Hot extrusion control method for stainless steel pipe machining process

Abstract

The invention provides a hot extrusion control method for a stainless steel pipe processing technology, and relates to the technical field of metal extrusion, and the hot extrusion control method comprises the following steps: carrying out continuous heating control and real-time temperature monitoring to obtain temperature monitoring data; when the preset temperature interval is met, inputting a temperature-pressure prediction model to obtain predicted pressure data; if the preset pressure interval is not met, continuously raising the temperature and heating until the preset pressure interval is met, and taking the heated temperature monitoring data as an initial temperature control parameter; obtaining an initial speed control parameter based on a pressure-speed calibration table; carrying out extrusion control, and recording the steel pipe finished product quality; optimization is carried out by taking optimization of the quality of the finished steel pipe as a target, and an optimization control parameter combination is obtained; and carrying out hot extrusion optimization. The technical problems that according to a traditional method, hot extrusion parameter control is not accurate enough, parameter adjustment generally depends on past experience, and consequently the machining quality of the stainless steel pipe is poor, and the production efficiency is low are solved.

Description

Technical Field

[0001] The invention relates to the technical field of metal extrusion, and in particular to a hot extrusion control method for a stainless steel pipe processing technology. Background Art

[0002] Hot extrusion is a common metal processing technology, which is widely used in industries such as stainless steel pipes. In the hot extrusion process, the metal billet is heated and then extruded under a certain pressure to obtain the desired shape and size of the product. However, there are some technical problems in traditional hot extrusion processing, which limit product quality and production efficiency. On the one hand, the traditional hot extrusion processing does not accurately control various parameters, resulting in unstable product quality; on the other hand, traditional hot extrusion processing often requires a long debugging time and manual intervention, and the production efficiency is low, which cannot meet the needs of large-scale production. Summary of the invention

[0003] The present application provides a hot extrusion control method for stainless steel pipe processing technology, aiming to solve the technical problems that traditional methods are not accurate enough in parameter control of hot extrusion, and parameter adjustment usually relies on past experience, resulting in poor processing quality and low production efficiency of stainless steel pipes.

[0004] In view of the above problems, the present application provides a hot extrusion control method for a stainless steel pipe processing technology.

[0005] The first aspect disclosed in the present application provides a hot extrusion control method for a stainless steel pipe processing process, the method is applied to a hot extrusion control device, the hot extrusion control device includes a heating unit and an extrusion unit, the method includes: after the target metal billet is transferred to the heating unit, the heating unit is activated to perform continuous heating control of the metal billet, and the temperature monitoring device of the heating unit is activated to perform real-time temperature monitoring of the heating unit to obtain temperature monitoring data; when the temperature monitoring data meets the preset temperature range, the current temperature monitoring data is input into a temperature-pressure prediction model to perform pressure prediction and obtain predicted pressure data; if the predicted pressure data does not meet the preset pressure range, the temperature is continuously increased and heated until the predicted pressure data meets the preset pressure range. When the preset pressure range is met, the temperature monitoring data after heating is used as the initial temperature control parameter, and the current predicted pressure data is used as the initial pressure control parameter; based on the pressure-speed calibration table, the initial pressure control parameter is matched with the extrusion speed to obtain the initial speed control parameter; the target metal billet is transmitted to the extrusion unit, and extrusion control is performed based on the initial pressure control parameter and the initial speed control parameter, and the quality of the finished steel pipe is recorded as the preheating extrusion control result; with the goal of optimizing the quality of the finished steel pipe, the initial temperature control parameter, the initial pressure control parameter and the initial speed control parameter are optimized to obtain an optimized control parameter combination; the hot extrusion optimization of the target metal billet is performed with the optimized control parameter combination.

[0006] The second aspect disclosed in the present application provides a hot extrusion control system for a stainless steel pipe processing technology, the system is applied to a hot extrusion control device, the hot extrusion control device includes a heating unit and an extrusion unit, the system is used for the above-mentioned hot extrusion control method for a stainless steel pipe processing technology, the system includes: a temperature monitoring module, the temperature monitoring module is used to activate the heating unit to perform continuous heating control of the metal billet after the target metal billet is transferred to the heating unit, and activate the temperature monitoring device of the heating unit to perform real-time temperature monitoring of the heating unit to obtain temperature monitoring data; a pressure prediction module, the pressure prediction module is used to input the current temperature monitoring data into a temperature-pressure prediction model when the temperature monitoring data meets the preset temperature range, perform pressure prediction, and obtain predicted pressure data; an initial parameter acquisition module, the initial parameter acquisition module is used to continuously heat and heat if the predicted pressure data does not meet the preset pressure range until the predicted pressure data meets the preset pressure range. When the pressure data meets the preset pressure range, the temperature monitoring data after heating is used as the initial temperature control parameter, and the current predicted pressure data is used as the initial pressure control parameter; an extrusion speed matching module, the extrusion speed matching module is used to match the initial pressure control parameter with the extrusion speed based on the pressure-speed calibration table to obtain the initial speed control parameter; an extrusion control module, the extrusion control module is used to transfer the target metal billet to the extrusion unit, perform extrusion control based on the initial pressure control parameter and the initial speed control parameter, and record the quality of the finished steel pipe as the preheating extrusion control result; a parameter optimization module, the parameter optimization module is used to optimize the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter with the goal of optimizing the quality of the finished steel pipe to obtain an optimized control parameter combination; a hot extrusion optimization module, the hot extrusion optimization module is used to perform hot extrusion optimization of the target metal billet with the optimized control parameter combination.

[0007] According to a third aspect disclosed in the present application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any step of the first aspect disclosed in the present application is implemented.

[0008] The fourth aspect disclosed in the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the first aspect disclosed in the present application.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: By monitoring the temperature data in real time and inputting it into the temperature-pressure prediction model for prediction, the heating process can be controlled more accurately. By continuously heating the temperature until the predicted pressure meets the preset pressure range, and then using it as the initial pressure control parameter, the accuracy of pressure control during the extrusion process can be ensured; by matching the initial pressure control parameter with the speed based on the pressure-speed calibration table, the coordination of the extrusion speed and pressure can be ensured, thereby further improving the processing quality of the product; by optimizing the initial temperature control parameters, pressure control parameters and speed control parameters, and using the optimized parameter combination to optimize the hot extrusion of the metal billet, the production efficiency can be improved while improving the product quality. In summary, the hot extrusion control method for stainless steel pipe processing solves the problem of inaccurate temperature, pressure and speed control in traditional hot extrusion processing by accurately controlling parameters such as heating, pressure and speed, thereby improving the processing quality of stainless steel pipes.

[0010] 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

[0011] Figure 1 A schematic flow chart of a hot extrusion control method for a stainless steel pipe processing process provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a hot extrusion control system for a stainless steel pipe processing process provided in an embodiment of the present application; Figure 3 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application.

[0012] Explanation of reference numerals: temperature monitoring module 10 , pressure prediction module 20 , initial parameter acquisition module 30 , extrusion speed matching module 40 , extrusion control module 50 , parameter optimization module 60 , hot extrusion optimization module 70 . DETAILED DESCRIPTION

[0013] The embodiment of the present application provides a hot extrusion control method for stainless steel pipe processing technology, which solves the technical problems that traditional methods are not accurate enough in parameter control of hot extrusion, and parameter adjustment usually relies on past experience, resulting in poor processing quality and low production efficiency of stainless steel pipes.

[0014] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. 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.

[0015] like Figure 1 As shown, an embodiment of the present application provides a hot extrusion control method for a stainless steel pipe processing process, the method is applied to a hot extrusion control device, the hot extrusion control device includes a heating unit and an extrusion unit, the method includes: When the target metal blank is transferred to the heating unit, the heating unit is activated to perform continuous temperature rise heating control on the metal blank, and the temperature monitoring device of the heating unit is activated to perform real-time temperature monitoring on the heating unit to obtain temperature monitoring data; The hot extrusion control method for a stainless steel pipe processing process provided in an embodiment of the present application is applied to a hot extrusion control device, which includes a heating unit and an extrusion unit, wherein the heating unit includes a heating element for providing heat to heat up a metal blank, and also includes a temperature monitoring device for real-time monitoring of temperature changes during the heating process; the extrusion unit is used to extrude the heated metal blank, and extrude the metal blank into a desired shape, such as a steel pipe, by applying pressure.

[0016] First, the target metal blank is moved to the working area where the heating unit is located through a conveying device, such as a conveyor belt or a robotic arm. The heating element in the heating unit is started to provide heat to the metal blank, so that the metal blank is continuously heated at an appropriate rate. The temperature monitoring device of the heating unit is activated to start real-time monitoring of the temperature inside the heating unit. The temperature monitoring device can be a temperature sensor, a thermal imager, etc., which can accurately measure the temperature change in the heating unit. The temperature monitoring device continuously collects temperature data to obtain temperature monitoring data, that is, the real-time temperature information of the metal blank.

[0017] When the temperature monitoring data meets the preset temperature range, the current temperature monitoring data is input into the temperature-pressure prediction model to perform pressure prediction and obtain predicted pressure data; The preset temperature range is obtained by matching with the stainless steel pipe sample. The detailed matching method is described in detail in the subsequent steps. When the temperature monitoring data reaches the preset temperature range, it indicates that the metal billet has reached the expected heating temperature. At this time, these data are input into the temperature-pressure prediction model. The temperature-pressure prediction model is a mathematical model that models the relationship between temperature and pressure based on theoretical calculations. The temperature-pressure prediction model uses the input temperature monitoring data to predict the pressure according to the relationship defined in the model to determine the expected pressure value under given temperature conditions. The predicted pressure data is obtained through the calculation of the temperature-pressure prediction model, which represents the expected extrusion pressure that the metal billet should be subjected to at the current temperature.

[0018] If the predicted pressure data does not meet the preset pressure range, the temperature is continuously raised and heated until the predicted pressure data meets the preset pressure range, and the temperature monitoring data after heating is used as the initial temperature control parameter, and the current predicted pressure data is used as the initial pressure control parameter; The preset pressure range is set according to the actual situation, for example, based on the control range of the extrusion unit, the heating range of the metal billet, etc. After the temperature-pressure prediction model is calculated, if the predicted pressure data obtained fails to meet the preset pressure range requirements, the metal billet will continue to be heated. During the heating process, the heating unit will continue to provide heat to the metal billet to increase its temperature. During the continuous heating process, the predicted pressure data will be continuously monitored until it reaches the preset pressure range, then the heating will be stopped, and the temperature monitoring data at this time will be used as the initial temperature control parameter, and the current predicted pressure data will be used as the initial pressure control parameter.

[0019] Based on the pressure-speed calibration table, the initial pressure control parameter is matched with the extrusion speed to obtain the initial speed control parameter; The pressure-speed calibration table is a pre-established table that records the extrusion speeds corresponding to different pressures. This table is obtained based on experiments or theoretical analysis and is used to guide speed control during the extrusion process. According to the initial pressure control parameters, the extrusion speed that matches the initial pressure is found in the pressure-speed calibration table, thereby determining the initial speed control parameters, which represent the extrusion speed that should be taken under given initial pressure conditions.

[0020] The target metal blank is transferred to the extrusion unit, extrusion control is performed based on the initial pressure control parameter and the initial speed control parameter, and the quality of the finished steel pipe is recorded as a preheating extrusion control result; After the target metal billet is heated by the heating unit, it is transferred to the working area of ​​the extrusion unit. According to the determined initial pressure control parameters and initial speed control parameters, the metal billet begins to be extruded. The extrusion unit applies the corresponding pressure and controls the extrusion speed according to these parameter settings to extrude the metal billet into the shape of a steel pipe. After the extrusion is completed, the quality of the steel pipe is recorded, including the inspection of the size, shape, surface quality, etc. of the steel pipe. The quality of the finished steel pipe is obtained as the result of the preheating extrusion control.

[0021] With the goal of optimizing the quality of the finished steel pipe, the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter are optimized to obtain an optimized control parameter combination; Clearly define the optimization goal, that is, the quality of the finished steel pipe, including dimensional accuracy, surface quality, mechanical properties, etc. Select a suitable optimization algorithm to search for the optimal control parameter combination. Common optimization algorithms include genetic algorithm, particle swarm algorithm, simulated annealing algorithm, etc. Take the quality of the finished steel pipe as the optimization goal, and establish an optimization model. The model takes the initial temperature control parameters, pressure control parameters, and speed control parameters as inputs, and outputs quality-related evaluation indicators.

[0022] The optimization model is solved using the selected optimization algorithm to search for the optimal control parameter combination. During the optimization process, different parameter combinations will be continuously tried and their impact on the quality of the finished steel pipe will be evaluated. Each generated parameter combination will be evaluated and its impact on the quality of the finished steel pipe will be calculated. Based on the evaluation results, it is determined which parameter combinations can bring the best quality of the finished steel pipe. After the optimization algorithm search is completed, the optimal control parameter combination is obtained. This optimized control parameter combination is the optimization result of the initial temperature control parameter, pressure control parameter and speed control parameter.

[0023] The hot extrusion optimization of the target metal billet is performed using the optimized control parameter combination.

[0024] The control parameter combination obtained by the above optimization is applied to the hot extrusion control device, and these optimized control parameters include optimized temperature control parameters, pressure control parameters and speed control parameters. By using the optimized control parameter combination, the target metal billet is hot extruded. During the extrusion process, the heating unit is heated according to the optimized temperature control parameters, and the extrusion unit is extruded according to the optimized pressure and speed parameters, so as to achieve the best effect of the processing process and maximize the quality of the finished steel pipe.

[0025] Furthermore, obtaining the preset temperature range includes: According to the hot working record database of stainless steel pipes, multiple metal blank information and multiple hot working temperature sets of multiple stainless steel pipe samples are obtained; Establishing a mapping relationship between hot working temperature and melting failure index based on the plurality of metal blank information; Based on the mapping relationship between the thermal processing temperature and the melting failure index, the plurality of thermal processing temperature sets are analyzed to obtain a plurality of melting failure probability sets; Based on the preset melting failure probability, the plurality of melting failure probability sets are screened, and a plurality of screening hot working temperature sets are obtained according to the screening results; Based on the multiple screening hot working temperature sets, establishing multiple sample preset temperature intervals for the multiple stainless steel pipe samples; The target metal blank is matched with the plurality of metal blank information to obtain the preset temperature range of the current stainless steel pipe.

[0026] A hot working record database is established to store relevant data during the hot working of stainless steel pipes. This database includes processing parameters, process information, processing conditions and other data of different steel pipe samples. From the established hot working record database, metal blank information of multiple stainless steel pipe samples is retrieved and extracted. This metal blank information includes the material, size, chemical composition, physical properties and the like of the steel. Similarly, multiple hot working temperature sets of multiple stainless steel pipe samples are extracted from the hot working record database. These hot working temperature sets include the heating temperature during the heating process, which lays the foundation for subsequent analysis.

[0027] According to the hot working needs and process requirements, the heating temperature of each metal blank during the hot working process is determined, and the melting failure index is defined to describe the melting failure of the metal during the heating process. The melting failure index includes melting temperature, melting rate, thermal deformation rate, etc. Through statistical analysis, regression analysis and other methods, based on the collected metal blank information and the determined hot working temperature, a mapping relationship between hot working temperature and melting failure index is established. This mapping relationship is used to describe the probability of melting failure of the metal blank at a specific processing temperature.

[0028] Based on the established mapping relationship between hot working temperature and melting failure index, each hot working temperature is mapped to the corresponding melting failure index. According to the mapping relationship, the melting failure probability corresponding to each processing temperature can be calculated. The melting failure probabilities of each temperature point in each hot working temperature set obtained by analysis are summarized to obtain multiple melting failure probability sets. The elements in each set represent the melting failure probabilities under different hot working temperature conditions.

[0029] Determine a preset melting failure probability threshold, which is used to screen the hot processing temperature set. The preset melting failure probability can be set according to processing requirements, safety standards or quality requirements. Screen each melting failure probability set to find the temperature points that meet the preset melting failure probability requirements, that is, according to the preset melting failure probability threshold, screen out the temperature points below the probability threshold. According to the screening results, the temperature points that meet the preset melting failure probability requirements are combined into a new screening hot processing temperature set, which includes those processing temperature points whose predicted melting failure probability is lower than the preset threshold.

[0030] For each stainless steel pipe sample, a sample preset temperature interval is established according to its corresponding screening hot working temperature set. The sample preset temperature interval can be determined according to the lowest temperature and the highest temperature in the screening temperature set, that is, the preset temperature interval of each sample is the temperature range in its corresponding screening temperature set. The sample preset temperature interval of each stainless steel pipe sample is recorded to obtain multiple sample preset temperature intervals.

[0031] Obtain the metal blank information of the current stainless steel pipe, match the target metal blank information with the multiple metal blank information collected previously, and the matching process includes comparing the similarity and matching degree between the target metal blank and the existing information in the database. According to the matching results, find the metal blank information closest to the target metal blank, and use the preset temperature range of the selected matching metal blank as the preset temperature range of the current stainless steel pipe. This preset temperature range can be used as a reference during the processing to determine the heating temperature range to ensure the processing quality and stability of product performance.

[0032] Furthermore, constructing the temperature-pressure prediction model includes: Obtaining a temperature-flow stress variation curve when the heating unit heats the metal blank; Obtaining a rheological stress-pressure change curve when the extrusion unit extrude the metal blank; The temperature-rheological stress change curve and the rheological stress-pressure change curve are associated to establish the temperature-pressure prediction model.

[0033] Adopt the method of experiment or simulation, use the heating unit to heat the metal blank, keep the temperature control of the heating unit stable, and adjust the temperature according to the preset heating curve to simulate the heating situation in the actual production process, and measure the rheological stress data of the metal blank in real time. The rheological stress is the resistance of the material to deformation under stress, which can be measured by tensile testing machine, pressure sensor and other equipment. Record the real-time measured temperature and rheological stress data, and perform data analysis. According to the changes in temperature and rheological stress during the heating process, draw the temperature-rheological stress change curve with temperature as the horizontal axis and rheological stress as the vertical axis.

[0034] Similarly, the metal billet is extruded by an extrusion unit using an experimental or simulation method. During the extrusion process, the extrusion pressure data is measured in real time, and these data can be obtained through the pressure sensor on the extruder. The real-time measured rheological stress and extrusion pressure data are recorded and analyzed. According to the changes in rheological stress and pressure during the extrusion process, a rheological stress-pressure change curve is drawn with rheological stress as the horizontal axis and pressure as the vertical axis.

[0035] The collected temperature-rheological stress change curve and rheological stress-pressure change curve are preprocessed, including data cleaning, smoothing and normalization, to prepare the data for modeling. The temperature-rheological stress change curve and the rheological stress-pressure change curve are associated, which can be done by matching the data points of the two to find the corresponding pressure data points corresponding to the temperature and rheological stress.

[0036] According to the associated data points, regression analysis and other methods can be used to establish a temperature-pressure prediction model. In this model, temperature is used as the input variable, flow stress is used as the intermediate variable, and extrusion pressure is used as the output variable. Use the existing data set to train and verify the established prediction model by splitting the data set into a training set and a test set. Evaluate the established prediction model to check its prediction ability and generalization ability, and tune the model according to the evaluation results. After the model is established and tuned, it can be used in the actual production process. The model can predict the corresponding extrusion pressure based on the given temperature information to guide production operations and optimize production processes.

[0037] Furthermore, with the goal of optimizing the quality of the finished steel pipe, the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter are optimized to obtain an optimized control parameter combination, including: Based on the multiple melting failure probability sets, obtaining a current melting failure probability set of the stainless steel pipe; Performing a central tendency analysis on the melting failure probability set to obtain a representative melting failure probability; Calculating the difference between the representative melting failure probability and the preset melting failure probability to obtain a melting failure probability deviation value; Generate a target temperature control parameter with the goal of minimizing the melting failure probability deviation value; Based on the target temperature control parameter, in combination with the temperature-pressure prediction model and the pressure-speed calibration table, a target pressure control parameter and a target speed control parameter are obtained; An initial control parameter combination is constructed with the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter; a target control parameter combination is constructed with the target temperature control parameter, the target pressure control parameter, and the target speed control parameter; and control parameter optimization is performed based on the initial control parameter combination and the target control parameter combination to obtain the optimized control parameter combination.

[0038] The target metal blank information is matched with the multiple metal blank information collected previously, the metal blank information closest to the target metal blank is found, and the melting failure probability set corresponding to the metal blank is obtained as the melting failure probability set of the current stainless steel pipe.

[0039] Calculate the central tendency indicators of the melting failure probability set, such as the mean, median, mode, etc. These indicators can reflect the central position of the melting failure probability, that is, around which value it is concentrated, and obtain the representative melting failure probability after calculation.

[0040] The preset melting failure probability is a probability value set according to actual conditions or historical experience, which represents the normal condition of the target metal billet. The representative melting failure probability is subtracted from the preset melting failure probability to obtain the difference between the two, i.e., the melting failure probability deviation value. This difference can be positive or negative. A positive number indicates that the actual melting failure probability is higher than the preset value, and a negative number indicates that the actual melting failure probability is lower than the preset value.

[0041] The melting failure probability deviation value is used as the objective function to optimize the target parameters. The goal of the objective function is to make the melting failure probability deviation value as close to zero as possible, even if the actual melting failure probability is consistent with the expected value. Define the spatial range of temperature control parameters, including the temperature range, heating rate, etc. These parameters will be adjusted during the optimization process to minimize the melting failure probability deviation value. Select a suitable optimization algorithm, such as gradient descent, genetic algorithm, simulated annealing, etc. These algorithms can search in the parameter space to find the temperature control parameter combination that minimizes the objective function.

[0042] For a given combination of temperature control parameters, the corresponding melting failure probability deviation value is calculated, and the selected optimization algorithm is used to iteratively search in the parameter space to find the temperature control parameter combination that minimizes the objective function until the stopping condition is reached. The final target temperature control parameters are determined to minimize the melting failure probability.

[0043] The determined target temperature control parameters are input into the temperature-pressure prediction model to predict the target extrusion pressure under the temperature condition and obtain the target pressure control parameters. The predicted target pressure value is used as input to find the corresponding target speed control parameters in the pressure-speed calibration table and obtain the target speed control parameters.

[0044] The initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter are integrated to obtain an initial control parameter combination; and the target temperature control parameter, the target pressure control parameter, and the target speed control parameter are integrated to obtain a target control parameter combination.

[0045] Take a key performance indicator in the production process, such as product quality, energy consumption, production efficiency, etc., as the objective function. Select an appropriate optimization algorithm based on the actual situation. Commonly used algorithms include gradient descent, genetic algorithm, simulated annealing algorithm, etc. Define the search space and possible value range of the control parameters, and set constraints to ensure that the parameters change within a reasonable range. Set a stop condition, such as reaching the maximum number of iterations, the objective function converges to a pre-set threshold, etc. When the stop condition is met, the optimization algorithm will stop searching and return the optimal control parameter combination.

[0046] The selected optimization algorithm is used to search for the optimal control parameter combination in the defined parameter space. The algorithm continuously adjusts the parameter value according to the change of the objective function until the stop condition is reached. Finally, the optimal control parameter combination is output and the optimized control parameter combination is applied to actual production to improve the efficiency of the production process and product quality.

[0047] Furthermore, performing a central tendency analysis on the melting failure probability set to obtain a representative melting failure probability includes: Calculating the standard deviation of the melting failure probability set to obtain a melting failure probability deviation coefficient; When the melting failure probability deviation coefficient is greater than or equal to the preset deviation coefficient, performing mean calculation on the melting failure probability set to obtain a probability mean as the representative melting failure probability; When the melting failure probability deviation coefficient is less than a preset deviation coefficient, a mode analysis is performed on the melting failure probability set to obtain a probability mode as the representative melting failure probability.

[0048] The melting failure probability deviation coefficient is a statistic used to measure the degree of dispersion of the melting failure probability set. Specifically, all probability values ​​in the melting failure probability set are first added up, and then divided by the total number of probability values ​​to obtain the average value. Then, the deviation between each probability value and the average value is calculated, and then the squares of these deviations are summed, and finally divided by the total number of probability values ​​and the square root is taken to obtain the standard deviation. The calculation formula is as follows: ; in, is the standard deviation, is the i-th melting failure probability, n represents the total number of melting failure probability concentration probabilities, is the mean of the n melting failure probabilities.

[0049] Finally, the ratio of the standard deviation to the mean is calculated to obtain the deviation coefficient of the melting failure probability, which is expressed in the form of a percentage. By calculating the deviation coefficient of the melting failure probability set, the degree of discreteness of the probability set can be evaluated, and then the stability of the melting failure probability can be understood. If the deviation coefficient is small, it means that the probability value is relatively stable, otherwise it means that the probability value has large fluctuations.

[0050] When the melting failure probability deviation coefficient is greater than or equal to the preset deviation coefficient, it means that the discreteness of the melting failure probability is high, and the entire probability set cannot be directly used as the representative melting failure probability. Therefore, it is necessary to reduce the discreteness of the probability set to obtain a more stable representative melting failure probability. In this case, the mean of the melting failure probability set is calculated as the representative melting failure probability, that is, the fluctuation of each value in the probability set is offset by taking the average value, so as to obtain a more stable estimated value. Specifically, all probability values ​​in the melting failure probability set are added, and then divided by the total number of probability values ​​to obtain an average value, which is used as the representative melting failure probability.

[0051] When the melting failure probability deviation coefficient is less than the preset deviation coefficient, it means that the discrete degree of the melting failure probability is relatively small, and the mode of the probability set can be used as the representative melting failure probability. The mode refers to the value with the highest frequency in a set of data. Specifically, the melting failure probability set is analyzed to determine the probability value with the highest frequency. This value is the mode of the probability set, and the determined mode is used as the representative melting failure probability for subsequent analysis.

[0052] Furthermore, based on the initial control parameter combination and the target control parameter combination, performing control parameter optimization to obtain the optimized control parameter combination includes: Establishing a three-dimensional optimization space, wherein the three-dimensional optimization space has a preset temperature range and a preset pressure range as constraints; In the three-dimensional optimization space, the initial control parameter combination is mapped as the optimization starting point, and the target control parameter combination is mapped as the optimization direction, wherein the mapping formula is as follows: ; in, represents the i-th optimization item of the initial control parameter combination, is the jth optimization element of the i-th optimization item, i=1,2,3, j=1,2,3...,D, D is the number of j-th optimization elements, represents the lower bound of the jth optimized element, represents the upper bound of the jth optimized element, and U is a uniformly distributed random function; Between the optimization starting point and the optimization direction, the control parameters are mutated to obtain a mutated control parameter combination, wherein the mutation formula is as follows: ; Among them, M is the combination of variation control parameters, is the target control parameter combination; Randomly exchange the elements in the target control parameter combination and the elements of the same optimization item in the variation control parameter combination to obtain a first test control parameter combination; The initial control parameter combination is replaced and updated with the first experimental control parameter combination, and iterative optimization is performed to obtain the optimized control parameter combination.

[0053] According to the previous steps, the preset temperature range and the preset pressure range are obtained, and the temperature control parameters, pressure control parameters, and speed control parameters are used as the three axes of the three-dimensional coordinate system. The preset temperature range and the preset pressure range are mapped to the three-dimensional coordinate system, wherein the extrusion speed changes with the pressure, and after mapping, a cuboid is formed, which is the three-dimensional optimization space. In this way, the control parameter combination in the optimization process can be ensured to be within the preset temperature range and the preset pressure range, so as to ensure the safety and stability of the production process.

[0054] The initial control parameter combination is mapped to the corresponding coordinate axis of the three-dimensional optimization space to obtain a coordinate point as the optimization starting point; the target control parameter combination is mapped to the corresponding coordinate axis of the three-dimensional optimization space to obtain another coordinate point as the optimization direction, wherein the mapping formula is as follows: ; in, represents the i-th optimization item of the initial control parameter combination, and the value range of i is 1 to, It can represent temperature control parameters, pressure control parameters and speed control parameters; represents the jth optimization element in the i-th optimization item in the control parameter combination, where the value range of j starts from 1 and goes up to D, where D is the number of jth optimization elements. is the lower bound of the jth optimization element, i.e., the minimum acceptable value of the control parameter. represents the upper bound of the jth optimization element, that is, the maximum acceptable value of the control parameter, Indicates that the upper bound and the lower bound A value is randomly generated according to a uniform distribution.

[0055] Through this formula, an initial control parameter combination can be randomly generated as the starting point for optimization. At the same time, a target control parameter combination can be determined as the direction of optimization. This method can be explored in multiple dimensions to find the optimal control parameter combination.

[0056] Between the optimization starting point and the optimization direction, the control parameters are mutated to obtain a mutated control parameter combination, wherein the mutation formula is as follows: ; Among them, P represents the optimization starting point, that is, the initial control parameter combination, represents the jth optimization element in the i-th optimization item in the target control parameter combination, that is, the optimization direction, It represents a random number uniformly generated in the range [0,1]. The 3 in the formula is a coefficient used to control the degree of variation.

[0057] Through this mutation process, a certain amount of randomness can be introduced into the optimization process, so as to better explore the search space and find a better combination of control parameters.

[0058] In the target control parameter combination and the variation control parameter combination, for the same optimization item, an element is randomly selected for exchange, so that the exchange can be performed within the same optimization item, avoiding interference between different optimization items. The selected element in the target control parameter combination is exchanged with the element of the same optimization item in the variation control parameter combination. After the exchange is completed, the new control parameter combination obtained is the first experimental control parameter combination.

[0059] Through this process, a certain degree of randomness can be introduced on the basis of retaining the target control parameter combination and the variation control parameter combination to generate the control parameter combination for the first trial, so as to further explore the search space and find a better control parameter combination.

[0060] The first test control parameter combination and the initial control parameter combination are compared, and according to the performance of the two, the first test control parameter combination replaces the initial control parameter combination as a new initial control parameter combination, or the initial control parameter combination is retained.

[0061] Repeat the above steps for multiple iterations. After each iteration, if the pre-set stop condition is reached, such as reaching the maximum number of iterations, converging to a certain threshold, etc., the iteration is stopped. Otherwise, the next iteration is continued. Finally, the optimal control parameter combination is selected as the optimization result.

[0062] Furthermore, replacing and updating the initial control parameter combination with the first test control parameter combination, performing iterative optimization, and obtaining the optimized control parameter combination includes: Based on the hot extrusion simulation model, the first test control parameter combination is simulated, and the simulation result is evaluated based on the hot extrusion quality evaluation index to obtain the first test parameter quality evaluation value; Based on the hot extrusion simulation model, the initial control parameter combination is simulated, and the simulation result is evaluated based on the hot extrusion quality evaluation index to obtain the initial parameter quality evaluation value; If the first test parameter quality evaluation value is less than the initial parameter quality evaluation value, continue to use the initial control parameter combination as the starting point for optimization; If the first test parameter quality evaluation value is greater than or equal to the initial parameter quality evaluation value, the first test control parameter combination is used to replace the initial control parameter combination as the starting point for updating and optimizing; Iterate the optimization until the convergence condition is reached and obtain the final optimized control parameter combination.

[0063] Use simulation software to establish a simulation model of the hot extrusion process, which can accurately simulate the changes in key parameters such as temperature, pressure, and deformation during the hot extrusion process. The first experimental control parameter combination is used as the input parameter of the simulation model, and the simulation is run in the established hot extrusion simulation model to simulate the heating, extrusion, and deformation of the metal billet during the hot extrusion process.

[0064] The simulation results are evaluated according to the hot extrusion quality evaluation indicators, which include indicators for the quality of steel pipe finished products, melting failure probability, mechanical properties, etc. The evaluation results of each evaluation indicator are weighted and summed to obtain the quality evaluation value of the first test parameter, which is used to measure the quality of the first test control parameter combination.

[0065] In the same way, in the established hot extrusion simulation model, the initial control parameter combination is used to run the simulation, and the hot extrusion quality evaluation index is used to evaluate the simulation results to obtain the initial parameter quality evaluation value.

[0066] Compare the first test parameter quality evaluation value with the initial parameter quality evaluation value. If the first test parameter quality evaluation value is less than the initial parameter quality evaluation value, it means that the quality of the initial control parameter combination is better. In this case, continue to use the initial control parameter combination as the starting point for optimization, re-execute the control parameter optimization process, and try to find a better control parameter combination.

[0067] If the first test parameter quality evaluation value is greater than or equal to the initial parameter quality evaluation value, it means that the quality of the first test parameter combination is better. The first test control parameter combination is then used to replace the initial control parameter combination as the updated optimization starting point, so as to continue to look for a better control parameter combination in the next round of optimization.

[0068] Set the termination condition of the iteration, such as setting the maximum number of iterations or defining the threshold of the control parameter change as the convergence condition, and start from the updated optimization starting point to execute the iterative process of control parameter optimization. In each iteration, perform simulation according to the current control parameter combination and evaluate the quality of the simulation results. According to the evaluation results, adjust the control parameter combination and generate a new test parameter combination to determine whether the convergence condition is met. If so, stop the iteration, otherwise continue to the next round of iteration. When the convergence condition is met, the iterative process stops, and the control parameter combination obtained at this time is the final optimized control parameter combination.

[0069] Through the iterative optimization process, the control parameter combination is continuously adjusted and its effect is evaluated until the convergence condition is met. The optimal control parameter combination can be obtained, thereby achieving the optimization of the hot extrusion process and the best quality production.

[0070] In summary, the hot extrusion control method for stainless steel pipe processing provided in the embodiment of the present application has the following technical effects: 1. By monitoring the temperature data in real time and inputting it into the temperature-pressure prediction model for prediction, the heating process can be controlled more accurately. By continuously heating the temperature until the predicted pressure meets the preset pressure range, it is used as the initial pressure control parameter to ensure the accuracy of pressure control during the extrusion process. 2. By matching the initial pressure control parameters based on the pressure-speed calibration table, the coordination of extrusion speed and pressure can be ensured, thereby further improving the processing quality of the product; 3. By optimizing the initial temperature control parameters, pressure control parameters and speed control parameters, and using the optimized parameter combination to optimize the hot extrusion of metal billets, the production efficiency can be improved while improving product quality.

[0071] In summary, the hot extrusion control method for stainless steel pipe processing solves the problem of inaccurate temperature, pressure and speed control in traditional hot extrusion processing by accurately controlling parameters such as heating, pressure and speed, thereby improving the processing quality of stainless steel pipes.

[0072] Based on the same inventive concept as the hot extrusion control method for stainless steel pipe processing in the above-mentioned embodiment, Figure 2As shown, the present application provides a hot extrusion control system for a stainless steel pipe processing process, the system is applied to a hot extrusion control device, the hot extrusion control device includes a heating unit and an extrusion unit, and the system includes: A temperature monitoring module 10, wherein the temperature monitoring module 10 is used to activate the heating unit to perform continuous heating control of the metal blank after the target metal blank is transferred to the heating unit, and to activate the temperature monitoring device of the heating unit to perform real-time temperature monitoring of the heating unit to obtain temperature monitoring data; A pressure prediction module 20, wherein when the temperature monitoring data meets a preset temperature range, the pressure prediction module 20 is used to input the current temperature monitoring data into a temperature-pressure prediction model to perform pressure prediction and obtain predicted pressure data; An initial parameter acquisition module 30, wherein if the predicted pressure data does not satisfy the preset pressure interval, the initial parameter acquisition module 30 is used to continuously increase the temperature and heat until the predicted pressure data satisfies the preset pressure interval, and then use the temperature monitoring data after heating as the initial temperature control parameter and the current predicted pressure data as the initial pressure control parameter; An extrusion speed matching module 40, the extrusion speed matching module 40 is used to perform extrusion speed matching on the initial pressure control parameter based on a pressure-speed calibration table to obtain an initial speed control parameter; An extrusion control module 50, the extrusion control module 50 is used to transfer the target metal billet to the extrusion unit, perform extrusion control based on the initial pressure control parameter and the initial speed control parameter, and record the quality of the finished steel pipe as a preheating extrusion control result; A parameter optimization module 60, wherein the parameter optimization module 60 is used to optimize the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter to obtain an optimized control parameter combination with the goal of optimizing the quality of the finished steel pipe; The hot extrusion optimization module 70 is used to perform hot extrusion optimization of the target metal billet using the optimization control parameter combination.

[0073] Furthermore, the system further includes a preset temperature range acquisition module to perform the following operation steps: According to the hot working record database of stainless steel pipes, multiple metal blank information and multiple hot working temperature sets of multiple stainless steel pipe samples are obtained; Establishing a mapping relationship between hot working temperature and melting failure index based on the plurality of metal blank information; Based on the mapping relationship between the thermal processing temperature and the melting failure index, the plurality of thermal processing temperature sets are analyzed to obtain a plurality of melting failure probability sets; Based on the preset melting failure probability, the plurality of melting failure probability sets are screened, and a plurality of screening hot working temperature sets are obtained according to the screening results; Based on the multiple screening hot working temperature sets, establishing multiple sample preset temperature intervals for the multiple stainless steel pipe samples; The target metal blank is matched with the plurality of metal blank information to obtain the preset temperature range of the current stainless steel pipe.

[0074] Furthermore, the system also includes a model building module to perform the following steps: Obtaining a temperature-flow stress variation curve when the heating unit heats the metal blank; Obtaining a rheological stress-pressure change curve when the extrusion unit extrude the metal blank; The temperature-rheological stress change curve and the rheological stress-pressure change curve are associated to establish the temperature-pressure prediction model.

[0075] Furthermore, the system further includes an optimization control parameter combination acquisition module to perform the following operation steps: Based on the multiple melting failure probability sets, obtaining a current melting failure probability set of the stainless steel pipe; Performing a central tendency analysis on the melting failure probability set to obtain a representative melting failure probability; Calculating the difference between the representative melting failure probability and the preset melting failure probability to obtain a melting failure probability deviation value; Generate a target temperature control parameter with the goal of minimizing the melting failure probability deviation value; Based on the target temperature control parameter, in combination with the temperature-pressure prediction model and the pressure-speed calibration table, a target pressure control parameter and a target speed control parameter are obtained; An initial control parameter combination is constructed with the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter; a target control parameter combination is constructed with the target temperature control parameter, the target pressure control parameter, and the target speed control parameter; and control parameter optimization is performed based on the initial control parameter combination and the target control parameter combination to obtain the optimized control parameter combination.

[0076] Furthermore, the system further includes a failure probability acquisition module to perform the following operation steps: Calculating the standard deviation of the melting failure probability set to obtain a melting failure probability deviation coefficient; When the melting failure probability deviation coefficient is greater than or equal to the preset deviation coefficient, performing mean calculation on the melting failure probability set to obtain a probability mean as the representative melting failure probability; When the melting failure probability deviation coefficient is less than a preset deviation coefficient, a mode analysis is performed on the melting failure probability set to obtain a probability mode as the representative melting failure probability.

[0077] Furthermore, the system further includes an iterative optimization module to perform the following operation steps: Establishing a three-dimensional optimization space, wherein the three-dimensional optimization space has a preset temperature range and a preset pressure range as constraints; In the three-dimensional optimization space, the initial control parameter combination is mapped as the optimization starting point, and the target control parameter combination is mapped as the optimization direction, wherein the mapping formula is as follows: ; in, represents the i-th optimization item of the initial control parameter combination, is the jth optimization element of the i-th optimization item, i=1,2,3, j=1,2,3...,D, D is the number of j-th optimization elements, represents the lower bound of the jth optimized element, represents the upper bound of the jth optimized element, and U is a uniformly distributed random function; Between the optimization starting point and the optimization direction, the control parameters are mutated to obtain a mutated control parameter combination, wherein the mutation formula is as follows: ; Among them, M is the combination of variation control parameters, is the target control parameter combination; Randomly exchange the elements in the target control parameter combination and the elements of the same optimization item in the variation control parameter combination to obtain a first test control parameter combination; The initial control parameter combination is replaced and updated with the first experimental control parameter combination, and iterative optimization is performed to obtain the optimized control parameter combination.

[0078] Furthermore, the system also includes an optimization module to perform the following operation steps: Based on the hot extrusion simulation model, the first test control parameter combination is simulated, and the simulation result is evaluated based on the hot extrusion quality evaluation index to obtain the first test parameter quality evaluation value; Based on the hot extrusion simulation model, the initial control parameter combination is simulated, and the simulation result is evaluated based on the hot extrusion quality evaluation index to obtain the initial parameter quality evaluation value; If the first test parameter quality evaluation value is less than the initial parameter quality evaluation value, continue to use the initial control parameter combination as the starting point for optimization; If the first test parameter quality evaluation value is greater than or equal to the initial parameter quality evaluation value, the first test control parameter combination is used to replace the initial control parameter combination as the starting point for updating and optimizing; Iterate the optimization until the convergence condition is reached and obtain the final optimized control parameter combination.

[0079] Through the above detailed description of the hot extrusion control method for stainless steel pipe processing technology, those skilled in the art can clearly understand the hot extrusion control system for stainless steel pipe processing technology in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0080] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the processor of the computer device is used to provide computing and control capabilities; the memory of the computer device includes a non-volatile storage medium and an internal memory, wherein the non-volatile storage medium stores an operating system, a computer program and a database, and the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium; the network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a hot extrusion control method for a stainless steel pipe processing process.

[0081] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[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 description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A hot extrusion control method for stainless steel pipe processing technology, characterized in that: The method is applied to a hot extrusion control device, the hot extrusion control device includes a heating unit and an extrusion unit, and the method includes: When the target metal blank is transferred to the heating unit, the heating unit is activated to perform continuous temperature rise heating control on the metal blank, and the temperature monitoring device of the heating unit is activated to perform real-time temperature monitoring on the heating unit to obtain temperature monitoring data; When the temperature monitoring data meets the preset temperature range, the current temperature monitoring data is input into the temperature-pressure prediction model to perform pressure prediction and obtain predicted pressure data; If the predicted pressure data does not meet the preset pressure range, the temperature is continuously raised and heated until the predicted pressure data meets the preset pressure range, and the temperature monitoring data after heating is used as the initial temperature control parameter, and the current predicted pressure data is used as the initial pressure control parameter; Based on the pressure-speed calibration table, the initial pressure control parameter is matched with the extrusion speed to obtain the initial speed control parameter; The target metal blank is transferred to the extrusion unit, extrusion control is performed based on the initial pressure control parameter and the initial speed control parameter, and the quality of the finished steel pipe is recorded as a preheating extrusion control result; With the goal of optimizing the quality of the finished steel pipe, the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter are optimized to obtain an optimized control parameter combination; The hot extrusion optimization of the target metal billet is performed using the optimized control parameter combination.

2. The method according to claim 1, characterized in that Obtaining the preset temperature range includes: According to the hot working record database of stainless steel pipes, multiple metal blank information and multiple hot working temperature sets of multiple stainless steel pipe samples are obtained; Establishing a mapping relationship between hot working temperature and melting failure index based on the plurality of metal blank information; Based on the mapping relationship between the thermal processing temperature and the melting failure index, the plurality of thermal processing temperature sets are analyzed to obtain a plurality of melting failure probability sets; Based on the preset melting failure probability, the plurality of melting failure probability sets are screened, and a plurality of screening hot working temperature sets are obtained according to the screening results; Based on the multiple screening hot working temperature sets, establishing multiple sample preset temperature intervals for the multiple stainless steel pipe samples; The target metal blank is matched with the plurality of metal blank information to obtain the preset temperature range of the current stainless steel pipe.

3. The method according to claim 1, characterized in that Constructing the temperature-pressure prediction model includes: Obtaining a temperature-flow stress variation curve when the heating unit heats the metal blank; Obtaining a rheological stress-pressure change curve when the extrusion unit extrude the metal blank; The temperature-rheological stress change curve and the rheological stress-pressure change curve are associated to establish the temperature-pressure prediction model.

4. The method according to claim 2, characterized in that With the goal of optimizing the quality of the finished steel pipe, the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter are optimized to obtain an optimized control parameter combination, including: Based on the multiple melting failure probability sets, obtaining a current melting failure probability set of the stainless steel pipe; Performing a central tendency analysis on the melting failure probability set to obtain a representative melting failure probability; Calculating the difference between the representative melting failure probability and the preset melting failure probability to obtain a melting failure probability deviation value; Generate a target temperature control parameter with the goal of minimizing the melting failure probability deviation value; Based on the target temperature control parameter, in combination with the temperature-pressure prediction model and the pressure-speed calibration table, a target pressure control parameter and a target speed control parameter are obtained; An initial control parameter combination is constructed with the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter; a target control parameter combination is constructed with the target temperature control parameter, the target pressure control parameter, and the target speed control parameter; and control parameter optimization is performed based on the initial control parameter combination and the target control parameter combination to obtain the optimized control parameter combination.

5. The method according to claim 4, characterized in that Performing a central tendency analysis on the melting failure probability set to obtain a representative melting failure probability includes: Calculating the standard deviation of the melting failure probability set to obtain a melting failure probability deviation coefficient; When the melting failure probability deviation coefficient is greater than or equal to the preset deviation coefficient, performing mean calculation on the melting failure probability set to obtain a probability mean as the representative melting failure probability; When the melting failure probability deviation coefficient is less than a preset deviation coefficient, a mode analysis is performed on the melting failure probability set to obtain a probability mode as the representative melting failure probability.

6. The method according to claim 4, characterized in that Based on the initial control parameter combination and the target control parameter combination, performing control parameter optimization to obtain the optimized control parameter combination includes: Establishing a three-dimensional optimization space, wherein the three-dimensional optimization space has a preset temperature range and a preset pressure range as constraints; In the three-dimensional optimization space, the initial control parameter combination is mapped as the optimization starting point, and the target control parameter combination is mapped as the optimization direction, wherein the mapping formula is as follows: ； in, represents the i-th optimization item of the initial control parameter combination, is the jth optimization element of the i-th optimization item, i=1,2,3, j=1,2,3...,D, D is the number of j-th optimization elements, represents the lower bound of the jth optimized element, represents the upper bound of the jth optimized element, and U is a uniformly distributed random function; Between the optimization starting point and the optimization direction, the control parameters are mutated to obtain a mutated control parameter combination, wherein the mutation formula is as follows: ； Among them, M is the combination of variation control parameters, Target control parameter combination; Randomly exchange the elements in the target control parameter combination and the elements of the same optimization item in the variation control parameter combination to obtain a first test control parameter combination; The initial control parameter combination is replaced and updated with the first experimental control parameter combination, and iterative optimization is performed to obtain the optimized control parameter combination.

7. The method according to claim 6, characterized in that The initial control parameter combination is replaced and updated with the first test control parameter combination, and iterative optimization is performed to obtain the optimized control parameter combination, including: Based on the hot extrusion simulation model, the first test control parameter combination is simulated, and the simulation result is evaluated based on the hot extrusion quality evaluation index to obtain the first test parameter quality evaluation value; Based on the hot extrusion simulation model, the initial control parameter combination is simulated, and the simulation result is evaluated based on the hot extrusion quality evaluation index to obtain the initial parameter quality evaluation value; If the first test parameter quality evaluation value is less than the initial parameter quality evaluation value, continue to use the initial control parameter combination as the starting point for optimization; If the first test parameter quality evaluation value is greater than or equal to the initial parameter quality evaluation value, the first test control parameter combination is used to replace the initial control parameter combination as the starting point for updating and optimizing; Iterate the optimization until the convergence condition is reached and obtain the final optimized control parameter combination.

8. A hot extrusion control system for stainless steel pipe processing technology, characterized in that: The system is applied to a hot extrusion control device, the hot extrusion control device includes a heating unit and an extrusion unit, the system is used to implement the hot extrusion control method for a stainless steel pipe processing process according to any one of claims 1 to 7, and the system includes: A temperature monitoring module, wherein the temperature monitoring module is used to activate the heating unit to perform continuous heating control of the metal blank after the target metal blank is transferred to the heating unit, and activate the temperature monitoring device of the heating unit to perform real-time temperature monitoring of the heating unit to obtain temperature monitoring data; A pressure prediction module, wherein when the temperature monitoring data meets a preset temperature range, the pressure prediction module inputs the current temperature monitoring data into a temperature-pressure prediction model to perform pressure prediction and obtain predicted pressure data; An initial parameter acquisition module, wherein if the predicted pressure data does not satisfy the preset pressure interval, the initial parameter acquisition module is used to continuously increase the temperature and heat until the predicted pressure data satisfies the preset pressure interval, and the temperature monitoring data after heating is used as the initial temperature control parameter and the current predicted pressure data is used as the initial pressure control parameter; An extrusion speed matching module, the extrusion speed matching module is used to perform extrusion speed matching on the initial pressure control parameter based on a pressure-speed calibration table to obtain an initial speed control parameter; An extrusion control module, the extrusion control module is used to transfer the target metal billet to the extrusion unit, perform extrusion control based on the initial pressure control parameter and the initial speed control parameter, and record the quality of the finished steel pipe as a preheating extrusion control result; A parameter optimization module, wherein the parameter optimization module is used to optimize the initial temperature control parameter, the initial pressure control parameter, and the initial speed control parameter with the goal of optimizing the quality of the finished steel pipe to obtain an optimized control parameter combination; A hot extrusion optimization module is used to perform hot extrusion optimization of the target metal billet using the optimization control parameter combination.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the hot extrusion control method for a stainless steel pipe processing process according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the hot extrusion control method for a stainless steel pipe processing process according to any one of claims 1 to 7 are implemented.

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