Tire electromagnetic induction heating system control method based on multi-physics field coupling simulation

Abstract

The invention discloses a tire electromagnetic induction heating system control method based on multi-physics field coupling simulation. The method comprises the following steps: carrying out multi-physics field coupling simulation modeling; performing system optimization analysis; deploying a real-time control system; and performing performance verification and iterative optimization. According to the method, control over the complex physical process is achieved through multi-physics field coupling simulation, a closed-loop coupling system of physical entity-digital twinborn body-intelligent optimization-real-time control is constructed, data two-way circulation and state real-time synchronization are achieved, and the accuracy and timeliness of system regulation and control are improved; the global optimization of the system performance is realized by adopting a multi-objective optimization method, and the problem of local optimization is avoided; and through deep integration of a digital twinning technology and real-time control, predictability and controllability of the production process are realized, and the tire quality is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of tire vulcanization equipment technology, specifically relating to a control method for a tire electromagnetic induction heating system based on multi-physics field coupling simulation. Background Technology

[0002] Tire vulcanization is a critical process in tire manufacturing, directly impacting tire lifespan, safety performance, and fuel economy. Traditional tire vulcanization processes primarily use steam or superheated water as heating media, resulting in inherent drawbacks such as high energy consumption, low heat transfer efficiency, and insufficient temperature control precision. Statistics show that in a typical tire production process, the vulcanization step accounts for approximately 60% of the total energy consumption, while the thermal energy utilization rate is generally below 40%. This inefficiency not only increases production costs but also contradicts the current trend of green and low-carbon development in manufacturing.

[0003] Electromagnetic induction heating technology, as a novel heating method, has gained widespread attention in the tire vulcanization field in recent years due to its high efficiency, cleanliness, and strong controllability. This technology uses an alternating magnetic field to induce eddy currents inside metal molds or tire components, achieving direct heating and avoiding the multiple energy losses inherent in traditional heat conduction methods. However, electromagnetic induction heating systems still face several technical challenges in practical applications: First, due to the skin effect and edge effect of the electromagnetic field, the heating rates of different parts of the tire (such as the crown, shoulder, and sidewall) vary significantly, leading to inconsistent vulcanization levels and affecting product quality. Second, the system involves complex coupling effects of electromagnetic, temperature, flow, and structural fields, making it difficult to achieve global optimization of system parameters using traditional experience-based trial-and-error methods. Third, the nonlinear and time-varying characteristics of the system make it difficult for traditional PID control methods to achieve ideal control results, often resulting in temperature overshoot or excessively long settling times.

[0004] In the field of multiphysics coupling simulation and optimal control, existing technologies have gaps: on the one hand, most studies focus only on a single physical field or simple two-way coupling, failing to fully consider the complex interactions between electromagnetic, thermal, fluid, and solid-state multiple physical fields; on the other hand, optimal design and real-time control are often treated as two separate processes, lacking an integrated solution from simulation to control. These technological limitations make it difficult for existing tire electromagnetic induction heating systems to achieve an optimal balance in terms of energy consumption, efficiency, and quality. Summary of the Invention

[0005] To overcome the problems existing in the prior art, this invention provides a control method for a tire electromagnetic induction heating system based on multi-physics field coupling simulation. Through multi-physics field coupling simulation technology, the electromagnetic field, temperature field, flow field and structural field in the tire electromagnetic induction heating system are simulated and optimized in a coordinated manner, thereby achieving precise control of the heating process and optimized design of system parameters.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A control method for a tire electromagnetic induction heating system based on multiphysics coupling simulation includes the following steps:

[0008] (1) Multi-physics field coupling simulation modeling: A parametric three-dimensional model including tire mold, electromagnetic coil, nitrogen flow channel and vulcanizing container is established. Then, an electromagnetic-thermal-fluid-solid multi-physics field bidirectional coupling simulation model is established and analyzed to fully reproduce the physical phenomena in the tire electromagnetic induction heating process.

[0009] (2) System optimization analysis: Taking coil structure parameters and process parameters as design variables, and heating time, temperature uniformity, energy consumption and maximum stress as optimization objectives, a multi-objective genetic algorithm is used to solve the Pareto optimal solution set, complete the optimization of the simulation model, and output the reduced-order model;

[0010] (3) Deploy a real-time control system: Construct a real-time predictive control system that includes a reduced-order model, a sensor input module, a fuzzy PID control module, and an actuator output module, which communicates with the field PLC controller, collects temperature sensor data from multiple points inside the capsule in real time, predicts the temperature distribution trend through the reduced-order model, and dynamically adjusts the coil power and nitrogen valve opening using the fuzzy PID algorithm.

[0011] (4) Performance verification and iterative optimization: By comparing the predicted values ​​with the actual sensor measurements, the control accuracy and reliability of the system are verified; and a continuous optimization mechanism for the system is established, feeding back the actual production data to the simulation model and updating the model parameters regularly.

[0012] Preferably, in step (1), a multiphysics coupling simulation model is established in the ANSYS Workbench platform. Based on the feedback data of the physical entity, the ANSYS Workbench platform runs the electromagnetic-thermal-fluid-solid multiphysics coupling simulation model to complete the accurate mapping between the virtual model and the physical entity. Then, parameterization and automated analysis are carried out, performance prediction and virtual verification are performed, and the high-precision model and performance prediction results are output to step (2).

[0013] Preferably, step (1) is achieved through the following steps: First, the three-dimensional model is established in ANSYS DesignModeler. The design variables of the three-dimensional model include coil spacing, flow channel diameter and container wall thickness. These parameters will be used as design variables for system optimization in the subsequent optimization process.

[0014] Secondly, after completing the geometric modeling, a complete coupled simulation process was built in the ANSYS Workbench platform. In the electromagnetic field analysis, the coil excitation frequency range was set to 1-100kHz, the eddy current loss distribution was calculated and transferred to the temperature field as a heat source; the temperature field analysis considered the temperature dependence of material properties and achieved bidirectional coupling with the electromagnetic field through a feedback iterator; the flow field analysis set the nitrogen inlet pressure and mass flow rate, and performed conjugate heat transfer calculation with the temperature field through the system coupling component; the structural field analysis received the flow field pressure load through another coupling component and calculated the transient deformation process of the container expansion.

[0015] Preferably, in step (2), when designing experiments and modeling response surfaces using the DesignXplorer module, coil structure parameters and process parameters are used as design variables, and heating time, temperature uniformity, energy consumption and maximum stress are used as optimization objectives. At the same time, a uniformly distributed Pareto optimal solution set is obtained by non-dominated sorting and crowding calculation using a multi-objective genetic algorithm.

[0016] Preferably, in step (3), the high-fidelity model output by ANSYS Workbench is imported into the Twin Builder platform to generate a reduced-order model, and multi-point temperature sensor data inside the capsule are collected in real time. The temperature distribution trend is predicted through the reduced-order model, and the coil power and nitrogen valve opening are dynamically adjusted using the fuzzy PID algorithm.

[0017] Preferably, the fuzzy PID controller, based on expert experience and simulation analysis results, takes temperature deviation and its rate of change as input variables and adaptively adjusts PID parameters through a fuzzy inference mechanism, effectively overcoming the nonlinear and time-varying characteristics of the system.

[0018] Preferably, in step (4), a historical process parameter database is established to record the process parameters and product quality data of each successful run; the system's automatic correction algorithm automatically adjusts the model parameters according to the deviation between the actual production data and the simulation prediction values; and a periodic re-optimization mechanism is set up to automatically start the re-optimization process when the system performance indicators show a downward trend.

[0019] Compared with the prior art, the present invention has the following advantages:

[0020] 1. This invention achieves control of complex physical processes through multi-physics coupling simulation, breaking through the technical bottleneck of the separation between simulation, optimization, and control. It constructs a closed-loop coupling system of "physical entity - digital twin - intelligent optimization - real-time control," realizing bidirectional data flow and real-time state synchronization, thus improving the accuracy and timeliness of system regulation. Furthermore, it adopts a multi-objective optimization method to achieve global optimization of system performance, avoiding local optima problems. Through the deep integration of digital twin technology and real-time control, it achieves predictability and controllability of the production process, resulting in a significant improvement in tire quality.

[0021] 2. This invention can improve the uniformity of tire vulcanization temperature, avoid the problem of uneven heating in different parts of the tire, improve the qualified rate of finished tires, and shorten the heating time and reduce energy consumption.

[0022] 3. This invention is not only applicable to tire vulcanization processes to promote the transformation and upgrading of the tire manufacturing industry towards intelligence and green development; it can also be extended to other industrial heating fields that require precise temperature control, and has broad application prospects and technical promotion value. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the closed-loop coupled system in this invention, consisting of a physical entity, a digital twin, an intelligent optimization engine, and a real-time predictive control system.

[0024] Figure 2 This is a diagram showing the multi-physics coupling relationship of a tire electromagnetic vulcanization system.

[0025] Figure 3 A multi-physics coupling simulation architecture for tire electromagnetic vulcanization systems;

[0026] Figure 4 Optimize the control process for the tire electromagnetic vulcanization system. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] This invention relates to a control method for a tire electromagnetic induction heating system based on multiphysics field coupling simulation, such as... Figure 1 As shown, it includes four stages: multiphysics coupled simulation modeling, system optimization analysis, deployment of real-time control system, and performance verification and iterative optimization. These four stages operate in a closed-loop coupled system consisting of physical entities, digital twins, intelligent optimization engines, and real-time predictive control systems. The digital twin consists of a multiphysics coupled simulation model that has been dynamically calibrated with real-time data and a reduced-order model (ROM) driven by it and used for real-time calculation.

[0029] As a preferred embodiment, the present invention provides a tire electromagnetic induction heating system control method based on multiphysics field coupling simulation, comprising the following steps:

[0030] (1) Multiphysics coupling simulation modeling: By integrating solvers such as Maxwell, TransientThermal, Fluent, and TransientStructural on the ANSYS Workbench platform, an electromagnetic-thermal bidirectional coupling model of temperature-related material properties was established. The thermal-fluid coupling and fluid-structure coupling analyses were realized through the System Coupling component. The multiphysics coupling relationships are as follows: Figure 2 As shown, the ANSYS Workbench platform runs an electromagnetic-thermal-fluid-solid multiphysics coupled simulation model based on physical entity feedback data. Temperature and pressure sensors are used to collect real-time measurements of the temperature and pressure of the tire's electromagnetic induction heating system, transmitting these measurements to the model. Simultaneously, the process effects are fed back to the model, completing data feedback and state synchronization. By comparing the deviations between predicted and measured values, key physical parameters of the model are dynamically corrected, thus completing data feedback and model calibration, achieving a precise mapping between the virtual model and the physical entity. Parametric and automated analysis is then performed, executing performance prediction and virtual verification, and simulation-driven optimization is used to output the high-precision model and performance prediction results to the intelligent optimization engine.

[0031] Specifically, a parametric 3D model containing a tire mold, electromagnetic coil, nitrogen flow channel, and vulcanizing container needs to be established in ANSYS DesignModeler. This model is the unified geometric basis and physical space carrier for electromagnetic-thermal-fluid-solid multiphysics coupling simulation. Its precise geometric structure and assembly relationship directly define the region for electromagnetic field calculation, the domain for fluid flow, the solid boundary for heat transfer, and the entity for structural analysis. It is the spatial framework for the interaction of all physical fields in the coupling simulation. Among them, key design variables such as coil spacing, flow channel diameter, and container wall thickness are set as parameters. In the optimization process of step (2), these parameters will be used as core design variables. A series of sample points will be generated through the Latin hypercube experimental design method to drive the automated coupling simulation process, so as to obtain the performance response such as heating time, temperature uniformity, and energy consumption corresponding to each sample point. Then, the Kriging response surface approximation model will be constructed, and finally, a multi-objective genetic algorithm will be used to perform global search and iteration in the design space composed of these variables to obtain the parameter combination that makes the comprehensive performance Pareto optimal, and complete the system optimization. Special attention needs to be paid to the parametric expression of geometric features during the modeling process to ensure that the geometric model can be automatically updated in the subsequent optimization process. After completing the geometric modeling, a complete coupled simulation process was built in the ANSYS Workbench platform. The electromagnetic field analysis set the coil excitation frequency range to 1-100kHz, calculated the eddy current loss distribution, and transferred it as a heat source to the temperature field. The temperature field analysis considered the temperature dependence of material properties and achieved bidirectional coupling with the electromagnetic field through the Feedback Iterator. The flow field analysis set the nitrogen inlet pressure and mass flow rate, and performed conjugate heat transfer calculations with the temperature field through the System Coupling component. The structural field analysis received the flow field pressure load through another System Coupling component and calculated the transient deformation process of the container expansion.

[0032] like Figure 3 As shown, the above coupling simulation is achieved through the following specific steps:

[0033] S1, Electromagnetic Field Analysis Stage: Perform alternating magnetic field calculations, and based on the alternating magnetic field calculation results, conduct eddy current loss metering and use it as a heat source to transfer to the temperature field for analysis. The material properties are updated and fed back to this stage based on the subsequent analysis results.

[0034] S2. Temperature field analysis stage: Using the volume heat source obtained in step S1) as input, transient heat conduction calculation is performed to complete the temperature distribution calculation. The output wall temperature is transferred to the flow field analysis stage, and the temperature result is fed back to step S1) to update the material properties. This stage is controlled by an electromagnetic-thermal coupling controller to achieve bidirectional coupling.

[0035] S3, Flow Field Analysis Stage: Using the wall temperature, nitrogen inlet pressure and mass flow rate output in step S2), the system coupling component performs conjugate heat transfer calculations with the temperature field and outputs the gas pressure load to the structural field analysis stage. This stage is controlled by a thermal-fluid coupling controller.

[0036] S4. Structural Field Analysis Stage: Using the gas pressure load output in step S3) as input, another SystemCoupling component performs stress and deformation analysis to obtain structural displacement and deformation results and feed them back to the flow field analysis in step S). Based on the stress and deformation results, mesh deformation calculation is performed to calculate the transient deformation process of container expansion, and the results are fed back to the flow field analysis in step S3) to form a closed loop. This stage is coupled and controlled by a fluid-structure interaction controller.

[0037] (2) System optimization analysis: Using coil structure parameters and process parameters as design variables, and heating time, temperature uniformity, energy consumption and maximum stress as optimization objectives, a multi-objective genetic algorithm is used to solve the Pareto optimal solution set. When the optimization algorithm converges iteratively and the obtained Pareto front performance distribution (specifically referring to the trade-off relationship between objectives such as heating time, temperature uniformity, energy consumption and maximum stress) meets the preset engineering constraints and comprehensive performance expectations (specifically referring to each objective value reaching the acceptable range, such as heating time being lower than the set threshold, temperature uniformity being higher than the minimum requirement, energy consumption being within the limit and maximum stress being lower than the allowable stress of the material), one or more optimal design schemes are selected from the Pareto optimal solution set, and a reduced-order model ROM for real-time control is trained and exported based on the simulation data corresponding to these schemes, and then the process is moved to step (3). If the objective is not achieved, the process is returned to step (1).

[0038] Specifically, such as Figure 4 As shown, this invention employs a physics-based intelligent optimization engine method. When designing DOE experiments and modeling response surfaces using the DesignXplorer module, it optimizes coil structural parameters (coil spacing, channel diameter) and process parameters (current frequency, nitrogen mass flow rate) with heating time, temperature uniformity, energy consumption, and maximum stress as the optimization objectives. Simultaneously, it applies the multi-objective genetic algorithm MOGA to solve for the Pareto optimal solution set. Specifically, this invention uses the Kriging response surface method to construct an approximate model between the design variables and the objective function, effectively reducing the optimization computation cost. Furthermore, it obtains a uniformly distributed Pareto optimal solution set through non-dominated sorting and crowding calculation using the multi-objective genetic algorithm.

[0039] Fifty sets of experimental samples were generated using the Latin hypercube sampling method, and performance response data for each sample point were obtained through an automated simulation process. Based on these sample data, a Kriging response surface model was constructed to replace the computationally intensive complete simulation process, significantly improving optimization efficiency. Subsequently, a multi-objective genetic algorithm was applied for optimization, setting heating time and energy consumption as minimization objectives and temperature uniformity as maximization objectives, while limiting the maximum equivalent stress to within the material yield strength and the pressure drop to below 0.1 MPa. The optimization process employed an adaptive search strategy, continuously improving the quality of the solution through genetic operations such as crossover and mutation, ultimately obtaining a uniformly distributed Pareto optimal solution, thereby obtaining the optimal parameter combination.

[0040] (3) Deploy a real-time control system: Construct a real-time predictive control system that includes a sensor input module, a ROM prediction module, a fuzzy PID control module and an actuator output module. The system communicates with the field PLC controller, collects temperature sensor data from multiple points inside the capsule in real time, predicts the temperature distribution trend through the ROM prediction model, and dynamically adjusts the coil power and nitrogen valve opening using the fuzzy PID algorithm.

[0041] Specifically, the reduced-order model output from ANSYS Workbench (exported from step 3) is imported into the Twin Builder platform to generate a reduced-order model ROM. A real-time control system is then constructed, including the generated reduced-order model ROM, a sensor input module, a fuzzy PID control module, and an actuator output module, completing the construction of the digital twin model. This system communicates with the field PLC controller via the OPC UA protocol, acquiring real-time temperature sensor data from multiple points within the container. The ROM model predicts temperature distribution trends, and the fuzzy PID algorithm dynamically adjusts the coil power size, power, and nitrogen valve opening. The fuzzy PID controller adaptively adjusts the proportional, integral, and derivative parameters based on the temperature deviation and its rate of change. The proportional coefficient uses a piecewise linear adjustment strategy based on the deviation magnitude, the integral time constant is dynamically adjusted based on the system response speed, and the derivative action is primarily activated for rapidly changing processes. This control strategy effectively overcomes the system's nonlinearity and time-delay characteristics, ensuring temperature control accuracy within ±2°C. If the temperature deviation at various points within the capsule exceeds the set value, temperature and pressure data are re-acquired, and PID control is re-implemented. Figure 4 As shown.

[0042] (4) Performance verification and iterative optimization: By comparing the predicted values ​​with the actual sensor measurements, the control accuracy and reliability of the system are verified; and a continuous optimization mechanism for the system is established, feeding back the actual production data to the simulation model, updating the model parameters regularly, and completing the closed-loop coupling of simulation-optimization-control.

[0043] Specifically, the control accuracy and reliability of the system are verified by comparing the predicted values ​​of the digital twin model with the actual sensor measurements. A continuous optimization mechanism is established for the system, feeding actual production data back to the simulation model and periodically updating the reduced-order model parameters to adapt to equipment aging and process changes. This includes: establishing a historical process parameter database to record process parameters and product quality data for each successful run; developing an automatic model correction algorithm to automatically adjust model parameters based on the deviation between actual production data and simulation predictions; and setting a periodic re-optimization mechanism to automatically initiate the re-optimization process when system performance indicators show a downward trend. Through the implementation of this invention, the consistency of tire vulcanization quality is improved, the scrap rate is reduced, and energy consumption is decreased, achieving efficient, precise, and intelligent control of the tire vulcanization process, providing strong technical support for the digital transformation of the tire manufacturing industry.

[0044] The above embodiments are merely specific examples to further illustrate the purpose, technical solution, and beneficial effects of the present invention, and the present invention is not limited thereto. Any modifications, equivalent substitutions, improvements, etc., made within the scope of the disclosure of the present invention are included within the protection scope of the present invention.

Claims

1. A control method for a tire electromagnetic induction heating system based on multi-physical field coupling simulation, characterized in that, The method comprises the following steps: (1) Multi-physical field coupling simulation modeling: a parameterized three-dimensional model including a tire mold, an electromagnetic coil, a nitrogen flow channel, and a vulcanization container is established, then a multi-physical field coupling simulation model of electromagnetic-thermal-flow-solid is established, and analysis is performed, and physical phenomena in the tire electromagnetic induction heating process are completely reproduced; (2) System optimization analysis: taking the coil structure parameters and process parameters as design variables, taking the heating time, temperature uniformity, energy consumption, and maximum stress as optimization objectives, applying a multi-objective genetic algorithm to solve a Pareto optimal solution set, completing optimization of the simulation model, and outputting a reduced-order model; (3) Deploying a real-time control system: a real-time prediction control system including the reduced-order model output in step (2), a sensor input module, a fuzzy PID control module, and an actuator output module is constructed, the system communicates with a field PLC controller, real-time acquisition of multi-point temperature sensor data in the capsule is performed, a temperature distribution trend is predicted through the reduced-order model, and the coil power and nitrogen valve opening are dynamically adjusted by using the fuzzy PID algorithm; (4) Performance verification and iterative optimization: the control accuracy and reliability of the system are verified by comparing the predicted value with the actual sensor measured value; and a continuous optimization mechanism of the system is established, and actual production data is fed back to the simulation model, and model parameters are updated regularly.

2. The method according to claim 1, wherein: In step (1), the multi-physical field coupling simulation modeling is established in the ANSYS Workbench platform, the ANSYS Workbench platform is based on physical entity feedback data, the electromagnetic-thermal-flow-solid multi-physical field coupling simulation model is run, the accurate mapping of the virtual model and the physical entity is completed, the parameterization and automatic analysis are carried out, the performance prediction and virtual verification are performed, and the high-precision model and the performance prediction result are output to step (2).

3. The method of claim 1, wherein: Step (1) is realized by the following steps, First, the three-dimensional model is established in ANSYS DesignModeler, the design variables of the three-dimensional model include coil spacing, flow channel diameter, and container wall thickness, and these parameters will be used as design variables for system optimization in the subsequent optimization process; Second, after completing the geometric modeling, a complete coupling simulation process is built in the ANSYS Workbench platform, the electromagnetic field analysis sets the coil excitation frequency range to 1-100 kHz, the eddy current loss distribution is calculated and is transmitted to the temperature field as a heat source; the temperature field analysis considers the temperature dependence of material properties, and realizes the bidirectional coupling with the electromagnetic field through a feedback iterator; the flow field analysis sets the nitrogen inlet pressure and mass flow rate, and performs conjugate heat transfer calculation with the temperature field through a system coupling component; the structural field analysis receives the flow field pressure load through another coupling component, and calculates the transient deformation process of the container expansion.

4. The method of claim 1, wherein: In step (2), When experimental design and response surface modeling are performed through the DesignXplorer module, the coil structure parameters and process parameters are taken as design variables, the heating time, temperature uniformity, energy consumption, and maximum stress are taken as optimization objectives, and the non-dominated sorting and crowding degree calculation of the multi-objective genetic algorithm are used to obtain a uniformly distributed Pareto optimal solution set.

5. The control method of a tire electromagnetic induction heating system based on multi-physical field coupling simulation according to claim 1, characterized in that: In step (3), the high-fidelity model exported by ANSYS Workbench is imported into the Twin Builder platform to generate a reduced-order model. Real-time data from multiple temperature sensors inside the capsule are collected, and the temperature distribution trend is predicted using the reduced-order model. The fuzzy PID algorithm is used to dynamically adjust the coil power and nitrogen valve opening.

6. The method of claim 5, wherein: The fuzzy PID controller takes temperature deviation and its rate of change as input variables based on expert experience and simulation analysis results. It adjusts PID parameters adaptively through fuzzy inference mechanism, effectively overcoming the nonlinear and time-varying characteristics of the system.

7. The method of claim 1, wherein the method is based on multi-physical field coupling simulation of a tire electromagnetic induction heating system. In step (4), a historical process parameter database is established to record process parameters and product quality data for each successful run. The system automatically corrects the algorithm by adjusting model parameters based on the deviation between actual production data and simulation prediction values. A periodic re-optimization mechanism is set up to automatically start the re-optimization process when the system performance indicators show a downward trend.

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