Remote control system and method of hot press

Abstract

The invention discloses a remote control system and method for a hot press, and relates to the technical field of coupling analysis, and the method comprises the steps: collecting physical field data, and uploading the data to a remote control center; based on the physical field data, constructing a dynamic physical field coupling analysis model, and dynamically correcting the physical field data to realize physical field association; according to a dynamic correction result, automatically adjusting parameters of the hot press by using a multi-physics field adaptive optimization algorithm in combination with physics field data and the dynamic correction result; the adjusted hot press parameters are directly transmitted to a hot press remote control unit, and operation adjustment is completed; according to the method, the coupling relation among the temperature field, the pressure field and the stress field is defined and dynamically corrected through the multi-physics field coupling analysis model, and correlation analysis and dynamic adjustment of the physics fields are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of coupling analysis, in particular to a remote control system of a hot press and a method thereof. Background Art

[0002] As an important processing equipment, hot press is widely used in the fields of composite material molding, electronic component packaging, etc. Its core lies in achieving high-precision processing and performance optimization of materials by controlling heating and pressure. With the development of industrial intelligence, traditional hot presses are gradually evolving towards intelligence and remote control. Collecting data through sensors and combining algorithm optimization parameters has become an important trend in the development of hot press technology. However, most hot presses are still mainly based on the measurement and control of a single physical field, and fail to fully consider the coupling relationship between temperature field, pressure field and stress field, resulting in limited processing accuracy and consistency. In addition, most existing control systems are localized single-machine control, which makes it difficult to achieve remote real-time adjustment and lacks the ability to dynamically respond to complex real-time working conditions, restricting the further improvement of production efficiency and equipment intelligence.

[0003] In response to the above problems, existing technologies still face many challenges in multi-physical field coupling analysis and optimization control; first, traditional hot presses are difficult to effectively collect and analyze data from multiple physical fields, and ignore the dynamic coupling between temperature fields, pressure fields and stress fields, resulting in delayed or inaccurate parameter adjustments, which in turn affects processing stability and process effects; second, existing technologies lack efficient adaptive optimization algorithms, and are unable to correct parameters based on real-time data, let alone combine historical data for intelligent prediction and decision-making; finally, traditional systems are difficult to achieve remote and precise control, especially in complex processing scenarios, where the adjustment of equipment operating parameters often relies on manual intervention, resulting in low efficiency and large errors. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a remote control method for a hot press to solve the problem of analyzing a variety of physical field data.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a remote control method for a hot press, which includes collecting physical field data and uploading it to a remote control center; constructing a dynamic physical field coupling analysis model based on the physical field data, dynamically correcting the physical field data, and realizing physical field association; according to the results after dynamic correction, using a multi-physical field adaptive optimization algorithm in combination with the physical field data and the results of dynamic correction to automatically adjust the hot press parameters; and directly transmitting the adjusted hot press parameters to the hot press remote control unit to complete the operation adjustment.

[0008] As a preferred solution of the remote control method of a hot press machine described in the present invention, the physical field data includes temperature field data, pressure field data, and stress field data.

[0009] As a preferred solution of the remote control method of a hot press described in the present invention, the specific steps of constructing a dynamic physical field coupling analysis model are as follows:

[0010] The input is defined as physical field data, and the output is the physical field data corrected after dynamic adjustment;

[0011] Define the coupling relationship between temperature field data and pressure field data;

[0012] Define the coupling relationship between temperature field data and stress field data;

[0013] Define the coupling relationship between pressure field data and stress field data;

[0014] According to the defined physical field data coupling relationship, the physical field data is dynamically adjusted and corrected. The expression is:

[0015]

[0016] in, is the partial derivative of temperature with respect to time, is the sign of the partial derivative, T is the temperature, C 1 is the thermal conductivity constant, is the Laplace operator, T(u,v,t) is the time-varying temperature distribution on the two-dimensional plane, Q(u,v,t) is the time-varying heat source term on the two-dimensional plane, f(P(u,v,t)) is the pressure field coupling term, P(u,v,t) is the time-varying pressure distribution on the two-dimensional plane, g(σ(x,y,z,t)) is the stress field coupling term, σ(x,y,z,t) is the time-varying stress distribution in three-dimensional space, u is the horizontal coordinate of the two-dimensional plane, v is the vertical coordinate of the two-dimensional plane, t is the time variable, x is the horizontal coordinate of the three-dimensional space, y is the vertical coordinate of the three-dimensional space, and z is the height position in the three-dimensional space;

[0017] According to the real-time collected temperature field data, the heat source item is dynamically adjusted and the corrected temperature field data is output;

[0018] According to the real-time collected pressure field data, the pressure distribution is dynamically adjusted and the corrected pressure field data is output;

[0019] According to the real-time collected stress field data, the stress distribution is dynamically adjusted and the corrected stress field data is output;

[0020] An online calibration mechanism is introduced to adaptively adjust and calibrate physical field data.

[0021] As a preferred solution of the remote control method of a hot press described in the present invention, wherein: the online calibration mechanism is introduced to adaptively adjust and calibrate the physical field data, and the specific steps are:

[0022] Input the corrected temperature field data, dynamically adjust the thermal conductivity constant, and output the calibrated thermal conductivity constant;

[0023] Input the corrected stress field data, dynamically adjust the stress field parameters, and output the calibrated stress distribution;

[0024] Input the corrected pressure field data and the corrected stress field data, dynamically calibrate the parameters of the pressure field coupling term and the stress field coupling term, and output the calibrated pressure field coupling coefficient and the stress field coupling coefficient.

[0025] As a preferred solution of the remote control method of a hot press described in the present invention, the method uses a multi-physical field adaptive optimization algorithm combined with physical field data and dynamic correction results to automatically adjust the parameters of the hot press. The specific steps are:

[0026] According to the corrected physical field data, the hot press parameters are dynamically adjusted through the multi-physical field adaptive optimization algorithm, combined with the historical operation data and the output of the dynamic physical field coupling analysis model. The expression is:

[0027] U o =min{∫[(T t -T'(u,v,t)) 2 +(P t -P'(u,v,t)) 2 +(σ t -σ'(x,y,z,t)) 2 ]dV};

[0028] Among them, U o is the optimization objective function, min is the minimization operator, ∫ is the spatial integral symbol, dV is the volume unit, T t is the target temperature distribution, T'(u,v,t) is the actual temperature field distribution that changes with time on the two-dimensional plane, (T t -T'(u,v,t)) 2 is the square of the temperature error, P t is the target pressure distribution, P'(u,v,t) is the actual pressure distribution on the two-dimensional plane that changes with time, (P t -P'(u,v,t)) 2 is the square of the pressure error, σ tis the target stress distribution, σ'(x, y, z, t) is the actual stress distribution in three-dimensional space that changes with time, (σ t -σ'(x,y,z,t)) 2 is the square of the stress error;

[0029] According to the comparison results between real-time physical field data and optimization targets, the judgment threshold is set;

[0030] According to the optimized target temperature distribution, the heat conduction constant and the heat source term are adjusted to generate the target temperature curve;

[0031] According to the optimized target pressure distribution, the pressure field coupling coefficient is adjusted to generate a pressure distribution curve;

[0032] Generate the hot pressing time setting value according to the optimization target stress distribution and optimization results.

[0033] As a preferred solution of the remote control method of a hot press machine described in the present invention, wherein: the setting of the judgment threshold value comprises the following specific steps:

[0034] When T'(u,v,t) <T t , then increase the heating power;

[0035] When T'(u,v,t)>T t , then reduce the heating power;

[0036] When T'(u,v,t)=T t , the current heating power remains unchanged;

[0037] When P'(u,v,t) <P t , then increase the applied pressure;

[0038] When P'(u,v,t)>P t , then reduce the pressure;

[0039] When P'(u,v,t)=P t , then keep the current pressure unchanged;

[0040] When σ'(x,y,z,t)<σ t , then adjust the pressure field coupling term and increase the loading pressure;

[0041] When σ'(x,y,z,t)>σ t , then adjust the pressure field coupling term to reduce the loading pressure;

[0042] When σ'(x,y,z,t)=σ t , the current loading pressure remains unchanged.

[0043] As a preferred solution of the remote control method of a hot press machine described in the present invention, wherein: the adjusted hot press machine parameters are directly transmitted to the hot press machine remote control unit to complete the operation adjustment, and the specific steps are:

[0044] The adjusted parameters are converted into execution instructions for the hot press and sent to the hot press control center via radio communication;

[0045] Adjust the heating power distribution according to the target temperature curve;

[0046] According to the pressure distribution curve, uniform force is achieved;

[0047] Adjust the heat pressing time according to the heat pressing time setting value.

[0048] In the second aspect, the present invention provides a remote control system for a hot press, comprising an acquisition module, a correction module, an adjustment module and an operation module; the acquisition module is used to acquire physical field data and upload it to a remote control center; the correction module is used to construct a dynamic physical field coupling analysis model based on the physical field data, dynamically correct the physical field data, and realize physical field association; the adjustment module is used to automatically adjust the hot press parameters according to the results after dynamic correction, using a multi-physical field adaptive optimization algorithm in combination with the physical field data and the results of dynamic correction; the operation module is used to directly transmit the adjusted hot press parameters to the hot press remote control unit to complete the operation adjustment.

[0049] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of a remote control method for a hot press as described in the first aspect of the present invention is implemented.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the remote control method for a hot press as described in the first aspect of the present invention is implemented.

[0051] The beneficial effects of the present invention are as follows: the present invention defines and dynamically corrects the coupling relationship between the temperature field, pressure field and stress field through a multi-physical field coupling analysis model, thereby realizing correlation analysis and dynamic adjustment of the physical field; real-time acquisition of physical field data and introduction of an online calibration mechanism solve the problem of inaccurate control caused by neglect of the interaction of multiple physical fields in traditional technologies; the dynamic coupling effect between the physical fields can be accurately captured, making the physical field data more accurate, and providing a reliable basis for subsequent parameter optimization; ultimately, the processing consistency and accuracy during the operation of the hot press are effectively improved, the demand for high-quality processing under complex working conditions is met, and the intelligence and reliability of the equipment are significantly enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0053] Figure 1 This is a flow chart of a remote control method for a hot press in Example 1.

[0054] Figure 2 This is a module diagram of a remote control system for a hot press in Example 1. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0058] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a remote control method for a hot press, comprising the following steps:

[0059] S1. Collect physical field data and upload it to the remote control center.

[0060] Furthermore, the physical field data includes temperature field data, pressure field data, stress field data and historical operation data;

[0061] Install temperature sensors, pressure sensors and stress sensors on key components of the hot press (such as hot press plate, mold and hydraulic press);

[0062] Real-time collection of physical field data generated during hot pressing: temperature distribution (temperature field data), pressure distribution (pressure field data) and stress concentration state (stress field data), and uploading the collected data to the remote control center using a wireless communication module (such as Wi-Fi or LoRa);

[0063] The temperature field data is the temperature distribution on the hot press plate surface recorded in the form of discrete coordinates and collected by the temperature sensor;

[0064] The pressure field data is the pressure distribution on the hot platen surface, collected by the pressure sensor;

[0065] The stress field data is the stress concentration in the contact area between the hot press plate and the workpiece, which is collected by the stress sensor and recorded as;

[0066] Historical operation data refers to the historical records related to equipment performance, operating status or physical field parameters accumulated during the operation of the equipment. It includes historical physical field data, operation parameter records (such as heating power, pressure setting value, hot pressing time, etc. of the hot press) and operation result records (such as finished product quality, fault information, etc.);

[0067] It should be noted that by installing temperature, pressure and stress sensors on the key components of the hot press and collecting physical field data (temperature field, pressure field, stress field) during the hot pressing process in real time, comprehensive monitoring of the hot pressing process is achieved; the data is uploaded to the remote control center using a wireless communication module to ensure data real-time and transmission efficiency; this step accurately obtains the temperature distribution, pressure distribution and stress concentration on the surface of the hot pressing plate, providing high-quality data support for subsequent multi-physical field coupling analysis and parameter optimization, significantly improving the ability to recognize the processing status and laying the foundation for high-precision remote control.

[0068] S2. Based on the physical field data, a dynamic physical field coupling analysis model is constructed to dynamically correct the physical field data and realize physical field association.

[0069] The input is defined as physical field data, and the output is the physical field data corrected after dynamic adjustment;

[0070] Define the coupling relationship between temperature field data and pressure field data. The distribution of pressure will affect the heat transfer efficiency of the contact interface. For example, the greater the pressure, the faster the heat transfer rate. Therefore, it is necessary to introduce the influence of the pressure field on the temperature field, which can be expressed as:

[0071] f(P(u,v,t))=a·P(u,v,t) b ;

[0072] Among them, f(P(u,v,t)) is the pressure field coupling term, which represents the influence of pressure distribution on temperature field. P(u,v,t) is the pressure distribution that changes with time on a two-dimensional plane. a is the proportional coefficient of pressure to heat conduction, which represents the basic intensity of the influence of pressure on heat conduction. Its specific value depends on the physical properties of the material and experimental data. For example, if a=0.5, it means that the change in heat conduction efficiency is 0.5 times under unit pressure. b is the nonlinear degree of the influence of pressure on heat conduction, which represents the changing trend of heat conduction efficiency with increasing pressure. If b=1, it represents a linear relationship. If b>1 or b<1, it represents a nonlinear relationship. For example, if b=1.2, it means that the influence of pressure on heat conduction is nonlinear, and this influence will become more significant with increasing pressure. a and b are pressure field coupling coefficients, which represent the nonlinear influence of pressure on heat conduction.

[0073] Define the coupling relationship between temperature field data and stress field data. Temperature changes will cause thermal expansion or contraction of the material, which in turn affects the stress distribution inside the material. For example, when the temperature gradient is large, stress concentration is likely to occur, which can be expressed as:

[0074]

[0075] σ=E*ε;

[0076] Among them, g(σ(x,y,z,t)) is the stress field coupling term, c is the proportionality coefficient when the stress is less than the yield strength, which indicates the influence of stress on temperature. Its specific value depends on the thermal expansion coefficient and elastic modulus of the material. For example, if c=0.3, it means that the temperature change is 0.3 times under unit stress. d is a proportionality coefficient when the stress is greater than or equal to the yield strength, which indicates the nonlinear influence of stress on temperature. Its specific value also depends on the plastic deformation characteristics of the material and experimental data. For example, if d=0.1, it means that under high stress, the temperature change is proportional to the square of the stress. σ(x,y,z,t) is the stress distribution that changes with time in three-dimensional space. c and d are stress field coupling coefficients. σ is stress, E is elastic modulus, ε is strain, and σ y is the yield strength of the material, which indicates the stress value at which the material begins to undergo plastic deformation. This value can be obtained through mechanical testing of the material (such as tensile testing). For example, if σ y =100MPa, it means that when the stress reaches 100MPa, the material begins to undergo plastic deformation.

[0077] Define the coupling relationship between pressure field data and stress field data. The uneven distribution of pressure will directly lead to changes in material deformation and stress concentration areas, which can be expressed as:

[0078] σ l(x,y,z,t)=h(P(u,v,t));

[0079] Among them, σ l (x, y, z, t) is the local stress. Local stress refers to the stress value measured at a specific location and time point of the material. It describes the stress state at a point (x, y, z) and a time t in three-dimensional space. h(P(u, v, t)) is the mapping function from pressure to local stress, which indicates how pressure affects the local stress distribution. It can be a linear function or a nonlinear function, depending on the constitutive relationship of the material. P(u, v, t) is the pressure distribution on a two-dimensional plane that changes with time.

[0080] The linear relationship is expressed as:

[0081] h(P(u,v,t))=k·P(u,v,t);

[0082] Where k is a proportionality coefficient, which indicates the degree of influence of pressure on local stress. For example, if k = 0.8, it means that under unit pressure, the change of local stress is 0.8 times;

[0083] The nonlinear relationship is expressed as:

[0084] h(P(u,v,t))=k 1 ·P(u,v,t)+k 2 P(u,v,t) 2 ;

[0085] Among them, k 1 is the proportionality coefficient of the linear term, which indicates the linear effect of pressure on local stress at low pressure. That is, when the pressure is small, the change in local stress is proportional to the pressure. For example, if k 1 =0.6, it means that under unit pressure, the change of local stress is 0.6 times, which means that in the low pressure range, the local stress increases linearly with the increase of pressure, k 2 is the proportionality coefficient of the quadratic term, which indicates the nonlinear effect of pressure on local stress under high pressure. Specifically, it describes that as pressure increases, the change in local stress is no longer a simple linear relationship, but grows quadratically. For example, if k 2 =0.1, it means that under high pressure, the change of local stress is not only proportional to the pressure, but also proportional to the square of the pressure, with a proportionality coefficient of 0.1, which indicates that within the high pressure range, the local stress will increase faster with the increase of pressure.

[0086] k 1 and k 2The specific values ​​of k are usually determined by experimental data or the constitutive relationship of the material. These coefficients reflect the mechanical response characteristics of the material under different pressure levels. 1 It mainly describes the elastic behavior of materials under low pressure, that is, within the elastic range, stress is proportional to strain (Hooke's law), k 2 It describes the plastic behavior or nonlinear behavior of materials under high pressure, that is, after exceeding a certain critical pressure, the stress-strain relationship of the material is no longer linear, but exhibits nonlinear characteristics.

[0087] According to the defined physical field data coupling relationship, the dynamic physical field coupling analysis model continuously receives the real-time collected physical field data, and dynamically adjusts and corrects the physical field data according to the defined coupling relationship. The expression is:

[0088]

[0089] in, It is the partial derivative of temperature over time, indicating the rate of change of temperature T with time t at the coordinate point (u, v,). It is the symbol of partial derivative, which indicates the rate of change of a function with respect to a certain variable while other variables remain unchanged. T is the temperature, C 1 is the thermal conductivity constant, which indicates the thermal conductivity of the material. is the Laplace operator, which represents the spatial diffusion rate of temperature on a two-dimensional plane. T(u,v,t) is the temperature distribution that varies with time on a two-dimensional plane. Q(u,v,t) is the heat source term that varies with time on a two-dimensional plane, which represents the heat input at the coordinate point (u,v) by the hot press. f(P(u,v,t)) is the pressure field coupling term, which represents the influence of pressure distribution on the temperature field. P(u,v,t) is the pressure distribution that varies with time on a two-dimensional plane. g(σ(x,y,z,t)) is the stress field coupling term, which represents the influence of stress concentration on the temperature field. σ(x,y,z,t) is the stress distribution that varies with time in three-dimensional space. u is the horizontal coordinate of the two-dimensional plane, v is the vertical coordinate of the two-dimensional plane, t is the time variable, which represents the change of the physical field with time. x is the horizontal coordinate of the three-dimensional space, y is the vertical coordinate of the three-dimensional space, and z is the height position in the three-dimensional space.

[0090] This formula describes the dynamic changes of the temperature field, integrates the heat source term, pressure field coupling term and stress field coupling term into the model, and realizes the precise correlation and dynamic adjustment between multiple physical fields; it can reflect the changes of temperature with time and space in real time, and dynamically optimize the heat source configuration and hot pressing parameters based on the influence of pressure distribution and stress concentration on heat conduction; through this formula, precise control of the temperature distribution of the hot press can be achieved, ensuring the uniformity and stability of the hot pressing process, improving processing quality and efficiency, and avoiding local overheating or stress concentration problems.

[0091] According to the real-time collected temperature field data, the heat source item is dynamically adjusted (if the temperature deviates from the target value, it is corrected by adjusting the heating power), and the corrected temperature field data is output;

[0092] According to the real-time collected pressure field data, the pressure distribution is dynamically adjusted (the pressure application area is corrected), and the corrected pressure field data is output;

[0093] According to the real-time collected stress field data, the stress distribution is dynamically adjusted, the material mechanical response parameters (such as elastic modulus and yield strength) are updated to adapt to the current stress distribution, and the corrected stress field data is output;

[0094] An online calibration mechanism is introduced to adaptively adjust and calibrate physical field data.

[0095] S2.1. Introduce an online calibration mechanism to adaptively adjust and calibrate physical field data. Specifically,

[0096] Input the corrected temperature field data, dynamically adjust the thermal conductivity constant according to the corrected temperature field data to adapt to environmental changes, and output the calibrated thermal conductivity constant;

[0097] Input the corrected stress field data, dynamically adjust the stress field parameters (elastic modulus and yield strength of the material) based on the corrected stress field data and its correlation with external conditions (such as temperature, strain, damage, etc.), enhance the adaptability of the material, and output the calibrated stress distribution;

[0098] The corrected pressure field data and the corrected stress field data are input, and the parameters of the pressure field coupling term and the stress field coupling term are dynamically calibrated according to the corrected pressure field data and the corrected stress field data, and the calibrated pressure field coupling coefficient and the stress field coupling coefficient are output.

[0099] It should be noted that by constructing a dynamic physical field coupling analysis model, combining the real-time collected physical field data (temperature field, pressure field, stress field) and their coupling relationship, the physical field data is dynamically corrected and adjusted to achieve the association and adaptive optimization of multiple physical fields; based on the coupling model, the heat source term, pressure distribution and material mechanical parameters (such as elastic modulus and yield strength) are dynamically adjusted to adapt to different processing conditions; an online calibration mechanism is introduced to adaptively adjust the thermal conductivity constant and coupling coefficient to further improve the model accuracy and material response capability; this step effectively improves the operating stability and processing accuracy of the hot press, ensuring optimized hot pressing effects under complex working conditions and extending the life of the equipment.

[0100] S3. According to the results after dynamic correction, the multi-physics field adaptive optimization algorithm is used to combine the physical field data and the results of dynamic correction to automatically adjust the parameters of the hot press.

[0101] Furthermore, according to the corrected physical field data, the multi-physics field adaptive optimization algorithm is used to dynamically adjust the hot press parameters by combining the historical operation data and the output of the dynamic physical field coupling analysis model. The expression is:

[0102] U o =min{∫[(T t -T'(u,v,t)) 2 +(P t -P'(u,v,t)) 2 +(σ t -σ'(x,y,z,t)) 2 ]dV};

[0103] Among them, U o is the optimization objective function. The smaller its value is, the better the optimization effect is. min is the minimization operator, which means that the value of the objective function should be minimized as much as possible by adjusting the model parameters to achieve the optimal state. ∫ is the spatial integral symbol, which means that the integral calculation is performed on the entire working area in the hot press. dV is the volume unit (micro volume element), which means the volume accumulation of the integral in three-dimensional space. t is the target temperature distribution, which is defined by the process requirements. T'(u,v,t) is the actual temperature distribution that changes with time on a two-dimensional plane. It is the actual temperature distribution calculated by the dynamic correction formula. It is affected by the heat conduction constant, heat source term, pressure coupling term and stress coupling term. (T t -T'(u,v,t)) 2 is the square of the temperature error, which represents the square of the error between the target temperature distribution and the actual temperature distribution, P tis the target pressure distribution, which is defined according to the process requirements and represents the expected pressure field distribution. P'(u,v,t) is the actual pressure distribution on the two-dimensional plane that changes with time. It describes the change of the pressure field on the two-dimensional plane with time t during the operation of the hot press. (P t -P'(u,v,t)) 2 is the square of the pressure error, which represents the square of the error between the target pressure distribution and the actual pressure distribution, σ t is the target stress distribution, which describes the expected stress distribution inside the workpiece during processing to ensure that the workpiece will not be damaged or deformed due to stress concentration. σ'(x, y, z, t) is the actual stress distribution that changes with time in three-dimensional space. It describes the change of the stress field in three-dimensional space with time t during the operation of the hot press. (σ t -σ'(x,y,z,t)) 2 is the square of the stress error, which represents the square of the error between the target stress distribution and the actual stress distribution;

[0104] This formula achieves comprehensive optimization of temperature, pressure and stress distribution by constructing an optimization objective function and minimizing the target value; it can accurately evaluate the deviation in the hot pressing process by calculating the square of the error between the target value and the actual distribution and integrating it in the entire working area; combined with dynamic adjustment of model parameters, the formula can guide the hot press to optimize the temperature, pressure and stress distribution in real time and generate the optimal control strategy; this formula effectively improves the accuracy and uniformity of the hot pressing process, ensures the molding quality of the workpiece, and reduces energy consumption and material loss.

[0105] According to the comparison results between the real-time physical field data and the optimization target, a judgment threshold is set to determine whether the hot press parameters need to be adjusted;

[0106] According to the optimized target temperature distribution, the heat conduction constant and the heat source term are adjusted to optimize the temperature field distribution and generate the target temperature curve;

[0107] According to the optimized target pressure distribution, the pressure field coupling coefficient is adjusted to solve the problem of uneven local pressure or abnormal pressure points, and the pressure distribution curve is generated;

[0108] According to the optimization target stress distribution and optimization results, the elastic modulus and yield strength of the material are dynamically updated, the stress field distribution is optimized, the stress concentration is reduced, and the hot pressing time setting value is generated.

[0109] It should be noted that threshold judgment is a preliminary screening mechanism to determine whether adjustment is needed. Its purpose is to avoid frequent adjustments and maintain stability;

[0110] Optimization adjustment is a precise execution mechanism used to provide specific optimization solutions when adjustment is needed to ensure efficient and high-quality machining processes.

[0111] S3.1. Set the judgment threshold, specifically,

[0112] When T'(u,v,t) <T t , then increase the heating power;

[0113] When T'(u,v,t)>T t , then reduce the heating power;

[0114] When T'(u,v,t)=T t , the current heating power remains unchanged;

[0115] When P'(u,v,t) <P t , then increase the applied pressure;

[0116] When P'(u,v,t)>P t , then reduce the pressure;

[0117] When P'(u,v,t)=P t , then keep the current pressure unchanged;

[0118] When σ'(x,y,z,t)<σ t , then adjust the pressure field coupling term and increase the loading pressure;

[0119] When σ'(x,y,z,t)>σ t , then adjust the pressure field coupling term to reduce the loading pressure;

[0120] When σ'(x,y,z,t)=σ t , the current loading pressure remains unchanged.

[0121] It should be noted that the core function of the online calibration mechanism is to ensure the accuracy of the physical field data and the reliability of the model, which belongs to the stage of data processing and model calibration; while the automatic adjustment of the hot press parameters is based on the calibrated data, and the control parameters are generated and executed through the optimization algorithm, which directly affects the operation of the hot press equipment; the two have different functions and distinct levels, but they work closely together as a whole to achieve high precision and high efficiency in remote control of the hot press.

[0122] It should be noted that by automatically adjusting the hot press parameters based on dynamically corrected physical field data and combining a multi-physical field adaptive optimization algorithm, accurate optimization and dynamic control of the temperature field, pressure field and stress field are achieved; by minimizing the objective function and combining real-time data, historical operation data and the output of the coupling analysis model, the thermal conductivity constant, heat source term, pressure field coupling coefficient and material mechanical parameters (such as elastic modulus and yield strength) are dynamically adjusted to optimize the temperature, pressure and stress distribution, generate the target curve and hot pressing time setting value; the threshold judgment mechanism avoids frequent adjustments and maintains overall stability; this step significantly improves processing accuracy, efficiency and equipment adaptability, ensuring high-quality hot pressing effects.

[0123] S4. The adjusted heat press parameters are directly transmitted to the heat press remote control unit to complete the operation adjustment.

[0124] Furthermore, the adjusted parameters are converted into execution instructions for the heat press and sent to the heat press control center via radio communication;

[0125] According to the target temperature curve, adjust the heating power distribution to ensure uniform surface temperature of the hot press plate;

[0126] According to the pressure distribution curve, the problem of insufficient or overpressure in some areas can be solved to achieve uniform stress.

[0127] According to the hot pressing time setting value, adjust the hot pressing time to ensure that the workpiece material is fully formed without overheating or overpressure.

[0128] It should be noted that the precise operation adjustment of the hot press is achieved by converting the adjusted hot press parameters into specific execution instructions and transmitting them to the hot press remote control unit. According to the target temperature curve, the heating power distribution is dynamically adjusted to ensure uniform surface temperature of the hot press plate; according to the pressure distribution curve, the pressure application area is optimized to solve the problem of insufficient or overpressure in the local area and achieve uniform force; according to the hot press time setting value, the hot press time is accurately controlled to prevent overheating or overpressure of the workpiece material and ensure the molding quality; this step directly applies the optimization results to the equipment operation, significantly improving the stability and processing accuracy of the hot press process, while improving the equipment operation efficiency and product consistency.

[0129] The present embodiment also provides a remote control system for a hot press, comprising: an acquisition module, a correction module, an adjustment module and an operation module; the acquisition module acquires physical field data and uploads it to a remote control center; the correction module constructs a dynamic physical field coupling analysis model based on the physical field data, dynamically corrects the physical field data, and realizes physical field association; the adjustment module automatically adjusts the hot press parameters according to the results after dynamic correction by using a multi-physical field adaptive optimization algorithm combined with the physical field data and the results of dynamic correction; the operation module directly transmits the adjusted hot press parameters to the hot press remote control unit to complete the operation adjustment.

[0130] This embodiment also provides a computer device, which is suitable for a remote control method of a hot press machine, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a remote control method of a hot press machine as proposed in the above embodiment.

[0131] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0132] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a remote control method for a hot press machine as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0133] In summary, the present invention defines and dynamically corrects the coupling relationship between temperature field, pressure field and stress field through a multi-physical field coupling analysis model, realizes correlation analysis and dynamic adjustment of physical fields; collects physical field data in real time and introduces an online calibration mechanism, which solves the problem of inaccurate control caused by ignoring the interaction of multiple physical fields in traditional technology; can accurately capture the dynamic coupling effect between various physical fields, make physical field data more accurate, and provide a reliable basis for subsequent parameter optimization; finally, effectively improves the processing consistency and accuracy in the operation of the hot press, meets the demand for high-quality processing under complex working conditions, and significantly enhances the intelligence and reliability of the equipment.

[0134] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A remote control method for a hot press, characterized in that: include, Collect physical field data and upload it to the remote control center; Based on the physical field data, a dynamic physical field coupling analysis model is constructed to dynamically correct the physical field data and realize physical field association; According to the results of dynamic correction, the multi-physics field adaptive optimization algorithm is used to automatically adjust the parameters of the hot press by combining the physical field data and the results of dynamic correction; The adjusted heat press parameters are directly transmitted to the heat press remote control unit to complete the operation adjustment.

2. A remote control method for a hot press as claimed in claim 1, characterized in that: The physical field data includes temperature field data, pressure field data, stress field data and historical operation data.

3. A remote control method for a hot press as claimed in claim 2, characterized in that: The specific steps of constructing the dynamic physical field coupling analysis model are as follows: The input is defined as physical field data, and the output is the physical field data corrected after dynamic adjustment; Define the coupling relationship between temperature field data and pressure field data; Define the coupling relationship between temperature field data and stress field data; Define the coupling relationship between pressure field data and stress field data; According to the defined physical field data coupling relationship, the physical field data is dynamically adjusted and corrected. The expression is: in, is the partial derivative of temperature with respect to time, is the sign of the partial derivative, T is the temperature, C1 is the thermal conductivity constant, is the Laplace operator, T(u,v,t) is the time-varying temperature distribution on the two-dimensional plane, Q(u,v,t) is the time-varying heat source term on the two-dimensional plane, f(P(u,v,t)) is the pressure field coupling term, P(u,v,t) is the time-varying pressure distribution on the two-dimensional plane, g(σ(x,y,z,t)) is the stress field coupling term, σ(x,y,z,t) is the time-varying stress distribution in three-dimensional space, u is the horizontal coordinate of the two-dimensional plane, v is the vertical coordinate of the two-dimensional plane, t is the time variable, x is the horizontal coordinate of the three-dimensional space, y is the vertical coordinate of the three-dimensional space, and z is the height position in the three-dimensional space; According to the real-time collected temperature field data, the heat source item is dynamically adjusted and the corrected temperature field data is output; According to the real-time collected pressure field data, the pressure distribution is dynamically adjusted and the corrected pressure field data is output; According to the real-time collected stress field data, the stress distribution is dynamically adjusted and the corrected stress field data is output; An online calibration mechanism is introduced to adaptively adjust and calibrate physical field data.

4. A remote control method for a hot press as claimed in claim 3, characterized in that: The online calibration mechanism is introduced to adaptively adjust and calibrate the physical field data. The specific steps are: Input the corrected temperature field data, dynamically adjust the thermal conductivity constant, and output the calibrated thermal conductivity constant; Input the corrected stress field data, dynamically adjust the stress field parameters, and output the calibrated stress distribution; Input the corrected pressure field data and the corrected stress field data, dynamically calibrate the parameters of the pressure field coupling term and the stress field coupling term, and output the calibrated pressure field coupling coefficient and the stress field coupling coefficient.

5. A remote control method for a hot press as claimed in claim 4, characterized in that: The multi-physics field adaptive optimization algorithm is used to automatically adjust the parameters of the hot press by combining the physical field data and the results of dynamic correction. The specific steps are: According to the corrected physical field data, the hot press parameters are dynamically adjusted through the multi-physical field adaptive optimization algorithm, combined with the historical operation data and the output of the dynamic physical field coupling analysis model. The expression is: U o =min{∫[(T t -T'(u,v,t)) 2 +(P t -P'(u,v,t)) 2 +(σ t -σ'(x,y,z,t)) 2 ]dV}; Among them, U o is the optimization objective function, min is the minimization operator, ∫ is the spatial integral symbol, dV is the volume unit, T t is the target temperature distribution, T'(u,v,t) is the actual temperature distribution that changes with time on the two-dimensional plane, (T t -T'(u,v,t)) 2 is the square of the temperature error, P t is the target pressure distribution, P'(u,v,t) is the actual pressure distribution on the two-dimensional plane that changes with time, (P t -P'(u,v,t)) 2 is the square of the pressure error, σ t is the target stress distribution, σ'(x, y, z, t) is the actual stress distribution in three-dimensional space that changes with time, (σ t -σ'(x,y,z,t)) 2 is the square of the stress error; According to the comparison results between real-time physical field data and optimization targets, the judgment threshold is set; According to the optimized target temperature distribution, the heat conduction constant and the heat source term are adjusted to generate the target temperature curve; According to the optimized target pressure distribution, the pressure field coupling coefficient is adjusted to generate a pressure distribution curve; Generate the hot pressing time setting value according to the optimization target stress distribution and optimization results.

6. A remote control method for a hot press as claimed in claim 5, characterized in that: The specific steps of setting the judgment threshold are as follows: When T'(u,v,t) <T t , then increase the heating power; When T'(u,v,t)>T t , then reduce the heating power; When T'(u,v,t)=T t , the current heating power remains unchanged; When P'(u,v,t) <P t , then increase the applied pressure; When P'(u,v,t)>P t , then reduce the pressure; When P'(u,v,t)=P t , then keep the current pressure unchanged; When σ'(x,y,z,t)<σ t , then adjust the pressure field coupling term and increase the loading pressure; When σ'(x,y,z,t)>σ t , then adjust the pressure field coupling term to reduce the loading pressure; When σ'(x,y,z,t)=σ t , the current loading pressure remains unchanged.

7. A remote control method for a hot press as claimed in claim 6, characterized in that: The adjusted heat press parameters are directly transmitted to the heat press remote control unit to complete the operation adjustment. The specific steps are: The adjusted parameters are converted into execution instructions for the hot press and sent to the hot press control center via radio communication; Adjust the heating power distribution according to the target temperature curve; According to the pressure distribution curve, uniform force is achieved; Adjust the heat pressing time according to the heat pressing time setting value.

8. A remote control system for a hot press, based on a remote control method for a hot press according to any one of claims 1 to 7, characterized in that: Including, acquisition module, correction module, adjustment module and operation module; The acquisition module is used to collect physical field data and upload it to the remote control center; The correction module is used to construct a dynamic physical field coupling analysis model based on the physical field data, dynamically correct the physical field data, and realize physical field association; The adjustment module is used to automatically adjust the parameters of the hot press according to the results of the dynamic correction by using a multi-physics field adaptive optimization algorithm in combination with the physical field data and the results of the dynamic correction; The operation module is used to directly transmit the adjusted hot press parameters to the hot press remote control unit to complete the operation adjustment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the remote control method of a hot press machine according to any one of claims 1 to 7 are implemented.

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

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