out effective and fine division; specifically, mesh division is a key step in digital simulation, which directly affects the accuracy and computational efficiency of the simulation; different mesh unit sizes can be set according to the structural characteristics of the aluminum alloy wheel casting; usually the mesh unit size of the wheel casting is set smaller, while the mesh unit size of the mold is set larger, based on which the simulation of the wheel casting can be effectively reflected while improving computational efficiency; by performing 2D and 3D mesh division, the mesh division of the entire model is finally completed; at the same time, attention should also be paid to the continuity between mesh units to ensure the connection between nodes, edges, and faces of different parts. ‌

[0058] Setting parameters‌: Determine the selection of wheel hub and mold materials, obtain the material's thermophysical properties and high-temperature mechanical properties, and set relevant process parameters, including: pouring temperature, cooling system, and filling pressure. The setting of these parameters is crucial to the accuracy of the simulation and can effectively ensure the simulation results and the quality of the final casting. Specifically, in the simulation pre-processing stage, it is necessary to set the material properties of the wheel hub and mold, and select suitable materials, such as: A356.2 aluminum alloy, because it has good casting properties and mechanical properties and is suitable for low-pressure casting processes. In addition, it is also necessary to set other related process parameters, such as: pouring temperature, cooling system, filling pressure, etc. The setting of the above parameters can directly affect the simulation results and the quality of the casting.‌

[0059] Finite Element Analysis: Finite element analysis is used to analyze the temperature field of the forging model, including analyzing the temperature of each small unit to obtain the temperature distribution of the entire forging model. Specifically, when performing finite element analysis on the temperature field of the casting model, the distribution of the temperature field is calculated using numerical analysis methods to simulate the process of liquid metal filling the mold, that is, numerical simulation of the filling process. The distribution of the temperature field has a significant impact on the chemical composition, structure, and physical properties of the casting product, so it must be accurately analyzed.

[0060] Digital simulation: ProCAST software is used to digitally simulate the low-pressure casting process of aluminum alloy wheels, studying the temperature field, solidification field, and the distribution of defects such as shrinkage and shrinkage cavities that are easily formed in the aluminum alloy wheel forging process; by adjusting process parameters such as pouring temperature, cooling system, and filling pressure, the casting process is optimized, casting defects are reduced, and casting quality is improved; it also includes analyzing the temperature of each small unit to ultimately obtain the temperature distribution of the entire forging model; specifically, by simulating the filling and solidification process of the casting through digital simulation technology, various phenomena in the forging process can be predicted, such as: simulating the process of liquid metal filling the casting mold, that is, filling Numerical simulation of the molding process can help predict and avoid defects in the casting process, such as cold shuts and air holes. Another example is the simulation of the solidification process of liquid metal, including the temperature drop and phase change. This step is crucial for predicting the quality and performance of castings, and can help optimize the casting process and improve the yield of castings. Finite element analysis is then used to analyze the stress field during the casting process. This can comprehensively simulate the temperature field distribution, filling and solidification processes during the casting process, and effectively predict possible stress concentration and deformation to ensure that the quality and performance of the castings meet the requirements, thereby optimizing the production process and improving the yield of castings.

[0061] Verification results: Compare the simulation results with the experimental data to verify the accuracy of the simulation, including comparing the obtained simulation results with the change patterns in the actual forging process, analyzing the quality problems of the castings based on the simulation results, and adjusting the process parameters according to the analysis results, including comparing the location and degree of shrinkage holes and shrinkage defects of aluminum alloy wheels in the simulation results with the actual forging results, optimizing the process to achieve the purpose of improving casting quality and production efficiency and ensuring the reliability of the simulation. Specifically, when comparing the simulation results with the experimental data, if the simulation results are consistent with the actual production situation, it means that the simulation is effective, and then adjust the corresponding process parameters according to the simulation results, such as adjusting the pouring temperature and cooling system, to reduce casting defects and improve the quality of the castings. To ensure the accuracy of the simulation and the quality of the castings, multiple iterative simulations can be performed, and the simulation result with the highest casting quality can be taken as the best result. The parameters in actual production can be corrected according to the various parameters set in this simulation result to achieve the best casting quality and the lowest defect rate.

[0062] Based on this, through the above steps, simulation software can be used to numerically simulate the low-pressure casting process of aluminum alloy wheels, optimize process parameters, and improve the quality and performance of castings.

[0063] Step 2: Automatic control: Multiple sensing devices are deployed in the forging process equipment to obtain various monitoring data, the operating status of the forging process equipment is determined based on the monitoring data, and each forging equipment is automatically controlled based on the operating status. Step 2: Multiple sensing devices are deployed in the forging process equipment, specifically including the following steps:

[0064] Data Acquisition: Position data, pressure data, temperature data, and forging production images are collected in real time through displacement sensors, pressure sensors, temperature sensors, vision systems, and safety light curtains installed in various forging scenarios. Specifically, the industrial computer is connected to an external control center, which receives the aforementioned data and outputs control signals after analysis and processing. Data communication channels are also set up to transmit data from each slave station to the control center, while the data receiving module sends the control signals to the industrial computer to realize automated production, thereby achieving precise control of the production process.

[0065] Data processing: Receive various data in each forging scene, and compare and analyze each data with the threshold value respectively, obtain the operating status of the process equipment in each forging scene, and feed it back to the industrial computer; specifically, after receiving the above-mentioned multiple data information, the control center can clean and store the data, and form a database in this way; then extract a part of the data as a data sample, and use the extraction of the middle value method to obtain the normal threshold value corresponding to each data, and use its threshold value as a reference for data analysis, specifically: compare each currently received data with the corresponding threshold value respectively. If it is within the normal threshold value, the current forging scene is judged to be normal; compare each currently received data with the corresponding threshold value respectively. If it exceeds or is lower than the normal threshold value, it is judged that the current data is abnormal, and then it is judged that the forging scene is abnormal and affects the forging quality, and the abnormal signal is fed back to the industrial computer.

[0066] Equipment control: The industrial computer receives the operating status of the process equipment in each forging scenario and sends corresponding control signals to each process equipment to drive and control the operation of each process equipment. Specifically, when the industrial computer receives an abnormal signal, such as when the temperature data in the temperature field exceeds the normal threshold after comparison, the industrial computer sends a cooling command to the temperature output devices in the temperature field, such as heaters, to reduce the temperature in the temperature field. The temperature sensor continues to monitor the temperature data until the temperature data in the temperature field is within the normal threshold range, thereby ensuring the quality of the casting and realizing fully automatic and intelligent operation.

[0067] Specifically, the data receiving operation status of position data, pressure data, and temperature data is monitored in real time, and an abnormal operation alarm is issued when the data receiving operation is abnormal, including:

[0068] Real-time monitoring of data reception operating parameters of position data, pressure data, and temperature data; wherein the data reception operating parameters include data reception delay ratio, data reception bit error rate, and data reception interruption rate;

[0069] Obtaining a first data reception indicator parameter using the data reception delay ratio and the data reception bit error rate;

[0070] The first data reception indicator parameter is obtained by the following formula:

[0071]

[0072] Among them, G 01 Indicates the first data reception indicator parameter; n indicates the number of data receptions; R i represents the data reception delay rate corresponding to the i-th data reception; W i R represents the data reception bit error rate corresponding to the i-th data reception; b W represents the standard deviation of the data reception delay rate corresponding to n data receptions; b C represents the standard deviation of the data reception bit error rate corresponding to n data receptions; rw Indicates the maximum difference between the data reception delay rate and the data reception bit error rate corresponding to n data receptions; C 01 represents a preset first difference reference value;

[0073] Comparing the first data reception indicator parameter with a preset first indicator parameter threshold;

[0074] When the first data reception index parameter exceeds a preset first index parameter threshold, the data reception interruption rate is retrieved and combined with the first data reception index parameter to perform an abnormality determination on the data reception operation state.

[0075] The technical solution described above provides the following benefits: It monitors the real-time status of position, pressure, and temperature data reception, ensuring both real-time and accuracy. By evaluating three key parameters—data reception delay ratio, data reception bit error rate, and data reception interruption rate—it comprehensively reflects the overall performance and stability of the data reception system.

[0076] By calculating the first data reception indicator parameter, this solution comprehensively considers the data reception delay ratio and data reception bit error rate, enabling more accurate determination of data reception anomalies. The calculation formula for the first data reception indicator parameter takes into account the number of data receptions, the delay rate and bit error rate for each data reception, and their standard deviations. It also considers the maximum difference between the delay rate and the bit error rate, helping to detect subtle anomalies in the data reception process. When the first data reception indicator parameter exceeds the preset first indicator parameter threshold, the solution further utilizes the data reception interruption rate to incorporate this into anomaly determination. This strategy considers both the real-time and accuracy of data reception and the continuity of data (via the data reception interruption rate), thereby improving the accuracy and reliability of anomaly determination. Once a data reception operation anomaly is determined, the solution promptly issues an alarm, notifying relevant personnel for resolution. This helps promptly identify and resolve issues, prevents data loss or error accumulation, and ensures stable system operation and data integrity. The preset first difference reference value and first indicator parameter threshold can be adjusted based on actual needs, making the solution adaptable to different application scenarios and performance requirements. In addition, the scheme can be further extended, such as introducing more data reception parameters or adopting more complex algorithms to improve the accuracy and efficiency of anomaly detection.

[0077] In summary, this technical solution can effectively improve the stability and reliability of the data receiving system and ensure the real-time and accuracy of data by real-time monitoring and comprehensive evaluation of the data receiving operating status, combined with accurate data receiving anomaly detection, flexible anomaly judgment strategy and timely anomaly alarm.

[0078] Specifically, when the first data reception indicator parameter exceeds a preset first indicator parameter threshold, the data reception interruption rate is retrieved and combined with the first data reception indicator parameter to perform abnormal determination on the data reception operation state, including:

[0079] When the first data reception indicator parameter exceeds a preset first indicator parameter threshold, retrieving a data reception interruption rate;

[0080] Obtaining a second data reception indicator parameter using the first data reception indicator parameter and the data reception interruption rate;

[0081] The second data reception indicator parameter is obtained by the following formula:

[0082]

[0083] Among them, G 02 Indicates the second data reception indicator parameter; n indicates the number of data reception times; Z i represents the data reception interruption rate corresponding to the i-th data reception; R i represents the data reception delay rate corresponding to the i-th data reception; W i represents the data reception bit error rate corresponding to the i-th data reception; C rz Indicates the maximum value of the difference between the data reception delay rate and the data reception interruption rate corresponding to n data receptions; C wz Indicates the maximum difference between the data reception interruption rate and the data reception bit error rate corresponding to n data receptions; C 02 Indicates the preset second difference reference value; C 03 Indicates a preset third difference reference value;

[0084] comparing the second data reception indicator parameter with a preset second indicator parameter threshold;

[0085] When the second data reception index parameter exceeds the preset second index parameter threshold, it is determined that the data reception operation state is abnormal, and an abnormality alarm is issued.

[0086] The technical effect of the above technical solution is that when the first data reception indicator parameter exceeds the preset first indicator parameter threshold, the solution not only takes into account the delay and error conditions of data reception, but also further introduces the key parameter of data reception interruption rate. By combining these three parameters, the operating status of data reception can be more comprehensively evaluated, thereby enhancing the ability of anomaly detection. By calculating the second data reception indicator parameter, the solution can comprehensively consider the mutual influence between the data reception delay rate, the data reception error rate, and the data reception interruption rate. The calculation formula of the second data reception indicator parameter includes the maximum difference between these parameters (C rz and C wz ), which helps to capture complex abnormal patterns in the data reception process. At the same time, C in the formula 02 and C 03 As the preset second difference reference value and third difference reference value, they can be adjusted according to the actual application scenario and requirements. This flexibility enables the solution to adapt to different data reception environments and performance requirements. 02When the preset threshold of the second indicator parameter is exceeded, the solution determines that the data reception operation status is abnormal and issues anomaly alarm is issued. This strategy ensures the reliability and timeliness of anomaly detection, helping to promptly identify and resolve issues in the data reception process. By monitoring the data reception operation status in real time and issuing prompt alarms when anomalies occur, the solution prevents problems such as data loss and error accumulation, thereby improving the stability and reliability of the entire system. The solution's technical framework and algorithm design are relatively simple and straightforward, making them easy to understand and implement. Furthermore, the introduction of configurable parameters and flexible anomaly detection strategies facilitates future expansion and maintenance of the solution.

[0087] In summary, this technical solution improves the accuracy and reliability of data reception anomaly detection by introducing the key parameter of data reception interruption rate and combining it with data reception delay rate and data reception bit error rate for comprehensive evaluation. Furthermore, this solution features flexible parameter configuration, a reliable anomaly determination strategy, and ease of scalability and maintenance, making it applicable to a variety of complex data reception environments and application scenarios.

[0088] Step 3. Testing process: Conduct comprehensive testing on the chemical composition, crystal structure and mechanical properties of each raw material to ensure the quality of the raw materials; the testing process includes but is not limited to the following equipment: spectrum analyzer, metallographic analyzer, metallographic microscope, Brinell hardness tester and tensile testing machine; specifically, according to the digital workshop product manufacturing process and product quality requirements, before the raw materials are put on the line, after the forging blanks are off the line, after heat treatment, and after machining (pre-machining, machining, finishing), the manufacturing process quality data must be tested by automatic testing equipment to ensure the quality of the raw materials.

[0089] Step 4: Forging process: Introducing digital simulation and automatic control technology into the current forging process, the aluminum rod in the raw material is cut, heated, forged, cold spun, and heat treated to complete the forging process of the aluminum alloy wheel; Step 4 includes the following steps:

[0090] Cutting and weight measurement: Use a fully automatic cutting machine to automatically cut the aluminum rods, and then perform weight testing on the cut aluminum rods; ensure that the weight of each product is strictly controlled within ±0.1kg, and eliminate unqualified products; specifically, before cutting, it is necessary to select an aluminum rod material suitable for forged wheels, which is the basic material for forged wheels, and conduct quality inspection on the selected aluminum rods. After the quality inspection is passed, the aluminum rods are polished to make the surface smooth, in preparation for subsequent processing, and the aluminum rods with smooth surfaces are cut to make the size required for manufacturing wheels; cutting is for the preliminary shaping of the aluminum rods, and measurement is to ensure that the aluminum rods meet the forging weight of aluminum alloy wheels.

[0091] Heating process: The cut aluminum bars are heated in a heating furnace to reach the optimal forgeable temperature, preparing for the subsequent forging process. Specifically, the cut aluminum bars are placed in a heating furnace for heating. After reaching the set temperature, the aluminum bars are taken out for forging.

[0092] Forging process: A high-tonnage hydraulic press completes the initial forging, forming forging, and expansion processes, achieving a denser microstructure under high pressure and improving product performance. After the aluminum bar reaches the set temperature, it is removed and forged into a blank using a forging press. The blank is then spin-formed using a spinning machine to form the basic shape of the wheel hub. After spinning, the aluminum hub undergoes heat treatment to improve material properties. The heat-treated aluminum hub is lathe-machined for further precise shaping. The lathe-machined hub is then polished again for a smooth surface. The hub is then punched according to structural requirements until the final shape is formed.

[0093] Powerful cold spinning: Use high-performance spinning machines to complete the cold spinning process, ensuring that the product's size, precision and appearance quality meet qualified standards. Specifically, the spinning machine model that can be used is: German-LEIFEID spinning machine automatically completes the cold spinning process, thereby ensuring that the product's size, precision and appearance quality are superior to the same industry level.

[0094] Enhanced T6 heat treatment: Through the integrated heat treatment system of high-temperature solid solution, quenching, and low-temperature aging, the mechanical strength and hardness of the wheel hub are improved. Specifically, by selecting the model: Sanjian brand enhanced T6 heat treatment furnace, the spun blank is subjected to continuous high-temperature solid solution, quenching, low-temperature aging, and integrated heat treatment system treatment, making the aluminum alloy system inside the product more stable and improving the mechanical strength and hardness of the wheel hub.

[0095] The beneficial effects achieved by the above content are: By introducing digital simulation technology and automated control technology, precise operations are performed using automated control and digital simulation to ensure that the quality and performance of aluminum alloy wheels meet high standards, meeting the modern automobile's requirements for lightweight, energy-saving and high performance.

[0096] Working principle: By introducing digital simulation technology and automated control technology, digital simulation technology is used to fully replicate the aluminum alloy wheel forging process and forging results, so that workers can predict the aluminum alloy wheel forging effect based on the current equipment parameters and various monitoring data in the scene, thereby facilitating workers to adjust the parameters of the forging equipment and scene parameters in advance; secondly, by deploying a variety of sensor equipment in various forging scenes, relying on the sensor equipment to monitor various data in the forging scenes, it is ensured that the forging environment is most suitable for the needs of aluminum alloy wheel forging, thereby improving the accuracy and effect of aluminum alloy wheel forging.

[0097] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0098] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. Aluminum alloy wheel forging process based on automated control and digital simulation, characterized in that: The following steps are involved: Step 1: Digital Simulation: Obtain historical aluminum alloy wheel forging process data, simulate the temperature field distribution, filling, and solidification processes during the aluminum alloy wheel forging process using the finite element method (FEM), compare the simulated forging results of the aluminum alloy wheel with the actual forging results of the historical aluminum alloy wheel, obtain simulation results of the temperature field, solidification field, and the distribution of shrinkage and shrinkage defects that are prone to form in the aluminum alloy wheel forging process, and adjust the parameters of the current forging equipment and scenario based on the simulation results. The finite element method (FEM) simulation of the temperature field distribution, filling, and solidification processes during the forging process in Step 1 specifically includes the following steps: Modeling and Meshing: The aluminum alloy wheel was modeled using UGNX software. The 3D model created with UGNX was imported into ProCAST software for 2D and 3D meshing. Modeling factors included the wheel's diameter and width, as well as the chamfers and grooves on the wheel surface. Meshing the 3D model required varying mesh densities for different parts of the model to ensure effective and detailed meshing of contour curves and areas with small curvature radii. Setting parameters: Determine the selection of wheel and mold materials, obtain the material's thermophysical and high-temperature mechanical properties, and set relevant process parameters, including pouring temperature, cooling system, and filling pressure, to ensure the simulation results and the quality of the final casting; Finite Element Analysis: The temperature field of the forging model is analyzed using the finite element analysis method, including analyzing the temperature of each small unit to obtain the temperature distribution of the entire forging model; Digital Simulation: ProCAST software is used to digitally simulate the low-pressure casting process for aluminum alloy wheels. The simulation examines the temperature field, solidification field, and the distribution of defects that easily form shrinkage and shrinkage cavities during the aluminum alloy wheel forging process. By adjusting the pouring temperature, cooling system, and filling pressure, the casting process is optimized, casting defects are reduced, and casting quality is improved. Verify results: Compare simulation results with experimental data to verify the accuracy of the simulation, including comparing the simulation results with the variation patterns in the actual forging process, analyzing casting quality issues based on the simulation results, and adjusting process parameters based on the analysis results to optimize the process. Step 2: Automatic control: Multiple sensor devices are deployed in the forging process equipment to obtain various monitoring data, and the operating status of the forging process equipment is determined based on the monitoring data. Based on the operating status, each forging equipment is automatically controlled; Step 3: Testing process: Conduct comprehensive testing on the chemical composition, crystal structure and mechanical properties of each raw material to ensure the quality of the raw materials; Step 4. Forging process: Introducing digital simulation and automatic control technology into the current forging process, the aluminum rods in the raw material are cut, heated, forged, cold spun and heat treated to complete the forging process of aluminum alloy wheels.

2. The aluminum alloy wheel forging process based on automated control and digital simulation according to claim 1, characterized in that: The second step comprises the following steps: Data Collection: Displacement sensors, pressure sensors, temperature sensors, vision systems, and safety light curtains installed in various forging scenes collect real-time position data, pressure data, temperature data, and forging production images during the forging process. Data processing: Receives data from each forging scenario, compares and analyzes each data point with the threshold, obtains the operating status of the process equipment in each forging scenario, and feeds it back to the industrial computer; Equipment control: The industrial computer receives the operating status of the process equipment in each forging scene and sends corresponding control signals to each process equipment to drive and control the operation of each process equipment.

3. The aluminum alloy wheel forging process based on automated control and digital simulation according to claim 2, characterized in that: Monitor the data reception status of position data, pressure data, and temperature data in real time, and issue an abnormality alarm when data reception is abnormal, including: Real-time monitoring of data reception operating parameters of position data, pressure data, and temperature data; wherein the data reception operating parameters include data reception delay ratio, data reception bit error rate, and data reception interruption rate; Obtaining a first data reception indicator parameter using the data reception delay ratio and the data reception bit error rate; The first data reception indicator parameter is obtained by the following formula: ； Among them, G 01 Indicates the first data reception indicator parameter; n indicates the number of data receptions; R i represents the data reception delay rate corresponding to the i-th data reception; W i R represents the data reception bit error rate corresponding to the i-th data reception; b W represents the standard deviation of the data reception delay rate corresponding to n data receptions; b C represents the standard deviation of the data reception bit error rate corresponding to n data receptions; rw Indicates the maximum difference between the data reception delay rate and the data reception bit error rate corresponding to n data receptions; C 01 represents a preset first difference reference value; Comparing the first data reception indicator parameter with a preset first indicator parameter threshold; When the first data reception index parameter exceeds a preset first index parameter threshold, the data reception interruption rate is retrieved and combined with the first data reception index parameter to perform an abnormality determination on the data reception operation state.

4. The aluminum alloy wheel forging process based on automated control and digital simulation according to claim 3, characterized in that: When the first data reception indicator parameter exceeds a preset first indicator parameter threshold, the data reception interruption rate is retrieved and combined with the first data reception indicator parameter to perform an abnormality determination on the data reception operation state, including: When the first data reception indicator parameter exceeds a preset first indicator parameter threshold, retrieving a data reception interruption rate; Obtaining a second data reception indicator parameter using the first data reception indicator parameter and the data reception interruption rate; The second data reception indicator parameter is obtained by the following formula: ； Among them, G 02 Indicates the second data reception indicator parameter; n indicates the number of data reception times; Z i represents the data reception interruption rate corresponding to the i-th data reception; R i represents the data reception delay rate corresponding to the i-th data reception; W i represents the data reception bit error rate corresponding to the i-th data reception; C rz Indicates the maximum value of the difference between the data reception delay rate and the data reception interruption rate corresponding to n data receptions; C wz Indicates the maximum difference between the data reception interruption rate and the data reception bit error rate corresponding to n data receptions; C 02 Indicates the preset second difference reference value; C 03 Indicates a preset third difference reference value; comparing the second data reception indicator parameter with a preset second indicator parameter threshold; When the second data reception index parameter exceeds the preset second index parameter threshold, it is determined that the data reception operation state is abnormal, and an abnormality alarm is issued.

5. The aluminum alloy wheel forging process based on automated control and digital simulation according to claim 1, characterized in that: In the fourth step, the aluminum rod in the raw material is cut, heated, forged, cold-spun and heat-treated, which specifically includes the following steps: Cutting and weighing: The aluminum bars are automatically cut by a fully automatic cutting machine, and the weight of the cut aluminum bars is tested; Heating process: The cut aluminum bars are heated in a heating furnace to reach the optimal forgeable temperature, preparing for the subsequent forging process. Forging process: High-tonnage hydraulic presses complete the initial forging, forming forging, and expansion processes, achieving a denser structure and improving product performance under high pressure. Powerful cold spinning: Using high-performance spinning machines to complete the cold spinning process, ensuring that the product's size, precision and appearance quality meet qualified standards; Enhanced T6 heat treatment: Through an integrated heat treatment system of high-temperature solid solution, quenching, and low-temperature aging, the mechanical strength and hardness of the wheel are improved.

6. The aluminum alloy wheel forging process based on automated control and digital simulation according to claim 1, characterized in that: In the verification results, the process parameters were adjusted according to the analysis results, including: comparing the location and extent of shrinkage holes and shrinkage defects of aluminum alloy wheels in the simulation results with the actual forging results.

7. The aluminum alloy wheel forging process based on automated control and digital simulation according to claim 5, characterized in that: During the forging process, the aluminum rod is removed after reaching the set temperature. A forging press is used to forge the preheated aluminum rod into a blank, which is then spun into shape using a spinning machine to form the basic shape of the wheel. The aluminum wheel is heat treated after spinning and then lathe-machined. The lathe-machined wheel is then polished again and holes are punched according to structural requirements until it is finally formed.

8. The aluminum alloy wheel forging process based on automated control and digital simulation according to claim 5, characterized in that: During cutting and weighing, it is necessary to ensure that the weight of each product is strictly controlled within ±0.1kg, and unqualified products are rejected.

9. The aluminum alloy wheel forging process based on automated control and digital simulation according to claim 1, characterized in that: The detection process in step three includes but is not limited to the following equipment: a spectrum analyzer, a metallographic analyzer, a metallographic microscope, a Brinell hardness tester and a tensile testing machine.

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