Optimal control method of foaming of automobile control device based on multivariate temperature coupling analysis

Abstract

The present application provides a foaming optimization control method for an automobile control device based on multivariate temperature coupling analysis, which relates to the field of manufacturing process control technology, including: based on the support frame attribute characteristics and material component ratio, according to the predetermined foaming processing stage and the foaming evaluation function, in the simulated foaming space, the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter are simulated and optimized to generate the optimal temperature control strategy; according to the predetermined foaming processing stage and the optimal temperature control strategy, the foaming control of the target steering wheel is executed. This application can solve the technical problems that the traditional foaming process focuses on the control of a single temperature, cannot effectively consider the multiple effects of different temperature changes on the foaming reaction, and easily leads to uneven foaming, unstable foam structure, etc.; by accurately controlling the four temperature control parameters, the mutual influence and coupling relationship between the temperatures can be fully considered to ensure that the temperature distribution of the foaming process is uniform and the foam structure is stable.

Description

Technical Field

[0001] The present application relates to the technical field of manufacturing process control, and in particular to a foaming optimization control method for an automobile control device based on multivariate temperature coupling analysis. Background Art

[0002] In the production process of automobile steering wheels, the foaming process is a crucial step. This process forms a uniform foam structure on the surface and inner cavity of the steering wheel through the reaction of the foaming agent and the base material. This not only affects the weight and comfort of the steering wheel, but also determines its safety performance and durability.

[0003] The traditional automobile steering wheel foaming process usually focuses on the control of a single temperature parameter, such as only paying attention to the heating temperature during the foaming process. Although this method can ensure the smooth progress of the foaming reaction to a certain extent, with the development of technology, more and more manufacturers have found that single temperature control can no longer meet the increasingly complex quality requirements in the foaming process. Summary of the invention

[0004] The purpose of this application is to provide a foaming optimization control method for an automobile control device based on multivariate temperature coupling analysis, in order to solve the technical problems that the traditional automobile steering wheel foaming process focuses more on the control of a single temperature and cannot effectively consider the multiple effects of different temperature changes on the foaming reaction, which easily leads to uneven foaming, unstable foam structure, etc.

[0005] In view of the above problems, the present application provides a foaming optimization control method for an automobile control device based on multivariate temperature coupling analysis, including: configuring a first temperature control parameter, a second temperature control parameter, a third temperature control parameter and a fourth temperature control parameter; obtaining the support frame property characteristics of the target steering wheel, and the material component ratio of the foaming liquid; combining the support frame property characteristics and the material component ratio, according to a predetermined foaming processing stage and a foaming evaluation function, in a simulated foaming space, simulating and optimizing the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter to generate an optimal temperature control strategy; according to the predetermined foaming processing stage and the optimal temperature control strategy, executing the foaming control of the target steering wheel.

[0006] The technical solution provided in this application has at least the following technical effects or advantages:

[0007] By configuring the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter; obtaining the support frame property characteristics of the target steering wheel and the material component ratio of the foaming liquid; combining the support frame property characteristics and the material component ratio, according to the predetermined foaming processing stage and the foaming evaluation function, in the simulated foaming space, the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter are simulated and optimized to generate an optimal temperature control strategy; according to the predetermined foaming processing stage and the optimal temperature control strategy, the foaming control of the target steering wheel is executed; that is, by simultaneously controlling the four temperature control parameters of ambient temperature, equipment preheating temperature, workpiece preheating temperature and foaming process heating temperature, the mutual influence and coupling relationship between temperatures can be fully considered to ensure uniform temperature distribution and stable foam structure in the foaming process, and ultimately improve the overall foaming effect of the steering wheel.

[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 according to the contents of the specification, and 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 specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 This is a flow chart of a foaming optimization control method for an automobile control device based on multivariate temperature coupling analysis in this application;

[0011] Figure 2 This is a flow chart of generating an optimal temperature control strategy in a foaming optimization control method for an automobile control device based on multivariate temperature coupling analysis in the present application. DETAILED DESCRIPTION

[0012] This application provides a foaming optimization control method for automobile control devices based on multivariate temperature coupling analysis, which solves the problem that the traditional automobile steering wheel foaming process focuses on the control of a single temperature and cannot effectively consider the multiple effects of different temperature changes on the foaming reaction, which easily leads to technical problems such as uneven foaming and unstable foam structure. By simultaneously controlling the four temperature control parameters of ambient temperature, equipment preheating temperature, processed product preheating temperature and foaming process heating temperature, the mutual influence and coupling relationship between temperatures can be fully considered to ensure uniform temperature distribution and stable foam structure during the foaming process, and ultimately improve the overall foaming effect of the steering wheel.

[0013] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0014] For examples, please see the attached Figure 1 The present application provides a foaming optimization control method for an automobile control device based on multivariate temperature coupling analysis, which specifically includes the following steps:

[0015] S100: configuring a first temperature control parameter, a second temperature control parameter, a third temperature control parameter, and a fourth temperature control parameter.

[0016] Furthermore, step S100 of the present application also includes:

[0017] S110: The first temperature control parameter is the ambient temperature, the second temperature control parameter is the support frame preheating temperature, the third temperature control parameter is the mold preheating temperature, and the fourth temperature control parameter is the heating temperature in the foaming process.

[0018] Specifically, first, the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter are configured. The first temperature control parameter is the ambient temperature, wherein the ambient temperature is one of the external factors affecting the entire foaming process. Changes in ambient temperature not only affect the heating effect of the equipment, but also directly affect the initial temperature of the mold, the support frame and the foaming liquid, thereby affecting the stability of the foaming reaction. During the foaming process, fluctuations in external temperature may lead to an uneven reaction rate of the foaming agent, resulting in an unstable foam structure. Therefore, precise control of ambient temperature is crucial to optimizing the foaming process.

[0019] The second temperature control parameter is the preheating temperature of the support frame. The support frame is a key part that supports the shape of the steering wheel during the foaming process. Its temperature state directly affects the expansion and molding of the foaming liquid. Since the support frame is in contact with the foaming liquid, its surface temperature has a greater impact on the heat conduction of the foaming process. Too high or too low support frame temperature will affect the expansion of the foam, resulting in uneven foaming and even defects. By preheating the support frame, the negative impact of temperature deviation on the foam structure can be effectively avoided, thereby improving the foaming quality.

[0020] The third temperature control parameter is the mold preheating temperature, where the mold preheating temperature is another important factor in controlling the expansion process of the foaming liquid. The temperature change of the mold will affect the heat conduction efficiency between the foaming liquid and the mold. Too low a temperature may cause the foaming liquid to expand too quickly, resulting in uneven bubbles, while too high a temperature may cause poor foam fluidity and affect the stability of the foam. By accurately controlling the mold preheating temperature, it can be ensured that the foaming liquid can expand evenly in the mold, thereby obtaining an ideal foam structure and quality.

[0021] The fourth temperature control parameter is the heating temperature in the foaming process. In the foaming process, the heating temperature is the core temperature control parameter of the foaming reaction. The expansion of the foaming agent, the reaction speed and the stability of the foam are closely related to the heating temperature. In the heating process, if the temperature control is not accurate, the foaming agent may react too fast or too slow, thereby affecting the quality and uniformity of the foam. By accurately controlling the heating temperature in the foaming process, it can be ensured that the foaming process is carried out within the optimal temperature range, and finally a stable and uniform foam structure is obtained.

[0022] The interaction and coupling relationship between these four temperature control parameters in the foaming process are very complex. Each temperature control parameter not only affects the foaming process independently, but also interacts with each other. For example, fluctuations in ambient temperature may affect the preheating effect of the support frame and the mold, and the adjustment of the heating temperature may affect the expansion rate of the foaming liquid. In order to cope with these complex temperature changes, traditional temperature control methods often cannot meet the quality consistency and precision requirements in the foaming process. Therefore, the optimization control method based on multivariate temperature coupling analysis globally optimizes these four temperature control parameters to ensure that the temperature changes in each link are within the optimal range, maximize the foaming effect, and reduce quality fluctuations and production defects. Through the reasonable configuration and control of these four temperature control parameters, the temperature changes in the foaming process can be adjusted more accurately, avoiding problems such as uneven foaming and unstable foam structure caused by inaccurate temperature control in traditional methods, and improving the foaming quality and overall performance of the steering wheel.

[0023] S200: Acquire the property characteristics of the support frame of the target steering wheel and the material composition ratio of the foaming liquid.

[0024] Furthermore, step S200 of the present application also includes:

[0025] S210: The support frame property characteristics include structural characteristics, dimensional parameters and heat transfer characteristics, and the material composition ratio includes raw material type and raw material ratio.

[0026] Specifically, the support frame property characteristics of the target steering wheel and the material composition ratio of the foaming liquid are obtained, wherein, as shown in Table 1, the support frame property characteristics include structural characteristics, dimensional parameters and heat transfer characteristics. The structural characteristics of the support frame refer to the geometric shape, surface characteristics and pore structure of the support frame, etc. These structural characteristics determine the contact area between the support frame and the foaming liquid and the heat conduction path. For example, if the surface of the support frame is smooth, the heat transfer may be relatively uniform; if the support frame has a porous structure or a complex geometric shape, the heat transfer may vary greatly; the size of the support frame (such as thickness, length, width, etc.) is directly The thermal conductivity of the support frame determines the efficiency of heat transfer between the support frame and the foaming liquid. The material of the support frame (such as metal, plastic, etc.) affects its thermal conductivity, thereby affecting the speed of heat transfer. The higher the thermal conductivity of the support frame material, the faster the heat transfer rate and the more uniform the temperature change. Conversely, materials with lower thermal conductivity will lead to uneven temperature and affect the foaming process.

[0027]

[0028] Table 1

[0029] The material composition ratio includes the raw material type and the raw material ratio. As shown in Table 2, the foaming liquid is usually composed of a variety of chemical raw materials, mainly including foaming agents, resins, hardeners, plasticizers, etc. The thermal stability, reaction rate and expansion characteristics of each raw material are different, which determine the overall performance of the foaming liquid. For example, the type of foaming agent (such as chemical foaming agent, physical foaming agent, etc.) directly affects the density and structure of the foam; the type of resin affects the viscosity and fluidity of the foaming liquid, thereby affecting the uniformity and stability of the foam; the type and ratio of the hardener will affect the curing rate and strength after foaming, and then affect the hardness and durability of the final product. Among them, the raw material ratio of the foaming liquid (such as the proportion of foaming agent, the proportion of resin, the proportion of hardener, etc.) has a significant effect on the foaming effect. Different raw material ratios will lead to different foaming reaction behaviors, affecting the size, uniformity and stability of the foam. For example, too high a proportion of foaming agent may cause the foam to expand too quickly, resulting in an unstable foam structure; and too high a proportion of resin may cause the foam density to be too large, affecting the foaming effect.

[0030]

[0031] Table 2

[0032] By fully acquiring the support frame property characteristics of the target steering wheel and the material composition ratio of the foaming liquid, the temperature control requirements during the foaming process can be effectively analyzed, and the temperature control strategy can be optimized and adjusted based on this information. This process helps to achieve precise temperature control management and ensure that the temperature, reaction rate and foam structure during the foaming process are in the optimal state, thereby greatly improving product quality.

[0033] S300: In combination with the support frame property characteristics and material composition ratio, according to the predetermined foaming process stage and the foaming evaluation function, in the simulated foaming space, the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter are simulated and optimized to generate an optimal temperature control strategy.

[0034] Further, if Figure 2 As shown, step S300 of the present application also includes:

[0035] S310: Configuring a predetermined foaming processing stage, wherein the predetermined foaming processing stage includes a pre-stage, a preheating stage, a reaction stage, a heat preservation stage and a cooling stage, and each stage is marked with a processing time.

[0036] Specifically, in the foaming optimization control method of automobile control devices based on multivariate temperature coupling analysis, configuring the predetermined foaming processing stage is a key step, which can more accurately control the temperature changes and processing time of each stage, ensure the stability of the foaming process and product quality. The predetermined foaming processing stage includes multiple steps, each step corresponding to different temperature control targets and time requirements.

[0037] Configure the predetermined foaming processing stage, wherein the predetermined foaming processing stage includes the pre-stage, preheating stage, reaction stage, insulation stage and cooling stage, and each stage is marked with a processing time. The pre-stage refers to the preheating stage of the support frame and the mold. The main task of this stage is to preheat the support frame and the mold to ensure that they reach the required initial temperature. This is to avoid the impact of low temperature on the foaming reaction and reduce the lag time of heat transfer. During the preheating process, the temperature of the support frame and the mold needs to be steadily increased until it reaches the predetermined temperature range. The time of the preheating stage is usually determined by the material properties and the heating rate. The time of this stage depends on the material and shape of the support frame and the mold and the performance of the heating equipment. Usually, the preheating stage of the mold and the support frame lasts from a few minutes to tens of minutes.

[0038] The preheating stage refers to the preheating stage of the foaming reaction. In this stage, the foaming liquid begins to enter the mold and is heated by an external heating source to raise the temperature of the foaming liquid to the temperature required for the reaction. The temperature increase in this stage is crucial to ensure the uniformity of the foaming reaction. The appropriate preheating temperature can accelerate the foaming reaction while avoiding premature reaction or incomplete reaction. Among them, the time of the preheating stage is usually short, usually a few minutes, depending on the type of foaming liquid and the heating rate.

[0039] The reaction stage refers to the foaming reaction stage. In the reaction stage, the chemical reaction of the foaming liquid begins, the foaming agent releases gas, and the liquid foams and expands. This stage requires strict control of temperature and time to ensure the stability and uniformity of the foam. Too high or too low temperature may lead to uneven foaming and affect the structure of the foam. For example, the reaction temperature is usually kept within a stable range to ensure that the foaming liquid reacts fully and the foam expands evenly. At the same time, the temperature control requirements at this stage are relatively high, because the foaming reaction is a temperature-sensitive process, and any temperature fluctuations may lead to inconsistent foaming effects. Among them, the reaction stage usually lasts from a few minutes to more than ten minutes, and the specific time depends on the reaction characteristics of the foaming liquid and the required foam density.

[0040] The main task of the insulation stage is to keep the temperature within the ideal range, ensure the completion of the foaming reaction and stabilize the foam structure. This stage is mainly to stabilize the foam structure produced during the foaming process and reduce any unnecessary temperature changes. At this stage, the temperature is usually kept at the temperature at the end of the reaction stage to ensure the stability of the foaming effect; among them, the time of the insulation stage is more flexible, usually a few minutes to tens of minutes, depending on the stability requirements of the foam. The cooling stage is the last stage of the foaming process. The main purpose is to solidify the foaming liquid and maintain the stability of the foam structure. During the cooling process, the temperature is gradually reduced to ensure that the foamed product will not deform or the foam structure will be uneven. Too fast a cooling rate may cause a large temperature difference between the surface and the inside of the foam, affecting the final quality of the foam. An appropriate cooling rate helps stabilize the quality of the product; among them, the cooling stage is relatively long, usually tens of minutes to hours, and the specific time depends on the size and temperature difference of the foamed product.

[0041] In the predetermined foaming processing stage, more precise foaming process control can be achieved by subdividing the temperature control targets and processing time of each stage. The temperature and duration of each stage are set to ensure that the temperature changes in the foaming process can maximize the optimization of the foam structure and avoid quality problems caused by unstable or inappropriate temperature. Optimizing the control parameters and processing time of these stages can not only improve the foaming effect, but also improve the quality and consistency of the final product.

[0042] S320: Constructing a simulated foaming space in combination with the support frame property characteristics, material composition ratio and predetermined foaming processing stage.

[0043] Furthermore, step S320 of the present application also includes:

[0044] S321: Taking the support frame attribute characteristics, material composition ratio and predetermined foaming process stage as similarity retrieval constraints, a sample temperature control scheme set is obtained based on big data retrieval, and product quality parameters after foaming according to different sample temperature control schemes are obtained to obtain a sample quality parameter set, wherein the quality parameters include foam density, pore distribution uniformity coefficient and pore size consistency coefficient; S322: Using the sample temperature control scheme set and the sample quality parameter set, supervised training and verification training of the random forest are performed until convergence, and the first simulated foaming branch is harvested; S323: Simulation modeling is performed on the first temperature control device, the second temperature control device, the third temperature control device and the fourth temperature control device, and the first simulated temperature control branch is constructed by fusion, and the simulated foaming space is generated in combination with the first simulated foaming branch.

[0045] Specifically, first, the historical temperature control data set is queried through the big data platform and intelligent retrieval technology, taking the support frame attribute characteristics, material composition ratio and predetermined foaming process stage as similarity retrieval constraints. Through feature matching algorithms (such as similarity-based matching or clustering-based retrieval methods), a set of sample temperature control schemes that are most similar to the current target steering wheel foaming process can be obtained to obtain a sample quality parameter set, wherein the sample temperature control scheme includes a first temperature control parameter (ambient temperature), a second temperature control parameter (support frame preheating temperature), a third temperature control parameter (mold preheating temperature) and a fourth temperature control parameter (foaming process heating temperature), and the fourth temperature control parameter includes a plurality of fourth sub-temperature control parameters of the preheating stage, the reaction stage, the insulation stage and the cooling stage. Next, obtain the product quality parameters after foaming according to different sample temperature control schemes. This can be obtained through historical product quality inspection data to obtain a sample quality parameter set, where the quality parameters include foam density, pore distribution uniformity coefficient and pore size consistency coefficient. Foam density refers to the mass density of the foam material after foaming, which is usually related to the release amount of the foaming agent, the foaming temperature and the duration of the foaming process. Too high or too low foam density will affect the strength and performance of the foamed product; the uniformity of pore distribution in the foam reflects the stability and uniformity of the foaming process. An ideal foamed product should have a uniform pore distribution to ensure consistency in product quality. The larger the pore distribution uniformity coefficient, the more uniform the pore distribution; the consistency of pore size in the foam affects the appearance and functionality of the final product. Inconsistent pore size will lead to uneven material strength, which may cause product defects. The larger the pore size consistency coefficient, the higher the pore size consistency.

[0046] Next, the sample temperature control scheme is used as input, the sample quality parameter is used as supervision, and the sample temperature control scheme set and the sample quality parameter set are used as training data to perform supervised training and validation training on the random forest. First, the sample temperature control scheme set and the quality parameter set are divided into a training set and a validation set in proportion (for example, 80% training set, 20% validation set); then, the random forest algorithm starts to build multiple decision trees based on the training set. The training process of each tree is to randomly select a part of the data from the sample temperature control scheme as input (features), and predict the corresponding quality parameters through these inputs; each tree will select a random child during training. The features of the set (part of the four temperature control parameters) are used to split the decision nodes to reduce overfitting and improve the generalization ability of the model; each layer of the tree is split according to the relationship between the features and the target quality parameters until the tree reaches the preset maximum depth or the split nodes no longer provide effective gains; then, as multiple decision trees are built, the model will continuously adjust the splitting method of the decision tree by comparing with the actual quality parameters (sample quality parameter set) to reduce the prediction error; each decision tree gives a prediction result, and the final prediction value is obtained by voting on the prediction results of all trees (averaging for regression problems). Further, the trained random forest model is evaluated using the validation set. The performance of the model is evaluated by comparing the difference between the foaming quality parameters predicted by the model and the true value, and calculating the error (for example, mean square error MSE); during the validation process, if the prediction error of the model is large, the model may need to be further optimized (for example, adjusting the number, depth or other hyperparameters of the tree). Finally, when the training error and validation error tend to be stable and there is no obvious overfitting phenomenon, the training process stops. At this time, the trained random forest model has converged and can effectively predict the foaming quality through the input sample temperature control scheme, thus obtaining the first simulated foaming branch that has been trained.

[0047] In this technical solution, the purpose of the first simulation temperature control branch is to simulate and model four temperature control devices (first temperature control device, second temperature control device, third temperature control device and fourth temperature control device) to simulate the change of energy consumption in the temperature control process, so as to provide a basis for temperature control optimization. On the other hand, in the three-dimensional simulation space, the first temperature control device, the second temperature control device, the third temperature control device and the fourth temperature control device are simulated and modeled. For example, for the first temperature control device, the simulation model considers the adjustment mechanism of the ambient temperature, such as the working state of the air conditioning system, the refrigeration or heating equipment, the energy transmission efficiency, etc., and a physical model (such as the heat conduction equation) can be used to describe how the ambient temperature is affected by changes in the external environment and the power adjustment of the equipment. For the second temperature control device, the heating process of the support frame is simulated, including the material of the support frame, the heat conduction efficiency and the heat exchange process between the support frame and the equipment. By inputting the initial temperature and heating power of the support frame, the time and energy consumption required for the support frame to reach the set preheating temperature are predicted. For the third temperature control device, the preheating process of the mold is simulated, considering the material properties of the mold, the temperature conduction characteristics, and the time and power consumption in the preheating stage. By simulating the heating curve of the mold, the energy consumption and time consumption at different preheating temperatures are predicted. For the fourth temperature control device, the temperature change during the foaming process is simulated, and the heating power and heating efficiency at different stages are considered. According to the heating requirements in the actual foaming process, the heating power and temperature curve are adjusted to simulate the energy consumption and temperature response of the device. The energy consumption of each temperature control device is modeled, and the energy consumption is usually closely related to factors such as the power consumption, heating time, and heat transfer efficiency of the device. In the simulation, the energy conservation equation based on the law of physics or the data-driven regression model can be used to estimate the energy consumption of each temperature control device, and the energy consumption data of all devices are integrated to obtain a total energy consumption prediction model. This model can predict the energy demand in the entire foaming process under different temperature control settings, and the first simulated temperature control branch is constructed by integration, wherein the output of the first simulated temperature control branch is the predicted result of the energy consumption during the temperature control process (including the energy consumption of each device and the total energy consumption of the entire system). Finally, the simulated foaming space is generated according to the first simulated temperature control branch and the first simulated foaming branch.

[0048] S330: configuring a temperature control parameter adjustment space, wherein the temperature control parameter adjustment space includes a first temperature control parameter threshold, a second temperature control parameter threshold, a third temperature control parameter threshold and a fourth temperature control parameter threshold, wherein the fourth temperature control parameter threshold includes a preheating temperature control parameter interval, a reaction temperature control parameter interval, an insulation temperature control parameter interval and a cooling temperature control parameter interval; S340: randomly selecting a plurality of temperature control parameters from the first temperature control parameter threshold, the second temperature control parameter threshold, the third temperature control parameter threshold and the fourth temperature control parameter threshold for combination to generate a plurality of initial temperature control schemes.

[0049] Specifically, a temperature control parameter adjustment space is configured, wherein the temperature control parameter adjustment space refers to the temperature parameter adjustment range within each stage, which can be set according to the actual processing scenario, including the first temperature control parameter threshold, the second temperature control parameter threshold, the third temperature control parameter threshold and the fourth temperature control parameter threshold, wherein the fourth temperature control parameter threshold includes the preheating temperature control parameter interval, the reaction temperature control parameter interval, the insulation temperature control parameter interval and the cooling temperature control parameter interval. Then, a plurality of temperature control parameters are randomly selected from the first temperature control parameter threshold, the second temperature control parameter threshold, the third temperature control parameter threshold and the fourth temperature control parameter threshold for combination, wherein the fourth temperature control parameter preheating stage, reaction stage, insulation stage and cooling stage of the plurality of sub-temperature parameters, combined with the predetermined foaming processing stage (marked with the stage duration) generate a plurality of initial temperature control schemes.

[0050] S350: Utilizing the simulated foaming space and the foaming evaluation function, performing simulation optimization according to the multiple initial temperature control schemes, and outputting the optimal temperature control strategy.

[0051] Furthermore, step S350 of the present application also includes:

[0052] S351: Input the multiple initial temperature control schemes into the first simulated foaming branch respectively to predict the foaming results, and output multiple predicted quality parameters, wherein the predicted quality parameters include predicted foam density, predicted pore distribution uniformity coefficient and predicted pore size consistency coefficient; S352: Input the multiple initial temperature control schemes into the first simulated temperature control branch respectively to perform heating energy consumption analysis, and output multiple simulated energy consumptions.

[0053] Specifically, the multiple initial temperature control schemes are respectively input into the first simulated foaming branch to predict the foaming results, and multiple predicted quality parameters are output, wherein the predicted quality parameters include predicted foam density, predicted cell distribution uniformity coefficient, and predicted cell size consistency coefficient. On the other hand, the multiple initial temperature control schemes are respectively input into the first simulated temperature control branch to perform heating energy consumption simulation analysis, and multiple simulated energy consumptions are output after being summarized.

[0054] S353: Utilizing the foaming evaluation function, a plurality of foaming fitness levels are obtained according to the plurality of predicted quality parameters and the plurality of simulated energy consumption evaluations.

[0055] Furthermore, step S353 of the present application also includes:

[0056] S3531: The expression of the foaming evaluation function is: ;

[0057] in, For foaming adaptability, is the energy consumption weight, Foam density weight, is the weight of foaming uniformity, is the foaming consistency weight, To simulate energy consumption, To predict foam density, is the standard foam density, To predict the cell distribution uniformity coefficient, is the coefficient of consistency for predicting cell size.

[0058] Specifically, in the foaming evaluation function, is the foaming fitness, the greater the fitness, the better the foaming effect; is the energy consumption weight, Foam density weight, is the weight of foaming uniformity, is the foaming consistency weight, To simulate energy consumption, To predict foam density, is the standard foam density (representing the expected foam density value, determined based on process requirements or product specifications), To predict the cell distribution uniformity coefficient, To predict the cell size consistency coefficient. By constructing a foaming evaluation function, the foaming fitness of the temperature control scheme can be accurately quantified, providing support for the subsequent optimization of the temperature control scheme, and improving the accuracy and efficiency of the optimization. The foaming evaluation function is further used to generate multiple foaming fitnesses of multiple initial temperature control schemes based on the multiple predicted quality parameters and multiple simulated energy consumption calculations.

[0059] S354: Taking the temperature control parameter adjustment space as a constraint and based on the multiple foaming adaptabilities, a temperature control scheme simulation is performed to optimize the temperature control scheme, and the optimal temperature control strategy is output.

[0060] Furthermore, step S354 of the present application also includes:

[0061] S3541: The initial temperature control scheme is regarded as the initial solution, and multiple initial solutions are arranged from large to small according to the foaming fitness, to construct an initial solution sequence, and the first K solutions of the initial solution sequence are selected as optimal solutions, and the last Q solutions are selected as inferior solutions, wherein the sum of Q and K is the number of initial solutions, Q is U times K, and U is an integer greater than 10 and less than 30; S3542: According to the K optimal solutions, the Q inferior solutions are randomly clustered with equal values ​​to obtain Q solution thresholds, and the optimal solutions within the solution thresholds are used as the optimization direction, and the inferior solutions within the solution thresholds are optimized and adjusted according to the predetermined optimization step length, to obtain To Q updated solution thresholds, where if the adjusted inferior solution exceeds the temperature control parameter adjustment space, this update will not be performed; S3543: the Q updated solution thresholds are judged, if within the same updated solution threshold, the fitness of the inferior solution is greater than the fitness of the superior solution, the superior solution is replaced by the inferior solution; S3544: continue to iterate and optimize until the optimization number constraint is reached, output Q latest solution thresholds, and select the solution threshold with the largest sum of fitness among the Q latest solution thresholds as the optimal solution threshold, and output the optimal solution of the optimal solution threshold as the optimal temperature control strategy.

[0062] Specifically, first, the initial temperature control scheme is regarded as the initial solution, and multiple initial solutions are arranged from large to small according to the foaming fitness to construct an initial solution sequence; then, the first K solutions of the initial solution sequence are selected as optimal solutions, and the last Q solutions are set as inferior solutions, wherein the sum of Q and K is the number of initial solutions, Q is U times of K, U is an integer greater than 10 and less than 30, and the specific values ​​of Q and K can be set according to the number of initial solutions; then, according to the K optimal solutions, the Q inferior solutions are randomly and equally clustered to obtain Q solution thresholds, wherein the number of inferior solutions in each solution threshold is the same; further, the optimal solution in the solution threshold is taken as the optimization direction, and the inferior solutions in the solution threshold are optimized and adjusted according to the predetermined optimization step length (including the temperature adjustment step length of each temperature control parameter), wherein if the adjusted inferior solution exceeds the temperature control parameter adjustment space, this update is not performed, and Q updated solution thresholds are obtained. Then, the Q updated solution thresholds are judged. If within the same updated solution threshold, the fitness of the inferior solution is greater than the fitness of the superior solution, the superior solution is replaced by the inferior solution. The same method is used to continue iterative optimization until the optimization number constraint is reached, and Q latest solution thresholds are output. The solution threshold with the largest sum of fitness among the Q latest solution thresholds is selected as the optimal solution threshold, and the superior solution of the optimal solution threshold is output as the optimal temperature control strategy.

[0063] By introducing a multi-solution collaborative optimization mechanism of superior and inferior solutions, random equivalent clustering and gradual adjustment of step size, the globality and accuracy of the optimization process are enhanced; while improving the quality of the foaming process, the energy consumption is effectively controlled, and it has strong global exploration and fine adjustment capabilities. This method can not only avoid local optimal solutions and ensure the global optimal temperature control strategy, but also achieve precise control of the foaming process through the optimization of multi-dimensional quality indicators; finally, the optimized temperature control strategy performs well in terms of quality, efficiency and energy consumption, and has high efficiency, stability and energy-saving technical effects.

[0064] S400: Executing foaming control of the target steering wheel according to the predetermined foaming process stage and the optimal temperature control strategy.

[0065] Specifically, the foaming control of the target steering wheel is finally performed according to the predetermined foaming process stage and the optimal temperature control strategy. The foaming control based on the optimal temperature control strategy ensures the foaming quality, energy consumption and process stability through precise regulation of each stage, and realizes efficient, energy-saving and stable foaming control.

[0066] In summary, the foaming optimization control method of an automobile control device based on multivariate temperature coupling analysis provided by the present application has the following technical effects:

[0067] By configuring the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter; obtaining the support frame property characteristics of the target steering wheel and the material component ratio of the foaming liquid; combining the support frame property characteristics and the material component ratio, according to the predetermined foaming processing stage and the foaming evaluation function, in the simulated foaming space, the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter are simulated and optimized to generate an optimal temperature control strategy; according to the predetermined foaming processing stage and the optimal temperature control strategy, the foaming control of the target steering wheel is executed; that is, by simultaneously controlling the four temperature control parameters of ambient temperature, equipment preheating temperature, workpiece preheating temperature and foaming process heating temperature, the mutual influence and coupling relationship between temperatures can be fully considered to ensure uniform temperature distribution and stable foam structure in the foaming process, and ultimately improve the overall foaming effect of the steering wheel.

[0068] 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.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A foaming optimization control method for automobile control devices based on multivariate temperature coupling analysis, characterized in that the method include: Configure a first temperature control parameter, a second temperature control parameter, a third temperature control parameter and a fourth temperature control parameter, wherein the first temperature control parameter is the ambient temperature, the second temperature control parameter is the support frame preheating temperature, the third temperature control parameter is the mold preheating temperature, and the fourth temperature control parameter is the heating temperature in the foaming process; Obtaining the property characteristics of the support frame of the target steering wheel and the material composition ratio of the foaming liquid; In combination with the support frame property characteristics and material composition ratio, according to the predetermined foaming process stage and the foaming evaluation function, in the simulated foaming space, the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter are simulated and optimized to generate an optimal temperature control strategy; executing foaming control of the target steering wheel according to the predetermined foaming process stage and the optimal temperature control strategy; In combination with the support frame property characteristics and material composition ratio, according to the predetermined foaming process stage and the foaming evaluation function, in the simulated foaming space, the first temperature control parameter, the second temperature control parameter, the third temperature control parameter and the fourth temperature control parameter are simulated and optimized to generate an optimal temperature control strategy, including: Configure the predetermined foaming processing stages, wherein the predetermined foaming processing stages include a pre-stage, a preheating stage, a reaction stage, a heat preservation stage, and a cooling stage, and each stage is marked with a processing time; Constructing a simulated foaming space in combination with the support frame property characteristics, material composition ratio and a predetermined foaming processing stage; Configuring a temperature control parameter adjustment space, wherein the temperature control parameter adjustment space includes a first temperature control parameter threshold, a second temperature control parameter threshold, a third temperature control parameter threshold, and a fourth temperature control parameter threshold, wherein the fourth temperature control parameter threshold includes a preheating temperature control parameter interval, a reaction temperature control parameter interval, a heat preservation temperature control parameter interval, and a cooling temperature control parameter interval; Randomly selecting a plurality of temperature control parameters from the first temperature control parameter threshold, the second temperature control parameter threshold, the third temperature control parameter threshold, and the fourth temperature control parameter threshold for combination to generate a plurality of initial temperature control schemes; Using the simulated foaming space and the foaming evaluation function, performing simulation optimization according to the multiple initial temperature control schemes, and outputting the optimal temperature control strategy; Combining the support frame property characteristics, material composition ratio and predetermined foaming process stage, a simulated foaming space is constructed, including: Taking the support frame attribute characteristics, material composition ratio and predetermined foaming process stage as similarity retrieval constraints, a sample temperature control scheme set is obtained based on big data retrieval, and product quality parameters after foaming according to different sample temperature control schemes are obtained to obtain a sample quality parameter set, wherein the quality parameters include foam density, cell distribution uniformity coefficient and cell size consistency coefficient; Using the sample temperature control scheme set and the sample quality parameter set, supervised training and validation training are performed on the random forest until convergence, and the first simulated foaming branch is harvested; The first temperature control device, the second temperature control device, the third temperature control device and the fourth temperature control device are simulated and modeled, and a first simulated temperature control branch is constructed by integration, and the simulated foaming space is generated in combination with the first simulated foaming branch.

2. The foaming optimization control method for automobile control device based on multivariate temperature coupling analysis according to claim 1 is characterized in that: The support frame property characteristics include structural characteristics, dimensional parameters and heat transfer characteristics, and the material composition ratio includes raw material type and raw material ratio.

3. The foaming optimization control method for automobile control device based on multivariate temperature coupling analysis according to claim 1 is characterized in that: Using the simulated foaming space and the foaming evaluation function, performing simulation optimization according to the multiple initial temperature control schemes, and outputting the optimal temperature control strategy, including: Inputting the multiple initial temperature control schemes into the first simulated foaming branch respectively to predict the foaming results, and outputting multiple predicted quality parameters, wherein the predicted quality parameters include predicted foam density, predicted cell distribution uniformity coefficient, and predicted cell size consistency coefficient; Inputting the multiple initial temperature control schemes into the first simulated temperature control branch respectively to perform heating energy consumption analysis, and outputting multiple simulated energy consumptions; Using the foaming evaluation function, a plurality of foaming fitness levels are obtained according to the plurality of predicted quality parameters and a plurality of simulated energy consumption evaluations; Taking the temperature control parameter adjustment space as a constraint and based on the multiple foaming adaptabilities, a temperature control scheme simulation is performed to output the optimal temperature control strategy.

4. The foaming optimization control method for automobile control device based on multivariate temperature coupling analysis according to claim 3 is characterized in that: The expression of the foaming evaluation function is: Among them, FIT is foaming fitness, S1 is energy consumption weight, S2 is foam density weight, S3 is foaming uniformity weight, S4 is foaming consistency weight, W is simulation energy consumption, M is predicted foam density, m is standard foam density, RT is predicted pore distribution uniformity coefficient, and PT is predicted pore size consistency coefficient.

5. The foaming optimization control method for automobile control device based on multivariate temperature coupling analysis according to claim 3 is characterized in that: Taking the temperature control parameter adjustment space as a constraint and based on the multiple foaming adaptabilities, a temperature control scheme simulation optimization is performed to output the optimal temperature control strategy, including: The initial temperature control scheme is regarded as an initial solution, and multiple initial solutions are arranged from large to small according to the foaming fitness, an initial solution sequence is constructed, and the first K solutions of the initial solution sequence are selected as optimal solutions, and the last Q solutions are selected as inferior solutions, wherein the sum of Q and K is the number of initial solutions, Q is U times K, and U is an integer greater than 10 and less than 30; According to the K optimal solutions, Q inferior solutions are randomly clustered with equal values ​​to obtain Q solution thresholds. The optimal solutions within the solution thresholds are used as the optimization direction. The inferior solutions within the solution thresholds are optimized and adjusted according to the predetermined optimization step length to obtain Q updated solution thresholds. If the adjusted inferior solution exceeds the temperature control parameter adjustment space, this update is not performed. The Q update solution thresholds are judged, and if within the same update solution threshold, the fitness of the inferior solution is greater than the fitness of the superior solution, the superior solution is replaced by the inferior solution; Continue to iterate and optimize until the optimization number constraint is reached, output Q latest solution thresholds, select the solution threshold with the largest sum of fitness among the Q latest solution thresholds as the optimal solution threshold, and output the optimal solution of the optimal solution threshold as the optimal temperature control strategy.

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