Curing methods, curing apparatus, electronic devices, and computer-readable storage media

Abstract

This application relates to a curing method, curing apparatus, electronic device, and computer-readable storage medium. The curing method includes: acquiring characteristic data of the working condition; acquiring a curing model matching the characteristic data from a curing model library; and inputting curing energy into the colloid bonded to the workpiece according to preset parameters corresponding to the curing model to cure the colloid. The curing model library includes multiple curing models. The curing method provided by this application utilizes a curing model matching the characteristic data of the working condition to cure the colloid, adapting to various working conditions and improving the curing effect of the colloid.

Description

Technical Field

[0001] This application relates to the technical field of curing technology, specifically to a curing method, curing apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] Sheet metal structural components widely use adhesive bonding processes in the automotive, aerospace, and home appliance industries. Traditional sheet metal adhesive curing mainly relies on natural air drying or oven / hot air heating. Natural curing cycles can take several hours, severely restricting production line speed. Oven heating consumes a lot of energy, easily causing thermal deformation of sheet metal and damage to the paint surface. Furthermore, it has poor adaptability to different materials, sheet thicknesses, and adhesive types, with fixed parameters that cannot be automatically adjusted according to working conditions. Summary of the Invention

[0003] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, this application provides a curing method, a curing apparatus, an electronic device, and a computer-readable storage medium.

[0004] This application provides a curing method, including: Acquire characteristic data of the working conditions; Obtain a solidified model from the solidified model library that matches the feature data; According to the preset parameters corresponding to the curing model, curing energy is input into the colloid bonded to the workpiece to cure the colloid; The solidified model library includes multiple solidified models.

[0005] According to one embodiment of this application, the curing energy includes laser energy and infrared energy, and the step of inputting curing energy into the colloid bonded to the workpiece according to the preset parameters of the curing model to cure the colloid includes: The laser is focused on the deep region of the colloid; Infrared light covers the coating path and edge blind areas of the colloid, providing additional heat to the surface of the colloid.

[0006] According to one embodiment of this application, the step of inputting curing energy into the colloid bonded to the workpiece according to the preset parameters of the curing model to cure the colloid includes: The energy loss during the curing process is obtained, and the input increment of the curing energy is corrected based on the energy loss. Obtain the predicted degree of curing of the colloid, and determine whether to stop inputting the curing energy into the colloid based on the predicted degree of curing.

[0007] According to an embodiment of this application, the step of obtaining the energy loss during the curing process includes: The temperature fields of the workpiece and the colloid are obtained in real time; The energy loss is calculated based on the temperature field.

[0008] According to an embodiment of this application, the step of obtaining the temperature field of the workpiece and the colloid in real time includes: Obtain the master equation for anisotropic heat conduction of the workpiece; Obtain the interfacial contact thermal resistance boundary conditions between the workpiece and the colloid; The temperature field is obtained based on the anisotropic heat conduction master equation and the interface contact thermal resistance boundary condition. The governing equation for anisotropic heat conduction is:

[0009] in, The in-plane thermal diffusivity is... The normal thermal diffusivity is... The density of the workpiece, For specific heat capacity, This is the heat source term within the volume; The interface contact thermal resistance boundary condition is as follows:

[0010] in, The temperature on the workpiece side of the interface. This refers to the temperature on the colloid side of the interface. The thermal conductivity of one side of the workpiece. The thermal conductivity of one side of the colloid is given. For interfacial contact thermal resistance, This refers to the interface contact position between the workpiece and the colloid.

[0011] According to one embodiment of this application, the formula for calculating the energy loss is:

[0012] in, The energy loss mentioned above. The in-plane conduction loss of the workpiece is denoted as . The normal transmission loss of the workpiece is denoted as . For surface convection loss, For surface radiation loss, This refers to interfacial thermal resistance loss.

[0013] According to one embodiment of this application, the in-plane conduction loss and the normal conduction loss are obtained by the temperature field using the standard integral form of Fourier's law; the surface convection loss is obtained by the temperature field using the standard integral form of Newton's cooling formula; and the surface radiation loss is obtained by the temperature field using the standard integral form of Stefan-Boltzmann's law. The interfacial thermal resistance loss is:

[0014] in, The heat flux density through the interface, For the interface area, For time step, This represents the interfacial contact thermal resistance.

[0015] According to an embodiment of this application, the step of obtaining the predicted degree of curing of the colloid includes: An improved model of the curing kinetics of the colloid was obtained; The predicted degree of cure is calculated based on the improved curing kinetics model. The improved curing kinetics model is as follows:

[0016] in, All are preset values. This refers to the reference temperature at the center point of the workpiece surface monitored by the infrared thermometer. This is the preset effective temperature drop induced by thermal conductivity.

[0017] According to an embodiment of this application, the step of determining whether to stop inputting the curing energy to the colloid based on the predicted degree of curing includes: When the predicted degree of curing is greater than or equal to the preset curing value, and the temperature of the colloid meets the preset temperature threshold range within the preset time period, the input of the curing energy is stopped immediately. If the predicted degree of curing is less than the preset curing value or the temperature of the colloid within the preset time does not meet the preset temperature threshold range, then the curing energy is kept input.

[0018] According to an embodiment of this application, the step of obtaining the characteristic data of the working condition includes: The material of the workpiece, the thickness of the workpiece, the thickness of the colloid, the type of the colloid, the ambient temperature, and the curvature of the workpiece surface are collected in real time to form six-dimensional working condition data. The operating condition data is normalized, outlier removal is performed, and weighted fault tolerance verification is applied to obtain the feature data.

[0019] According to one embodiment of this application, the weight of the material of the workpiece is 0.2, the weight of the thickness of the workpiece is 0.25, the weight of the thickness of the colloid is 0.2, the weight of the type of colloid is 0.2, the weight of the ambient temperature is 0.05, and the weight of the curvature of the workpiece surface is 0.1.

[0020] According to one embodiment of this application, the preset parameters include laser operating parameters, infrared operating parameters, tooling parameters, and irradiation pose parameters; The preset parameters also include calibration values. , , and ; in, The in-plane thermal conductivity of the workpiece is... The normal thermal conductivity of the workpiece is... Interfacial contact thermal resistance This is to induce an effective temperature drop due to thermal conductivity.

[0021] According to an embodiment of this application, the calibration steps for the thermally induced effective temperature drop include: Use an infrared thermometer to measure the reference temperature at the center point of the workpiece surface; A miniature thermocouple is embedded inside the colloid to monitor the effective temperature; The effective temperature drop induced by thermal conductivity is obtained by subtracting the effective temperature from the reference temperature. Record the effective temperature drop induced by thermal conductivity under different combinations, and establish... = The calibration database or empirical correlation; in, The in-plane thermal conductivity of the workpiece is... The thickness of the workpiece. The thickness of the colloid. The ambient air velocity.

[0022] According to one embodiment of this application, each of the solidified models has a corresponding preset boundary threshold; the step of obtaining a solidified model that matches the feature data includes: A fuzzy clustering matching algorithm is used to calculate the membership degree of the feature data relative to a preset boundary threshold of multiple fixed models; The model with the highest membership degree is selected as the matching result.

[0023] This application also provides a curing apparatus, which uses the method described in the above embodiments to cure the colloid; The curing device includes a sensor module, a control module, and an energy input module. The sensor module is used to acquire operating condition data, the control module is used to convert the operating condition data into the feature data, and control the energy input module to input the curing energy into the colloid to cure the colloid.

[0024] This application also provides an electronic device, including: Memory, which stores program data; and A processor, coupled to the memory, executes the program data during operation to implement the hardening method as described in the above embodiments.

[0025] This application also provides a computer-readable storage medium storing program data, which, when executed by a processor, is used to implement the solidification method described in the above embodiments.

[0026] In the curing method provided in this application, the curing model and the characteristic data of the working conditions are matched, which can be used to cure the colloid in a targeted manner to adapt to different working conditions and improve the curing efficiency and curing effect of the colloid. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic flowchart of an embodiment of the curing method of this application; Figure 2 yes Figure 1 A schematic flowchart of an embodiment of step S300 of the curing method shown; Figure 3 This is a schematic diagram of the structure of an embodiment of the workpiece and colloid of this application; Figure 4 This is a schematic flowchart of another embodiment of the curing method of this application; Figure 5 This is a schematic diagram of the laser-infrared differential energy distribution and closed-loop compensation principle in this application; Figure 6 This is a schematic diagram of the energy model-tooling-pose linkage mapping relationship of this application; Figure 7 This is a schematic diagram of the structure of an embodiment of the curing device of this application; Figure 8 This is a schematic diagram of the structure of an embodiment of the electronic device of this application; Figure 9 This is a schematic diagram of the structure of an embodiment of the computer-readable storage medium of this application. Detailed Implementation

[0029] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the application. Similarly, the following embodiments are only some, not all, embodiments of the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0030] The terms "first," "second," and "third" used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movement of components in a specific posture (as shown in the figures). If the specific posture changes, the directional indication will also change accordingly. The terms "comprising" and "having," and any variations thereof, in the embodiments of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or components inherent to these processes, methods, products, or devices.

[0031] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0032] This application provides a curing method, such as... Figure 1 and Figure 3 As shown, the curing method includes: Step S100: Obtain characteristic data of the working condition.

[0033] Step S200: Obtain a fixed model that matches the feature data from the fixed model library.

[0034] Step S300: Input curing energy into the colloid 20 bonded to the workpiece 10 according to the preset parameters corresponding to the curing model to cure the colloid 20; The solidified model library includes multiple solidified models.

[0035] The curing model library in this application can set multiple curing models for various working conditions. Depending on the changes in working conditions, different curing models can be switched to cure the colloid 20, so as to adapt to different working conditions and improve curing efficiency and curing effect.

[0036] In some embodiments, workpiece 10 can be a sheet metal structural component, which is a component formed from a thin metal sheet and can bear / transmit force / provide spatial support. In some other embodiments, workpiece 10 can also be a die-cast part or other structural component. Adhesive 20 can be an adhesive layer bonded to the sheet metal, and the material of adhesive 20 can be polyurethane, epoxy resin, or other materials. Adhesive 20 can be disposed between two workpieces 10 to bond the two workpieces 10 together.

[0037] In some embodiments, the curing energy includes laser energy and infrared energy, and step S300 includes: The laser is focused on the deep region of colloid 20.

[0038] Infrared light covers the coating path and edge blind areas of colloid 20, and provides additional heat to the surface of colloid 20.

[0039] The curing method of this application uses laser-infrared composite energy to cure colloid 20. Compared with single laser curing, the infrared surface radiation in laser-infrared composite curing eliminates the edge blind zone outside the coating trajectory and provides gentle heat replenishment to the surface layer, resulting in more uniform cross-linking and improved curing consistency. It also avoids the risk of overheating and ablation caused by adding surface energy to a single laser to cover deeper layers. This method is more adaptable to thick colloid layers and light-colored / low-absorption colloid systems, and is more likely to meet production line cycle requirements. The differentiated composite energy control strategy addresses the problem of uneven curing inside and outside colloid 20 from a mechanistic perspective, resulting in significantly better curing uniformity than single light source or simple synchronous irradiation schemes.

[0040] In some embodiments, step S100 includes: The material of workpiece 10, the thickness of workpiece 10, the thickness of colloid 20, the type of colloid 20, the ambient temperature, and the curvature of the surface of workpiece 10 are collected in real time to form six-dimensional working condition data.

[0041] The operating condition data is normalized, outlier removal is performed, and weighted fault tolerance verification is applied to obtain the feature data.

[0042] In some embodiments, operating condition data can be collected by multiple sensors. After data preprocessing, feature data is obtained from the operating condition data, and the data collected by each sensor is weighted according to the importance of the corresponding parameter.

[0043] In some embodiments, the weight of the material of the workpiece 10 is 0.2, the weight of the thickness of the workpiece 10 is 0.25, the weight of the thickness of the colloid 20 is 0.2, the weight of the type of colloid 20 is 0.2, the weight of the ambient temperature is 0.05, and the weight of the surface curvature of the workpiece 10 is 0.1.

[0044] In some embodiments, such as Figure 2 As shown, step S300 includes: Step S310: Obtain the energy loss during the curing process, and correct the input increment of the curing energy based on the energy loss.

[0045] Step S320: Obtain the predicted degree of curing of colloid 20, and determine whether to stop inputting curing energy into colloid 20 based on the predicted degree of curing.

[0046] When laser and infrared light irradiate the colloid 20, they cannot be completely absorbed by the colloid 20. There are energy losses such as conduction loss and radiation loss, which cause the effective curing reaction energy of the colloid 20 to be less than the input curing energy. This application corrects the input increment of curing energy based on the energy loss, and can replenish the energy loss in real time during the curing process to ensure that the effective curing reaction energy meets the requirements and improve the curing effect.

[0047] In some embodiments, such as Figure 4 As shown, the solidification method includes: Step S1: Automatic acquisition and preprocessing of working condition parameters; Step S2: Pre-construction of model library and matching of working conditions; Step S3: Model switching and tooling linkage; Step S4: Differentiated composite energy output; Step S5: Real-time closed-loop dynamic energy compensation of multi-model coupling; Step S6: Real-time calculation and accurate determination of solidification endpoint; Step S7: Data storage and model iterative optimization.

[0048] Step S1: Automatic acquisition and preprocessing of operating parameters.

[0049] A multi-sensor fusion module, consisting of a material identification sensor, a laser thickness gauge, a visual adhesive coating inspection camera, and a temperature and humidity sensor, can collect six-dimensional working condition data in real time, including the material, thickness, adhesive coating thickness, type of adhesive 20, ambient temperature, and surface curvature of the workpiece 10. The collected working condition data is normalized, outlier removed, and subjected to multi-sensor weighted fault tolerance verification to ensure that the overall acquisition error is ≤1%.

[0050] Step S2: Model library pre-construction and working condition matching.

[0051] In some embodiments, a standardized curing model library based on the coupling of three models—a composite energy input balance model, a sheet metal anisotropic heat conduction correction model, and a colloidal curing kinetics improvement model—can be pre-constructed.

[0052] The process of constructing the composite energy input balance model is as follows: Laser effectively absorbs energy (J): (1) In the formula, Laser power (W) The laser irradiation time is denoted as s. To match the laser wavelength (nm) and coating thickness (mm) Related laser absorption coefficient.

[0053] Infrared energy absorption (J): (2) In the formula, Infrared power density (W / cm²) 2 ), Effective adhesive application area (cm²) 2 ), Infrared irradiation time (s) The surface temperature is fed back in real time by the infrared thermometer. (°C) and sheet metal surface emissivity Adaptive correction of infrared absorption coefficient.

[0054] Total input energy (J): (3) Total energy loss (J): (4) In the formula, The in-plane conduction loss is J. The loss is the normal (vertical) conduction loss (J) of the sheet metal. For surface convection loss (J). denoted as surface radiation loss (J). The thermal resistance loss at the adhesive layer interface is (J).

[0055] Effective reaction energy of colloid 20 curing (J): (5) The curing energy input to colloid 20 in step S300 is the total input energy. The power of the laser and infrared light can be set according to the total input energy. The input increment of curing energy can also be corrected by adjusting the power of the laser and infrared light to make up for the total energy loss.

[0056] In some embodiments, the step of obtaining the energy lost during the curing process includes: The temperature field of workpiece 10 and colloid 20 is obtained in real time.

[0057] Energy loss is calculated based on the temperature field.

[0058] In some embodiments, the step of obtaining the temperature fields of the workpiece 10 and the colloid 20 in real time includes: Obtain the master equation for anisotropic heat conduction of workpiece 10.

[0059] Obtain the boundary conditions of the interfacial contact thermal resistance between workpiece 10 and colloid 20.

[0060] The temperature field is obtained based on the main governing equation of anisotropic heat conduction and the boundary conditions of interfacial contact thermal resistance.

[0061] In some embodiments, such as Figure 3 As shown, Figure 3 In the coordinate system, the x-axis and y-axis are parallel to the surface of workpiece 10, and the z-axis is perpendicular to the thickness direction of workpiece 10 and points downwards. The in-plane thermal conductivity of the workpiece 10 region... Thermal conductivity along the x and y directions, and in the thickness direction Along the z-direction. Colloid 20 is located between two workpieces 10. The junction 30 between workpiece 10 and colloid 20 ( The interfacial contact thermal resistance is Temperature jumps on both sides of the interface = ( Temperature on side 10 of the workpiece (This refers to the temperature on the colloid side 20).

[0062] During the manufacturing process, sheet metal materials undergo multiple rolling processes, which elongates the microscopic grains along the rolling direction, resulting in significant anisotropy in thermal conductivity: thermal conductivity along the sheet surface direction... Much greater than the thermal conductivity along the thickness direction (perpendicular to the plate surface). Furthermore, the interface between the sheet metal and the adhesive layer is not an ideally smooth surface; microscopic gaps exist, contributing to the interfacial thermal resistance. This causes temperature jumps on both sides of the interface.

[0063] The isotropic Fourier law commonly used in existing technologies cannot accurately describe the heat transfer behavior of such composite structures, which will lead to a deviation of 15% to 30% between the calculated and actual heat loss values, resulting in inaccurate subsequent energy compensation.

[0064] This application establishes anisotropic heat conduction master equation, or anisotropic heat conduction correction model, based on the anisotropic characteristics of the workpiece's thermal conductivity, to more accurately calculate heat loss. The complete derivation process of the sheet metal anisotropic heat conduction correction model is as follows: For anisotropic media, the heat flux density vector is related to the temperature gradient through the thermal conductivity tensor k: (6) For sheet metal materials, a coordinate system is adopted such that the xy plane is parallel to the sheet surface, and the z-axis is along the sheet thickness direction. Due to the macroscopic orthotropic anisotropy of the material, the thermal conductivity tensor can be diagonalized as: (7) In the formula, Thermal conductivity in the planar direction (W / m·K). is the thermal conductivity in the thickness direction (W / m·K).

[0065] By taking the internal micro-elements of the sheet metal and applying the law of conservation of energy, we can obtain: (8) In the formula, Sheet metal density (kg / m³) 3 ), Specific heat capacity (J / kg·K) For the heat source term within the volume (W / m) 3 This section represents laser and infrared energy deposition.

[0066] Substituting the anisotropic Fourier law into the equation, we expand the divergence term: (9) assumed and The in-plane thermal diffusivity is defined as constant in its respective direction. = and normal thermal diffusivity = Then the master equation for anisotropic heat conduction in sheet metal is obtained: (10) in, The in-plane thermal diffusivity is... The normal thermal diffusivity is... The density of workpiece 10, For specific heat capacity, This is the heat source term within the volume.

[0067] Compared to the classical isotropic equations, the core modification of the governing equations lies in distinguishing between in-plane and normal thermal diffusivity coefficients. For typical cold-rolled steel sheets, The ratio can reach 1.3 to 1.8; while for aluminum alloy plates, the ratio is approximately 1.1 to 1.4. Ignoring this difference in the calculation process will lead to an underestimation of in-plane heat dissipation and an overestimation of heat transfer in the thickness direction.

[0068] When workpiece 10 and colloid 20 are bonded, the surface roughness of the bonding surface can lead to non-ideal contact. Therefore, the curing model of this application introduces a thermal resistance boundary condition at the adhesive layer interface. The derivation process of the thermal resistance boundary condition at the adhesive layer interface is as follows: At the sheet metal-adhesive layer interface ( There is interfacial contact thermal resistance. (m) 2 ·K / W).

[0069] in The physical definition is the temperature difference across the interface. With heat flow per unit area through the interface The ratio, that is: (11) The value depends on factors such as surface roughness, contact pressure, and interfacial medium, with a typical value in the range of 1×10⁻⁶. -6 ~1×10 -4 m 2 Within the K / W range.

[0070] When the heat flow is continuous on both sides of the adhesive layer interface, but the temperature jumps, the boundary condition for the contact thermal resistance of the adhesive layer interface is defined as follows: (12) in, The temperature on the workpiece side of the interface (°C). The temperature on the colloid side of the interface is (°C). The thermal conductivity (W / m·K) of one side of workpiece 10. The thermal conductivity (W / m·K) of one side of colloid 20. For interfacial contact thermal resistance, This represents the interface contact location between workpiece 10 and colloid 20. This boundary condition precisely quantifies the additional energy loss caused by interfacial thermal resistance.

[0071] When introduced After boundary conditions, the interfacial thermal resistance loss of the adhesive layer This can be expressed as: (13) in Heat flux density through the interface (W·m) 2 The value is calculated from the temperature gradient across the interface. Interface area (m 2 ), The time step is in seconds. This represents the interfacial contact thermal resistance.

[0072] Based on the anisotropic heat conduction master equation (Equation 10) and the interface thermal resistance boundary condition (Equation 11), the finite difference method is used with a spatial step size of = =0.5mm =0.1mm, time step Discrete solution is performed at 0.01s to obtain the temperature field inside the sheet metal and adhesive layer in real time. T ( x,y,z,t ).

[0073] The specific implementation process is as follows: Step S10: Mesh generation and equation discretization.

[0074] The sheet metal and adhesive layer areas are arranged in spatial steps. = =0.5mm =0.1mm divided into fine mesh nodes, time step =0.01s.

[0075] For internal nodes (nodes not adjacent to the boundary), the temperature is directly updated using the discrete form of the anisotropic heat conduction governing equation (Equation 10). The core of this approach lies in the difference between the in-plane and normal thermal diffusivity coefficients:

[0076]

[0077] Among them, superscript Represents a time step, subscript Represents the coordinates of a spatial node.

[0078] Adjacent nodes on both sides of the interface between sheet metal and adhesive layer and : Fourier's law for sheet metal is: (15) The Fourier law for the adhesive layer side is: (16) Interface thermal resistance is defined as: (17) Solve the above three equations simultaneously and eliminate one of them. , The expression for the interfacial heat flux density flowing from the sheet metal to the adhesive layer is obtained as follows: (18) Step S20: Apply boundary conditions and time step.

[0079] Convection and radiation boundary conditions are applied to the upper surface of the sheet metal and the lower surface of the adhesive layer. Starting from the initial conditions (all nodes equal to ambient temperature), the internal heat source is updated sequentially at each time step. The process involves calculating the internal node temperatures, calculating the interface node temperatures, applying external boundary conditions, and iterating step-by-step to obtain the temperature field of the entire region at each time step. .

[0080] Step S30: Temperature difference acquisition.

[0081] After the solution is obtained, the temperature difference at any point on the adhesive interface is the temperature at that point. Its value is given by the following formula: (19) in, The heat flux and material temperature rise change dynamically during the solution process; The larger the value, the greater the temperature jump at the interface under the same heat flux.

[0082] Starting from the temperature field, calculate the in-plane conduction loss separately. Normal transmission loss Surface convection loss and surface radiation loss .

[0083] In some embodiments, in-plane conduction loss and normal conduction loss are obtained from the temperature field using the standard integral form of Fourier's law; surface convection loss is obtained from the temperature field using the standard integral form of Newton's law of cooling; and surface radiation loss is obtained from the temperature field using the standard integral form of Stefan-Boltzmann's law. The sum of all loss terms is the total loss. Substituting this into the composite energy input balance model (Equation 5), the effective reaction energy for curing colloid 20 can be obtained. .

[0084] In some embodiments, the step of obtaining the predicted degree of cure of colloid 20 includes: An improved model of curing kinetics for colloid 20 was obtained.

[0085] The degree of curing is predicted based on an improved curing kinetics model.

[0086] The improved curing kinetics model in this application is based on the classic Kamal-Sourour model.

[0087] The derivation process of the improved curing kinetics model of colloid 20 is as follows: The classic Kamal-Sourour autocatalytic curing model assumes that the temperature of the entire reaction system is uniform and equal to the set temperature, and the expression is as follows: (20) Among them, degree of curing This represents the percentage of colloid 20 that has reacted out of the total amount, and its value is [0,1]. The rate constant follows an Arrhenius temperature dependence. (twenty one) However, in the case of sheet metal bonding curing, the adhesive 20 is not in a thermally isolated state: the sheet metal, as a high thermal conductivity heat sink, continuously conducts the input heat out along the in-plane direction, making the actual temperature of the adhesive layer significantly lower than the reference temperature measured by the infrared thermometer or thermocouple. Ignoring this effect will lead to an overestimation of the degree of curing, resulting in premature shutdown and insufficient actual curing.

[0088] The amendments in this application do not directly modify the reaction order in the classical autocatalytic curing model. m , n Instead, it attributes the thermal conductivity effect of sheet metal to a temperature correction of the reaction rate.

[0089] First, this application defines the difference between the actual temperature of the adhesive layer and the reference temperature measured by an infrared thermometer or thermocouple as the effective temperature drop induced by thermal conductivity. .set up The reference temperature (K) at the center point of the sheet metal surface monitored by the infrared thermometer is [value missing]. The effective temperature at which the curing reaction of colloid 20 actually occurs is [value missing]. (K), we get: (twenty two) In the formula The effective temperature drop (K) induced by thermal conductivity depends on the in-surface thermal conductivity of the sheet metal. Sheet metal thickness Adhesive thickness and ambient air velocity Other heat dissipation parameters, i.e. .

[0090] Secondly, the effective temperature By embedding the Arrhenius equation, the effective rate constant is obtained. : (twenty three) Furthermore, With the original The ratio is defined as the correction function. : (twenty four) because Therefore Index results Always less than Therefore The constant validity of this method ensures that the thermal conductivity effect always reduces the effective reaction rate, which is something that the empirical correction method lacks.

[0091] Finally, the complete improved model of curing kinetics is as follows: (25) Where R is the molar gas constant, with a value of 8.314. All are preset values. This refers to the reference temperature at the center point of the workpiece surface monitored by the infrared thermometer. This is the preset effective temperature drop induced by thermal conductivity.

[0092] In some embodiments, the curing kinetic parameters corresponding to Formula 25 ( The exothermic curves of colloid 20 were measured using differential scanning calorimetry (DSC) at different heating rates. The system automatically fitted the curves and stored them as the preset parameter set for the corresponding model.

[0093] The three models in the curing model (or energy model) are divided as follows: the composite energy input balance model establishes the overall energy account framework (effective curing reaction energy = total input energy - total energy loss); the sheet metal anisotropic heat conduction correction model is responsible for solving the temperature field inside the sheet metal and adhesive layer and calculating various losses; and the improved curing kinetics model of colloid 20 is used to calculate the degree of curing and determine the curing endpoint.

[0094] In some embodiments, the temperature input in the improved curing kinetics model (modified Kamal-Sourour model) of colloid 20 The curing degree is obtained by real-time detection of the center point of the sheet metal surface using an infrared thermometer. The curing degree integral is calculated based on the time step. =0.01s, iterating synchronously with the temperature field solution.

[0095] In some embodiments, the curing method further includes: Step S3: Model switching and tooling linkage: The solidified model library includes multiple solidified models, each with a corresponding preset boundary threshold; step S200 includes: A fuzzy clustering matching algorithm is used to calculate the membership degree of feature data relative to preset boundary thresholds of multiple fixed models.

[0096] The model with the highest membership degree is selected as the matching result.

[0097] In some embodiments, the six-dimensional feature data, after preprocessing operations such as normalization and outlier removal, is input into a fuzzy clustering matching algorithm to determine the membership degree with the working condition boundary thresholds of each model in the model library, and the optimal dedicated fixed model is automatically matched.

[0098] In some embodiments, the fuzzy clustering matching algorithm takes six-dimensional feature data as input, and the role of the six-dimensional feature data in the matching is: the sheet metal material and thickness jointly determine the thermal property level ( , The order of magnitude of the laser absorption coefficient and its heat dissipation capacity are determined by the type of colloid 20, which determines the curing temperature window and reaction kinetic parameters. The coating thickness affects the laser absorption coefficient and its value. Ambient temperature and humidity are used to correct for convection boundary conditions. The surface curvature of workpiece 10 determines the tooling clamping method and irradiation pose type. The fuzzy clustering matching algorithm calculates the membership degree between the current working condition feature data and the preset boundary thresholds of each model, and takes the one with the highest membership degree as the matching result.

[0099] In some embodiments, based on the solidified model architecture, the operating condition boundaries and parameters of the six typical standardized solidified models M1 to M6 obtained in advance through offline calibration experiments are as follows: M1: Aluminum sheet metal + acrylic quick-drying adhesive ≈167W / m·K, ≈142W / m·K, Calibration value approximately 5 × 10 -6 m 2 • K / W; Laser pulse mode 30–50 W, 20 kHz, infrared 0.8 W / cm 2 , The calibration value is approximately 3–8K; curing time is 5–10 seconds, and the peak temperature is ≤60℃; it is matched with a flexible multi-point non-rigid clamping fixture, with an irradiation distance of 180mm and vertical incidence.

[0100] M2: Carbon steel sheet metal + epoxy structural adhesive ≈52W / m·K, ≈48W / m·K, Calibration value approximately 1×10 -5 m 2 • K / W; Laser continuous mode 80–120 W, infrared 1.5 W / cm 2 , The calibration value is approximately 8–18 K; curing time is 20–30 s, with a peak temperature of 90–110 °C; it is matched with a four-point symmetrical rigid clamping fixture, and the irradiation distance is 150 mm.

[0101] M3: Stainless steel + silicone sealant ≈16W / m·K, ≈15W / m·K, Calibration value approximately 8 × 10 -6 m 2 • K / W; Laser continuous mode 100-150W, infrared 2.0W / cm 2 , The calibration value is approximately 5–12K; curing time is 40–60 seconds, with a peak temperature of 120–140℃; it is matched with high-temperature resistant heat-insulating partitioned support tooling, and the irradiation distance is 140mm.

[0102] M4: Galvanized steel sheet + polyurethane adhesive Laser pulse 20-40W, infrared 0.6W / cm 2 Curing time is 15-25 seconds, with a peak temperature of ≤80℃; it is matched with soft-coated low-pressure protective tooling.

[0103] M5: Thin sheet metal (thickness ≤ 1mm) + quick-drying adhesive The effect is significant. and Differences need to be calibrated separately; intermittent laser pulse mode, infrared micro-power heating, curing 8-15s, peak temperature ≤50℃; matched with full-area surface support vacuum adsorption fixture.

[0104] M6: Thick sheet metal (thickness ≥ 3mm) + high-strength structural adhesive The in-plane heat conduction path is long. Larger values ​​(calibrated values ​​approximately 15–30K); laser segmented gradient boost, infrared zoned power compensation, curing time 30–60s, peak temperature 100–130℃; matched with heavy-duty multi-point locking fixtures, supporting zoned attitude fine-tuning.

[0105] In some embodiments, M1 The calibration value is approximately 3-8K. The adjustment range is 3 to 8K. When a specific working condition is matched with the M1 model, the system determines the specific operating point value for this solidification from within the adjustment range based on the six-dimensional feature data.

[0106] Thermophysical parameters and interfacial thermal resistance values ​​not listed in M4~M6 should be pre-determined according to the specific material of workpiece 10 and the calibration scheme, and then written into the corresponding model parameter set in practical applications. , , and Configure matching settings.

[0107] After the solidified model and feature data are matched, the industrial control controller retrieves all preset parameters of the corresponding solidified model from the model library and completes two operations simultaneously within 10ms: First, retrieve the laser working mode, laser power, infrared power density, curing time, temperature rise rate, and peak temperature threshold corresponding to the model to complete the energy system initialization; Then, the tooling mapping parameters bound to the model are retrieved, and the tooling clamping distance, support point position, irradiation working distance, platform pitch attitude, and spot focal length compensation are adjusted through the servo control system.

[0108] Energy parameters and tooling parameters take effect synchronously in the same time sequence, with a linkage response time of no more than 10ms.

[0109] Step S4: Differentiated composite energy output.

[0110] After the curing process starts, the energy system starts dual-source output according to the parameters locked in step S3: the laser first focuses on the deep area of ​​colloid 20 and heats it rapidly with high energy density, so that the temperature of colloid 20 quickly breaks through the activation energy threshold of the curing reaction; then the short-wave infrared array covers the entire coating trajectory and edge blind area, and uniformly heats the surface of colloid 20 to prevent the temperature difference between the surface and the deep layer from being too large.

[0111] Step S5: Real-time closed-loop dynamic energy compensation with multi-model coupling.

[0112] During the curing process, the system uses the finite difference method to perform real-time discretization and solution of the sheet metal anisotropic heat conduction correction model and its interface thermal resistance boundary conditions in step S2. It calculates the anisotropic conduction loss, convection loss, radiation loss and total energy loss caused by the thermal resistance of the adhesive layer interface in the sheet metal surface and thickness direction. Furthermore, combined with the real-time temperature data of workpiece 10, the system corrects the laser and infrared energy input increments, strictly controls the overall temperature rise of the sheet metal and the peak temperature of the adhesive 20, and achieves dynamic and accurate compensation for energy loss.

[0113] In some embodiments, loss calculation and closed-loop compensation are performed in parallel with the differentiated composite energy output in step S4.

[0114] Step S6: Real-time calculation and accurate determination of the curing endpoint.

[0115] In some embodiments, the step of determining whether to stop inputting curing energy to the colloid 20 based on the predicted degree of curing includes: When the predicted degree of cure is greater than or equal to the preset cure value, and the temperature of colloid 20 within the preset time period meets the preset temperature threshold range, the input of curing energy is stopped immediately. If the predicted degree of curing is less than the preset curing value or the temperature of colloid 20 does not meet the preset temperature threshold range within the preset time, then the input curing energy is maintained.

[0116] In some embodiments, this application introduces an improved Kamal-Sourour curing kinetics model that corrects for the effective temperature drop induced by sheet metal thermal conductivity, and calculates the predicted degree of curing of colloid 20 in real time by integral calculation. When the predicted degree of curing is ≥95% and the temperature of colloid 20 meets the preset temperature threshold range of the model within a continuous preset time, the laser and infrared energy input is immediately stopped to complete the rapid curing of colloid 20. If the predicted degree of curing is less than 95% or the temperature of colloid 20 does not meet the preset temperature threshold range of the model within a continuous preset time, then the laser and infrared energy input will be maintained and dynamic compensation will be applied.

[0117] In some embodiments, the predicted degree of curing can be calculated using an improved curing kinetics model, and the temperature of colloid 20 can be calculated using a temperature field. The predicted degree of curing and the temperature of colloid 20 are then combined to determine whether the curing of colloid 20 is complete.

[0118] The calibration values ​​for each model are shown in parameters M1 to M6 in step S2, and will be continuously optimized with iterations in step S7.

[0119] Step S7: Data storage and data traceability.

[0120] After each curing process, the system automatically records the six-dimensional characteristic data, energy operation parameters, temperature curve, curing degree results, tooling adjustment parameters, and sheet metal deformation detection data for this working condition, forming a complete process data sample for future reference in similar solutions and for tracing back when problems arise.

[0121] In some embodiments, the preset parameters corresponding to the solidified model include laser working parameters, infrared working parameters, tooling parameters, and irradiation pose parameters.

[0122] In some embodiments, laser operating parameters include parameters such as laser power and laser irradiation time. Infrared operating parameters include parameters such as infrared power density and infrared irradiation time. Tooling parameters include parameters such as clamping method and clamping force / support spacing. Irradiation pose parameters include parameters such as working distance, platform attitude, and focal length compensation.

[0123] In some embodiments, the preset parameters also include calibration values. , , and ;in, The in-plane thermal conductivity of workpiece 10 is... The normal thermal conductivity of workpiece 10 is... Interfacial contact thermal resistance This is to induce an effective temperature drop due to thermal conductivity.

[0124] In some embodiments, the calibration steps for thermally induced effective temperature drop include: Use an infrared thermometer to measure the reference temperature at the center point of the workpiece surface.

[0125] A miniature thermocouple is embedded inside the colloid 20 to monitor the effective temperature.

[0126] Subtracting the effective temperature from the reference temperature yields the thermally induced effective temperature drop.

[0127] Record the effective temperature drop induced by thermal conductivity under different combinations, and establish = The calibration database or empirical correlation.

[0128] in, The in-plane thermal conductivity of workpiece 10 is... The thickness of workpiece 10, The thickness of colloid 20, For ambient air velocity, , , , Substituting the numerical values ​​into the empirical correlation, we can obtain the corresponding... The value.

[0129] In some embodiments, and The calibration scheme is as follows: using the laser flash method or the transient planar heat source method, samples are taken along the plate surface and thickness directions respectively, the anisotropic thermal diffusivity and specific heat capacity are measured, and the thermal conductivity is calculated in reverse.

[0130] In some embodiments, The calibration scheme is as follows: a steady-state heat flow test bench is built, precision thermocouples are arranged on both sides of the interface between the sheet metal and the adhesive layer simulated sample, a known heat flow is applied, and the interface temperature difference is measured. ,Depend on Calculated .

[0131] In some embodiments, The calibration scheme is as follows: Under standardized operating conditions, the temperature is monitored simultaneously using an infrared thermometer and a miniature thermocouple embedded inside the adhesive layer. The temperature difference under different combinations is recorded, and a calibration system is established. = The calibration database or empirical correlation.

[0132] In some embodiments, calibration values ​​obtained through a single calibration experiment may contain significant errors and cannot be directly stored in the solidified model. Once the accumulated valid process data samples reach a preset number, the system initiates an iterative optimization program to optimize each model in the model library. , , , The calibration values ​​and working condition matching boundary thresholds are corrected, and the updated parameters are written into the model library in step S2 for use in the curing control of subsequent batches. As the number of production batches increases, the parameters in the model library gradually approach the optimal values ​​under this working condition, and the matching accuracy and curing consistency gradually improve.

[0133] In some embodiments, the average value of the calibration values ​​obtained from multiple calibration experiments can be written into the solidified model as the final calibration value to avoid random errors.

[0134] In some embodiments, the multi-model coupled real-time closed-loop dynamic energy compensation S5 and the real-time calculation and accurate determination of the curing endpoint S6 run in parallel and are inputs to each other during the curing process. After the batch is completed, the data storage and model iterative optimization S7 feeds back the optimization results to the model library pre-constructed and the model library matching S2 to form a closed-loop iteration.

[0135] In some embodiments, such as Figure 5 As shown, the industrial controller 2 sets the power, operating mode, and infrared power density of the laser 3 and the short-wave infrared array 4 according to the preset parameters of the anisotropic heat conduction correction model 1. The laser beam emitted by the laser 3 is focused on the deep region of the colloid 20; the short-wave infrared array 4 emits an infrared beam covering the surface and edge blind areas of the colloid 20, while the infrared thermometer collects the temperature of the surface of the colloid 20 in real time. Feedback is sent to laser 3, which then calculates each loss component and the total loss based on the anisotropic heat conduction correction model 1, dynamically correcting the laser power of laser 3 and the output increment of infrared power density of short-wave infrared array 4, forming a closed loop.

[0136] In some embodiments, tooling parameters and irradiation pose parameters can be stored in a database, with each solidified model corresponding to a set of tooling parameters and irradiation pose parameters.

[0137] In some embodiments, such as Figure 6 As shown in the diagram, the left side represents the standardized solidified model group 100, which includes the selection input of 6 standardized solidified models (M1 to M6). The middle section shows the mapping relationship 200, where each model corresponds to a set of preset tooling parameters (clamping method, clamping force / support spacing, support point arrangement) and irradiation pose parameters (working distance, platform pitch attitude, and spot focal length compensation value). The right side illustrates the execution end: the servo tooling module 300 synchronously adjusts the clamping spacing and support point positions according to the mapping parameters, the three-axis motion platform 400 adjusts the irradiation distance and incident angle, and the spot adjustment mechanism 500 completes focal length compensation. This application transforms the traditional "offline debugging" into "online adaptive switching" through the integrated linkage of energy model, tooling, and irradiation pose, significantly shortening changeover time. Simultaneously, six-dimensional fuzzy clustering provides millisecond-level model matching, adapting to different materials, plate thicknesses, and adhesive types, and is suitable for flexible production lines with multiple product mixes.

[0138] In some embodiments, the tooling parameters corresponding to the curing model M1 are flexible multi-point non-rigid clamping; the support spacing is 120mm. The tooling parameters corresponding to the curing model M2 are four-point symmetrical rigid clamping; the clamping force is 200N / point; the support points are arranged in a 120×80mm rectangular pattern. The tooling parameters corresponding to the curing model M3 are high-temperature resistant heat-insulating partitioned support. The tooling parameters corresponding to the curing model M4 are soft-wrapped low-pressure protective clamping. The tooling parameters corresponding to the curing model M5 are full-area surface support vacuum adsorption. The tooling parameters corresponding to the curing model M6 are heavy-duty multi-point locking; supporting partitioned posture fine adjustment.

[0139] Existing curing methods simply irradiate the adhesive layer with a light source, lacking a quantitative coupling model based on heat transfer and curing kinetics. Process parameters rely on manual experience for setting. There is no automatic matching and switching mechanism between operating conditions and energy models, nor are sheet metal thermal conductivity, interface thermal resistance, and tooling boundary conditions incorporated into closed-loop control. This leads to surface overheating, internal under-curing, and uneven curing of the adhesive, and a severe disconnect between the energy model and the curing tooling conditions. Furthermore, existing technologies lack self-learning optimization capabilities, making it difficult to guarantee long-term process consistency. This application aims to address the problems of low efficiency and easy sheet metal thermal deformation caused by natural curing or oven curing, fixed curing parameters from a single light source without adaptive operating conditions, uneven curing and overheating / under-curing due to the lack of a quantitative coupling model and closed-loop control, the disconnect between the energy process model and the curing tooling requiring manual readjustment, and insufficient long-term stability and accuracy due to the lack of a self-learning mechanism. The curing method of this application integrates multi-sensor operating condition perception, multi-model coupled quantitative calculation, differentiated energy control using laser and infrared, and energy-tooling-pose linkage adaptive control.

[0140] To better understand the technical solution of this application, the following detailed discussion is provided in conjunction with specific embodiments.

[0141] Example 1: Aluminum sheet metal + acrylic fast-drying adhesive curing The sensor collected data on materials made of 6061 aluminum alloy ( =167W / m·K, =142W / m·K), board thickness 1.2mm, adhesive thickness 0.3mm, adhesive type is acrylic quick-drying adhesive, ambient temperature 25℃, surface curvature ≈0.

[0142] After normalization and verification, the operating data is input into the fuzzy clustering matching module and compared with 6 models to determine membership. The membership degree of M1 is 0.94, and it is automatically matched to model M1. The controller completes the switching within 10ms: the laser is set to pulse mode, power 40W, frequency 20kHz; infrared power density 0.8W / cm². 2 Peak temperature threshold ≤ 60℃; The tooling was switched to a flexible, multi-point, non-rigid clamping system with a three-point support spacing of 120mm and an irradiation distance of 180mm, with perpendicular incidence. After curing began, the laser irradiated the deep layers of the adhesive layer in a pulsed mode, while an infrared array covered the entire adhesive line and edge blind areas. The system used the finite difference method to solve the anisotropic heat conduction correction model in real time. =5×10 -6 m 2 • K / W), dynamically compensates for heat dissipation within the aluminum plate and interface thermal resistance loss. When a high heat dissipation rate is detected, the infrared power is automatically reduced from 0.8 W / cm². 2 Fine-tuned to 0.85 W / cm 2 At the same time, based on The Kamal-Sourour model (Equation 25) with 5K correction is used to calculate and predict the degree of curing in real time. At 6.5s, the predicted degree of curing reaches 95.2%, and the peak temperature of the adhesive layer is 58℃. Since it meets the threshold of ≤60℃ and has been in the temperature range for 0.5s, the system immediately stops laser and infrared output.

[0143] Tests showed that the adhesive layer was uniformly cured without bubbles or undercooked sections, the sheet metal deformation was 0.012mm / 100mm, and there was no surface damage.

[0144] Example 2: Carbon steel sheet metal + epoxy structural adhesive curing The sensor collected data on Q235 cold-rolled steel sheet ( =52W / m·K, =48W / m·K), board thickness 2.0mm, adhesive thickness 0.5mm, adhesive type epoxy structural adhesive, ambient temperature 22℃, surface curvature ≈0. After the operating condition data was verified, it was input into the fuzzy clustering matching module. The membership degree of M2 was 0.91, and it was automatically matched to model M2.

[0145] The controller switches between the following modes within 10ms: laser is set to continuous mode with a power of 100W; infrared power density is 1.5W / cm². 2 Peak temperature threshold: 90~110℃; maximum curing time: 30s; tooling switched to four-point symmetrical rigid clamping, with support points arranged in a 120mm×80mm rectangle, clamping force 200N / point, irradiation distance 150mm. During curing, the system uses the finite difference method to solve the anisotropic heat conduction correction model in real time. =1×10 -5 m 2 (·K / W), carbon steel has lower thermal conductivity than aluminum, but it has a larger plate thickness and higher total heat capacity, with an infrared power density of 1.4~1.6W / cm². 2 Dynamic range adjustment. Based on... The Kamal-Sourour model with a 15K correction was used to calculate and predict the degree of curing in real time. At 25 seconds, the predicted degree of curing reached 96.3%, with a peak temperature of 105℃, which met the threshold range of 90~110℃, and the machine was immediately stopped. Testing showed that the adhesive layer was uniformly cured, the hardness met the standard, the sheet metal temperature rise was 38℃, the deformation was 0.018mm / 100mm, and there were no under-cured or overheated areas.

[0146] The curing method described in this application can achieve directional energy control and precise temperature control, with the overall temperature rise of the sheet metal ≤40℃ and the deformation ≤0.02mm / 100mm. The energy consumption is reduced by more than 50% compared to the overall heating of the oven.

[0147] Comparative Example 1: Accuracy Comparison and Verification of Heat Loss Models Take a 1.2mm thick 6061 aluminum alloy plate, coat it with a 0.3mm acrylic adhesive layer, and irradiate it at a fixed point with a laser power of 40W for 10s. Arrange thermocouple arrays at 5mm, 10mm, 15mm, 20mm and 30mm away from the center of the spot along the direction of the plate surface to record the temperature.

[0148] The traditional isotropic model uses a single thermal conductivity k = 155 W / m·K ( and The arithmetic mean of the calculated values ​​was used, without interface thermal resistance correction. After 10 seconds, the temperatures at each measuring point were compared with the measured values. The temperature deviations at the five measuring points were 15.2%, 18.9%, 22.1%, 19.4%, and 17.8%, respectively, with a root mean square error (RMSE) of 18.7%. The anisotropic correction model used in this application adopts... =167W / m·K =142W / m·K =5×10 -6 m 2 ·K / W, after 10s of calculation, the temperature at each measuring point was compared with the measured value. The temperature deviations were 2.5%, 3.1%, 2.9%, 2.4%, and 3.2%, respectively, with an RMSE of 2.8%. The comparison results show that the model in this application reduces the heat loss calculation error from 18.7% in the traditional method to 2.8%, verifying the effectiveness of the anisotropic heat conduction correction model and the interface thermal resistance boundary condition.

[0149] Comparative Example 2: Comparison and Verification of Curing Degree Prediction Accuracy During the curing process of Example 1, the traditional Kamal-Sourour model (without...) was simultaneously employed. Correction, use directly The curing degree was calculated using the reaction temperature as a control. The traditional model calculates and displays the curing degree at 5.0 seconds. =95.1%, if the machine is stopped based on this, the actual curing degree inside the adhesive layer, as measured by actual tests, is only 82%, indicating obvious under-curing; while the modified model of this application ( =5K) was determined to be 95.2% at 6.5s, and the actual curing degree was 96%, with the predicted value and the measured value being highly consistent.

[0150] This application can be used in the fields of sheet metal bonding curing and photoelectric energy coupling curing technology. By combining the anisotropic heat conduction master equation with the interface thermal resistance boundary conditions, the calculation error of sheet metal heat loss is greatly reduced. By correcting the Arrhenius equation through thermal conductivity-induced effective temperature drop, the accuracy of curing degree prediction is improved, and the determination of curing endpoint is accurate and reliable. The curing cycle time is compressed by 5-60 seconds, realizing online rapid curing on the production line, with an efficiency improvement of 2-3 orders of magnitude compared with natural curing.

[0151] This application embodiment also provides a curing device 40, such as Figure 7 As shown, the curing device 40 uses the curing method of the above embodiment to cure the colloid 20; the curing device 40 includes a sensor module 410, a control module 420 and an energy input module 430. The sensor module 410 is used to acquire working condition data, the control module 420 is used to convert the working condition data into feature data, and control the energy input module 430 to input curing energy into the colloid 20 to cure the colloid 20.

[0152] In some embodiments, the energy input module 430 may include a laser 3 and a short-wave infrared array 4.

[0153] This application also provides an electronic device 50, such as... Figure 8 As shown, the electronic device 50 includes a memory 510 and a processor 520. The memory 510 stores program data; the processor 520 is coupled to the memory 510, and the processor 520 executes the program data during operation to implement the solidification method of the above embodiment.

[0154] This application embodiment also provides a computer-readable storage medium 60, such as... Figure 9 As shown, the computer-readable storage medium 60 stores program data 610, which, when executed by the processor 520, is used to implement the solidification method of the above embodiments.

[0155] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. Any equivalent device or equivalent process transformation made based on the content of this application specification and drawings, or direct or indirect application in other related technical fields, are similarly included in the patent protection scope of this application.

Claims

1. A curing method characterized by, include: Acquire characteristic data of the working conditions; Obtain a solidified model from the solidified model library that matches the feature data; According to the preset parameters corresponding to the curing model, curing energy is input into the colloid bonded to the workpiece to cure the colloid; The solidified model library includes multiple solidified models.

2. The curing method according to claim 1, characterized by, The curing energy includes laser energy and infrared energy. The step of inputting curing energy into the colloid bonded to the workpiece according to the preset parameters of the curing model to cure the colloid includes: The laser is focused on the deep region of the colloid; Infrared light covers the coating path and edge blind areas of the colloid, providing additional heat to the surface of the colloid.

3. The curing method according to claim 1, characterized in that, The step of inputting curing energy into the colloid bonded to the workpiece according to the preset parameters of the curing model to cure the colloid includes: The energy loss during the curing process is obtained, and the input increment of the curing energy is corrected based on the energy loss. Obtain the predicted degree of curing of the colloid, and determine whether to stop inputting the curing energy into the colloid based on the predicted degree of curing.

4. The curing method according to claim 3, characterized in that, The steps for obtaining the energy lost during the curing process include: The temperature fields of the workpiece and the colloid are obtained in real time; The energy loss is calculated based on the temperature field.

5. The curing method according to claim 4, characterized in that, The step of obtaining the temperature fields of the workpiece and the colloid in real time includes: Obtain the master equation for anisotropic heat conduction of the workpiece; Obtain the interfacial contact thermal resistance boundary conditions between the workpiece and the colloid; The temperature field is obtained based on the anisotropic heat conduction master equation and the interface contact thermal resistance boundary condition. The governing equation for anisotropic heat conduction is: in, The in-plane thermal diffusivity is... The normal thermal diffusivity is... The density of the workpiece, For specific heat capacity, This is the heat source term within the volume; The interface contact thermal resistance boundary condition is as follows: in, The temperature on the workpiece side of the interface. This refers to the temperature on the colloid side of the interface. The thermal conductivity of one side of the workpiece. The thermal conductivity of one side of the colloid is given. For interfacial contact thermal resistance, This refers to the interface contact position between the workpiece and the colloid.

6. The curing method according to claim 4, characterized in that, The formula for calculating the energy loss is: in, The energy loss mentioned above. The in-plane conduction loss of the workpiece is denoted as . The normal transmission loss of the workpiece is denoted as . For surface convection loss, For surface radiation loss, This refers to the interfacial thermal resistance loss.

7. The curing method according to claim 6, characterized in that, The in-plane conduction loss and the normal conduction loss are obtained from the temperature field using the standard integral form of Fourier's law; the surface convection loss is obtained from the temperature field using the standard integral form of Newton's cooling formula; the surface radiation loss is obtained from the temperature field using the standard integral form of Stefan-Boltzmann's law. The interfacial thermal resistance loss is: in, The heat flux density through the interface, For the interface area, For time step, This represents the interfacial contact thermal resistance.

8. The curing method according to claim 3, characterized in that, The step of obtaining the predicted degree of cure of the colloid includes: An improved model of the curing kinetics of the colloid was obtained; The predicted degree of cure is calculated based on the improved curing kinetics model. The improved curing kinetics model is as follows: in, All are preset values. This refers to the reference temperature at the center point of the workpiece surface monitored by the infrared thermometer. This is the preset effective temperature drop induced by thermal conductivity.

9. The curing method according to claim 3, characterized in that, The step of determining whether to stop inputting the curing energy into the colloid based on the predicted degree of curing includes: When the predicted degree of curing is greater than or equal to the preset curing value, and the temperature of the colloid within the preset time period meets the preset temperature threshold range, the input of the curing energy is immediately stopped. If the predicted degree of curing is less than the preset curing value or the temperature of the colloid within the preset time does not meet the preset temperature threshold range, then the curing energy is kept input.

10. The curing method according to claim 1, characterized in that, The steps for acquiring the characteristic data of the working condition include: The material of the workpiece, the thickness of the workpiece, the thickness of the colloid, the type of the colloid, the ambient temperature, and the curvature of the workpiece surface are collected in real time to form six-dimensional working condition data. The operating condition data is normalized, outlier removal is performed, and weighted fault tolerance verification is applied to obtain the feature data.

11. The curing method according to claim 10, characterized in that, The weight of the workpiece material is 0.2, the weight of the workpiece thickness is 0.25, the weight of the colloid thickness is 0.2, the weight of the colloid type is 0.2, the weight of the ambient temperature is 0.05, and the weight of the workpiece surface curvature is 0.

1.

12. The curing method according to claim 1, characterized in that, The preset parameters include laser operating parameters, infrared operating parameters, tooling parameters, and irradiation pose parameters; The preset parameters also include calibration values. , , and ; in, The in-plane thermal conductivity of the workpiece is... The normal thermal conductivity of the workpiece is... Interfacial contact thermal resistance This is to induce an effective temperature drop due to thermal conductivity.

13. The curing method according to claim 12, characterized in that, The calibration steps for the effective temperature drop induced by thermal conductivity include: Use an infrared thermometer to measure the reference temperature at the center point of the workpiece surface; A miniature thermocouple is embedded inside the colloid to monitor the effective temperature; The effective temperature drop induced by thermal conductivity is obtained by subtracting the effective temperature from the reference temperature. Record the effective temperature drop induced by thermal conductivity under different combinations, and establish... = The calibration database or empirical correlation; in, The in-plane thermal conductivity of the workpiece is... The thickness of the workpiece. The thickness of the colloid. The ambient air velocity.

14. The curing method according to claim 1, characterized in that, Each of the solidification models has a corresponding preset boundary threshold; the step of obtaining a solidification model that matches the feature data includes: A fuzzy clustering matching algorithm is used to calculate the membership degree of the feature data relative to a preset boundary threshold of multiple fixed models; The model with the highest membership degree is selected as the matching result.

15. A curing apparatus, characterized in that, The curing device cures the colloid using the method described in any one of claims 1-14; The curing device includes a sensor module, a control module, and an energy input module. The sensor module is used to acquire operating condition data, the control module is used to convert the operating condition data into the feature data, and control the energy input module to input the curing energy into the colloid to cure the colloid.

16. An electronic device, characterized in that, include: Memory, which stores program data; as well as A processor, coupled to the memory, which, during operation, executes the program data to implement the hardening method as described in any one of claims 1-14.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program data, which, when executed by a processor, is used to implement the solidification method as described in any one of claims 1-14.

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