Infrared powder curing process parameter rationality prediction method based on data analysis

Abstract

The invention relates to an infrared powder curing process parameter rationality prediction method based on data analysis. The method comprises the following steps: step 1, variable determination; 2, establishing a data acquisition module: acquiring heating data involved in the infrared powder curing process under different conditions by changing the variables in the step 1; step 3, establishing a data analysis module: performing statistical analysis on the heating data related to the infrared powder curing process acquired by the data acquisition module; 4, constructing an infrared rapid heating and curing prediction model: constructing the infrared rapid heating and curing prediction model through statistical analysis of the heating data by a data analysis module; and 5, verifying the prediction model, and adjusting infrared powder curing process parameters according to a prediction result. According to the method, the infrared rapid heating curing prediction model is constructed, and the infrared powder curing process parameters are adjusted according to the prediction result, so that the controllability and reliability of the powder curing process can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of infrared heating powder curing, and particularly relates to a method for predicting the rationality of infrared powder curing process parameters based on data analysis. Background Art

[0002] In the past decade, due to the advantages of powder coating such as green environmental protection with zero VOC emissions, durable and high-temperature resistant coatings, and low comprehensive production costs, it is gradually replacing paint coating. Whether it is large-scale logistics equipment (such as containers), construction machinery, the automotive industry, or small-scale products like mobile phones, household appliances, and general industries, "changing paint to powder" has become an irresistible trend. However, from a process technology perspective, there are significant differences in the curing temperature between powder coating and paint coating. The energy consumption of powder curing is much higher than that of paint drying. Using traditional hot air circulation heating technology, thick-walled workpieces require a longer hot air circulation time, resulting in high energy consumption and a large floor area in the workshop. Therefore, this has become a common difficulty restricting powder coating of thick-walled parts.

[0003] Therefore, the infrared rapid heating curing technology has emerged. Compared with the traditional hot air circulation heating technology, infrared heating has the following remarkable characteristics: ① High thermal efficiency, low usage cost, and high output energy. In a short time, the heat source can reach the maximum radiant energy output state, and the heat can be directly transferred without any heating medium, which can quickly shorten the powder curing time and has a low usage cost. ② Clean and pollution-free energy. These two significant advantages give the infrared rapid heating curing technology a strong market advantage in the powder coating industry.

[0004] However, there is a fatal phenomenon in the current industry regarding the infrared rapid heating curing technology: Currently, no domestic manufacturer can theoretically calculate the temperature rise law and temperature uniformity of the infrared field used for powder curing of workpieces by infrared heating, and it can only be based on on-site experiments. The reason is that there is no systematic selection calculation basis for the infrared heating temperature field in China yet, so the quantitative relationship cannot be determined. However, the temperature curve is one of the important indicators for powder curing.

[0005] Therefore, there is an urgent need to develop an infrared rapid heating curing prediction model that can meet the on-site workpiece conditions, is convenient and fast, meet the industry needs, and promote the innovation of the coating process. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for predicting the rationality of infrared powder curing process parameters based on data analysis. By constructing an infrared rapid heating curing prediction model and adjusting the infrared powder curing process parameters according to the prediction results, the controllability and reliability of the powder curing process can be effectively improved.

[0007] The present invention solves its technical problems through the following technical solutions:

[0008] A method for predicting the rationality of infrared powder curing process parameters based on data analysis, comprising the following steps:

[0009] Step 1, variable determination: clarify that the infrared radiation distance, workpiece thickness, hot air circulation time, and infrared radiation time are key variables affecting the heating effect;

[0010] Step 2, establish a data acquisition module: by changing the variables in Step 1, collect the heating data involved in the infrared powder curing process under different conditions;

[0011] Step 3, establish a data analysis module: statistically analyze the heating data involved in the infrared powder curing process collected by the data acquisition module;

[0012] Step 4, construct an infrared rapid heating and curing prediction model: through the statistical analysis of the heating data by the data analysis module, construct an infrared rapid heating and curing prediction model;

[0013] Step 5, verify the prediction model, and adjust the infrared powder curing process parameters according to the prediction results.

[0014] Further, the heating data involved in the infrared powder curing process collected under different conditions in Step 2 includes the data under the following conditions:

[0015] (1) The temperature rise data of workpieces with different thicknesses and different areas under pure hot air conditions;

[0016] (2) Collect the temperature rise data of workpieces with different thicknesses and different areas under the combined action of infrared and hot air;

[0017] (3) Collect the temperature rise data of workpieces with different infrared radiation distances over time.

[0018] Further, the infrared rapid heating and curing prediction model constructed in Step 4 is:

[0019] Q = Q1 + Q2 + Q0, where Q0 is the initial heat value of the workpiece, and Q1 is calculated from the energy change over time with different areas, different thicknesses, and different infrared radiation distances in the infrared and hot air stages;

[0020] In a thermodynamic experiment, calculate the total heat absorption Q1 of the workpiece under infrared radiation, and its expression is: Q1 = ρ1·S·k1 + k2·t1. The physical meanings and dependencies of the parameters in the formula are as follows:

[0021] ρ1 is the heat absorption efficiency of the workpiece, which is a quadratic function of the infrared action time t1 and is determined by fitting experimental data. Its general form is: ρ1·t1 = a·t1 2+b·t1 + c; The measured values of ρ1 corresponding to different times within the interval [0, T] are used to solve for the coefficients a, b, and c using the least squares method.

[0022] k1 is the heat absorption coefficient varying with the distance d between the infrared source and the workpiece. Experiments show that it satisfies an exponential decay relationship: k1·d = k 1,0 ·e -λd , and the decay coefficient λ is determined through non-linear regression.

[0023] k2 is the influence coefficient of heat absorption by the thickness h and is related to the thermal diffusivity α of the workpiece, satisfying h0 is the reference thickness of 60 mm.

[0024] The expressions of ρ1 and k1 are fitted from the original experimental data, and a thermodynamic model is derived:

[0025] Q1 = ρ1·S·k1 + k2·t1, where ρ1 is a quadratic function of the workpiece varying with the infrared action time t1, obtained through the above experiments; S is the surface area of the workpiece; k1 is the heat absorption coefficient of the workpiece varying with the infrared distance, obtained through experiments; k2 is the heat absorption coefficient of workpieces with different thicknesses but the same area, and t1 is the infrared action time.

[0026] The total heat absorption of the workpiece under pure hot air convection is expressed as: The physical meanings and dependencies of the parameters in the formula are as follows:

[0027] ρ2 is the dynamic response coefficient of the workpiece to hot air and is a piecewise quadratic function of time t2:

[0028]

[0029] The piecewise characteristic of this function is that when the time t2 is within the interval [0, t c , the function is a quadratic polynomial; when t2 ∈ (t c , T], the function is reset to a new quadratic polynomial with t c as the origin.

[0030] is the heat absorption coefficient of hot air convection, depending on the hot air action area A and the temperature gradient and satisfies:

[0031]

[0032] The physical meanings and dependencies of the parameters in the formula are as follows:

[0033] Nu(A): is the Nusselt number, depending on the hot air action area A, and characterizes the convective heat transfer efficiency.

[0034] λair: Thermal conductivity of air, L: characteristic length;

[0035] is the non - linear correction term, β is the enhancement coefficient, is the temperature gradient, reflecting the additional heat transfer effect of the non - uniform temperature field;

[0036] k4(h,α): Coupling coefficient of thickness and thermal diffusivity, derived from the non - Fourier heat conduction model:

[0037] k4 = (α·τq) / (h 3 )(1 - e^ (-h / (ατT) ));

[0038] α: Thermal diffusivity of the material;

[0039] τq: Heat flux relaxation time, describing the delay characteristic of heat flux response;

[0040] h: Characteristic thickness of the workpiece;

[0041] τT: Temperature gradient relaxation time, reflecting the relaxation effect of the dynamic equilibrium of the temperature field;

[0042] The thermodynamic model derived by fitting the expressions of ρ2\k3 and k4 from the original experimental data:

[0043] Q2 = ρ2·S·k3 + k4·t2;

[0044] In the formula, ρ2 is a quadratic function of the workpiece changing with the hot - air action time t2, obtained through the above experiments; S is the surface area of the workpiece; k3 is the heat absorption coefficient of the workpiece varying with different hot - air areas, obtained through experiments; k4 is the heat absorption coefficient of workpieces with different thicknesses but the same area, and t2 is the hot - air action time.

[0045] The advantages and positive effects of the present invention are:

[0046] 1. The infrared powder curing process parameter rationality prediction method based on data analysis of the present invention can effectively improve the controllability and reliability of the powder curing process by constructing an infrared rapid heating curing prediction model and adjusting the infrared powder curing process parameters according to the prediction results.

[0047] 2. The rationality prediction method for infrared powder curing process parameters based on data analysis in the present invention can better meet the curing requirements of different types of powder coatings and thick workpieces by constructing an infrared rapid heating curing prediction model, significantly improving the prediction accuracy of the infrared powder curing process, greatly reducing the trial-and-error cost, and enhancing the efficiency.

[0048] 3. The rationality prediction method for infrared powder curing process parameters based on data analysis in the present invention optimizes the infrared powder curing process, reduces material loss and production cost, and improves economic benefits.

[0049] 4. The rationality prediction method for infrared powder curing process parameters based on data analysis in the present invention can accurately predict the key parameters during the infrared powder curing process through a data acquisition module and a data analysis module, ensuring the consistency and stability of the powder-cured products and enhancing the product quality.

[0050] 5. The rationality prediction method for infrared powder curing process parameters based on data analysis in the present invention determines variables and provides customized solutions for the special requirements of different industries and products, such as the curing of thick workpieces with large or complex shapes.

[0051] 6. The rationality prediction method for infrared powder curing process parameters based on data analysis in the present invention promotes the intelligent and automated process of infrared powder curing technology, contributing to the sustainable development of the manufacturing industry. Description of the Drawings

[0052] Figure 1 It is a flowchart of the rationality prediction method for infrared powder curing process parameters based on data analysis;

[0053] Figure 2 It is a test curve of pure hot air at 185°C;

[0054] Figure 3 It is a test curve of pure hot air at 175°C;

[0055] Figure 4 It is a test curve of workpieces with different thicknesses under the combined action of infrared and hot air;

[0056] Figure 5 It is a test curve of workpieces with different areas under the combined action of infrared and hot air;

[0057] Figure 6 It is an infrared test curve with an infrared radiation distance of 200 mmr;

[0058] Figure 7 It is an infrared test curve with an infrared radiation distance of 250 mmr;

[0059] Figure 8It is the infrared test curve with an infrared radiation distance of 300mmr;

[0060] Figure 9 It is the temperature rise law of workpieces with the same area and different thicknesses under the condition of pure hot air at 185℃;

[0061] Figure 10 They are curves obtained by different treatment methods;

[0062] Figure 11 It is the temperature change law of workpieces at different infrared radiation distances;

[0063] Figure 12 It is the comparison between the predicted temperature curve and the actual verification result of a 125mm*250mm*80mm workpiece. Specific implementation mode

[0064] The present invention will be further described in detail below through specific embodiments. The following embodiments are only descriptive and not restrictive, and the protection scope of the present invention cannot be limited thereby.

[0065] Such as Figure 1 shown, a method for predicting the rationality of infrared powder curing process parameters based on data analysis includes the following steps:

[0066] Step 1, variable determination: Define the infrared radiation distance, workpiece thickness, hot air circulation time, and infrared radiation time as the key variables affecting the heating effect;

[0067] According to the radiation propagation law, heat exchange law, and heat conduction law, it can be known that the heat absorption law of the workpiece under the combined action of infrared and hot air is the combined action of radiative heat absorption and convective heat absorption. Therefore, as long as the heat absorption coefficient of the combined action is determined, the temperature rise law of the workpiece can be obtained; at the same time, according to the law of heat conduction, the temperature rise law of the workpiece at different times can be obtained. The actual infrared radiation heat absorption is affected by environmental factors more. Therefore, according to the requirements of the powder curing process, the present invention defaults the hot air temperature, workpiece material, powder coating, and infrared radiation surface temperature to the same conditions, and takes the infrared radiation distance (the distance between the workpiece and the infrared), workpiece thickness, workpiece size, hot air circulation time, and infrared radiation time as the key variables affecting the rationality of the powder curing process parameters, to obtain the temperature rise law of the workpiece in the infrared powder curing furnace and form a relatively accurate mathematical model prediction method.

[0068] Step 2, establish a data acquisition module: By changing the variables in Step 1, collect the heating data involved in the infrared powder curing process under different conditions;

[0069] Test plan 1:

[0070] Powder parameters: Yellow, high-temperature powder, curing temperature 180℃, 10min;

[0071] Powder coating thickness: 80 - 100μm;

[0072] Set temperature of infrared panel: 500°C, infrared wavelength range: 2 - 10um;

[0073] Collect the heating data of workpieces with different thicknesses and areas under pure hot air conditions, as Figure 2 shown;

[0074] The heating of the workpiece is mainly related to the air temperature, infrared temperature, infrared distance, and the material, thickness, heat absorption surface area, and weight of the workpiece itself. Under the condition of pure hot air at 185°C, the heating data of workpieces with different thicknesses, the same length and height, and the material of Q235B are shown in Table 1 below:

[0075] Serial number Workpiece length (mm) Workpiece height (mm) Surface area (m2) Weight (KG) Workpiece thickness (mm) 1 125 250 0.1075 14.718 60 2 125 250 0.115 17.172 70 3 125 250 0.115 19.625 80

[0076] Table 1 Heating test plan of workpieces under 185°C pure hot air conditions

[0077] Test plan 2:

[0078] Powder parameters: yellow, high-temperature powder, curing temperature 180°C, 10 min;

[0079] Powder coating thickness: 80 - 100μm;

[0080] Set temperature of infrared panel: 500°C, infrared wavelength range: 2 - 10um;

[0081] Collect the heating data of workpieces with different thicknesses and areas under pure hot air conditions, as Figure 3 shown;

[0082] Temperature of pure hot air: 175°C; workpiece material Q235B, test workpieces are shown in Table 2:

[0083] The heating of the workpiece is mainly related to the air temperature, infrared temperature, infrared distance, and the material, thickness, heat absorption surface area, and weight of the workpiece itself. Under the condition of pure hot air at 175°C, the heating data of workpieces with different thicknesses, the same length and height, and the material of Q235B are shown in Table 1 below:

[0084] Serial number Workpiece length (mm) Workpiece height (mm) Surface area (m2) Weight (KG) Workpiece thickness (mm) 1 125 250 0.1075 14.718 60 2 125 250 0.115 17.172 70 3 125 250 0.1225 19.625 80

[0085] Table 2 Heating test plan of workpieces under 175°C pure hot air conditions

[0086] Test plan 3:

[0087] Powder parameters: yellow, high-temperature powder, curing temperature 180°C, 10 min;

[0088] Powder coating thickness: 80 - 100μm;

[0089] Set temperature of the infrared panel: 500°C, infrared wavelength range: 2 - 10μm;

[0090] Collect the temperature rise data of workpieces with different thicknesses and areas under the combined action of infrared and hot air, as Figure 4 、 Figure 5 shown;

[0091] Under the condition of a pure hot air temperature of 210°C and an infrared radiation distance (the distance between the workpiece and the infrared) of 150mm, with an infrared temperature of 500°C, the temperature rise curves of workpieces with different thicknesses, lengths, heights, and made of Q235B are tested as shown in Table 3:

[0092]

[0093] Table 3 Temperature rise test scheme of workpieces under the combined action of infrared and hot air

[0094] Test scheme 4:

[0095] Powder parameters: yellow, high-temperature powder, curing temperature 180°C, 10min;

[0096] Powder coating thickness: 80 - 100μm;

[0097] Set temperature of the infrared panel: 500°C, infrared wavelength range: 2 - 10μm;

[0098] Collect the temperature rise data of workpieces with different infrared radiation distances over time, as Figure 6 、 Figure 7 、 Figure 8 shown;

[0099] Under the condition of a pure hot air temperature of 210°C and infrared radiation distances (the distance between the workpiece and the infrared) of 200mm, 250mm, and 300mm respectively, with an infrared temperature of 500°C, for workpieces with the same length, height, surface area, and infrared radiation area and made of Q235B, the temperature rise comparison is shown in Table 4:

[0100]

[0101] Table 4 Temperature rise test scheme of workpieces with different infrared radiation distances over time

[0102] Step 3, establish a data analysis module: statistically analyze the heating data involved in the infrared powder curing process collected by the data acquisition module;

[0103] such as Figure 9As shown in the figure, for workpieces with different thicknesses under the condition of pure hot air at the same area, the temperature rise law with time shows an obvious linear relationship with the thickness. By extracting and analyzing the characteristic coefficients of the data, the change law of the heat absorption coefficient when the workpiece with the same area changes in the thickness direction is obtained. At the same time, the data of the comparative experiment shows that the heat absorption coefficients of workpieces with the same area and different thicknesses under the condition of pure hot air at 175 °C are the same as those obtained under the above-mentioned hot air condition of 185 °C. Based on this, the heat absorption coefficients of workpieces with the same area and different thicknesses can be obtained. Repeating the same method above, the heat absorption coefficients of workpieces with the same area, different thicknesses under infrared and hot air conditions can also be obtained.

[0104] As Figure 10 shown, for the temperature rise curves of workpieces with different areas and the same thickness under infrared and hot air conditions with time, by using linear regression, quadratic function regression, logarithmic regression and exponential regression, and through comparative analysis, a very obvious quadratic function regression curve can be obtained, with a small error and a high curve fitting degree. From this quadratic function regression analysis, the change law of the heat absorption coefficient of workpieces with different areas under the action of infrared and hot air with time can be obtained. At the same time, by using the change law of the above-mentioned workpieces with different thicknesses to correct this quadratic function, the calculation formula for the heat absorption of workpieces with different areas and different thicknesses under the action of infrared and hot air can be obtained. Repeating the same method above, the heat absorption calculation formula of workpieces with the same area, different areas and different thicknesses under the condition of pure hot air can also be obtained.

[0105] As Figure 11 shown, for the temperature rise law curve of the workpiece with time at the same workpiece, the same hot air temperature, the same infrared temperature and different infrared radiation distances, it can be seen that the infrared radiation intensity remains at a high efficiency between 150 - 250 mm, and the attenuation is obvious at 300 mm. By extracting and analyzing the characteristic coefficients of the data, the influence law of the total heat absorption coefficient and the distance at different infrared distances is obtained, which is an exponential change law.

[0106] Step 4: Construct an infrared rapid heating and curing prediction model: Through the statistical analysis of heating data by the data analysis module, an infrared rapid heating and curing prediction model is constructed;

[0107] Using the standardization methods such as data analysis and feature extraction in Step 3, the data is normalized to a unified range. Through the analysis of these individual influencing factors such as infrared radiation distance, different radiation areas, workpiece thickness change, and infrared action time, the complete calculation formula for the prediction method of the rationality of powder curing process parameters is obtained:

[0108] Q = Q1 + Q2 + Q0, where Q0 is the initial calorific value of the workpiece, and Q1 is the calculation of the energy change with time at different areas, different thicknesses, and different infrared radiation distances in the infrared and hot air stages;

[0109] In a thermodynamic experiment, calculate the total heat absorption Q1 of the workpiece under infrared radiation. Its expression is: Q1 = ρ1·S·k1 + k2·t1. The physical meanings and dependencies of each parameter in the formula are as follows:

[0110] ρ1 is the heat absorption efficiency of the workpiece, which is a quadratic function of the infrared action time t1 and is determined by fitting experimental data. Its general form is: ρ1·t1 = a·t1 2 + b·t1 + c; Measure the values of ρ1 corresponding to different times of t1 in the interval [0, T], and use the least squares method to solve the coefficients a, b, and c;

[0111] k1 is the heat absorption coefficient that varies with the distance d between the infrared source and the workpiece. Experiments show that it satisfies the exponential decay relationship: k1·d = k 1,0 ·e -λd , and determine the decay coefficient λ through nonlinear regression;

[0112] k2 is the influence coefficient of heat absorption for the thickness h, which is related to the thermal diffusivity α of the workpiece and satisfies h0 is the reference thickness of 60 mm;

[0113] Fit the expressions of ρ1 and k1 from the original experimental data and derive the thermodynamic model:

[0114] Q1 = ρ1·S·k1 + k2·t1, where ρ1 is a quadratic function of the workpiece varying with the infrared action time t1 and is obtained through the above experiments; S is the surface area of the workpiece; k1 is the heat absorption coefficient of the workpiece varying with the infrared distance and is obtained through experiments; k2 is the heat absorption coefficient of workpieces with the same area but different thicknesses, and t1 is the infrared action time;

[0115] The total heat absorption of the workpiece under pure hot air convection is expressed as: The physical meanings and dependencies of each parameter in the formula are as follows:

[0116] ρ2 is the dynamic response coefficient of the workpiece to hot air, which is a piecewise quadratic function of time t2:

[0117]

[0118] The piecewise characteristic of this function is: when the time t2 is in the interval [0, t c , the function is a quadratic polynomial; when t2 ∈ (t c , T], the function is reset to a new quadratic polynomial with t c as the origin;

[0119] is the heat absorption coefficient of hot air convection, which depends on the hot air action area A and the temperature gradient and satisfies:

[0120]

[0121] The physical meanings and dependencies of the parameters in the formula are as follows:

[0122] Nu(A): Nusselt number, which depends on the hot air action area A and characterizes the convective heat transfer efficiency;

[0123] λair: Thermal conductivity of air, and L is the characteristic length;

[0124] is the non-linear correction term, and β is the strengthening coefficient. is the temperature gradient, which reflects the additional heat transfer effect of the non-uniform temperature field;

[0125] k4(h,α): Coupling coefficient of thickness and thermal diffusivity, derived from the non-Fourier heat conduction model:

[0126] k4 = (α·τq) / (h 3 )(1 - e^ (-h / (ατT) ));

[0127] α: Thermal Diffusivity of the material;

[0128] τq: Heat Flux Relaxation Time, which describes the delay characteristic of heat flux response;

[0129] h: Characteristic thickness of the workpiece;

[0130] τT: Temperature Gradient Relaxation Time, which reflects the relaxation effect of the dynamic equilibrium of the temperature field;

[0131] Derive the thermodynamic model by fitting the expressions of ρ2\k3 and k4 from the original experimental data:

[0132] Q2 = ρ2·S·k3 + k4·t2;

[0133] In the formula, ρ2 is a quadratic function of the workpiece changing with the hot air action time t2, obtained through the above experiments; S is the surface area of the workpiece; k3 is the heat absorption coefficient of the workpiece varying with different hot air areas, obtained through experiments; k4 is the heat absorption coefficient of workpieces with different thicknesses but the same area, and t2 is the hot air action time.

[0134] Step 5: Verify the prediction model and adjust the infrared powder curing process parameters according to the prediction results:

[0135] Input the initial temperature of the workpiece, the infrared action distance, the infrared action time, the surface area of the workpiece, the weight of the workpiece, and the thickness of the workpiece. According to the established infrared rapid heating and curing prediction model, output the hot air time of the workpiece, the total curing time, and the specific temperature to which the workpiece rises. The infrared powder curing process parameters can be adjusted according to the prediction results output by the infrared rapid heating and curing prediction model to achieve the best curing effect.

[0136] As Figure 12 shown, for a workpiece of 125mm * 250mm * 80mm, the conclusion obtained through comparative analysis of experiments and the infrared rapid heating and curing prediction model is that the temperature error between the experimental results and the workpiece of the infrared rapid heating and curing prediction model is controlled within plus or minus 5 °C, meeting the requirements of the industry.

[0137] The present invention constructs an infrared rapid heating and curing prediction model to accurately obtain the empirical temperature rise formula during the infrared powder curing process, thereby obtaining the optimal infrared curing process parameters according to the actual situation of the product, improving the product quality, and reducing the production cost.

[0138] The present invention has broad application prospects and significant economic value. By determining variables, a data acquisition module, and a data analysis module, an infrared rapid heating and curing prediction model is constructed and the prediction model is verified, which can effectively improve the controllability and reliability of the powder curing process and provide important support for the intelligent upgrading of the manufacturing industry. It is hoped that through the popularization and application of the present invention, the infrared heating technology progress and industrial development in the coating field can be promoted.

[0139] Although the embodiments and drawings of the present invention are disclosed for illustrative purposes, those skilled in the art can understand that various substitutions, changes, and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the scope of the present invention is not limited to the content disclosed in the embodiments and drawings.

Claims

1. A method for predicting the rationality of infrared powder curing process parameters based on data analysis, characterized in that: It includes the following steps: Step 1, Variable determination: Define the infrared radiation distance, workpiece thickness, hot air circulation time, and infrared radiation time as the key variables affecting the heating effect; Step 2, Establish a data acquisition module: By changing the variables in Step 1, collect the heating data involved in the infrared powder curing process under different conditions; Step 3, Establish a data analysis module: Statistically analyze the heating data involved in the infrared powder curing process collected by the data acquisition module; Step 4, Construct an infrared rapid heating and curing prediction model: Through the statistical analysis of the heating data by the data analysis module, construct an infrared rapid heating and curing prediction model; Step 5, Verify the prediction model and adjust the infrared powder curing process parameters according to the prediction results.

2. The rationality prediction method for infrared powder curing process parameters based on data analysis according to claim 1, characterized in that: The heating data involved in the infrared powder curing process collected under different conditions in Step 2 includes the data under the following conditions: (1) The temperature rise data of workpieces with different thicknesses and different areas under pure hot air conditions; (2) Collect the temperature rise data of workpieces with different thicknesses and different areas under the combined action of infrared and hot air; (3) Collect the temperature rise data of workpieces with different infrared radiation distances over time.

3. The method for predicting the rationality of infrared powder curing process parameters based on data analysis according to claim 1, characterized in that: The infrared rapid heating and curing prediction model constructed in Step 4 is: Q = Q1 + Q2 + Q0, where Q0 is the initial heat value of the workpiece, and Q1 is calculated based on the energy change over time with different areas, different thicknesses, and different infrared radiation distances in the infrared and hot air stages; In the thermodynamic experiment, calculate the total heat absorption Q1 of the workpiece under infrared radiation. Its expression is: Q1 = ρ1·S·k1 + k2·t1. The physical meanings and dependencies of each parameter in the formula are as follows: ρ1 is the heat absorption efficiency of the workpiece, which is a quadratic function of the infrared action time t1 and is determined by fitting experimental data. Its general form is: ρ1·t1 = a·t1 2 + b·t1 + c; The ρ1 values corresponding to different moments of t1 in the interval [0, T] are measured experimentally, and the coefficients a, b, and c are solved using the least squares method; k1 is the heat absorption coefficient varying with the distance d between the infrared source and the workpiece. Experiments show that it satisfies the exponential decay relationship: k1·d = k 1,0 ·e -λd , and the attenuation coefficient λ is determined by non-linear regression; $k_2$ is the influence coefficient of the thickness $h$ on heat absorption, which is related to the thermal diffusivity $\alpha$ of the workpiece and satisfies $h_0$ is the reference thickness of 60 mm; Fit the expressions of ρ1 and k1 from the original experimental data and derive the thermodynamic model: Q1 = ρ1·S·k1 + k2·t1, where ρ1 is a quadratic function of the workpiece changing with the infrared action time t1, obtained through the above experiments; S is the surface area of the workpiece; k1 is the heat absorption coefficient of the workpiece changing with the infrared distance, obtained through experiments; k2 is the heat absorption coefficient of workpieces with the same area but different thicknesses, and t1 is the infrared action time; The total heat absorption of the workpiece under the action of pure hot air convection, and its expression is: The physical meanings and dependencies of the parameters in the formula are as follows: ρ2 is the dynamic response coefficient of the workpiece to hot air, which is a piecewise quadratic function of time t2: The piecewise characteristic of this function is as follows: when the time t2 is in the interval [0, t c , the function is a quadratic polynomial; when t2 ∈ (t c , T], the function is reset to a new quadratic polynomial with t c as the origin; is the heat absorption coefficient of hot air convection, which depends on the hot air acting area A and the temperature gradient Satisfy: The physical meanings and dependencies of each parameter in the formula are as follows: Nu(A): The Nusselt number, which depends on the hot air action area A and characterizes the convective heat transfer efficiency; λair: The thermal conductivity of air, and L is the characteristic length; is the non-linear correction term, and β is the strengthening coefficient. is the temperature gradient, reflecting the additional heat transfer effect of the non-uniform temperature field. k4(h,α): The coupling coefficient of thickness and thermal diffusivity, derived from the non-Fourier heat conduction model: k4 = (α·τq) / (h 3 )(1 - e^ (-h / (ατT) )); α: The thermal diffusivity of the material; τq: The heat flux relaxation time, which describes the delay characteristic of the heat flux response; h: The characteristic thickness of the workpiece; τT: The temperature gradient relaxation time, which reflects the relaxation effect of the dynamic balance of the temperature field; Derive the thermodynamic model by fitting the expressions of ρ2\k3 and k4 from the original experimental data: Q2 = ρ2·S·k3 + k4·t2; where ρ2 is a quadratic function of the workpiece changing with the hot air action time t2, obtained from the above experiments; S is the surface area of the workpiece; k3 is the heat absorption coefficient of the workpiece with different hot air areas, obtained through experiments; k4 is the heat absorption coefficient of workpieces with the same area but different thicknesses, and t2 is the hot air action time.

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