Quality on-line monitoring method in plate stamping forming process

Abstract

The invention provides an online quality monitoring method in the plate stamping forming process, and relates to the technical field of intelligent manufacturing, the method comprises the following steps: collecting the real-time downward pressing speed and the real-time downward pressing amount of stamping forming equipment for a target component; inputting the real-time pressing speed and the real-time pressing amount into a quality prediction model to obtain the real-time strain rate and the real-time stress of each part of the target component; based on the real-time strain rate, whether the stamping forming equipment causes cracking or wrinkling of the target component or not is judged, and whether the component strength of the target component reaches a preset strength standard or not is judged according to the real-time stress; and if it is determined that the target component is cracked or wrinkled by the stamping forming equipment, or the component strength of the target component does not reach the preset strength standard, parameters of the stamping forming equipment are adjusted. According to the invention, the quality of the component in the stamping forming process can be monitored in real time.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent manufacturing, and specifically to an online quality monitoring method during a sheet metal stamping process. Background Art

[0002] Sheet metal forming is a metalworking technology widely used in the manufacturing industry, which mainly involves the use of dies and presses to shape metal sheets into specific geometric shapes. This method is commonly used in the automotive, aviation, home appliance and construction industries, and can quickly and cost-effectively produce a large number of complex metal parts. During the stamping process, the sheet metal undergoes plastic deformation under pressure and is finally formed into the desired component shape through various stamping operations such as drawing, bending, shearing and punching. This process has high requirements on the performance of the material and requires precise control of process parameters to ensure product quality and structural integrity.

[0003] With the rapid development of the aerospace and automotive industries, lightweighting has become one of the development trends in the transportation field, and the proportion of lightweight materials such as high-strength steel, aluminum alloys and titanium alloys has continued to increase. The stamping process of metal sheets is an important component processing process. During the stamping process, the sheet is prone to defects such as cracking, wrinkling and insufficient strength, resulting in an increase in the scrap rate. The quality of the component during the stamping process is often directly related to the material flow, so it is of great significance to monitor the material flow in the stamping process of the component in real time and realize feedback control. However, the stamping die is often closed, so it cannot be monitored by visual means. The die often works under high temperature and high stress conditions, so it is difficult to arrange sensors inside. Therefore, real-time detection of material flow in the stamping process faces huge challenges. Summary of the invention

[0004] The present application provides a method for online quality monitoring during a sheet metal stamping process, which can achieve real-time monitoring of the quality of components during the stamping process.

[0005] In a first aspect of the present application, a method for online quality monitoring in a sheet metal stamping process is provided, the method comprising:

[0006] Collect the real-time pressing speed and real-time pressing amount of the stamping forming equipment for the target component;

[0007] Inputting the real-time pressing speed and the real-time pressing amount into a quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component;

[0008] Based on the real-time strain rate, determine whether the stamping equipment causes cracking or wrinkling of the target component, and based on the real-time stress, determine whether the component strength of the target component reaches a preset strength standard;

[0009] If it is determined that the stamping and forming equipment causes cracking or wrinkling of the target component, or the component strength of the target component does not meet the preset strength standard, the parameters of the stamping and forming equipment are adjusted.

[0010] On the basis of the above technical solution, preferably, before inputting the real-time pressing speed and the real-time pressing amount into the quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component, the method further includes:

[0011] Component BP neural network model, in which the pressing speed and pressing amount are used as input nodes of the input layer, and the strain rate and stress are used as output nodes of the output layer;

[0012] Obtain a preset data set, and divide the data set into a training set, a validation set, and a test set;

[0013] The BP neural network model is trained using the training set, the parameters of the BP neural network model are adjusted using the validation set, and the performance of the BP neural network model is evaluated using the test set to obtain the quality prediction model;

[0014] The quality prediction model is integrated into the monitoring system of the production line, and the collected pressing speed and pressing amount are input in real time, and the predicted strain rate and stress are output.

[0015] On the basis of the above technical solution, preferably, before the component BP neural network is constructed, the method further comprises:

[0016] Establish a 3D model of sheet metal stamping;

[0017] Setting boundary conditions for the sheet metal stamping, wherein the boundary conditions include sheet metal material properties, a pressing speed range, a pressing amount range, and friction conditions;

[0018] The three-dimensional model after setting the boundary conditions is run simulated, and the stress rate and stress distribution of the stamping component under different process conditions, as well as the pressing amount and pressing speed under different process conditions are recorded;

[0019] The stress rate, stress distribution, pressing amount and pressing speed are output to obtain the data set.

[0020] On the basis of the above technical solution, preferably, the real-time pressing speed and the real-time pressing amount are input into the quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component, which are specifically calculated by the following formula:

[0021]

[0022] σ(ε ′ ,ε,T)=σ y (ε ′ ,T)+(K(T)·ε n )·(1+C·log(ε ′ ))

[0023] Wherein, ε is the real-time strain rate, H is the thickness change rate of the target component per unit time, h is the current thickness of the target component, v is the real-time pressing speed, σ(ε',ε,T) is the real-time stress, σ y (∈,T) is the yield stress, K(T) is the hardening coefficient, ∈ is the accumulated strain, n is the hardening exponent, C is the rate sensitivity coefficient, and T is the material temperature.

[0024] On the basis of the above technical solution, preferably, judging whether the stamping forming equipment causes cracking or wrinkling of the target component based on the real-time strain rate specifically includes:

[0025] determining a critical strain rate of the target component;

[0026] It is determined whether the real-time strain rate is greater than or equal to the critical strain rate. If it is determined that the real-time strain rate is greater than or equal to the critical strain rate, it is determined that the stamping equipment will cause the target component to crack or wrinkle.

[0027] On the basis of the above technical solution, judging whether the component strength of the target component reaches a preset strength standard according to the real-time stress specifically includes:

[0028] Determining a stress threshold corresponding to a preset strength standard of the target component based on the yield strength and tensile strength of the target component;

[0029] It is determined whether the real-time stress is greater than or equal to the stress threshold; if it is determined that the real-time stress is greater than or equal to the stress threshold, it is determined that the component strength of the target component reaches the preset strength standard.

[0030] On the basis of the above technical solution, preferably, if it is determined that the stamping forming equipment causes the target component to crack or wrinkle, or the component strength of the target component does not reach the preset strength standard, the parameters of the stamping forming equipment are adjusted, specifically including:

[0031] Determining the component yield rate of the target component according to the stamping simulation result for the target component;

[0032] Determining whether the component yield rate is greater than or equal to a preset yield rate;

[0033] If it is determined that the component yield is less than the preset yield, the pressure distribution of the stamping equipment is adjusted according to the material properties and thickness of the target component to make the pressure distribution of the stamping equipment uniform.

[0034] In a second aspect of the present application, a device for online monitoring of quality during sheet metal stamping forming is provided, the device comprising an acquisition module, a processing module and a judgment module, wherein:

[0035] The acquisition module is used to collect the real-time pressing speed and real-time pressing amount of the stamping forming equipment for the target component;

[0036] The processing module is used to input the real-time pressing speed and the real-time pressing amount into a quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component;

[0037] The judgment module is used to judge whether the stamping forming equipment causes cracking or wrinkling of the target component based on the real-time strain rate, and judge whether the component strength of the target component reaches a preset strength standard according to the real-time stress;

[0038] The processing module is used to adjust the parameters of the stamping equipment if it is determined that the stamping equipment causes the target component to crack or wrinkle, or the component strength of the target component does not meet the preset strength standard.

[0039] On the basis of the above technical solution, preferably, the processing module is used to construct a BP neural network model, wherein the pressing speed and pressing amount are used as input nodes of the input layer, and the strain rate and stress are used as output nodes of the output layer;

[0040] The acquisition module is used to acquire a preset data set and divide the data set into a training set, a validation set and a test set;

[0041] The processing module is used to train the BP neural network model using the training set, adjust the parameters of the BP neural network model using the validation set, and evaluate the performance of the BP neural network model using the test set to obtain the quality prediction model;

[0042] The processing module is used to integrate the quality prediction model into the monitoring system of the production line, input the collected pressing speed and pressing amount in real time, and output the predicted strain rate and stress.

[0043] On the basis of the above technical solution, preferably, the processing module is used to establish a three-dimensional model of sheet metal stamping;

[0044] The processing module is used to set the boundary conditions of the sheet metal stamping, wherein the boundary conditions include sheet metal material properties, pressing speed range, pressing amount range and friction conditions;

[0045] The processing module is used to run a simulation on the three-dimensional model after setting the boundary conditions, and record the stress rate and stress distribution of the stamping component under different process conditions, as well as the pressing amount and pressing speed under different process conditions;

[0046] The processing module is used to output the stress rate, stress distribution, pressing amount and pressing speed to obtain the data set.

[0047] On the basis of the above technical solution, preferably, the processing module is used to input the real-time pressing speed and the real-time pressing amount into the quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component, which is specifically calculated by the following formula:

[0048]

[0049] σ(ε ′ ,ε,T)=σ y (ε ′ ,T)+(K(T)·ε n )·(1+C·log(ε ′ ))

[0050] Wherein, ε is the real-time strain rate, H is the thickness change rate of the target component per unit time, h is the current thickness of the target component, v is the real-time pressing speed, σ(ε',ε,T) is the real-time stress, σ y (∈,T) is the yield stress, K(T) is the hardening coefficient, ∈ is the accumulated strain, n is the hardening exponent, C is the rate sensitivity coefficient, and T is the material temperature.

[0051] On the basis of the above technical solution, preferably, the processing module is used to determine the critical strain rate of the target component;

[0052] The judgment module is used to judge whether the real-time strain rate is greater than or equal to the critical strain rate. If it is determined that the real-time strain rate is greater than or equal to the critical strain rate, it is determined that the stamping equipment will cause the target component to crack or wrinkle.

[0053] On the basis of the above technical solution, preferably, the processing module is used to determine the stress threshold corresponding to the preset strength standard of the target component based on the yield strength and tensile strength of the target component;

[0054] The judgment module is used to judge whether the real-time stress is greater than or equal to the stress threshold. If it is determined that the real-time stress is greater than or equal to the stress threshold, it is determined that the component strength of the target component reaches the preset strength standard.

[0055] On the basis of the above technical solution, preferably, the processing module is used to determine the component yield rate of the target component according to the stamping simulation result for the target component;

[0056] The judging module is used to judge whether the component yield rate is greater than or equal to a preset yield rate;

[0057] The processing module is used to adjust the pressure distribution of the stamping equipment according to the material properties and thickness of the target component to make the pressure distribution of the stamping equipment uniform if it is determined that the component yield is less than the preset yield.

[0058] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0059] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0060] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0061] 1. This application can realize real-time monitoring of component quality during the stamping process by integrating real-time data acquisition, dynamic model analysis and instant quality assessment methods. First, by installing sensors to collect real-time information about the pressing speed and pressing amount of the target component, these data directly reflect the dynamic conditions of the stamping process. By inputting these data into a pre-trained quality prediction model, the strain rate and stress of each part of the target component can be calculated in real time, which provides a scientific basis for evaluating whether the component will crack, wrinkle or fail to reach the required strength during the production process. By analyzing these real-time calculation results, the system can promptly identify quality problems and automatically adjust the parameters of the stamping equipment before problems occur, such as changing the pressing speed or adjusting the pressure distribution, thereby preventing defects and ensuring component quality.

[0062] 2. By building and applying a quality prediction model based on BP neural network, the real-time monitoring and control of component quality during stamping can be effectively achieved. By taking the real-time collected pressing speed and pressing amount as input, the model can predict the strain rate and stress of the component, thereby identifying the risks that may cause cracking, wrinkling or substandard strength in advance. This can significantly reduce the cost of trial and error, optimize the production process, reduce the scrap rate, and improve overall production efficiency.

[0063] 3. Integrating intelligent prediction models into the monitoring system of the production line enables the manufacturing process to be automatically adjusted before potential problems are discovered, ensuring that product quality meets preset standards and enhancing the reliability and stability of the manufacturing process. In addition, it helps to reduce labor and time costs because it reduces the reliance on continuous monitoring by professionals and achieves highly automated production control.

[0064] 4. The stress model disclosed in this application combines the yield stress, hardening coefficient, and strain rate sensitivity coefficient of the material, so that it can accurately reflect the stress response under different process conditions. In addition, the use of neural network models to predict and adjust process parameters can automatically adjust machine settings without intervening in the production process to optimize product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of a method for online quality monitoring in a sheet metal stamping forming process disclosed in an embodiment of the present application;

[0066] Figure 2 It is a module schematic diagram of a method for online quality monitoring in a sheet metal stamping forming process disclosed in an embodiment of the present application;

[0067] Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0068] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 203, judgment module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0069] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0070] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0071] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0072] Sheet metal stamping is a key metal processing technology widely used in the automotive, aviation, home appliance and construction industries. It mainly uses dies and presses to shape metal sheets into specific geometric shapes. With the increasing use of lightweight materials such as high-strength steel, aluminum alloys and titanium alloys, the importance of stamping has become increasingly prominent. However, during the stamping process, the sheet metal is prone to defects such as cracking and wrinkling. These problems are usually directly related to material flow, but due to the closed and high-temperature and high-stress environment of the stamping die, real-time monitoring of material flow through traditional vision or internal sensors is extremely challenging. Therefore, the development of an effective real-time monitoring and feedback control system is crucial to improving component quality and reducing scrap rates.

[0073] This embodiment discloses a method for online quality monitoring during sheet metal stamping. Figure 1 , including the following steps S110-S140:

[0074] S110, collecting the real-time pressing speed and real-time pressing amount of the stamping forming equipment for the target component.

[0075] The method for online quality monitoring during sheet metal stamping process disclosed in the embodiment of the present application is applied to a server, and the server includes but is not limited to electronic devices such as mobile phones, tablet computers, wearable devices, PCs (Personal Computers), etc., and can also be a background server running a method for online quality monitoring during sheet metal stamping process. The server can be implemented as an independent server or a server cluster composed of multiple servers.

[0076] Collecting the real-time pressing speed and real-time pressing amount of the stamping equipment for the target component is a key step to ensure production efficiency and product quality. In order to measure the pressing speed and pressing amount, it is necessary to select appropriate sensors. Position sensors are used to measure the position of the die or punch to calculate the pressing amount. Speed ​​sensors directly measure the moving speed of the punch. Although pressure sensors are mainly used to measure pressure, they can also indirectly infer speed and displacement through pressure changes.

[0077] Position sensors are usually installed between a moving part (such as a slide or punch) and a fixed part (such as a press bed). Speed ​​sensors can be installed on the moving part of the punch to monitor its speed in real time. Pressure sensors are installed in areas where the greatest force may be applied, such as near the die contact point.

[0078] Configure the data acquisition system to receive, store and process signals from sensors, and then process and analyze the collected data to support production decisions, including data filtering and smoothing, applying digital filtering technology, eliminating noise and interference, and improving data quality. Extract key features from time series data, such as maximum pressing speed, average speed, total pressing amount, etc.

[0079] S120, inputting the real-time pressing speed and the real-time pressing amount into the quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component.

[0080] In a possible implementation, before the real-time pressing speed and the real-time pressing amount are input into the quality prediction model to obtain the real-time strain rate and the real-time stress of each part of the target component, the method also includes: a component BP neural network model, wherein the pressing speed and the pressing amount are used as input nodes of the input layer, and the strain rate and the stress are used as output nodes of the output layer; obtaining a preset data set, and dividing the data set into a training set, a validation set, and a test set; using the training set to train the BP neural network model, using the validation set to adjust the parameters of the BP neural network model, and using the test set to evaluate the performance of the BP neural network model to obtain a quality prediction model; integrating the quality prediction model into the monitoring system of the production line, inputting the collected pressing speed and pressing amount in real time, and outputting the predicted strain rate and stress.

[0081] Specifically, in the stamping process, quality prediction using the BP neural network model is an advanced technical means to improve production efficiency and product quality. In the stamping process, quality prediction using the BP neural network model is an advanced technical means to improve production efficiency and product quality. Input layer design, the number of nodes in the input layer should correspond to the number of variables affecting the prediction. In this case, there are two input nodes, corresponding to the real-time pressing speed and the real-time pressing amount, respectively. The number of nodes and the number of layers in the hidden layer are usually determined based on the complexity of the problem and the characteristics of the data. The optimal hidden layer structure and number of nodes can be selected by experiment or by using heuristic methods such as cross-validation. The number of nodes in the output layer should correspond to the number of results to be predicted, namely strain rate and stress, so there should be two output nodes.

[0082] Obtain the data set for training the neural network and perform appropriate preprocessing. First, collect historical production data, including records of pressing speed, pressing amount, strain rate, and stress. Perform data cleaning (remove outliers and missing values), standardize or normalize input and output data to improve the efficiency and effectiveness of network training. Divide the entire data set into training set, validation set, and test set. A common ratio is 70% training set, 15% validation set, and 15% test set.

[0083] Train and adjust the neural network model using the training set and validation set. Use the training set to train the BP neural network. During training, adjust the network weights to minimize the prediction error (such as mean square error MSE). Use the validation set to adjust model parameters such as learning rate and batch size, as well as early stopping to avoid overfitting. Use the test set to evaluate the performance of the model. Analyze the prediction accuracy and stability of the model, and perform iterative optimization as needed.

[0084] Integrate the trained model into the production line monitoring system to achieve real-time quality prediction. Integrate the BP neural network model into the existing production monitoring system. Ensure that the model can receive real-time input data and quickly output prediction results. The system receives the pressing speed and pressing amount data of the stamping equipment in real time, predicts the strain rate and stress through the model, and displays this information for the operator to monitor. Adjust production parameters based on the prediction results, such as adjusting the pressing speed or changing the mold settings to optimize product quality.

[0085] In a possible implementation, before the component BP neural network, the method also includes: establishing a three-dimensional model of sheet metal stamping; setting boundary conditions for sheet metal stamping, the boundary conditions including sheet metal material properties, pressing speed range, pressing amount range and friction conditions; running a simulation of the three-dimensional model after setting the boundary conditions, recording the stress rate and stress distribution of the stamped component under different process conditions, as well as the pressing amount and pressing speed under different process conditions; outputting the stress rate, stress distribution, pressing amount and pressing speed to obtain a data set.

[0086] Specifically, before using the BP neural network model to predict the quality of sheet metal stamping, establishing a three-dimensional model of the sheet metal and performing numerical simulation are key steps, which provide the necessary data sets for training the neural network. First, it is necessary to use suitable computer-aided engineering (CAE) software such as Abaqus, ANSYS or SolidWorks to establish a three-dimensional model of sheet metal stamping. Draw the precise three-dimensional geometry of the sheet metal and the mold to ensure that all details such as the stamping location, thickness and shape accurately reflect the design intent. Mesh the three-dimensional model for finite element analysis. The density and type of the mesh (such as tetrahedron or hexahedron) have an important impact on the simulation accuracy and calculation time.

[0087] Input the material properties of the sheet, such as elastic modulus, yield strength, Poisson's ratio, etc. These data can usually be obtained from the material manual. Set the range of pressing speed and pressing amount used in the simulation. These parameters should reflect the conditions in actual production. Set the friction coefficient according to the actual contact conditions between the stamping die and the sheet.

[0088] Then use CAE software to run simulations to analyze stress and strain rate under different process parameters. Run simulations under set boundary conditions and record the stress rate and stress distribution of the sheet during the entire stamping process. Change the pressing speed and pressing amount and repeat the simulation to observe the impact of these changes on the stamping results.

[0089] After the simulation is completed, collect and output the required data sets, which will be used to train the BP neural network model. Extract data such as stress rate, stress distribution, pressing amount and pressing speed from the CAE software. Format the extracted data, such as saving it as a CSV or Excel file, for subsequent processing and analysis.

[0090] The real-time pressing speed and real-time pressing amount are input into the quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component, which are calculated by the following formula:

[0091]

[0092] σ(ε ′,ε,T)=σ y (ε ′ ,T)+(K(T)·ε n )·(1+C·log(ε ′ ))

[0093] Among them, ε is the real-time strain rate, H is the thickness change rate of the target component per unit time, h is the current thickness of the target component, v is the real-time pressing speed, σ(ε',ε,T) is the real-time stress, σ y (∈,T) is the yield stress, K(T) is the hardening coefficient, ∈ is the accumulated strain, n is the hardening exponent, C is the rate sensitivity coefficient, and T is the material temperature.

[0094] The strain rate (∈) is the rate of change of material deformation per unit time, which is defined as the ratio of the thickness change rate (H) to the current thickness (h). In the actual stamping process, the real-time pressing speed (v) of the punch can be approximately equivalent to the thickness change rate (H).

[0095] The stress model uses a classic plastic hardening model with an additional term for strain rate sensitivity. In this model, the yield stress σ y It can be determined by experimental data and is usually related to the strain rate and temperature of the material. The hardening coefficient K(T) describes the hardening behavior of the material after plastic deformation and is usually obtained through material testing. The hardening exponent n describes the degree of nonlinearity of the hardening behavior. The Clog(∈′) term takes into account the rate dependence of the material, that is, how the stress response of the material changes with the change of the deformation rate. By integrating these physical models and material parameters, the material stress state during the stamping process can be accurately predicted, thereby achieving better quality control and optimization in the production process.

[0096] S130, judging whether the stamping forming equipment causes cracking or wrinkling of the target component based on the real-time strain rate, and judging whether the component strength of the target component reaches the preset strength standard based on the real-time stress

[0097] In one possible implementation, whether the stamping equipment causes cracking or wrinkling of the target component is determined based on the real-time strain rate, specifically including: determining the critical strain rate of the target component; determining whether the real-time strain rate is greater than or equal to the critical strain rate, if it is determined that the real-time strain rate is greater than or equal to the critical strain rate, then it is determined that the stamping equipment will cause cracking or wrinkling of the target component.

[0098] Specifically, it is a key quality control process to determine whether the stamping equipment will cause cracking or wrinkling of the target component based on the judgment of real-time strain rate. The critical strain rate refers to the maximum allowable deformation rate at which the component begins to crack or wrinkle during the stamping process. The behavior of the material at different strain rates can be measured under controlled experimental conditions through material mechanics tests (such as tensile tests). Observe at which strain rate the material begins to show signs of cracking or wrinkling. Or analyze the data recorded in previous production, especially those cases known to cause cracking or wrinkling, to estimate the critical strain rate. It is also possible to combine material science and engineering experience to determine a safe strain rate range to avoid cracking and wrinkling.

[0099] Once the real-time strain rate data and the predetermined critical strain rate are available, a comparison can be made and a warning threshold can be set in the monitoring system. When the real-time strain rate reaches or exceeds the critical strain rate, the system issues a warning. When the system detects an excessively high strain rate, it automatically takes measures, such as slowing down the pressing speed or suspending the operation, to prevent damage to the component.

[0100] In a possible implementation, judging whether the component strength of the target component reaches a preset strength standard based on the real-time stress specifically includes: determining a stress threshold corresponding to the preset strength standard of the target component based on the yield strength and tensile strength of the target component; judging whether the real-time stress is greater than or equal to the stress threshold, and if it is determined that the real-time stress is greater than or equal to the stress threshold, then determining that the component strength of the target component reaches the preset strength standard.

[0101] Specifically, during the stamping process, it is crucial to ensure that the strength of the target component meets the preset standard. This requires a series of steps to monitor the real-time stress and compare it with the preset strength standard. First, the preset strength standard is determined based on the material properties of the target component, such as yield strength and tensile strength. Through material mechanics tests, such as standard tensile tests, the yield strength (that is, the minimum stress at which the material begins to deform permanently) and tensile strength (that is, the maximum stress that the material can withstand before breaking) of the material are obtained. Based on the yield strength and tensile strength obtained, a safe stress threshold is determined. Usually, this threshold is set below the yield strength to ensure that the component does not undergo permanent deformation during operation.

[0102] The collected real-time stress data is analyzed and compared with the preset stress threshold. The monitored stress data is continuously compared with the preset stress threshold through real-time monitoring software or system. If the monitored stress data is greater than or equal to the preset stress threshold, the system should issue a warning, indicating that the strength of the component has reached or exceeded the preset standard, and there may be a safety risk. Once it is determined that the real-time stress exceeds the preset standard, take immediate measures to avoid possible component damage or failure.

[0103] S140: If it is determined that the stamping equipment causes cracking or wrinkling of the target component, or the component strength of the target component does not meet the preset strength standard, the parameters of the stamping equipment are adjusted.

[0104] In one possible implementation, if it is determined that the stamping equipment causes cracking or wrinkling of the target component, or the component strength of the target component does not meet the preset strength standard, the parameters of the stamping equipment are adjusted, specifically including: determining the component yield of the target component based on the stamping simulation results for the target component; judging whether the component yield is greater than or equal to the preset yield; if it is determined that the component yield is less than the preset yield, adjusting the pressure distribution of the stamping equipment based on the material properties and thickness of the target component to make the pressure distribution of the stamping equipment uniform.

[0105] Specifically, according to the computer-aided engineering (CAE) software, stamping simulation is performed, and according to the design of the target component, an accurate three-dimensional model is established, and material properties and thickness data are input. The initial conditions of the simulation are configured, including pressure distribution, punch speed, die temperature, etc. The simulation is performed to predict the material behavior during the stamping process, especially focusing on stress, strain, and possible defect occurrence areas (such as cracking or wrinkling).

[0106] The simulation results are used to evaluate the yield of the component, i.e. the proportion of defect-free components. The simulation results are analyzed in detail to determine the yield of the component under the current pressure distribution. The yield obtained from the simulation is compared with the preset yield standard.

[0107] If the simulated yield is lower than the preset standard, the pressure distribution needs to be adjusted to improve the quality of the component. Based on the problem areas found in the simulation, identify the causes of uneven pressure distribution, such as improper die design, uneven material thickness, or inappropriate pressing speed. Adjust the die structure or change the settings of the stamping equipment as needed, such as adjusting the alignment of the punch, changing the punch speed, or optimizing the lubrication conditions. Use software tools to redesign the pressure distribution to ensure that the force applied to the component is more uniform. This may involve adjusting the geometry of the punch, changing the buffer material, or adjusting the support structure of the die. After adjusting the pressure distribution, rerun the stamping simulation to check how the new pressure distribution affects the stress state and yield of the component. Analyze the simulation results after the improvement measures to confirm whether the yield meets or exceeds the preset standards.

[0108] This embodiment also discloses an online quality monitoring device during the stamping process of a plate. Figure 2 The device includes an acquisition module 201, a processing module 202 and a judgment module 203, wherein:

[0109] The acquisition module 201 is used to collect the real-time pressing speed and real-time pressing amount of the stamping forming equipment for the target component.

[0110] The processing module 202 is used to input the real-time pressing speed and the real-time pressing amount into the quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component.

[0111] The judgment module 203 is used to judge whether the stamping equipment causes cracking or wrinkling of the target component based on the real-time strain rate, and to judge whether the component strength of the target component reaches a preset strength standard based on the real-time stress.

[0112] The processing module 202 is used to adjust the parameters of the stamping equipment if it is determined that the stamping equipment causes cracking or wrinkling of the target component, or the component strength of the target component does not meet the preset strength standard.

[0113] In a possible implementation, the processing module 202 is used to construct a BP neural network model, wherein the pressing speed and pressing amount are used as input nodes of the input layer, and the strain rate and stress are used as output nodes of the output layer.

[0114] The acquisition module 201 is used to acquire a preset data set and divide the data set into a training set, a validation set and a test set.

[0115] The processing module 202 is used to train the BP neural network model using the training set, adjust the parameters of the BP neural network model using the validation set, and evaluate the performance of the BP neural network model using the test set to obtain a quality prediction model.

[0116] The processing module 202 is used to integrate the quality prediction model into the monitoring system of the production line, input the collected pressing speed and pressing amount in real time, and output the predicted strain rate and stress.

[0117] In a possible implementation, the processing module 202 is used to establish a three-dimensional model of sheet metal stamping.

[0118] The processing module 202 is used to set the boundary conditions for sheet metal stamping, wherein the boundary conditions include sheet metal material properties, pressing speed range, pressing amount range and friction conditions.

[0119] The processing module 202 is used to run a simulation on the three-dimensional model after setting the boundary conditions, and record the stress rate and stress distribution of the stamping component under different process conditions, as well as the pressing amount and pressing speed under different process conditions.

[0120] The processing module 202 is used to output the stress rate, stress distribution, pressing amount and pressing speed to obtain a data set.

[0121] In a possible implementation, the processing module 202 is used to input the real-time pressing speed and the real-time pressing amount into the quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component, which are specifically calculated by the following formula:

[0122]

[0123] σ(ε ′ ,ε,T)=σ y (ε ′ ,T)+(K(T)·ε n )·(1+C·log(ε ′ ))

[0124] Among them, ε is the real-time strain rate, H is the thickness change rate of the target component per unit time, h is the current thickness of the target component, v is the real-time pressing speed, σ(ε',ε,T) is the real-time stress, σ y (∈,T) is the yield stress, K(T) is the hardening coefficient, ∈ is the accumulated strain, n is the hardening exponent, C is the rate sensitivity coefficient, and T is the material temperature.

[0125] In a possible implementation, the processing module 202 is used to determine a critical strain rate of a target component.

[0126] The judgment module 203 is used to judge whether the real-time strain rate is greater than or equal to the critical strain rate. If it is determined that the real-time strain rate is greater than or equal to the critical strain rate, it is determined that the stamping forming equipment will cause cracking or wrinkling of the target component.

[0127] In a possible implementation, the processing module 202 is used to determine a stress threshold corresponding to a preset strength standard of the target component based on the yield strength and tensile strength of the target component.

[0128] The judgment module 203 is used to judge whether the real-time stress is greater than or equal to the stress threshold. If it is determined that the real-time stress is greater than or equal to the stress threshold, it is determined that the component strength of the target component reaches a preset strength standard.

[0129] In a possible implementation, the processing module 202 is used to determine the component yield rate of the target component according to the stamping simulation result of the target component.

[0130] The judgment module 203 is used to judge whether the component yield rate is greater than or equal to a preset yield rate.

[0131] The processing module 202 is used to adjust the pressure distribution of the stamping equipment according to the material characteristics and thickness of the target component to make the pressure distribution of the stamping equipment uniform if it is determined that the component yield is less than the preset yield.

[0132] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0133] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0134] The communication bus 302 is used to realize the connection and communication between these components.

[0135] The user interface 303 may include a display screen (Display) and a camera (Camera), and the optional user interface 303 may also include a standard wired interface and a wireless interface.

[0136] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0137] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0138] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be at least one storage device located away from the aforementioned processor 301. The memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface 303 module, and an application for an online quality monitoring method during sheet metal stamping forming.

[0139] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program stored in the memory 305 for an online quality monitoring method in a sheet metal stamping process. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0140] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0141] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0143] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0145] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0146] The present application also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores instructions. When executed by one or more processors 301, the electronic device executes one or more of the methods described in the above embodiments.

[0147] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for online quality monitoring during sheet metal stamping process, characterized in that: The method comprises: Collect the real-time pressing speed and real-time pressing amount of the stamping forming equipment for the target component; Inputting the real-time pressing speed and the real-time pressing amount into a quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component; Based on the real-time strain rate, determine whether the stamping equipment causes cracking or wrinkling of the target component, and based on the real-time stress, determine whether the component strength of the target component reaches a preset strength standard; If it is determined that the stamping and forming equipment causes cracking or wrinkling of the target component, or the component strength of the target component does not meet the preset strength standard, the parameters of the stamping and forming equipment are adjusted.

2. The method for online quality monitoring during sheet metal stamping process according to claim 1, characterized in that: Before inputting the real-time pressing speed and the real-time pressing amount into the quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component, the method further includes: Component BP neural network model, in which the pressing speed and pressing amount are used as input nodes of the input layer, and the strain rate and stress are used as output nodes of the output layer; Obtain a preset data set, and divide the data set into a training set, a validation set, and a test set; The BP neural network model is trained using the training set, the parameters of the BP neural network model are adjusted using the validation set, and the performance of the BP neural network model is evaluated using the test set to obtain the quality prediction model; The quality prediction model is integrated into the monitoring system of the production line, and the collected pressing speed and pressing amount are input in real time, and the predicted strain rate and stress are output.

3. The method for online quality monitoring during sheet metal stamping process according to claim 2, characterized in that: Before the component BP neural network, the method further includes: Establish a 3D model of sheet metal stamping; Setting boundary conditions for the sheet metal stamping, wherein the boundary conditions include sheet metal material properties, a pressing speed range, a pressing amount range, and friction conditions; The three-dimensional model after setting the boundary conditions is run simulated, and the stress rate and stress distribution of the stamping component under different process conditions, as well as the pressing amount and pressing speed under different process conditions are recorded; The stress rate, stress distribution, pressing amount and pressing speed are output to obtain the data set.

4. The method for online quality monitoring during sheet metal stamping process according to claim 1, characterized in that: The real-time pressing speed and the real-time pressing amount are input into the quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component, which are specifically calculated by the following formula: in ′ ,e,T)=σ y (e ′ ,T)+(K(T)·e n )·(1+C·log(ε ′ )) Wherein, ε is the real-time strain rate, H is the thickness change rate of the target component per unit time, h is the current thickness of the target component, v is the real-time pressing speed, σ(ε',ε,T) is the real-time stress, σ y (∈,T) is the yield stress, K(T) is the hardening coefficient, ∈ is the accumulated strain, n is the hardening exponent, C is the rate sensitivity coefficient, and T is the material temperature.

5. The method for online quality monitoring during sheet metal stamping process according to claim 1, characterized in that: The step of judging whether the stamping equipment causes cracking or wrinkling of the target component based on the real-time strain rate specifically includes: determining a critical strain rate of the target component; It is determined whether the real-time strain rate is greater than or equal to the critical strain rate. If it is determined that the real-time strain rate is greater than or equal to the critical strain rate, it is determined that the stamping equipment will cause the target component to crack or wrinkle.

6. The method for online quality monitoring during sheet metal stamping process according to claim 1, characterized in that: The step of judging whether the component strength of the target component reaches a preset strength standard according to the real-time stress specifically includes: Determining a stress threshold corresponding to a preset strength standard of the target component based on the yield strength and tensile strength of the target component; It is determined whether the real-time stress is greater than or equal to the stress threshold; if it is determined that the real-time stress is greater than or equal to the stress threshold, it is determined that the component strength of the target component reaches the preset strength standard.

7. The method for online quality monitoring during sheet metal stamping process according to claim 1, characterized in that: If it is determined that the stamping equipment causes the target component to crack or wrinkle, or the component strength of the target component does not meet the preset strength standard, the parameters of the stamping equipment are adjusted, specifically including: Determining the component yield rate of the target component according to the stamping simulation result for the target component; Determining whether the component yield rate is greater than or equal to a preset yield rate; If it is determined that the component yield is less than the preset yield, the pressure distribution of the stamping equipment is adjusted according to the material properties and thickness of the target component to make the pressure distribution of the stamping equipment uniform.

8. An online quality monitoring device for sheet metal stamping process, characterized in that: The device comprises an acquisition module (201), a processing module (202) and a judgment module (203), wherein: The acquisition module (201) is used to collect the real-time pressing speed and real-time pressing amount of the stamping forming equipment for the target component; The processing module (202) is used to input the real-time pressing speed and the real-time pressing amount into a quality prediction model to obtain the real-time strain rate and real-time stress of each part of the target component; The judgment module (203) is used to judge whether the stamping forming equipment causes cracking or wrinkling of the target component based on the real-time strain rate, and to judge whether the component strength of the target component reaches a preset strength standard based on the real-time stress; The processing module (202) is used to adjust the parameters of the stamping equipment if it is determined that the stamping equipment causes the target component to crack or wrinkle, or the component strength of the target component does not meet the preset strength standard.

9. An electronic device, characterized in that: The electronic device comprises a processor (301), a communication bus (302), a memory (305), a user interface (303), a network interface (304) and a memory (305), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are both used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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