Rapid temperature control compensation method and system for hydraulic forming dies for rubber products

Abstract

The present invention belongs to the technical field of die temperature regulation, and discloses a method and system for rapid temperature control compensation of a hydraulic forming mold for a rubber product. The method comprises: executing a dynamic temperature control zoning strategy on the hydraulic forming mold for the rubber product based on key physical properties of the rubber compound before entering the mold to form N temperature control sub-areas; associating and binding the N temperature control sub-areas with temperature control actuators actually arranged in the mold cavity, establishing a hardware mapping index relationship, and constructing a hardware mapping index relationship table; dynamically modeling is performed based on the hardware mapping index relationship table and the operating process parameters of the N temperature control sub-areas per unit time to construct a dynamic heat transfer coupling matrix; adaptive temperature control compensation is performed on the N temperature control sub-areas based on the hardware mapping index relationship table and the dynamic heat transfer coupling matrix; the present application realizes real-time adaptation of the temperature control compensation strategy to the mold-compound combined working condition, breaking through the bottlenecks in non-adaptive regulation and boundary response lag.

Description

Technical Field

[0001] The present invention relates to the technical field of die temperature regulation, and more particularly to a method and system for rapid temperature control compensation of a hydraulic molding die for rubber products. Background Art

[0002] In the actual application of rubber products, the requirements for the molding quality and processing stability of rubber products continue to increase. Among them, the hydraulic molding process has become the mainstream method for processing rubber products due to its high molding pressure and good product density. The temperature control system in the hydraulic molding mold plays a decisive role in ensuring the sufficient vulcanization of the rubber compound, suppressing internal stress and improving the dimensional consistency of the product. However, rubber materials have significant physical characteristics such as high viscosity and low thermal conductivity. During the filling process, it is easy to form a center-edge thermal difference. In addition, the mold cavity structure is complex and the heating response is highly nonlinear, making the accuracy and uniformity of thermal field control a key factor affecting product quality.

[0003] Currently, common temperature control strategies mostly use mold geometry to define heating areas and control them through statically set heating templates and preset threshold parameters. Although they have certain engineering operability, the existing temperature control area division method relies on fixed structures and manual experience, and it is difficult to dynamically reflect the thermal diffusion behavior and local response characteristics of the rubber material, resulting in poor adaptability of the temperature control strategy and limited adjustment effect. The control parameter configuration lacks the ability to adaptively adjust online according to the actual thermal coupling state, and cannot meet the refined temperature control requirements under the changes in thermal interference coupling between multiple areas and asymmetric heat migration, which is prone to temperature rise lag and temperature control imbalance.

[0004] Therefore, there is an urgent need for a temperature control compensation method that can dynamically model the physical properties of the rubber and the regional response characteristics, and combine multi-dimensional thermal behavior indicators to realize adaptive control strategy configuration, so as to solve the key problems such as response lag and local control failure of the current rubber hydraulic forming mold temperature control system. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned purpose, the present invention provides the following technical solution: a rapid temperature control and compensation method for a hydraulic forming mold for rubber products, comprising:

[0006] Based on the key physical properties of the rubber compound before entering the mold, a dynamic temperature control zoning strategy is implemented for the rubber product hydroforming mold to form N temperature control sub-areas;

[0007] Associating and binding the N temperature control sub-areas with the temperature control actuators actually arranged in the mold cavity, establishing a hardware mapping index relationship, and constructing a hardware mapping index relationship table;

[0008] Based on the hardware mapping index relationship table and the operating process parameters of N temperature control sub-areas per unit time, dynamic modeling is performed to construct a dynamic heat transfer coupling matrix;

[0009] Adaptive temperature control compensation is performed on N temperature control sub-areas based on the hardware mapping index relationship table and the dynamic heat transfer coupling matrix.

[0010] Furthermore, the method for adaptively controlling and compensating the temperature of the rubber product hydraulic forming mold includes:

[0011] S300: Let the initial value of n be 1, and the value range of n be 1 to N;

[0012] S301: Calculating the self-response intensity index, coupling influence intensity index, and migration asymmetry set of the nth temperature control sub-region based on the dynamic heat transfer coupling matrix;

[0013] S302: Inputting the temperature control region type, key physical property parameters, self-response strength index, coupling influence strength index, and migration asymmetry set of the nth temperature control sub-region into a threshold setting model to obtain dynamically set self-response strength index thresholds and coupling influence strength index thresholds;

[0014] Based on the self-response intensity index threshold and the coupling influence intensity index threshold, a Boolean response label assignment operation is performed on the self-response intensity index, the coupling influence intensity index and the migration asymmetry set to construct a Boolean response label table;

[0015] Input the hardware mapping index relationship table, the Boolean response label table, the temperature control area type, the self-response strength index, the coupling influence strength index and the migration asymmetry set into the control instruction setting model to obtain the temperature control instruction set;

[0016] Send the temperature control instruction set to the corresponding actuator through the system control interface for execution to achieve adaptive temperature control compensation;

[0017] S303: Let n=n+1. If n is less than or equal to N, return to S301 to continue execution. If n is greater than N, end the current process.

[0018] Furthermore, the method for obtaining the migration asymmetry set of the nth temperature control sub-region specifically includes:

[0019] S400: Set the initial value of the index variable h to 1, and the value range of h is 1 to N;

[0020] S401: Obtain the element corresponding to the position (n, N+1-h) from the dynamic heat transfer coupling matrix, record it as the first element, and obtain the element corresponding to the position (N+1-h, n) from the dynamic heat transfer coupling matrix, record it as the second element;

[0021] S402: Subtract the second element from the first element and take the absolute value to obtain a migration asymmetry index. If the migration asymmetry index is greater than a preset migration asymmetry index threshold, the nth temperature control sub-region and the N+1-hth temperature control sub-region are constructed into a migration asymmetry combination, and the migration asymmetry combination is added to the migration asymmetry set.

[0022] S403: Let h = h + 1. If h is less than or equal to N, return to S401 to continue execution. If h is greater than N, end the current process.

[0023] Furthermore, the method for constructing the Boolean response label table includes:

[0024] If the migration asymmetry set is not empty, the Boolean response label of the migration asymmetry set is assigned a value of yes; if the migration asymmetry set is empty, the Boolean response label of the migration asymmetry set is assigned a value of no;

[0025] If the self-response strength indicator is greater than the self-response strength indicator threshold, the Boolean response label of the self-response strength indicator is assigned a value of yes; if the self-response strength indicator is less than or equal to the self-response strength indicator threshold, the Boolean response label of the self-response strength indicator is assigned a value of no;

[0026] If the coupling impact strength indicator is greater than the coupling impact strength indicator threshold, the Boolean response label of the coupling impact strength indicator is assigned a value of yes; if the coupling impact strength indicator is less than or equal to the coupling impact strength indicator threshold, the Boolean response label of the coupling impact strength indicator is assigned a value of no;

[0027] The self-response strength index, coupling influence strength index, migration asymmetry set and the corresponding Boolean response labels are constructed into a Boolean response label table.

[0028] Furthermore, the method for constructing the dynamic heat transfer coupling matrix includes:

[0029] Divide the unit time into Q time points;

[0030] Obtain the temperature and power corresponding to N temperature control sub-areas at Q time points, and construct the temperature set and power set corresponding to the N temperature control sub-areas;

[0031] Based on the temperature sets and power sets of N temperature control sub-areas, a thermal response linear regression model corresponding to each temperature control sub-area is constructed. The gain vector is calculated, the parameter vector estimate is updated, and the covariance matrix is ​​updated at Q time points of each thermal response linear regression model to obtain the heat transfer coupling coefficient vectors of the N temperature control sub-areas. With the heat transfer coupling coefficient vectors as rows, the N heat transfer coupling coefficient vectors are sequentially constructed into a dynamic heat transfer coupling matrix.

[0032] Furthermore, the method for constructing the hardware mapping index relationship table includes:

[0033] Extract the boundary 3D coordinates of N temperature control sub-areas and construct the coverage of the temperature control sub-areas;

[0034] Extract the working coverage area corresponding to each temperature control actuator according to the installation position of the temperature control actuator and the working coverage range of the temperature control actuator;

[0035] For each temperature control actuator, spatial overlap is calculated with the temperature control sub-area coverage of N temperature control sub-areas in turn to obtain the corresponding geometric intersection area. The geometric intersection area is divided by the working coverage area of ​​the temperature control actuator to obtain the temperature control area attribution ratio. The temperature control actuator is then assigned to the temperature control sub-area with the largest temperature control area attribution ratio. The temperature control actuator includes a heating element, a cooling channel, and a temperature acquisition sensor.

[0036] The temperature control actuators, temperature control area attribution ratios, and temperature control area types corresponding to the N temperature control sub-areas are constructed to obtain a hardware mapping index relationship table.

[0037] Furthermore, the method for forming N temperature control sub-regions includes:

[0038] S100: Divide the mold cavity into M mold cavity sub-areas; set the initial value of m to 1, and the value range of m to be 1 to M; obtain key physical properties of the rubber compound before entering the mold; the key physical properties include the initial temperature of the rubber compound, the apparent viscosity of the rubber compound, the density of the rubber compound, the target vulcanization temperature, the thermal conductivity of the rubber compound, the specific heat capacity of the rubber compound, the heat of vulcanization per unit mass of the rubber compound, and the activation energy of the vulcanization reaction of the rubber compound;

[0039] S101: Obtain the thickness from the mold wall to the center of the rubber material in the mth mold cavity sub-region, recorded as the mold cavity half thickness; obtain the mold surface heating temperature of the mth mold cavity sub-region; obtain the flow path length of the rubber material from the mold entry position to the mth mold cavity sub-region; obtain the effective pressure of the mth mold cavity sub-region;

[0040] S102: Calculating the temperature delay parameter, filling lag time and nonlinear temperature rise index of the m-th cavity sub-region based on key physical properties, cavity half-thickness, mold surface heating temperature, flow path length and effective pressure;

[0041] S103: Inputting the temperature delay parameter, filling lag time and nonlinear temperature rise index of the m-th cavity sub-region into the regional response evaluation model to obtain a corresponding regional response score;

[0042] S104: Set m = m + 1. If m is less than or equal to M, return to S102 and continue execution. If m is greater than M, obtain the regional response scores corresponding to the M mold cavity sub-regions and execute S105.

[0043] S105: Merging the M mold cavity sub-regions according to the M regional response scores to obtain N temperature control sub-regions.

[0044] Furthermore, a method for merging the M cavity sub-regions according to the M regional response scores to obtain N temperature control sub-regions includes:

[0045] S200: Set the initial value of m to 1; classify the M mold cavity sub-regions into temperature control region types based on the M region response scores to obtain the temperature control region types corresponding to the M mold cavity sub-regions; initialize the processing flags of the M mold cavity sub-regions to unprocessed; and set the initial value of the count variable N of the temperature control sub-region to 1;

[0046] S201: If the processing mark of the m-th mold cavity sub-region is unprocessed, then the mold cavity sub-regions adjacent to the m-th mold cavity sub-region, having the same temperature control region type and processing mark as unprocessed are constructed into the N-th set of regions to be merged and processed; if the processing mark of the m-th mold cavity sub-region is processed, then execute S203;

[0047] S202: Determine whether there is a mold cavity sub-region that is adjacent to the mold cavity sub-region in the Nth set of regions to be merged and processed, has the same temperature control region type, and is marked as unprocessed. If so, add the corresponding mold cavity sub-region to the Nth set of regions to be merged and process, and continue executing S202. If not, construct the mold cavity sub-region in the Nth set of regions to be merged and process into the Nth temperature control sub-region, set the corresponding processing mark to processed, and execute S203.

[0048] S203: Set m=m+1. If m is less than or equal to M, set N=N+1 and return to S201 to continue execution. If m is greater than M, N temperature control sub-areas are obtained and the current process ends.

[0049] Furthermore, the temperature control region types of the M mold cavity sub-regions are divided based on the M region response scores, and a method for obtaining the temperature control region types corresponding to the M mold cavity sub-regions includes:

[0050] A high response critical threshold and a low response critical threshold are preset, and the high response critical threshold is greater than the low response critical threshold; the M regional response scores are compared with the high response critical threshold and the low response critical threshold respectively. If the regional response score is greater than or equal to the high response critical threshold, the corresponding mold cavity sub-region is marked as a key temperature control region; if the regional response score is less than the high response critical threshold and greater than or equal to the low response critical threshold, the corresponding mold cavity sub-region is marked as a secondary response region; if the regional response score is less than the low response critical threshold, the corresponding mold cavity sub-region is marked as a conventional temperature control region.

[0051] A rapid temperature control and compensation system for a hydraulic forming mold for a rubber product, used to implement the rapid temperature control and compensation method for a hydraulic forming mold for a rubber product, comprises:

[0052] The temperature control zoning module implements a dynamic temperature control zoning strategy for the rubber product hydroforming mold based on the key physical properties of the rubber compound before it is put into the mold, forming N temperature control sub-areas;

[0053] A hardware mapping module is used to associate and bind N temperature control sub-areas with the temperature control actuators actually arranged in the mold cavity, establish a hardware mapping index relationship, and construct a hardware mapping index relationship table;

[0054] The matrix construction module dynamically models the dynamic heat transfer coupling matrix based on the hardware mapping index relationship table and the operating process parameters of N temperature control sub-areas per unit time;

[0055] The temperature control compensation module performs adaptive temperature control compensation on N temperature control sub-areas based on the hardware mapping index relationship table and the dynamic heat transfer coupling matrix.

[0056] Compared with the prior art, the technical effects and advantages of the rapid temperature control compensation method and system for rubber product hydraulic forming molds of the present invention are as follows:

[0057] This application constructs an adaptive temperature control compensation method and system for hydraulic forming molds for rubber products, achieving dynamic temperature control strategy optimization based on the physical properties of the rubber material, thermal diffusion behavior, and regional coupling characteristics, significantly improving the real-time performance of the mold cavity thermal field response and the uniformity of product vulcanization. Compared with the existing technology that relies on the mold geometry to preset heating areas and uses fixed thresholds and unified control templates for temperature adjustment, this application first constructs multi-dimensional regional thermal behavior indicators based on the key physical properties of the rubber material before entering the mold and the spatial configuration of the mold cavity, including temperature delay parameters, filling lag time, and nonlinear temperature rise indicators. Then, the regional response evaluation model obtained through training generates regional response scores, and combines them with adjacency rules to form adaptive temperature control sub-regions that reflect the sensitivity of thermal control.

[0058] On this basis, the present application further constructs a dynamic heat transfer coupling matrix to identify the coupling relationship between heat diffusion paths between regions, extracts self-response strength indicators, coupling influence strength indicators, and migration asymmetry sets, establishes a Boolean response label system, and realizes online adaptive correction of thresholds through a threshold setting model. Combined with the hardware mapping index relationship table constructed by the actual temperature control actuators, the present application uses the control instruction setting model to realize the automatic matching and adjustment instruction generation of differentiated control strategies based on multi-source thermal response states, thereby completing the closed-loop response chain from thermal behavior modeling to control signal output. This method not only effectively reduces the manual participation and subjectivity of temperature control area division and strategy setting, but also realizes the real-time adaptation of the temperature control compensation strategy to the mold-rubber joint working conditions. It has the combined advantages of high precision, high response, and high deployability, breaking through the technical bottlenecks of traditional temperature control strategies in single-valued logic, non-adaptive adjustment, and boundary response lag. Overall, the present application has achieved substantial improvements compared to the existing technology in terms of regional identification dimension, control strategy accuracy, and execution linkage structure, and has significant innovation and industrial value. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Schematic diagram of a rapid temperature control and compensation system for a rubber product hydraulic forming mold according to Example 1 of the present invention;

[0060] Figure 2 This is a flow chart of a rapid temperature control and compensation method for a rubber product hydraulic forming mold according to Example 2 of the present invention;

[0061] Figure 3 is a flow chart of a method for forming N temperature control sub-regions;

[0062] Figure 4 A flowchart of a method for constructing a hardware mapping index relationship table;

[0063] Figure 5 A flow chart of the method for constructing a dynamic heat transfer coupling matrix;

[0064] Figure 6 The present invention is a flow chart of a method for adaptive temperature control compensation of a hydraulic forming mold for rubber products. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present invention will be described in detail, clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. It should be noted that the specific embodiments described below are only used to better illustrate and describe the technical solutions of the present invention, and are intended to enable those skilled in the art to better understand and implement the present invention, and should not be construed as limiting the scope of protection of the present invention. Without departing from the spirit and essence of the present invention, those skilled in the art may modify, adjust or make equivalent replacements based on the contents disclosed in the present invention, and these should all be regarded as the scope of protection of the present invention.

[0066] Example 1

[0067] See also Figure 1 As shown, this embodiment discloses a rapid temperature control and compensation system for a hydraulic forming mold for rubber products, including a temperature control partition module, a hardware mapping module, a matrix construction module and a temperature control and compensation module. Each module realizes data transmission through wired and / or wireless connections.

[0068] The temperature control zoning module implements a dynamic temperature control zoning strategy for the rubber product hydraulic forming mold based on the key physical properties of the rubber compound before it is put into the mold, forming N temperature control sub-areas.

[0069] like Figure 3 As shown, the method for forming N temperature control sub-regions includes:

[0070] S100: Divide the mold cavity into M mold cavity sub-areas; let the initial value of m be 1, and the value range of m be 1 to M; obtain key physical properties of the rubber compound before it is put into the mold; the key physical properties include the initial temperature of the rubber compound, the apparent viscosity of the rubber compound, the density of the rubber compound, the target vulcanization temperature, the thermal conductivity of the rubber compound, the specific heat of the rubber compound, the heat of vulcanization per unit mass of the rubber compound, and the activation energy of the vulcanization reaction of the rubber compound; the initial temperature of the rubber compound is obtained by an infrared non-contact temperature measuring device; the apparent viscosity of the rubber compound, the density of the rubber compound, the target vulcanization temperature, the thermal conductivity of the rubber compound, the specific heat of the rubber compound, the heat of vulcanization per unit mass of the rubber compound, and the activation energy of the vulcanization reaction of the rubber compound are obtained from a formula database;

[0071] S101: Obtain the thickness from the mold wall to the center of the rubber material in the mth mold cavity sub-region, recorded as the mold cavity half-thickness; obtain the mold surface heating temperature of the mth mold cavity sub-region; the mold surface heating temperature can be measured in real time by optical fiber or multi-point thermocouple; obtain the length of the flow path through which the rubber material flows from the mold entry position to the mth mold cavity sub-region; obtain the effective pressure of the mth mold cavity sub-region; the effective pressure refers to the net pressure per unit area actually applied to the rubber material in the mth mold cavity sub-region when the mold clamping pressure applied by the hydraulic system is transmitted to the mold cavity through the mold plate, mold frame and rubber body in the mold clamping state, after deducting factors such as energy loss, geometric attenuation, and flow resistance obstacles; the effective pressure can be obtained by embedding a piezoresistive or piezoelectric film sensor;

[0072] S102: Calculating the temperature delay parameter, filling lag time and nonlinear temperature rise index of the m-th cavity sub-region based on key physical properties, cavity half-thickness, mold surface heating temperature, flow path length and effective pressure;

[0073] S103: Inputting the temperature delay parameter, filling lag time and nonlinear temperature rise index of the m-th cavity sub-region into the regional response evaluation model to obtain a corresponding regional response score;

[0074] S104: Set m = m + 1. If m is less than or equal to M, return to S102 and continue execution. If m is greater than M, obtain the regional response scores corresponding to the M mold cavity sub-regions and execute S105.

[0075] S105: Merging the M mold cavity sub-regions according to the M regional response scores to obtain N temperature control sub-regions.

[0076] The method for obtaining the temperature delay parameter includes:

[0077] ;

[0078] in, is the temperature delay parameter of the m-th cavity sub-region, is the cavity half thickness of the mth cavity sub-region, is the thermal diffusivity, , is a constant, is the thermal conductivity of the rubber compound, is the density of the rubber compound, is the specific heat capacity of the rubber compound, is a logarithmic function, is the mold surface heating temperature of the mth mold cavity sub-area, is the initial temperature of the rubber compound, The target vulcanization temperature. The mold surface heating temperature refers to the steady-state temperature reached by the heating device in the mold forming area (inner wall of the mold cavity) before the mold closing and pressure holding phase. This temperature constitutes the boundary heat source condition during the rubber compound heating process. The target vulcanization temperature refers to the control setpoint for the rubber compound temperature during the vulcanization reaction to achieve the desired degree of crosslinking (e.g., achieving optimal physical properties).

[0079] It should be noted that Represents the spatial scale of the heat transfer path. The heat conduction time increases with the square of the heat transfer path. Therefore, the thicker the cavity half-thickness, the more significant the heating time. Rubber molds usually have thicker walls, and the distance between the cavity center and the heating surface becomes the key factor in determining the heating rate. It is a comprehensive indicator to measure the thermal conductivity and thermal response speed of materials. is a coefficient used to simplify the multi-order heat conduction mode into the first-order dominant term, which is convenient for engineering approximation. Describes the time scale required for unit temperature transition, indicating the basic time required to transfer heat from the cavity wall to the center of the rubber compound under thermal diffusion conditions. This part increases with the square of the cavity thickness and decreases with the increase of thermal diffusivity. reflects The relative position of the entire temperature increase range, It indicates the temperature difference between the mold surface heating temperature and the initial temperature of the rubber compound, indicating the theoretical maximum temperature rise, that is, the upper limit of the temperature difference that the rubber compound may eventually reach if there is no heat loss; The larger it is, the greater the thermal driving force of the system and the greater the warming potential.

[0080] Indicates the temperature difference between the mold surface heating temperature and the target vulcanization temperature, indicating the remaining temperature difference that the system still needs to overcome when reaching the target vulcanization temperature; The smaller it is, the closer the rubber is to the temperature required for vulcanization and the shorter the heating time. This function dynamically characterizes the distribution of the target vulcanization temperature relative to the entire heating range. When the target vulcanization temperature is close to the mold surface heating temperature, the logarithmic term tends to be larger, indicating that the heating time can be significantly shortened. However, when the target vulcanization temperature is close to the initial temperature of the rubber compound, the logarithmic term is smaller, indicating that the heating time required is prolonged. It has good dynamic response characteristics and can achieve accurate time correction under different temperature rise conditions. Overall, it can be understood as an estimated value of the non-steady-state heat conduction time required to conduct from the cavity wall temperature to the center point temperature, which is used to measure the degree of temperature rise lag in the center of the cavity.

[0081] The method for obtaining the filling lag time includes:

[0082] ;

[0083] in, is the filling lag time of the m-th cavity sub-area, is the flow path length of the m-th cavity sub-region, is the effective pressure of the m-th cavity sub-region, The filling lag time is the apparent viscosity of the rubber compound. The filling lag time indicates the estimated time required for the rubber compound to flow from the center area of ​​the mold cavity to the edge area and complete the main filling process. It is used to determine whether there is a risk of delayed filling of the rubber compound to edge structures such as corners and ribs after the mold is closed. It is the core indicator for determining whether temperature equalization compensation needs to be delayed in the zone temperature control strategy.

[0084] It should be noted that The overall reflection of the coupling effect between the flow difficulty of the rubber itself and the spatial structure of the mold cavity is the main internal factor leading to filling delay; It represents the geometric impedance generated by the flow path of the rubber compound in the mold cavity. It reflects the physical characteristics that the filling resistance gradually increases as the flow path extends and the filling lag in the distal area is significant, especially in the complex rubber cavity structure. It indicates the resistance under unit shear rate and has a nonlinear relationship with the flow rate. It is an important physical property parameter that determines the flow rate of the rubber. The increase of this parameter means that the rubber is more viscous and the flow time is prolonged. It accurately reflects whether there is a time lag in the filling of the far end of the mold cavity, characterizes the actual flow time of high-viscosity rubber materials in complex mold cavities, and provides a basis for the temperature control system to judge whether the edge area is full. By constructing the filling lag time, this application realizes the time quantitative modeling of the flow process of rubber compound from the center of the mold cavity to the edge. The filling lag time comprehensively considers the coupling relationship between the viscosity of the rubber compound, the length of the structural path and the effective pressure, and provides theoretical support for whether the edge area of ​​the mold cavity has been completed. It helps the temperature control system to make early judgments on local thermal compensation, thereby significantly improving the vulcanization uniformity and product consistency.

[0085] The method for obtaining the nonlinear temperature rise index includes:

[0086] ;

[0087] in, is a nonlinear temperature rise index, The heat released by vulcanization per unit mass of rubber material, is the specific heat capacity of the rubber compound. The nonlinear temperature rise index refers to the theoretical temperature rise amplitude of the rubber compound per unit volume due to the exothermic heat of the vulcanization reaction, and the degree of nonlinear temperature jump caused by the exothermic heat of the reaction rubber compound itself. It is an important criterion for determining temperature control fluctuations caused by thermo-chemical behavior. By introducing the nonlinear temperature rise index, a quantitative assessment mechanism for the impact of the exothermic behavior of rubber vulcanization on the local temperature distribution in the mold cavity was established. This can accurately reflect the temperature jump trend caused by chemical exothermic reactions, effectively assisting the system in identifying potential temperature overshoot and uneven vulcanization between the edge and center, thereby achieving early pre-adjustment of the thermal field within the mold cavity and differentiated temperature control, improving product quality consistency and system response speed.

[0088] Methods for merging M cavity sub-regions according to M regional response scores to obtain N temperature control sub-regions include:

[0089] S200: Assume that the initial value of m is 1, and the value range of m is 1 to M; classify the M mold cavity sub-regions into temperature control region types based on the M region response scores, and obtain the temperature control region types corresponding to the M mold cavity sub-regions; initialize the processing flags of the M mold cavity sub-regions to unprocessed; and set the initial value of the count variable N of the temperature control sub-region to 1;

[0090] S201: If the processing mark of the m-th mold cavity sub-region is unprocessed, then the mold cavity sub-regions adjacent to the m-th mold cavity sub-region, having the same temperature control region type and processing mark as unprocessed are constructed into the N-th set of regions to be merged and processed; if the processing mark of the m-th mold cavity sub-region is processed, then execute S203;

[0091] S202: Determine whether there is a mold cavity sub-region that is adjacent to the mold cavity sub-region in the Nth set of regions to be merged and processed, has the same temperature control region type, and is marked as unprocessed. If so, add the corresponding mold cavity sub-region to the Nth set of regions to be merged and process, and continue executing S202. If not, construct the mold cavity sub-region in the Nth set of regions to be merged and process into the Nth temperature control sub-region, set the corresponding processing mark to processed, and execute S203.

[0092] S203: Set m=m+1. If m is less than or equal to M, set N=N+1 and return to S201 to continue execution. If m is greater than M, N temperature control sub-areas are obtained and the current process ends.

[0093] The training method of the regional response assessment model includes:

[0094] Pre-constructing a regional response evaluation dataset, wherein the regional response evaluation dataset includes PG group regional response evaluation data and regional response scores corresponding to the PG group regional response evaluation data, where PG is a positive integer; the regional response evaluation data includes a temperature delay parameter, a filling lag time, and a nonlinear temperature rise index; dividing the regional response evaluation dataset into a training set and a validation set, wherein the training set is used for learning parameters of the regional response evaluation model, and the validation set is used for real-time monitoring of the generalization performance and overfitting degree of the regional response evaluation model;

[0095] A deep neural network with a multi-layer perceptron as the core is used as the regional response assessment model. The regional response assessment data is input into the deep neural network after standardization and vectorization processing. The deep neural network consists of an input layer, a hidden layer and an output layer. Each hidden layer uses a nonlinear activation function to extract high-order features. The output layer uses a softmax activation function to obtain the probability distribution corresponding to each regional response score. Finally, the regional response score corresponding to the maximum probability is taken as the prediction result of the regional response assessment model. During the training process, the cross-entropy loss function is used as the optimization target, and a gradient descent optimization algorithm is used to update the network weights. An early stopping strategy is set: when the prediction accuracy on the validation set reaches or exceeds the preset threshold, the regional response assessment model is determined to have converged and training is terminated.

[0096] The method for dividing the M mold cavity sub-regions into temperature control region types based on the M region response scores and obtaining the temperature control region types corresponding to the M mold cavity sub-regions includes:

[0097] A high response critical threshold and a low response critical threshold are preset, and the high response critical threshold is greater than the low response critical threshold; the M regional response scores are compared with the high response critical threshold and the low response critical threshold respectively. If the regional response score is greater than or equal to the high response critical threshold, the corresponding mold cavity sub-region is marked as a key temperature control region; if the regional response score is less than the high response critical threshold and greater than or equal to the low response critical threshold, the corresponding mold cavity sub-region is marked as a secondary response region; if the regional response score is less than the low response critical threshold, the corresponding mold cavity sub-region is marked as a conventional temperature control region.

[0098] It should be noted that the value range of the regional response score is The high response critical threshold and the low response critical threshold are set by those skilled in the art based on the quantiles of the historical regional response score distribution. For example, the high response critical threshold is the 80th percentile of the regional response score distribution, i.e., 0.8, and the low response critical threshold is the 45th percentile of the regional response score distribution, i.e., 0.45. By setting the high response critical threshold and the low response critical threshold and classifying the mold cavity sub-regions based on the regional response score distribution, compared to the prior art, the present application can dynamically identify temperature-sensitive areas and rationally allocate temperature control resources according to the response strength.

[0099] Traditional temperature control systems usually divide the mold cavity into fixed heating zones based on the mold geometry or manufacturing drawings. This fails to reflect the differences in thermal responses of different zones during the actual molding process, and makes it difficult to cope with the changing working conditions of multiple rubber materials and multiple molds. In existing solutions, if regional precision is controlled, each sub-zone corresponds to a control channel, and a large number of heating elements and sampling modules need to be configured, resulting in high system complexity and cost. This application performs partition aggregation based on regional response scores, and can identify areas that are truly thermally sensitive or lagging for the current batch of rubber materials and the current mold combination, thereby realizing intelligent regional division that is "response-driven rather than structure-driven." By setting a response score threshold and combining regional adjacency to aggregate and merge regions, dynamic and adaptive construction of cavity temperature control sub-zones is achieved. Compared with traditional fixed heating zone control strategies, this method can flexibly divide temperature control zones according to actual thermal response behavior, improve temperature control precision and responsiveness, reduce control resource requirements, and enhance the system's adaptability to multi-rubber and multi-mold scenarios. It is a key link in realizing optimized control of intelligent temperature control systems by partitioning.

[0100] For example, in a preferred embodiment of the present invention, when the number N of temperature control sub-regions is 5, the system divides and merges the M mold cavity sub-regions into 5 temperature control sub-regions according to the aforementioned regional response score merging method. Each temperature control sub-region corresponds to one or more spatially adjacent sets of mold cavity sub-regions with the same response type, and is assigned a corresponding temperature control region type. In this embodiment, the temperature control sub-region numbers, mold cavity sub-region sets, and temperature control region types are shown in Table 1 below.

[0101] Table 1 Temperature control mapping table

[0102]

[0103] It should be noted that in a preferred embodiment of the present application, as shown in the temperature control mapping table, the area with the temperature control sub-area numbered 1 selects the cavity sub-area set {3, 4}, and the cavity sub-area set is composed of two adjacent cavity partitions at the end of the filling path. Based on the comprehensive judgment of the self-response strength index, coupling influence strength index and migration asymmetry set of the dynamic heat transfer coupling matrix, the temperature control sub-area is identified as the key temperature control area.

[0104] The hardware mapping module is used to associate and bind N temperature control sub-areas with temperature control actuators actually arranged in the mold cavity, establish a hardware mapping index relationship, and construct a hardware mapping index relationship table.

[0105] like Figure 4 As shown, the method for constructing the hardware mapping index relationship table includes:

[0106] Extract the boundary 3D coordinates of N temperature control sub-areas and construct the coverage of the temperature control sub-areas;

[0107] Extract the working coverage area corresponding to each temperature control actuator according to the installation position of the temperature control actuator and the working coverage range of the temperature control actuator;

[0108] For each temperature control actuator, spatial overlap is calculated with the temperature control sub-area coverage of N temperature control sub-areas in turn to obtain the corresponding geometric intersection area. The geometric intersection area is divided by the working coverage area of ​​the temperature control actuator to obtain the temperature control area attribution ratio. The temperature control actuator is then assigned to the temperature control sub-area with the largest temperature control area attribution ratio. The temperature control actuator includes a heating element, a cooling channel, and a temperature acquisition sensor.

[0109] The temperature control actuators, temperature control area attribution ratios, and temperature control area types corresponding to the N temperature control sub-areas are constructed to obtain a hardware mapping index relationship table.

[0110] An example of the hardware mapping index relationship table is shown in Table 2:

[0111] Table 2 Hardware mapping index relationship table

[0112]

[0113] In a preferred embodiment of the present application, as shown in the hardware mapping index relationship table, the area corresponding to the temperature control sub-area numbered 1 is identified as the key temperature control area, and based on the dynamic heat transfer coupling matrix and the Boolean response label table, it is determined that the temperature control sub-area needs to implement a differentiated fine temperature control strategy. The temperature control sub-area is precisely bound to the heating, cooling and temperature acquisition hardware through the hardware mapping index relationship table, wherein the heating element sequence includes electric heating components numbered JR1 and JR3, the cooling channel sequence is a liquid cooling path numbered LQ1, and the temperature acquisition sensor sequence is a multi-point thermocouple temperature measurement device numbered WD1 and WD2. On the one hand, the above configuration ensures that the area has a high power adjustment flexibility during the heating stage and can dynamically adjust the output power ratio according to the self-response strength index. On the other hand, through the coupling of the cooling channel and multi-point temperature measurement, it has the ability to correct temperature control lag and suppress boundary thermal disturbances.

[0114] In addition, the temperature control area attribution ratio is 0.77, indicating that this temperature control sub-region covers the vast majority of the temperature control area of ​​the corresponding cavity sub-region, and has a significant regional master control effect in the execution of the control strategy. Through the above hardware mapping relationship, when the system generates a set of control instructions, it can directly assign the adjustment target value and compensation type label to execution units such as JR1, JR3, and LQ1 based on the region type and thermal response index, forming a highly targeted closed-loop control instruction, realizing the whole process linkage control from regional characteristic identification to precise matching of execution elements, significantly improving temperature control accuracy, response speed and system stability, and supporting the core technology implementation path of the adaptive temperature control compensation method of this application.

[0115] It should be noted that by constructing the above-mentioned hardware mapping index relationship table, compared with the existing technology, this application realizes an automated and structured binding mechanism between temperature control areas and physical control elements, providing underlying support for the precise scheduling, signal mapping and channel instruction routing of regional temperature control strategies, significantly improving the intelligence level and adaptability of the system.

[0116] The matrix construction module performs dynamic modeling based on the hardware mapping index relationship table and the operating process parameters of N temperature control sub-areas per unit time to construct a dynamic heat transfer coupling matrix.

[0117] like Figure 5 As shown, the method for constructing the dynamic heat transfer coupling matrix includes:

[0118] Divide the unit time into Q time points;

[0119] Obtain the temperature and power corresponding to N temperature control sub-areas at Q time points, and construct the temperature set and power set corresponding to the N temperature control sub-areas;

[0120] Based on the temperature sets and power sets of N temperature control sub-areas, a thermal response linear regression model corresponding to each temperature control sub-area is constructed. The gain vector is calculated, the parameter vector estimate is updated, and the covariance matrix is ​​updated at Q time points of each thermal response linear regression model to obtain the heat transfer coupling coefficient vectors of the N temperature control sub-areas. With the heat transfer coupling coefficient vectors as rows, the N heat transfer coupling coefficient vectors are sequentially constructed into a dynamic heat transfer coupling matrix.

[0121] The construction method of the thermal response linear regression model is:

[0122] ;

[0123] in, is the temperature control sub-area at time point i The temperature at time i ranges from 1 to N, which is used to represent the temperature of N temperature control sub-areas at time points. The temperature at The value range is from 1 to Q. Indicates the jth temperature control sub-area at time point The power when represents the error term of the i-th temperature control sub-region, representing measurement error, non-modeled disturbance or system noise, is the heat transfer coupling coefficient, which is obtained by sequentially calculating the gain vector, updating the parameter vector estimate, and updating the covariance matrix for each thermal response linear regression model at Q time points. The temperature response process of the rubber mold temperature control system has thermal inertia and approximate linear hysteresis characteristics. The temperature will not respond instantaneously to the power input, so it can be approximated as a first-order hysteresis linear model. The thermal response linear regression model adopts a linear regression structure. Based on the physical property that the temperature response lags behind the power input in the thermal inertia system, a weighted relationship between the current temperature and the multi-region input power at the previous moment is constructed; by introducing the heat transfer coupling coefficient, multi-region thermal diffusion modeling is realized, taking into account both recognition accuracy and computational efficiency; the error term It provides robust tolerance to disturbances and offers an interpretable and online-updatable thermal behavior basis for subsequent optimal control strategies.

[0124] The calculation method of the gain vector includes:

[0125] ;

[0126] in, Indicates the temperature control sub-area i at time point The gain vector when is the covariance matrix of the ith temperature control sub-area (the covariance matrix dimension is N×N), indicating the time point The uncertainty of the estimated values ​​of each parameter is greater when the value is larger, the more uncertain it is. The initial value of the covariance matrix is , Initialize weights for the covariance, The value of is usually to , is the identity matrix; For N temperature control sub-areas at time point The power input vector composed of the power at time (the dimension of the power input vector is N×1); The forgetting factor is usually set to a value between 0.95 and 1. It is used to control whether to attach importance to new data and prevent historical estimates from being completely dominated. Exemplarily, it can be 0.95 in this application. express The transpose of It is used to measure the matching degree between the current input and the covariance structure and determine the overall gain. The whole acts as a regularization factor to ensure numerical stability and prevent oscillation or overfitting. Represents the projection of the input vector into the current covariance space. The overall effect is that when the number of times the new power input vector appears in the history is less than the preset number threshold and the difference between the temperature predicted by the model and the actual measured temperature is greater than the preset difference threshold, it indicates that the gain is large and the update is fast; otherwise, it indicates that the gain is small and remains stable.

[0127] For example, in this application, The value of is set to , is a 3rd order identity matrix, then the covariance matrix is .

[0128] The method for updating the parameter vector estimated value includes:

[0129] ;

[0130] in, The i-th temperature control sub-area at time point The estimated results of the heat transfer coupling coefficient when is the temperature control sub-area at time point i The heat transfer coupling coefficient estimation result at is . The update formula for the parameter vector estimate is structured as: new estimate = old estimate + gain × residual. This is used to dynamically correct the heat transfer coupling coefficient of the i-th temperature control sub-region within each sampling period. This implements an optimal minimum mean square error estimation mechanism that integrates new observation information while maintaining the continuity of historical estimates, providing the core adaptive update capability for constructing the dynamic heat transfer coupling matrix.

[0131] The updating method of the covariance matrix includes:

[0132] ;

[0133] in, is the temperature control sub-area at time point i The covariance matrix at the time of , the update formula of the covariance matrix is ​​used to dynamically adjust the uncertainty estimation of the heat transfer coupling coefficient of the i-th temperature control sub-region. The compression term in the observation direction is constructed by multiplying the current gain vector with the power input vector and the historical covariance matrix, and the forgetting factor is introduced , a recursive update mechanism based on directional information compression and historical memory decay is implemented. Its role is to ensure that the system maintains the numerical stability and sensitivity of parameter updates while gradually learning the thermal response rules, avoiding overfitting or premature convergence.

[0134] For example, in this application, the final dynamic heat transfer coupling matrix is .in, It represents the direct response intensity of the power input of the Nth temperature control sub-region to the temperature of the Nth temperature control sub-region.

[0135] The temperature control compensation module performs adaptive temperature control compensation on N temperature control sub-areas based on the hardware mapping index relationship table and the dynamic heat transfer coupling matrix.

[0136] like Figure 6 As shown, the method for adaptive temperature control compensation of the hydraulic forming mold of rubber products includes:

[0137] S300: Let the initial value of n be 1, and the value range of n be 1 to N;

[0138] S301: Calculating the self-response intensity index, coupling influence intensity index, and migration asymmetry set of the nth temperature control sub-region based on the dynamic heat transfer coupling matrix;

[0139] S302: Inputting the temperature control region type, key physical property parameters, self-response strength index, coupling influence strength index, and migration asymmetry set of the nth temperature control sub-region into a threshold setting model to obtain dynamically set self-response strength index thresholds and coupling influence strength index thresholds;

[0140] Based on the self-response intensity index threshold and the coupling influence intensity index threshold, a Boolean response label assignment operation is performed on the self-response intensity index, the coupling influence intensity index and the migration asymmetry set to construct a Boolean response label table;

[0141] The hardware mapping index relationship table, Boolean response label table, temperature control area type, self-response strength index, coupling influence strength index and migration asymmetry set are input into the control instruction setting model to obtain a temperature control instruction set; the control instruction includes a control cycle number, a temperature control actuator number, an adjustment target value and a compensation type label; the adjustment target value includes heating power percentage, cooling valve opening, PID reference temperature; the compensation type label includes preheating, delay, and limit.

[0142] Send the temperature control instruction set to the corresponding actuator through the system control interface (PLC, industrial bus or embedded hardware protocol) for execution to achieve adaptive temperature control compensation;

[0143] S303: Let n=n+1. If n is less than or equal to N, return to S301 to continue execution. If n is greater than N, end the current process.

[0144] It should be noted that the self-response strength index of the nth temperature control sub-region is the element corresponding to the nth row and nth column in the dynamic heat transfer coupling matrix; the self-response strength index represents the degree of response of the nth temperature control sub-region to the temperature change of its own power input. The larger the self-response strength index, the stronger the regional response and the more sensitive it is to temperature rise.

[0145] The method for obtaining the coupling influence strength index for the nth temperature-controlled sub-region specifically includes constructing a coupling influence element set from all elements in the nth row of the dynamic heat transfer coupling matrix, excluding the element corresponding to the nth row and nth column from the coupling influence element set, and then taking the absolute values ​​of all elements in the coupling influence element set and summing them to obtain the coupling influence strength index. The coupling influence strength index is used to identify thermally coupled-sensitive areas. A larger coupling influence strength index indicates that the temperature control of this area is susceptible to interference from changes in power input from other areas.

[0146] The method for obtaining the migration asymmetry set of the nth temperature control sub-region specifically includes:

[0147] S400: Set the initial value of the index variable h to 1, and the value range of h is 1 to N;

[0148] S401: Obtain the element corresponding to the position (n, N+1-h) from the dynamic heat transfer coupling matrix, record it as the first element, and obtain the element corresponding to the position (N+1-h, n) from the dynamic heat transfer coupling matrix, record it as the second element;

[0149] S402: Subtract the second element from the first element and take the absolute value to obtain a migration asymmetry index. If the migration asymmetry index is greater than a preset migration asymmetry index threshold, the nth temperature control sub-region and the N+1-hth temperature control sub-region are constructed into a migration asymmetry combination, and the migration asymmetry combination is added to the migration asymmetry set.

[0150] S403: Let h = h + 1. If h is less than or equal to N, return to S401 to continue execution. If h is greater than N, end the current process.

[0151] It should be noted that the migration asymmetry index threshold is set by the heat transfer coupling coefficient under multiple batches of different rubber materials and different mold structures.

[0152] For example, in this application, the maximum value of the heat transfer coupling coefficient in the dynamic heat transfer coupling matrix is ​​obtained, and the migration asymmetry index threshold is set to ;in is the empirical coefficient, Set according to the mold-experience coefficient mapping table, is the maximum value of the heat transfer coupling coefficient in the heat transfer coupling matrix.

[0153] The mold-experience coefficient mapping table is shown in Table 3:

[0154] Table 3 Mold-experience coefficient mapping table

[0155]

[0156] It should be noted that, in a preferred embodiment of the present application, the migration asymmetry set is used to identify regional combinations with strong thermal migration bias.

[0157] The regional combinations in the migration asymmetry set are significantly manifested in the actual operation process as directional differences in the geometric configuration of the heat diffusion path, uneven distribution of thermal conductivity of materials between regions, or significant differences in heat exchange coefficients under local boundary conditions, which in turn trigger the asymmetric characteristics of the bidirectional heat transfer effect, which has a significant impact on the dynamic balance of regional temperature and the stability of coupled regulation.

[0158] The training method of the threshold setting model includes:

[0159] Pre-constructing a threshold setting data set, the threshold setting data set including YZ group threshold setting data and self-response strength index thresholds and coupling influence strength index thresholds corresponding to the YZ group threshold setting data, where YZ is a positive integer, and the threshold setting data including temperature control area type, key physical property parameters, response strength index, coupling influence strength index, and migration asymmetry set; dividing the threshold setting data set into a training set and a validation set, the training set being used for learning threshold setting model parameters, and the validation set being used for real-time monitoring of the generalization performance and overfitting degree of the threshold setting model;

[0160] A deep neural network with a multilayer perceptron as the core is used as the threshold setting model. The threshold setting data is input into the deep neural network after standardization and vectorization processing. The deep neural network consists of an input layer, a hidden layer and an output layer. Each hidden layer uses a nonlinear activation function to extract high-order features. The output layer uses a softmax activation function to obtain the probability distribution corresponding to the respective response intensity index threshold and coupling influence intensity index threshold. Finally, the self-response intensity index threshold and coupling influence intensity index threshold corresponding to the maximum probability are taken as the prediction results of the threshold setting model. During the training process, the cross entropy loss function is used as the optimization target, and a gradient descent optimization algorithm is used to update the network weights. An early stopping strategy is set: when the prediction accuracy on the validation set reaches or exceeds the preset threshold, the threshold setting model is determined to have converged and the training is terminated.

[0161] The method for constructing the Boolean response label table includes:

[0162] If the migration asymmetry set is not empty, the Boolean response label of the migration asymmetry set is assigned a value of yes; if the migration asymmetry set is empty, the Boolean response label of the migration asymmetry set is assigned a value of no;

[0163] If the self-response strength indicator is greater than the self-response strength indicator threshold, the Boolean response label of the self-response strength indicator is assigned a value of yes; if the self-response strength indicator is less than or equal to the self-response strength indicator threshold, the Boolean response label of the self-response strength indicator is assigned a value of no;

[0164] If the coupling impact strength indicator is greater than the coupling impact strength indicator threshold, the Boolean response label of the coupling impact strength indicator is assigned a value of yes; if the coupling impact strength indicator is less than or equal to the coupling impact strength indicator threshold, the Boolean response label of the coupling impact strength indicator is assigned a value of no;

[0165] The self-response strength index, coupling influence strength index, migration asymmetry set and the corresponding Boolean response labels are constructed into a Boolean response label table.

[0166] An example of the Boolean response label table is shown in Table 4:

[0167] Table 4 Boolean response label table

[0168]

[0169] The training method of the control instruction setting model includes:

[0170] A control instruction setting data set is pre-constructed, comprising SD group control instruction setting data and a temperature control instruction set corresponding to the SD group control instruction setting data, where SD is a positive integer; the control instruction setting data set comprises a hardware mapping index relationship table, a Boolean response label table, a temperature control zone type, a response strength index, a coupling influence strength index, and a migration asymmetry set; the control instruction setting data set is divided into a training set and a validation set, the training set being used for learning control instruction setting model parameters, and the validation set being used for real-time monitoring of the generalization performance and overfitting degree of the control instruction setting model;

[0171] A deep neural network with a multilayer perceptron as the core is used as the control instruction setting model. The control instruction setting data is input into the deep neural network after standardization and vectorization processing. The deep neural network consists of an input layer, a hidden layer and an output layer. Each hidden layer uses a nonlinear activation function to extract high-order features. The output layer uses a Softmax activation function to obtain the probability distribution corresponding to each temperature control instruction set. Finally, the temperature control instruction set corresponding to the maximum probability is taken as the prediction result of the control instruction setting model. During the training process, the cross-entropy loss function is used as the optimization target, and a gradient descent optimization algorithm is used to update the network weights. An early stopping strategy is set: when the prediction accuracy on the validation set reaches or exceeds the preset threshold, the control instruction setting model is judged to have converged and training is terminated.

[0172] It should be noted that during the adaptive temperature control compensation process for rubber product hydroforming dies, a control instruction setting model is constructed based on the hardware mapping index relationship table, Boolean response label table, temperature control zone type, self-response strength index, coupling influence strength index, and migration asymmetry set, and a differentiated temperature control instruction set is output. This design has clear functional roles and logical rationality, specifically including:

[0173] The hardware mapping index relationship table binds control logic to physical actuators, ensuring that generated control commands are accurately mapped to actual hardware such as heaters, cooling channels, and sensors, ensuring robust and regionally targeted execution of control commands. The Boolean response label table quickly indicates whether each sub-region exhibits thermal response behaviors such as thermal hysteresis, coupling interference, or migration anomalies. This provides clear triggering criteria for control strategies, facilitating conditional flow diversion and control strength assessment within the command setting logic. Temperature control zone types reflect mold structural or functional characteristics (e.g., central core zone, edge transition zone), facilitating fine-tuning of control strategies within the control model within similar response labels, enhancing control refinement. Response strength and coupling influence indices, as continuous-value inputs of thermal response behavior, assist the control model in dynamically quantifying and adjusting control parameters (e.g., output power percentage, response delay, and PID slope) based on the degree of response. The migration asymmetry set identifies asymmetric heat transfer pairs between regions, enabling the regulation of adjacent regions to be restricted, staggered, or protected against overshoot or conflicting responses.

[0174] Compared with the temperature control method of "fixed threshold + unified strategy template" commonly used in the prior art, this application constructs a multi-dimensional thermal response feature system with self-response strength index, coupling influence strength index and migration asymmetry set as the core, and combines the hardware mapping index relationship table and regional type information to form a control instruction setting mechanism that can dynamically determine, respond differentially and accurately implement. This mechanism can not only realize differentiated identification and labeling expression of thermal response behaviors for different temperature control sub-regions, but also perform hierarchical matching and dynamic parameter adjustment of control strategies based on the judgment results, breaking through the coarse-grained judgment method of "whether to control or not without distinction of strength" of existing control systems. At the same time, the instruction generation process is closely related to the hardware control path, and the response characteristics can be directly mapped to specific execution elements, building a closed-loop path from thermal behavior perception to control instruction execution, and ensuring the speed and accuracy of system response. In addition, the application further introduces a migration asymmetry set to determine the direction of thermal offset between regions, avoid concurrent conflicts of control instructions in the coupling channel, and effectively avoid oscillation and overshoot problems caused by thermal disturbances between regions.

[0175] In summary, the control instruction setting mechanism proposed in this application is not only more refined in the dimension of thermal response modeling, but also more intelligent and adaptable in the control strategy generation and execution level. It can significantly improve the thermal uniformity, response speed and product vulcanization consistency of the multi-zone coupled temperature control system, breaking through the technical bottlenecks of traditional temperature control strategies in single-valued logic, non-adaptive adjustment and boundary response lag, and overall achieving substantial improvements and creative breakthroughs compared with existing technologies.

[0176] Example 2

[0177] See also Figure 2 As shown, this embodiment provides a rapid temperature control and compensation method for a hydraulic forming mold for a rubber product, comprising:

[0178] Based on the key physical properties of the rubber compound before entering the mold, a dynamic temperature control zoning strategy is implemented for the rubber product hydroforming mold to form N temperature control sub-areas;

[0179] Associating and binding the N temperature control sub-areas with the temperature control actuators actually arranged in the mold cavity, establishing a hardware mapping index relationship, and constructing a hardware mapping index relationship table;

[0180] Based on the hardware mapping index relationship table and the operating process parameters of N temperature control sub-areas per unit time, dynamic modeling is performed to construct a dynamic heat transfer coupling matrix;

[0181] Adaptive temperature control compensation is performed on N temperature control sub-areas based on the hardware mapping index relationship table and the dynamic heat transfer coupling matrix.

[0182] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0183] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A rapid temperature control and compensation method for a hydraulic forming mold for rubber products, characterized in that: include: Based on the key physical properties of the rubber compound before entering the mold, a dynamic temperature control zoning strategy is implemented for the rubber product hydroforming mold to form N temperature control sub-areas; Associating and binding the N temperature control sub-areas with the temperature control actuators actually arranged in the mold cavity, establishing a hardware mapping index relationship, and constructing a hardware mapping index relationship table; Based on the hardware mapping index relationship table and the operating process parameters of N temperature control sub-areas per unit time, dynamic modeling is performed to construct a dynamic heat transfer coupling matrix; The method for constructing the dynamic heat transfer coupling matrix includes: Divide the unit time into Q time points; Obtain the temperature and power corresponding to N temperature control sub-areas at Q time points, and construct the temperature set and power set corresponding to the N temperature control sub-areas; Based on the temperature sets and power sets of the N temperature-controlled sub-regions, a thermal response linear regression model corresponding to each temperature-controlled sub-region is constructed. For Q time points of each thermal response linear regression model, a gain vector is calculated, an estimated parameter vector is updated, and a covariance matrix is ​​updated. A heat transfer coupling coefficient vector of the N temperature-controlled sub-regions is obtained. The heat transfer coupling coefficient vector represents the direct response strength of the power input of the temperature-controlled sub-region to the temperature of the temperature-controlled sub-region itself. The N heat transfer coupling coefficient vectors are sequentially constructed into a dynamic heat transfer coupling matrix, with the heat transfer coupling coefficient vectors as rows. Adaptive temperature control compensation is performed on N temperature control sub-areas based on the hardware mapping index relationship table and the dynamic heat transfer coupling matrix.

2. The rapid temperature control and compensation method for a rubber product hydraulic forming mold according to claim 1, characterized in that: The method of adaptive temperature control compensation for rubber product hydraulic forming mold includes: S300: Let the initial value of n be 1, and the value range of n be 1 to N; S301: Calculating the self-response intensity index, coupling influence intensity index, and migration asymmetry set of the nth temperature control sub-region based on the dynamic heat transfer coupling matrix; S302: Inputting the temperature control region type, key physical property parameters, self-response strength index, coupling influence strength index, and migration asymmetry set of the nth temperature control sub-region into a threshold setting model to obtain dynamically set self-response strength index thresholds and coupling influence strength index thresholds; Based on the self-response intensity index threshold and the coupling influence intensity index threshold, a Boolean response label assignment operation is performed on the self-response intensity index, the coupling influence intensity index and the migration asymmetry set to construct a Boolean response label table; Input the hardware mapping index relationship table, the Boolean response label table, the temperature control area type, the self-response strength index, the coupling influence strength index and the migration asymmetry set into the control instruction setting model to obtain the temperature control instruction set; Send the temperature control instruction set to the corresponding actuator through the system control interface for execution to achieve adaptive temperature control compensation; S303: Let n=n+1. If n is less than or equal to N, return to S301 to continue execution. If n is greater than N, end the current process.

3. The rapid temperature control and compensation method for a rubber product hydraulic forming mold according to claim 2, characterized in that: The method for obtaining the migration asymmetry set of the nth temperature control sub-region specifically includes: S400: Set the initial value of the index variable h to 1, and the value range of h is 1 to N; S401: Obtain the element corresponding to the position (n, N+1-h) from the dynamic heat transfer coupling matrix, record it as the first element, and obtain the element corresponding to the position (N+1-h, n) from the dynamic heat transfer coupling matrix, record it as the second element; S402: Subtract the second element from the first element and take the absolute value to obtain a migration asymmetry index. If the migration asymmetry index is greater than a preset migration asymmetry index threshold, the nth temperature control sub-region and the N+1-hth temperature control sub-region are constructed into a migration asymmetry combination, and the migration asymmetry combination is added to the migration asymmetry set. S403: Let h = h + 1. If h is less than or equal to N, return to S401 to continue execution. If h is greater than N, end the current process.

4. The rapid temperature control and compensation method for a rubber product hydraulic forming mold according to claim 2, characterized in that: The method for constructing the Boolean response label table includes: If the migration asymmetry set is not empty, the Boolean response label of the migration asymmetry set is assigned a value of yes; if the migration asymmetry set is empty, the Boolean response label of the migration asymmetry set is assigned a value of no; If the self-response strength indicator is greater than the self-response strength indicator threshold, the Boolean response label of the self-response strength indicator is assigned a value of yes; if the self-response strength indicator is less than or equal to the self-response strength indicator threshold, the Boolean response label of the self-response strength indicator is assigned a value of no; If the coupling impact strength indicator is greater than the coupling impact strength indicator threshold, the Boolean response label of the coupling impact strength indicator is assigned a value of yes; if the coupling impact strength indicator is less than or equal to the coupling impact strength indicator threshold, the Boolean response label of the coupling impact strength indicator is assigned a value of no; The self-response strength index, coupling influence strength index, migration asymmetry set and the corresponding Boolean response labels are constructed into a Boolean response label table.

5. The rapid temperature control and compensation method for a rubber product hydraulic forming mold according to claim 1, characterized in that: The method for constructing the hardware mapping index relationship table includes: Extract the boundary 3D coordinates of N temperature control sub-areas and construct the coverage of the temperature control sub-areas; Extract the working coverage area corresponding to each temperature control actuator according to the installation position of the temperature control actuator and the working coverage range of the temperature control actuator; For each temperature control actuator, spatial overlap is calculated with the temperature control sub-area coverage of N temperature control sub-areas in turn to obtain the corresponding geometric intersection area. The geometric intersection area is divided by the working coverage area of ​​the temperature control actuator to obtain the temperature control area attribution ratio. The temperature control actuator is then assigned to the temperature control sub-area with the largest temperature control area attribution ratio. The temperature control actuator includes a heating element, a cooling channel, and a temperature acquisition sensor. The temperature control actuators, temperature control area attribution ratios, and temperature control area types corresponding to the N temperature control sub-areas are constructed to obtain a hardware mapping index relationship table.

6. The rapid temperature control and compensation method for a rubber product hydraulic forming mold according to claim 1, characterized in that: The method for forming N temperature control sub-regions includes: S100: Divide the mold cavity into M mold cavity sub-areas; set the initial value of m to 1, and the value range of m to be 1 to M; obtain key physical properties of the rubber compound before entering the mold; the key physical properties include the initial temperature of the rubber compound, the apparent viscosity of the rubber compound, the density of the rubber compound, the target vulcanization temperature, the thermal conductivity of the rubber compound, the specific heat capacity of the rubber compound, the heat of vulcanization per unit mass of the rubber compound, and the activation energy of the vulcanization reaction of the rubber compound; S101: Obtain the thickness from the mold wall to the center of the rubber material in the mth mold cavity sub-region, recorded as the mold cavity half thickness; obtain the mold surface heating temperature of the mth mold cavity sub-region; obtain the flow path length of the rubber material from the mold entry position to the mth mold cavity sub-region; obtain the effective pressure of the mth mold cavity sub-region; S102: Calculating the temperature delay parameter, filling lag time and nonlinear temperature rise index of the m-th cavity sub-region based on key physical properties, cavity half-thickness, mold surface heating temperature, flow path length and effective pressure; S103: Inputting the temperature delay parameter, filling lag time and nonlinear temperature rise index of the m-th cavity sub-region into the regional response evaluation model to obtain a corresponding regional response score; S104: Set m = m + 1. If m is less than or equal to M, return to S102 and continue execution. If m is greater than M, obtain the regional response scores corresponding to the M mold cavity sub-regions and execute S105. S105: Merging the M mold cavity sub-regions according to the M regional response scores to obtain N temperature control sub-regions.

7. The rapid temperature control and compensation method for a rubber product hydraulic forming mold according to claim 6, characterized in that: Methods for merging M cavity sub-regions according to M regional response scores to obtain N temperature control sub-regions include: S200: Set the initial value of m to 1; classify the M mold cavity sub-regions into temperature control region types based on the M region response scores to obtain the temperature control region types corresponding to the M mold cavity sub-regions; initialize the processing flags of the M mold cavity sub-regions to unprocessed; and set the initial value of the count variable N of the temperature control sub-region to 1; S201: If the processing mark of the m-th mold cavity sub-region is unprocessed, then the mold cavity sub-regions adjacent to the m-th mold cavity sub-region, having the same temperature control region type and processing mark as unprocessed are constructed into the N-th set of regions to be merged and processed; if the processing mark of the m-th mold cavity sub-region is processed, then execute S203; S202: Determine whether there is a mold cavity sub-region that is adjacent to the mold cavity sub-region in the Nth set of regions to be merged and processed, has the same temperature control region type, and is marked as unprocessed. If so, add the corresponding mold cavity sub-region to the Nth set of regions to be merged and process, and continue executing S202. If not, construct the mold cavity sub-region in the Nth set of regions to be merged and process into the Nth temperature control sub-region, set the corresponding processing mark to processed, and execute S203. S203: Set m=m+1. If m is less than or equal to M, set N=N+1 and return to S201 to continue execution. If m is greater than M, N temperature control sub-areas are obtained and the current process ends.

8. The rapid temperature control and compensation method for a rubber product hydraulic forming mold according to claim 7, characterized in that: The method for dividing the M mold cavity sub-regions into temperature control region types based on the M region response scores and obtaining the temperature control region types corresponding to the M mold cavity sub-regions includes: A high response critical threshold and a low response critical threshold are preset, and the high response critical threshold is greater than the low response critical threshold; the M regional response scores are compared with the high response critical threshold and the low response critical threshold respectively. If the regional response score is greater than or equal to the high response critical threshold, the corresponding mold cavity sub-region is marked as a key temperature control region; if the regional response score is less than the high response critical threshold and greater than or equal to the low response critical threshold, the corresponding mold cavity sub-region is marked as a secondary response region; if the regional response score is less than the low response critical threshold, the corresponding mold cavity sub-region is marked as a conventional temperature control region.

9. A rapid temperature control and compensation system for a hydraulic forming mold for rubber products, used to implement the rapid temperature control and compensation method for a hydraulic forming mold for rubber products according to any one of claims 1 to 8, characterized in that: include: The temperature control zoning module implements a dynamic temperature control zoning strategy for the rubber product hydroforming mold based on the key physical properties of the rubber compound before it is put into the mold, forming N temperature control sub-areas; A hardware mapping module is used to associate and bind N temperature control sub-areas with the temperature control actuators actually arranged in the mold cavity, establish a hardware mapping index relationship, and construct a hardware mapping index relationship table; The matrix construction module dynamically models the dynamic heat transfer coupling matrix based on the hardware mapping index relationship table and the operating process parameters of N temperature control sub-areas per unit time; The temperature control compensation module performs adaptive temperature control compensation on N temperature control sub-areas based on the hardware mapping index relationship table and the dynamic heat transfer coupling matrix.

Images