Transmission control method and system for thermal forming of carbon dioxide steel cylinder

Abstract

The invention discloses a transmission control method and system for thermal forming of a carbon dioxide steel cylinder, and relates to the field of heat treatment. The method comprises the following steps: receiving steel cylinder thermal forming transmission control parameters; transmission analysis is conducted, and steel deformation speed time sequence information is generated; analyzing through the mechanical property prediction network to generate a mechanical property prediction value; performing simulation through the digital twin model of the thermal forming equipment to generate a simulation steel cylinder model; comparing to generate a size deviation coefficient and a performance deviation coefficient; and performing thermal forming transmission control according to the steel cylinder thermal forming transmission control parameters. The technical problem that in the prior art, the predictability of steel cylinder thermal forming transmission control in the prior art is poor due to the fact that evaluation of the thermal forming control effect of pre-configured transmission control parameters is lacked is solved, and the predictability and stability of carbon dioxide steel cylinder thermal forming transmission control are improved. The failure rate and the rejection rate in the production process are reduced, and the production efficiency and the product quality are improved.

Description

Technical Field

[0001] The present application relates to the field of heat treatment technology, and specifically to a transmission control method and system for hot forming of carbon dioxide cylinders. Background Art

[0002] Thermoforming of CO2 cylinders is an important part of the cylinder manufacturing process. Its purpose is to make the cylinders achieve the expected shape and performance through heating and forming processes. With the continuous development of industrial technology, the manufacturing efficiency and precision requirements for cylinders are getting higher and higher. The existing transmission control methods for thermoforming of CO2 cylinders mainly rely on traditional mechanical transmission and temperature control systems. Although these methods can meet production needs to a certain extent, there are still some problems and shortcomings. For example, traditional mechanical transmission systems are easily affected by wear and deformation, resulting in reduced transmission accuracy and stability; at the same time, traditional temperature control systems often find it difficult to achieve precise control of temperature, affecting the molding quality and performance of the cylinders.

[0003] In summary, the prior art lacks an evaluation of the thermoforming control effect of pre-configured transmission control parameters, resulting in a technical problem in which the predictability of the thermoforming transmission control of steel cylinders in the prior art is poor. Summary of the invention

[0004] Based on this, it is necessary to provide a transmission control method and system for the hot forming of carbon dioxide cylinders to address the above-mentioned technical problems, which can solve the technical problem that the prior art lacks an evaluation of the hot forming management and control effects of pre-configured transmission control parameters, resulting in poor predictability of transmission control for hot forming of cylinders in the prior art. By improving the predictability and stability of transmission control for hot forming of carbon dioxide cylinders, the failure rate and scrap rate in the production process can be reduced, and the production efficiency and product quality can be improved.

[0005] In a first aspect, a transmission control method for hot forming of a carbon dioxide cylinder is provided, comprising: receiving transmission control parameters for hot forming of the cylinder, wherein the transmission control parameters for hot forming of the cylinder include shaft transmission speed timing information, steel feed speed timing information, steel temperature timing information, transmission direction timing information and transmission distance timing information; performing transmission analysis according to the shaft transmission speed timing information to generate steel deformation speed timing information; analyzing the steel deformation speed timing information and the steel temperature timing information through a mechanical property prediction network to generate a mechanical property prediction value; and The twin model performs simulation based on the shaft transmission speed timing information, the steel feed speed timing information, the steel temperature timing information, the transmission direction timing information and the transmission distance timing information to generate a simulated steel cylinder model; compares the simulated steel cylinder model with the reference steel cylinder model to generate a size deviation coefficient; compares the predicted mechanical property value with the expected mechanical property value to generate a performance deviation coefficient; when the size deviation coefficient is less than or equal to a first deviation threshold, and the performance deviation coefficient is less than or equal to a second deviation threshold, hot forming transmission control is performed according to the hot forming transmission control parameters of the steel cylinder.

[0006] In a second aspect, a transmission control system for hot forming of carbon dioxide cylinders is provided, the system comprising: a cylinder hot forming transmission control parameter receiving module, the cylinder hot forming transmission control parameter receiving module is used to receive cylinder hot forming transmission control parameters, wherein the cylinder hot forming transmission control parameters include shaft transmission speed timing information, steel feed speed timing information, steel temperature timing information, transmission direction timing information and transmission distance timing information; a transmission analysis module, the transmission analysis module is used to perform transmission analysis according to the shaft transmission speed timing information to generate steel deformation speed timing information; a mechanical property prediction value generation module, the mechanical property prediction value generation module is used to analyze the steel deformation speed timing information and the steel temperature timing information through a mechanical property prediction network to generate a mechanical property prediction value; a simulation cylinder model generation module, the simulation steel The bottle model generation module is used to generate a simulated steel cylinder model through the digital twin model of the hot forming equipment, based on the shaft transmission speed timing information, the steel feed speed timing information, the steel temperature timing information, the transmission direction timing information and the transmission distance timing information; the size deviation coefficient generation module, the size deviation coefficient generation module is used to compare the simulated steel cylinder model with the benchmark steel cylinder model to generate a size deviation coefficient; the performance deviation coefficient generation module, the performance deviation coefficient generation module is used to compare the predicted value of the mechanical property with the expected value of the mechanical property to generate a performance deviation coefficient; the hot forming transmission control module, the hot forming transmission control module is used to perform hot forming transmission control according to the steel cylinder hot forming transmission control parameters when the size deviation coefficient is less than or equal to the first deviation threshold and the performance deviation coefficient is less than or equal to the second deviation threshold.

[0007] The above-mentioned transmission control method and system for hot forming of carbon dioxide cylinders solves the technical problem that the prior art lacks an evaluation of the hot forming control effect of pre-configured transmission control parameters, resulting in poor predictability of transmission control for hot forming of cylinders in the prior art. By improving the predictability and stability of transmission control for hot forming of carbon dioxide cylinders, the failure rate and scrap rate in the production process are reduced, and the production efficiency and product quality are improved.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a transmission control method for thermoforming a carbon dioxide cylinder in one embodiment;

[0010] Figure 2A schematic flow chart of adding a dimension deviation coefficient of a transmission control method for thermoforming a carbon dioxide cylinder in one embodiment;

[0011] Figure 3 The present invention is a structural block diagram of a transmission control system for thermoforming a carbon dioxide cylinder in one embodiment.

[0012] Explanation of the accompanying drawings: steel cylinder hot forming transmission control parameter receiving module 11, transmission analysis module 12, mechanical property prediction value generating module 13, simulation steel cylinder model generating module 14, size deviation coefficient generating module 15, performance deviation coefficient generating module 16, hot forming transmission control module 17. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] like Figure 1 As shown, the present application provides a transmission control method for hot forming of a carbon dioxide cylinder, comprising:

[0015] Receive the transmission control parameters of the hot forming of the steel cylinder, wherein the transmission control parameters of the hot forming of the steel cylinder include shaft transmission speed timing information, steel feed speed timing information, steel temperature timing information, transmission direction timing information and transmission distance timing information.

[0016] Carbon dioxide cylinders are mainly made of high-strength low-alloy steel, which has the advantages of light weight, high strength, and good corrosion resistance. Its chemical composition mainly includes carbon, silicon, manganese, phosphorus, sulfur, etc. Carbon dioxide cylinder hot forming refers to heating the metal material used to manufacture the cylinder, such as high-strength low-alloy steel, to a certain temperature to make it reach a plastic state, and then changing its shape under the action of external forces, such as hydraulic equipment, to meet the design requirements of the carbon dioxide cylinder. Carbon dioxide cylinders have high requirements for air tightness, mechanical properties, etc. Therefore, a transmission control method for hot forming of carbon dioxide cylinders is provided, which can significantly improve the predictability and stability of the transmission control of hot forming of carbon dioxide cylinders, reduce the failure rate and scrap rate in the production process, and improve production efficiency and product quality.

[0017] The shaft transmission speed timing information refers to the change of the rotation speed of the main shaft, usually the main shaft of the spinning machine, over time during the hot forming process of the steel cylinder. The rotation speed of the main shaft directly affects the deformation speed and forming quality of the steel. By accurately controlling the rotation speed of the main shaft, it is possible to ensure that the steel is deformed evenly and stably during the hot forming process. The steel feed speed timing information describes the change of the moving speed of the steel along the spinning direction (usually the axial direction) over time during the hot forming process. The feed speed of the steel determines the deformation degree and forming efficiency of the steel during the hot forming process. By adjusting the feed speed of the steel, the deformation amount and deformation speed of the steel can be controlled, thereby ensuring the forming quality and production efficiency of the steel cylinder. The steel temperature timing information records the change of the temperature of the steel over time during the entire hot forming process. The temperature of the steel is an important factor affecting its plasticity and deformation ability. By real-time monitoring and controlling the temperature of the steel, it is possible to ensure that the steel is in the optimal temperature state during the hot forming process, thereby improving its plasticity and deformation ability and ensuring the forming quality of the steel cylinder. The transmission direction timing information describes the change of the transmission direction of the main shaft and the steel feed mechanism over time during the entire hot forming process. The change of the transmission direction directly affects the deformation path and the formed shape of the steel. By accurately controlling the change of the transmission direction, it can be ensured that the steel is deformed according to the predetermined path and shape, so as to obtain a steel cylinder that meets the requirements. The transmission distance timing information records the change of the distance moved by the main shaft and the steel feed mechanism over time during the entire hot forming process. The change of the transmission distance determines the deformation amount and the formed size of the steel during the hot forming process. By accurately controlling the change of the transmission distance, it can be ensured that the deformation amount and the formed size of the steel meet the design requirements, so as to obtain a steel cylinder that meets the standards.

[0018] Transmission analysis is performed based on the shaft transmission speed timing information to generate steel deformation speed timing information.

[0019] Spindle drive efficiency describes the ratio of actual power transferred from the motor to the spindle to the input power. This is usually a value between 0 and 1, depending on the design and efficiency of the drive system. Steels of different materials and thicknesses will have different deformation characteristics at the same transmission speed. For example, softer steels and thinner steels are easier to deform, while harder steels and thicker steels require higher transmission speeds to achieve the same deformation effect. The design of the spinning die has a direct impact on the deformation speed of the steel. The geometry, size and material of the die will affect the deformation behavior of the steel. Based on the above factors, the deformation speed of the steel at different time points can be calculated, and the time series information of the steel deformation speed can be generated.

[0020] According to the shaft transmission speed timing information, analysis is performed based on the standard transmission mapping function to obtain the standard speed timing information of the forming action arm, wherein the standard transmission mapping function refers to the mapping relationship between the forming action arm speed and the shaft transmission speed when the hot forming equipment is lossless; according to the hot forming equipment position number, the preset historical time zone transmission monitoring data is collected, wherein the preset historical time zone transmission monitoring data includes shaft transmission speed recording information, steel deformation distance recording information and steel deformation duration recording information; according to the shaft transmission speed recording information, the steel deformation distance recording information and the steel deformation duration recording information, transmission loss analysis is performed to obtain the transmission speed loss ratio; according to the transmission speed loss ratio, the standard speed timing information of the forming action arm is fitted to generate the steel deformation speed timing information.

[0021] The standard transmission mapping function describes the mapping relationship between the forming arm speed and the shaft transmission speed when the hot forming equipment is lossless. This is usually a mathematical formula or a lookup table. The shaft transmission speed value corresponding to each time point in the shaft transmission speed timing information is substituted into the standard transmission mapping function to calculate the standard speed value of the forming arm at the corresponding time point. The preset historical time zone refers to the time period used to analyze the transmission loss, which can be a few hours, a few days or longer in the past. According to the hot forming equipment bit number, the transmission monitoring data in the preset historical time zone is collected from the data recording system, including the shaft transmission speed recording information, the steel deformation distance recording information and the steel deformation time recording information. The collected transmission monitoring data is processed and analyzed to calculate the actual ratio between the shaft transmission speed and the steel deformation speed in each time period. The actual ratio is compared with the standard ratio when there is no loss, and the transmission speed loss ratio is calculated. This loss ratio reflects the transmission efficiency loss of the equipment during actual operation. The transmission speed loss ratio obtained in the above is applied to the standard speed timing information of the forming arm obtained by analyzing the standard speed timing information of the forming arm, and the speed value is adjusted on the calculated standard speed timing information of the forming arm. According to the adjusted forming arm speed timing information, combined with the steel deformation distance and duration record information, the actual deformation speed of the steel at each time point is calculated, thereby generating the steel deformation speed timing information.

[0022] According to the shaft transmission speed record information, analysis is performed based on the standard transmission mapping function to obtain the theoretical speed of the forming action arm; speed calculation is performed according to the deformation distance record information and the steel deformation time record information to obtain the monitoring speed of the forming action arm; the loss ratio of the monitoring speed of the forming action arm to the theoretical speed of the forming action arm is calculated to obtain the initial loss ratio set of the transmission speed; the initial loss ratio set of the transmission speed is de-accidentally fitted to generate the transmission speed loss ratio.

[0023] According to the shaft transmission speed record information, the theoretical speed of the forming arm is calculated using the known standard transmission mapping function. This mapping function represents the conversion relationship from the shaft transmission speed to the forming arm speed without any loss. Through this function, the theoretical speed of the forming arm at each time point can be obtained. The monitoring speed of the forming arm is calculated according to the deformation distance record information and the steel deformation time record information. This speed is obtained through actual measurement and reflects the speed of the forming arm in the actual working process. The monitoring speed at each time point can be obtained by dividing the deformation distance by the deformation time. With the theoretical speed and monitoring speed of the forming arm, the loss ratio of the transmission speed can be calculated. The loss ratio is obtained by dividing the monitoring speed by the theoretical speed, which reflects the deviation between the actual speed and the theoretical speed, that is, the efficiency loss in the transmission process. By calculating the loss ratio at each time point, an initial loss ratio set can be obtained. Since the initial loss ratio set may contain outliers caused by accidental factors (such as measurement errors, temporary instability of the equipment, etc.), it is necessary to remove the accidental fitting to obtain a more accurate transmission speed loss ratio. This process can be achieved through a variety of statistical and mathematical methods, depending on the characteristics of the data and the required accuracy. A common method is to use the average or median to represent the loss ratio of the entire collection. This method is simple and easy to use, but it may not fully reflect the distribution of the data. More advanced methods include the use of weighted average, least squares method, curve fitting, etc., which can take into account the weights of different data points and the relationship between them to obtain a more accurate loss ratio. After determining the transmission speed loss ratio, it can be used to evaluate the transmission efficiency of the equipment, guide the maintenance and optimization of the equipment, and predict the performance of the equipment in future operation.

[0024] The deformation velocity time series information of the steel material and the temperature time series information of the steel material are analyzed through a mechanical property prediction network to generate a mechanical property prediction value.

[0025] Ensure that both the deformation velocity time series information and the temperature time series information have been properly preprocessed so that they can be used as input to the network. This includes data cleaning (removing outliers or missing values), data normalization or standardization, and converting the time series data into a form suitable for network input (such as a time series window or sliding window). Select a suitable neural network model and build a new network for mechanical property prediction. Use historical data, including known steel deformation velocity time series information, temperature time series information, and corresponding mechanical property values, to train the network. During the training process, the network will learn to extract features from the input time series data and predict the corresponding mechanical property values. The weights and bias terms of the network are adjusted through the back propagation algorithm to minimize the error between the predicted value and the actual value. After the training is completed, the validation set is used to evaluate the performance of the network. The validation set is a data set independent of the training set, which is used to check whether the network is overfitting or underfitting. If the validation performance meets the requirements, the test set can be used to further evaluate the generalization ability of the network. The test set is a data set independent of both the training set and the validation set, which is used to simulate the performance of the network in actual applications. After the network is trained and verified, it can be used to generate new predictions of the mechanical properties of steel. The new steel deformation velocity and temperature time series information are provided as input to the network, and the network will output the corresponding mechanical property prediction values. These prediction values ​​can be used to guide decisions such as quality control, process optimization or material selection in the production process.

[0026] According to the steel model, the one-to-one corresponding mechanical property prediction network is activated, wherein the mechanical property prediction network includes a first mechanical property prediction channel, a second mechanical property prediction channel, and finally an Nth mechanical property prediction channel; the first mechanical property prediction channel, the second mechanical property prediction channel, and finally the Nth mechanical property prediction channel are traversed, and the steel deformation speed time series information and the steel temperature time series information are processed to generate the mechanical property prediction value; wherein the mechanical property prediction network construction step includes: according to the steel model, the steel deformation speed record time series information and the steel temperature record time series information are collected. information, a first mechanical property test characteristic value, a second mechanical property test characteristic value, until the Nth mechanical property test characteristic value; using the first mechanical property test characteristic value as supervision data, using the steel deformation speed record timing information, and the steel temperature record timing information as input data, training a long short-term memory neural network to generate the first mechanical property prediction channel; until using the Nth mechanical property test characteristic value as supervision data, using the steel deformation speed record timing information, and the steel temperature record timing information as input data, training a long short-term memory neural network to generate the Nth mechanical property prediction channel.

[0027] According to the steel model, the steel deformation speed record time series information and steel temperature record time series information are collected, and the first mechanical property test characteristic value, the second mechanical property test characteristic value and the Nth mechanical property test characteristic value of the corresponding steel model are collected. The collected steel deformation speed record time series information and steel temperature record time series information are cleaned, normalized and other preprocessing operations are performed to ensure that the mechanical property test characteristic value and the corresponding time series information are one-to-one corresponding in time. The first mechanical property test characteristic value is used as the supervision data (i.e., the target output), the steel deformation speed record time series information and the steel temperature record time series information are used as the input data, and the long short-term memory neural network (LSTM) is used to generate the first mechanical property prediction channel. The long short-term memory neural network (LSTM, Long Short-Term Memory) is a special recurrent neural network designed to solve the gradient vanishing and gradient explosion problems existing in traditional recurrent neural networks when processing long sequence data, and better capture the long-term dependencies in the sequence. Repeat the above steps, but use the second mechanical property test eigenvalue, the third mechanical property test eigenvalue, etc. as supervision data, and train the second mechanical property prediction channel, the third mechanical property prediction channel, etc. respectively, until the Nth mechanical property prediction channel is generated. Integrate all trained mechanical property prediction channels into a network to form a complete mechanical property prediction network. Activate the corresponding mechanical property prediction network according to the actual steel model used. Input the deformation speed timing information and temperature timing information of the steel to be predicted into the mechanical property prediction network. Traverse the first mechanical property prediction channel, the second mechanical property prediction channel until the Nth mechanical property prediction channel. Each channel will be processed according to the input timing information and output the corresponding mechanical property prediction value. Integrate the prediction results of all channels and output a complete set of mechanical property prediction values.

[0028] Through the digital twin model of the hot forming equipment, simulation is performed based on the shaft transmission speed timing information, the steel feed speed timing information, the steel temperature timing information, the transmission direction timing information and the transmission distance timing information to generate a simulated cylinder model.

[0029] Collect and organize the timing information of shaft transmission speed, steel feed speed, steel temperature, transmission direction and transmission distance. These data are usually stored in the form of time series, such as CSV files or tables in databases. Import these time series data into the program and perform necessary preprocessing, such as data cleaning, interpolation and normalization. The digital twin model is usually a complex simulation system that simulates the physical behavior of the hot forming equipment. This model may be a physics-based model, that is, using physical equations to describe the system behavior, or it may be a data-based model, that is, using machine learning or statistical methods to predict the system behavior. Input the preprocessed time series data into the digital twin model and run the simulation. During the simulation, the model simulates the operation of the hot forming equipment according to the input time series data, including shaft transmission, steel feeding, temperature change, transmission direction and transmission distance. After the simulation is completed, the model will output a series of simulation results, including shape changes and temperature distribution of steel during the hot forming process. These results can be used to generate a simulated cylinder model. The specific generation method depends on the function and output format of the simulation software. Typically, simulation results can be displayed using visualization tools and exported as 3D model files for further processing and analysis in CAD software. The generated simulated cylinder model needs to be compared and verified with the actual cylinder to evaluate the accuracy of the simulation. If there is a large difference between the simulation results and the actual situation, the digital twin model needs to be adjusted and optimized to improve the accuracy of the simulation.

[0030] The simulated steel cylinder model and the reference steel cylinder model are compared to generate a dimension deviation coefficient.

[0031] like Figure 2 As shown, a dimension deviation threshold is configured; the simulated steel cylinder model and the reference steel cylinder model are compared to obtain a dimension deviation modulus set; the proportion of the dimension deviation modulus set greater than or equal to the dimension deviation threshold is calculated, and is set as a first dimension deviation factor; the maximum value of the dimension deviation modulus set is calculated, and is set as a second dimension deviation factor; the first dimension deviation factor and the second dimension deviation factor are added to the dimension deviation coefficient.

[0032] Determine the key dimensions or feature points to be compared, which may be the diameter, height, curvature at a specific location, etc. of the cylinder. Extract the data of these key dimensions from the simulated cylinder model and the benchmark cylinder model. It is necessary to read the data from the 3D model file or export the data using CAD software. For each key dimension, calculate the difference between the simulation model and the benchmark model. This can be done by simple subtraction or more complex comparison methods such as least squares fitting. Convert the difference in each key dimension into a deviation coefficient. This can be done by dividing the difference by the benchmark dimension (or other appropriate reference value) to obtain a dimensionless deviation coefficient, i.e., the dimensional deviation coefficient.

[0033] According to actual needs, set one or more dimension deviation thresholds. These thresholds will be used to determine which dimension deviations need attention. Obtain the key dimension data of the two models and calculate the deviation modulus (i.e. the absolute value of the deviation) between them. Count the proportion of items in the dimension deviation modulus set that are greater than or equal to the dimension deviation threshold. This proportion reflects the prevalence of deviations exceeding the threshold. Find the maximum value in the dimension deviation modulus set. This maximum value represents the most serious degree of deviation. Add the first dimension deviation factor and the second dimension deviation factor to the dimension deviation coefficient in some way (such as weighted or directly merged).

[0034] The predicted mechanical property value is compared with the expected mechanical property value to generate a performance deviation coefficient.

[0035] Traverse the mechanical property attribute set and configure the mechanical property threshold set; assign weights to the mechanical property attribute set to generate a weight distribution result; calculate the proportion of the mechanical property prediction value and the mechanical property expected value that meet the mechanical property threshold set, and set it as the mechanical property quality factor; when the mechanical property quality factor is less than the quality factor threshold, set the performance deviation coefficient to infinity; when the mechanical property quality factor is greater than or equal to the quality factor threshold, perform weighted mean fitting on the mechanical property deviation modulus value set according to the weight distribution result to generate the performance deviation coefficient.

[0036] Clarify the mechanical properties. For example, for metal materials, you may be concerned about tensile strength, yield strength, elongation, etc. For each mechanical property, you need to set an acceptable threshold range. This range is usually based on design requirements, industry standards, or historical data. Different mechanical properties may have different effects on the overall performance of the product. Therefore, a weight needs to be assigned to each property to reflect its importance. The predicted value of each mechanical property will be compared with the expected value, and it will be determined whether they fall within the set threshold range. Then, the proportion of the number of properties that meet the threshold conditions to the total number of properties is calculated, and this proportion is the mechanical property quality factor. If the mechanical property quality factor is less than a certain set quality factor threshold, it means that the mechanical properties of the product do not meet the requirements, and the performance deviation coefficient is set to infinity (or a certain maximum value) to indicate that the product is unqualified. If the mechanical property quality factor is greater than or equal to the quality factor threshold, it means that the mechanical properties of the product basically meet the requirements, but there may still be some deviations. At this time, we will perform weighted mean fitting on the mechanical property deviation modulus set according to the weight distribution result to generate the performance deviation coefficient. The smaller the performance deviation coefficient, the closer the mechanical properties of the product are to the expected value.

[0037] When the size deviation coefficient is less than or equal to the first deviation threshold, and the performance deviation coefficient is less than or equal to the second deviation threshold, thermoforming transmission control is performed according to the thermoforming transmission control parameters of the steel cylinder.

[0038] The difference between the actual size and theoretical size of the steel cylinder is measured or calculated, and the size deviation coefficient is calculated. This coefficient is usually a quantitative indicator used to indicate the degree of deviation between the actual size and the theoretical size, and to evaluate the performance deviation of the steel cylinder. It involves the comparison between the actual value and the expected value of multiple mechanical performance indicators (such as tensile strength, yield strength, etc.). A performance deviation coefficient is obtained by calculating the deviation of these performance indicators and synthesizing them. After obtaining the size deviation coefficient and the performance deviation coefficient, they need to be compared with the preset thresholds. The first deviation threshold is the upper tolerance limit of the size deviation, and the second deviation threshold is the upper tolerance limit of the performance deviation. Only when the size deviation coefficient is less than or equal to the first deviation threshold, and the performance deviation coefficient is less than or equal to the second deviation threshold, will the next step be continued. If both of the above conditions are met, it means that the size and performance of the steel cylinder are within an acceptable range. At this time, the thermoforming transmission control can be performed according to the preset thermoforming transmission control parameters of the steel cylinder. These control parameters may include heating temperature, heating time, forming pressure, etc., which jointly determine the final shape and performance of the steel cylinder. According to the control parameters, the thermoforming equipment is started to heat and form the steel cylinder. Throughout the process, various parameters need to be closely monitored to ensure that they are always within the preset range to ensure the quality and performance of the cylinder. In this way, it can be ensured that the cylinder always maintains high quality and stable performance during the thermoforming process.

[0039] When the size deviation coefficient is greater than the first deviation threshold, or / and the performance deviation coefficient is greater than the second deviation threshold, based on the mechanical property prediction network and the digital twin model of the thermoforming equipment, the transmission control parameters of the thermoforming of the steel cylinder are optimized to generate the recommended transmission control parameters for the thermoforming of the steel cylinder; and the thermoforming transmission control is performed according to the recommended transmission control parameters for the thermoforming of the steel cylinder.

[0040] Calculate the size deviation coefficient and performance deviation coefficient and compare them with the respective set thresholds. If any deviation coefficient exceeds the corresponding threshold, parameter optimization is required. Use the existing mechanical property prediction network to predict the mechanical properties of the cylinder under different control parameters. At the same time, use the digital twin model of the thermoforming equipment to simulate the actual thermoforming process and predict the size deviation. These two tools will serve as the basis for parameter optimization. Set optimization goals, such as minimizing the weighted sum of size deviation and performance deviation, or meeting specific performance requirements. This needs to be determined according to specific production requirements and product standards. Based on the above optimization goals, use appropriate optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) to optimize the transmission control parameters of thermoforming of cylinders. In this process, the mechanical property prediction network and the digital twin model will provide prediction results under different parameter combinations for reference by the optimization algorithm. After parameter optimization, a set of optimized transmission control parameters for thermoforming of cylinders are obtained, which can reduce size deviation and performance deviation, or meet specific performance requirements. This set of parameters is the recommended transmission control parameters for thermoforming of cylinders. Based on the generated recommended transmission control parameters for thermoforming of cylinders, the thermoforming equipment is adjusted accordingly and the thermoforming process is started. During the thermoforming process, the parameters need to be closely monitored to ensure that they are always within the preset range to ensure the quality and performance of the cylinders. After the thermoforming is completed, the cylinders are subsequently inspected and evaluated to verify the validity of the recommended parameters. If any problems or defects are found, the parameters can be re-optimized based on the new data to further improve the thermoforming process. In this way, the transmission control parameters for thermoforming of cylinders can be dynamically adjusted according to the real-time deviation situation to ensure that the product always meets the high quality and stable performance requirements.

[0041] like Figure 3 As shown, the embodiment of the present application includes a transmission control system for thermoforming of a carbon dioxide cylinder, the system comprising:

[0042] The steel cylinder hot forming transmission control parameter receiving module 11 is used to receive the steel cylinder hot forming transmission control parameters, wherein the steel cylinder hot forming transmission control parameters include shaft transmission speed timing information, steel material feed speed timing information, steel material temperature timing information, transmission direction timing information and transmission distance timing information;

[0043] A transmission analysis module 12, the transmission analysis module 12 is used to perform transmission analysis according to the shaft transmission speed time series information to generate steel deformation speed time series information;

[0044] A mechanical property prediction value generation module 13, the mechanical property prediction value generation module 13 is used to analyze the steel deformation speed time series information and the steel temperature time series information through a mechanical property prediction network to generate a mechanical property prediction value;

[0045] A simulation steel cylinder model generation module 14, the simulation steel cylinder model generation module 14 is used to generate a simulation steel cylinder model by simulating the shaft transmission speed timing information, the steel feed speed timing information, the steel temperature timing information, the transmission direction timing information and the transmission distance timing information through a digital twin model of the hot forming equipment;

[0046] A size deviation coefficient generating module 15, wherein the size deviation coefficient generating module 15 is used to compare the simulated steel cylinder model with the reference steel cylinder model to generate a size deviation coefficient;

[0047] A performance deviation coefficient generating module 16, wherein the performance deviation coefficient generating module 16 is used to compare the predicted value of the mechanical property with the expected value of the mechanical property to generate a performance deviation coefficient;

[0048] The thermoforming transmission control module 17 is used to perform thermoforming transmission control according to the thermoforming transmission control parameters of the steel cylinder when the size deviation coefficient is less than or equal to the first deviation threshold and the performance deviation coefficient is less than or equal to the second deviation threshold.

[0049] Furthermore, the embodiment of the present application also includes:

[0050] A control parameter optimization module, wherein the control parameter optimization module is used to optimize the transmission control parameters of the steel cylinder thermoforming based on the mechanical property prediction network and the thermoforming equipment digital twin model to generate recommended transmission control parameters for the thermoforming of the steel cylinder when the size deviation coefficient is greater than the first deviation threshold, or / and the performance deviation coefficient is greater than the second deviation threshold;

[0051] A control module is used to perform thermoforming transmission control according to the recommended transmission control parameters for thermoforming of the steel cylinder.

[0052] Furthermore, the embodiment of the present application also includes:

[0053] An analysis module, the analysis module is used to analyze the shaft transmission speed timing information based on a standard transmission mapping function to obtain the standard speed timing information of the forming action arm, wherein the standard transmission mapping function refers to the mapping relationship between the forming action arm speed and the shaft transmission speed when the hot forming equipment is lossless;

[0054] A preset historical time zone transmission monitoring data acquisition module, the preset historical time zone transmission monitoring data acquisition module is used to collect preset historical time zone transmission monitoring data according to the hot forming equipment position number, wherein the preset historical time zone transmission monitoring data includes shaft transmission speed record information, steel deformation distance record information and steel deformation duration record information;

[0055] A transmission speed loss ratio obtaining module, the transmission speed loss ratio obtaining module is used to perform transmission loss analysis according to the shaft transmission speed record information, the steel deformation distance record information and the steel deformation time record information to obtain the transmission speed loss ratio;

[0056] A fitting module is used to fit the standard speed timing information of the forming action arm according to the transmission speed loss ratio to generate the steel deformation speed timing information.

[0057] Furthermore, the embodiment of the present application also includes:

[0058] A forming action arm theoretical speed acquisition module, the forming action arm theoretical speed acquisition module is used to obtain the forming action arm theoretical speed by analyzing the shaft transmission speed record information based on the standard transmission mapping function;

[0059] A speed calculation module, the speed calculation module is used to perform speed calculation according to the deformation distance record information and the steel deformation time record information to obtain the forming arm monitoring speed;

[0060] A loss ratio calculation module, the loss ratio calculation module is used to calculate the loss ratio of the monitoring speed of the forming action arm to the theoretical speed of the forming action arm, and obtain an initial loss ratio set of transmission speed;

[0061] The de-accidental fitting module is used to perform de-accidental fitting on the transmission speed initial loss ratio set to generate the transmission speed loss ratio.

[0062] Furthermore, the embodiment of the present application also includes:

[0063] An activation module, wherein the activation module is used to activate the one-to-one corresponding mechanical property prediction network according to the steel model, wherein the mechanical property prediction network includes a first mechanical property prediction channel, a second mechanical property prediction channel, and up to an Nth mechanical property prediction channel;

[0064] A time series information processing module, the time series information processing module is used to traverse the first mechanical property prediction channel, the second mechanical property prediction channel until the Nth mechanical property prediction channel, process the steel deformation speed time series information and the steel temperature time series information, and generate the mechanical property prediction value;

[0065] Wherein, the mechanical properties prediction network construction step includes:

[0066] An information collection module, wherein the information collection module is used to collect steel deformation speed record time series information, steel temperature record time series information, a first mechanical property test characteristic value, a second mechanical property test characteristic value, and up to an Nth mechanical property test characteristic value according to the steel model;

[0067] A long short-term memory neural network training module, wherein the long short-term memory neural network training module is used to train a long short-term memory neural network using the first mechanical property test characteristic value as supervision data, and using the steel deformation speed record time series information and the steel temperature record time series information as input data to generate the first mechanical property prediction channel;

[0068] An iteration module is used to train a long short-term memory neural network by using the Nth mechanical property test characteristic value as supervision data and the steel deformation speed record time series information and the steel temperature record time series information as input data to generate the Nth mechanical property prediction channel.

[0069] Furthermore, the embodiment of the present application also includes:

[0070] A size deviation threshold configuration module, wherein the size deviation threshold configuration module is used to configure the size deviation threshold;

[0071] A size deviation modulus value set acquisition module, the size deviation modulus value set acquisition module is used to compare the simulated steel cylinder model with the reference steel cylinder model to obtain a size deviation modulus value set;

[0072] A first size deviation factor module, the first size deviation factor module is used to calculate the proportion of the size deviation modulus value set greater than or equal to the size deviation threshold, and set it as a first size deviation factor;

[0073] A second size deviation factor module, the second size deviation factor module is used to calculate the maximum value of the size deviation module value set and set it as a second size deviation factor;

[0074] A size deviation coefficient adding module is used to add the first size deviation factor and the second size deviation factor into the size deviation coefficient.

[0075] Furthermore, the embodiment of the present application also includes:

[0076] A mechanical property threshold set configuration module, wherein the mechanical property threshold set configuration module is used to traverse the mechanical property attribute set and configure the mechanical property threshold set;

[0077] A weight distribution module, the weight distribution module is used to distribute weights to the mechanical performance attribute set and generate a weight distribution result;

[0078] A mechanical property quality factor module, wherein the mechanical property quality factor module is used to calculate the ratio of the mechanical property prediction value to the mechanical property expected value that satisfies the mechanical property threshold set, and sets the ratio as the mechanical property quality factor;

[0079] A performance deviation coefficient setting module, wherein the performance deviation coefficient setting module is used to set the performance deviation coefficient to infinity when the mechanical property quality factor is less than the quality factor threshold;

[0080] A weighted mean fitting module is used to perform weighted mean fitting on the mechanical property deviation modulus set according to the weight distribution result to generate the performance deviation coefficient when the mechanical property quality factor is greater than or equal to the quality factor threshold.

[0081] For a specific embodiment of a transmission control system for thermoforming of a carbon dioxide cylinder, please refer to the embodiment of a transmission control method for thermoforming of a carbon dioxide cylinder described above, which will not be described in detail here. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0082] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0083] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A transmission control method for thermoforming of carbon dioxide cylinders, characterized in that: include: Receiving the transmission control parameters of the hot forming of the steel cylinder, wherein the transmission control parameters of the hot forming of the steel cylinder include shaft transmission speed timing information, steel material feed speed timing information, steel material temperature timing information, transmission direction timing information and transmission distance timing information; Perform transmission analysis according to the shaft transmission speed time series information to generate steel deformation speed time series information; Analyzing the steel deformation velocity time series information and the steel temperature time series information through a mechanical property prediction network to generate a mechanical property prediction value; Through the digital twin model of the hot forming equipment, simulation is performed based on the shaft transmission speed timing information, the steel feed speed timing information, the steel temperature timing information, the transmission direction timing information and the transmission distance timing information to generate a simulated steel cylinder model; Comparing the simulated steel cylinder model with the reference steel cylinder model to generate a dimensional deviation coefficient; Comparing the predicted mechanical property value with the expected mechanical property value to generate a performance deviation coefficient; When the size deviation coefficient is less than or equal to the first deviation threshold, and the performance deviation coefficient is less than or equal to the second deviation threshold, thermoforming transmission control is performed according to the thermoforming transmission control parameters of the steel cylinder.

2. A transmission control method for thermoforming of a carbon dioxide cylinder as claimed in claim 1, characterized in that: Also includes: When the size deviation coefficient is greater than the first deviation threshold, or / and the performance deviation coefficient is greater than the second deviation threshold, based on the mechanical property prediction network and the digital twin model of the thermoforming equipment, the transmission control parameters of the thermoforming of the steel cylinder are optimized to generate the recommended transmission control parameters of the thermoforming of the steel cylinder; Thermoforming transmission control is performed according to the recommended transmission control parameters for thermoforming of steel cylinders.

3. The transmission control method for thermoforming of a carbon dioxide cylinder according to claim 1, characterized in that: Transmission analysis is performed according to the shaft transmission speed time series information to generate steel deformation speed time series information, including: According to the shaft transmission speed timing information, analysis is performed based on a standard transmission mapping function to obtain standard speed timing information of the forming action arm, wherein the standard transmission mapping function refers to a mapping relationship between the forming action arm speed and the shaft transmission speed when the hot forming equipment is lossless; According to the position number of the hot forming equipment, the preset historical time zone transmission monitoring data is collected, wherein the preset historical time zone transmission monitoring data includes shaft transmission speed record information, steel deformation distance record information and steel deformation duration record information; Perform transmission loss analysis according to the shaft transmission speed record information, the steel deformation distance record information and the steel deformation time record information to obtain a transmission speed loss ratio; The standard speed timing information of the forming action arm is fitted according to the transmission speed loss ratio to generate the steel deformation speed timing information.

4. A transmission control method for thermoforming of a carbon dioxide cylinder as claimed in claim 3, characterized in that: Performing transmission loss analysis according to the shaft transmission speed record information, the steel deformation distance record information, and the steel deformation time record information to obtain a transmission speed loss ratio includes: According to the shaft transmission speed record information, analysis is performed based on the standard transmission mapping function to obtain the forming action arm theoretical speed; Perform speed calculation according to the deformation distance record information and the steel deformation time record information to obtain the forming arm monitoring speed; Calculating the loss ratio of the monitoring speed of the forming action arm to the theoretical speed of the forming action arm to obtain a set of initial loss ratios of transmission speeds; The transmission speed initial loss ratio set is subjected to a de-random fitting to generate the transmission speed loss ratio.

5. The transmission control method for thermoforming of a carbon dioxide cylinder according to claim 1, characterized in that: The deformation velocity time series information of the steel material and the temperature time series information of the steel material are analyzed through a mechanical property prediction network to generate a mechanical property prediction value, including: According to the steel model, activating the one-to-one corresponding mechanical property prediction network, wherein the mechanical property prediction network includes a first mechanical property prediction channel, a second mechanical property prediction channel, and up to an Nth mechanical property prediction channel; Traversing the first mechanical property prediction channel, the second mechanical property prediction channel, and finally the Nth mechanical property prediction channel, processing the steel deformation velocity time series information and the steel temperature time series information, and generating the mechanical property prediction value; Wherein, the mechanical properties prediction network construction step includes: According to the steel model, collect the steel deformation speed record time series information, the steel temperature record time series information, the first mechanical property test characteristic value, the second mechanical property test characteristic value until the Nth mechanical property test characteristic value; Using the first mechanical property test characteristic value as supervision data, and using the steel deformation speed record time series information and the steel temperature record time series information as input data, training a long short-term memory neural network to generate the first mechanical property prediction channel; Until the Nth mechanical property test characteristic value is used as supervision data, the steel deformation speed record time series information and the steel temperature record time series information are used as input data, the long short-term memory neural network is trained to generate the Nth mechanical property prediction channel.

6. The transmission control method for thermoforming of carbon dioxide cylinders according to claim 1, characterized in that: Comparing the simulated steel cylinder model with the reference steel cylinder model, a dimension deviation coefficient is generated, including: Configure size deviation threshold; Comparing the simulated steel cylinder model with the reference steel cylinder model to obtain a size deviation modulus value set; Calculate the proportion of the size deviation modulus value set that is greater than or equal to the size deviation threshold, and set it as a first size deviation factor; Calculate the maximum value of the size deviation modulus value set and set it as the second size deviation factor; The first size deviation factor and the second size deviation factor are added to the size deviation coefficient.

7. The transmission control method for thermoforming of carbon dioxide cylinders according to claim 1, characterized in that: Comparing the predicted mechanical property value with the expected mechanical property value to generate a performance deviation coefficient, including: Traverse the mechanical property attribute set and configure the mechanical property threshold set; Performing weight distribution on the mechanical performance attribute set to generate a weight distribution result; Calculate the ratio of the predicted value of the mechanical property to the expected value of the mechanical property that satisfies the mechanical property threshold set, and set it as the mechanical property quality factor; When the mechanical property quality factor is less than the quality factor threshold, the performance deviation coefficient is set to infinity; When the mechanical property quality factor is greater than or equal to the quality factor threshold, a weighted mean fitting is performed on the mechanical property deviation modulus value set according to the weight distribution result to generate the performance deviation coefficient.

8. A transmission control system for thermoforming of carbon dioxide cylinders, characterized in that: The system is used to execute a transmission control method for thermoforming a carbon dioxide cylinder according to any one of claims 1 to 7, and the system comprises: A steel cylinder thermoforming transmission control parameter receiving module, the steel cylinder thermoforming transmission control parameter receiving module is used to receive the steel cylinder thermoforming transmission control parameters, wherein the steel cylinder thermoforming transmission control parameters include shaft transmission speed timing information, steel material feed speed timing information, steel material temperature timing information, transmission direction timing information and transmission distance timing information; A transmission analysis module, the transmission analysis module is used to perform transmission analysis according to the shaft transmission speed time series information to generate steel deformation speed time series information; A mechanical property prediction value generation module, wherein the mechanical property prediction value generation module is used to analyze the steel deformation velocity time series information and the steel temperature time series information through a mechanical property prediction network to generate a mechanical property prediction value; A simulation steel cylinder model generation module, the simulation steel cylinder model generation module is used to generate a simulation steel cylinder model by simulating the shaft transmission speed timing information, the steel feed speed timing information, the steel temperature timing information, the transmission direction timing information and the transmission distance timing information through a digital twin model of the hot forming equipment; A size deviation coefficient generating module, wherein the size deviation coefficient generating module is used to compare the simulated steel cylinder model with the reference steel cylinder model to generate a size deviation coefficient; A performance deviation coefficient generating module, wherein the performance deviation coefficient generating module is used to compare the predicted value of the mechanical property with the expected value of the mechanical property to generate a performance deviation coefficient; A thermoforming transmission control module, wherein the thermoforming transmission control module is used to perform thermoforming transmission control according to the thermoforming transmission control parameters of the steel cylinder when the size deviation coefficient is less than or equal to a first deviation threshold and the performance deviation coefficient is less than or equal to a second deviation threshold.

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