ProCAST-based large workpiece heat treatment heating temperature-up time prediction method

Abstract

The invention relates to a large workpiece heat treatment heating temperature-up time prediction method based on ProCAST, belongs to the technical field of heat treatment analog simulation, and solves the problem that a method for specifically simulating and predicting the large workpiece heat treatment heating temperature-up time by adopting ProCAST software is not disclosed in the prior art. The invention provides the large workpiece heat treatment heating temperature-up time prediction method based on ProCAST, the method is based on ProCAST software, the accuracy is high, the method is simple, and a simulation setting method, a simulation parameter use range, a simulation and actual comparison method, a simulation accuracy quantitative judgment method and a simulation parameter adjustment method for simulating large workpiece heat treatment by the ProCAST software are defined. Simulation is matched with the actual situation, the method is high in timeliness, simulation is conducted in the workpiece production process, simulation parameters are further corrected according to the actually-measured temperature of the surface in the early stage of heat treatment, and the simulation accuracy is guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of heat treatment simulation technology, and in particular to a method for predicting the heating-to-temperature time of a large workpiece during heat treatment based on ProCAST. Background Art

[0002] In the casting or forging production process, heat treatment of the workpiece is generally involved, especially for large castings and forgings, the heat treatment time is very long, even up to dozens of days. It is inconvenient to measure the internal temperature of the workpiece during heat treatment heating or cooling. Generally, the time for the core to reach temperature is judged based on experience, but it is not accurate. If the heating or cooling time is too short, the inside of the workpiece does not reach the specified temperature, which will cause quality problems; if the heating time is too long, the inside of the workpiece has already reached the temperature, which will extend the production cycle and cause energy waste. Therefore, accurately judging the temperature inside the workpiece during heat treatment is very necessary to improve product quality, increase production efficiency, and reduce production costs.

[0003] Finite element simulation is widely used to guide actual production. It is a more reasonable method to combine simulation methods to predict temperature changes during heat treatment. However, the accuracy of the simulation should also be considered. In order to find the optimal simulation parameters, the simulation should be compared with the actual production situation to verify the optimization of the simulation parameters. Since the applicable simulation parameters for each heat treatment will change, and the heat treatment time of large workpieces is long, the simulation parameters can be further corrected according to the measured temperature values ​​of the surface in the early stage of heat treatment, and the internal temperature time of the workpiece can be accurately predicted, so as to determine the furnace time. Through parameter correction, the influence of complex conditions on site can be reduced to ensure the accuracy of the simulation.

[0004] ProCAST software can simulate processes such as filling, solidification and heat treatment, but there is no public research on the key points of ProCAST heat treatment temperature field simulation and the accuracy of the simulation, and there is also little optimization verification combined with actual measured temperature values. Summary of the invention

[0005] In view of the above analysis, the present invention aims to provide a method for predicting the temperature field of large workpiece heat treatment based on ProCAST, so as to solve the problems that the prior art does not disclose a method for simulating and predicting the heating time of large workpiece heat treatment using ProCAST software, the existing method cannot determine the accuracy of the simulation, and does not clarify at least one of the following problems: the scope of use of simulation parameters, the method for adjusting simulation parameters, etc.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A method for predicting the heating-to-temperature time of a large workpiece during heat treatment based on ProCAST specifically comprises the following steps:

[0008] S1: Establish a three-dimensional model of workpiece heat treatment;

[0009] S2: Divide the three-dimensional model into grids in ProCAST software, set the heat treatment simulation parameters, and start the view factor calculation;

[0010] S3: Use ProCAST software to simulate and calculate the temperature field of the workpiece during heat treatment to obtain the temperature field simulation results;

[0011] S4: Extracting the measured temperature value: When the workpiece is actually heat treated, select 1 to 2 temperature measurement positions for temperature measurement. After the workpiece is heated in the heat treatment furnace for ≥48h, extract the temperature value of the temperature measurement data;

[0012] S5: Compare the temperature value in the simulation result of step S3 at the same position with the temperature value extracted in step S4 to determine whether the simulation result of step S3 is credible. If not, readjust the heat treatment simulation parameters of step S2 until the simulation result of step S3 is credible.

[0013] Furthermore, step S1 includes simplifying the three-dimensional model of the heat treatment, wherein the three-dimensional model includes the workpiece, the auxiliary tool and the heat treatment furnace.

[0014] Furthermore, in step S2, the simulation parameters include material property parameters, interface heat transfer coefficient, workpiece surface blackness value, heat treatment furnace wall blackness value, workpiece convection heat transfer coefficient in the furnace and furnace temperature.

[0015] Furthermore, the material physical property parameters include thermal conductivity, specific heat capacity and density.

[0016] Furthermore, in step S4, the temperature measurement data of each temperature measurement point includes the temperatures at different heat treatment stages.

[0017] Furthermore, the number of temperature values ​​compared in step S5 is at least 10.

[0018] Furthermore, step S5 specifically includes: comparing the temperature difference and the average temperature difference between the temperature value in the simulation result in step S3 and the extracted measured temperature value at at least 10 corresponding time points, and determining whether the simulation result in step S3 is credible.

[0019] Furthermore, if the temperature differences are all less than 5% and the average temperature difference is less than 2%, it is determined that the simulation result of step S3 is credible, otherwise, the heat treatment simulation parameters of step S2 are readjusted until the simulation result of step S3 is credible.

[0020] Furthermore, the heat treatment simulation parameters of step S2 are readjusted to include the blackness value of the workpiece surface and / or the blackness value of the heat treatment furnace wall.

[0021] Furthermore, the prediction method also includes S6: extracting the temperature-reaching time of the slowest heating point in the core of the workpiece from the credible simulation results. After reaching the required temperature, it can be taken out of the furnace for cooling. The temperature-reaching time is the heat treatment furnace-out time.

[0022] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0023] The present invention provides a method for predicting the heating time of a large workpiece heat treatment based on ProCAST. The method is based on ProCAST software, has high accuracy, and is simple. It clarifies the simulation setting method, simulation parameter usage range, simulation and actual comparison method, simulation accuracy quantitative judgment, and simulation parameter adjustment method of ProCAST software simulating the heat treatment of large workpieces. The simulation is close to the actual situation, and the method has strong timeliness. It is simulated in the workpiece production process, and the simulation parameters are further corrected according to the measured surface temperature in the early stage of heat treatment to ensure the accuracy of the simulation; the method of the present invention is used for simulation prediction, which can provide effective guidance for actual production.

[0024] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0026] Figure 1 It is a flow chart of a method for predicting temperature field of heat treatment of large workpieces based on ProCAST of the present invention;

[0027] Figure 2 The three-dimensional model diagram in Example 1 of the present invention;

[0028] Figure 3 is a curve showing the change of the actual workpiece heat treatment temperature over time in Example 1 of the present invention;

[0029] Figure 4 : is a position diagram of actual temperature measurement points in Example 1 of the present invention;

[0030] Figure 5 A comparison diagram of the simulated temperature value and the measured temperature value in Example 1 of the present invention;

[0031] Figure 6 This is a time chart of the heat treatment out of the furnace in Example 1 of the present invention.

[0032] Reference numerals:

[0033] 1-rotor, 2-shim, 3-heat treatment furnace. DETAILED DESCRIPTION

[0034] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the present invention and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0035] A specific embodiment of the present invention, as Figure 1 As shown, a method for predicting the heating-to-temperature time of a large workpiece during heat treatment based on ProCAST is disclosed, which specifically includes the following steps:

[0036] S1: Establish a three-dimensional model of workpiece heat treatment;

[0037] S2: Divide the three-dimensional model into grids in ProCAST software, set the heat treatment simulation parameters, and start the view factor calculation;

[0038] S3: Use ProCAST software to simulate and calculate the temperature field of the workpiece during heat treatment to obtain the temperature field simulation results;

[0039] S4: Extracting the measured temperature value: When the workpiece is actually heat treated, select 1 to 2 temperature measurement positions for temperature measurement. After the workpiece is heated in the heat treatment furnace for ≥48h, extract the temperature value of the temperature measurement data;

[0040] S5: Compare the temperature value in the simulation result of step S3 at the same position with the temperature value extracted in step S4 to determine whether the simulation result of step S3 is credible. If not, readjust the heat treatment simulation parameters of step S2 until the simulation result of step S3 is credible.

[0041] The present invention provides a method for predicting the heating-to-temperature time of a large workpiece heat treatment based on ProCAST. The method is based on ProCAST software, has high accuracy, is simple in method, clarifies the simulation setting method, simulation parameter usage range, simulation and actual comparison method, simulation accuracy quantitative judgment, and simulation parameter adjustment method of ProCAST software simulating the heat treatment of large workpieces. The simulation is close to the actual situation.

[0042] In the prior art, simulation and actual verification are usually carried out after the actual production is completed. Appropriate simulation parameters are determined by comparing simulation with actual practice, which are used to simulate and predict the subsequent production process. However, the actual production situation is complicated, and the applicable simulation parameters for each heat treatment are not consistent. The method of the present invention has strong timeliness, and the simulation is carried out during the production process of the workpiece, and the simulation parameters are further corrected according to the measured surface temperature in the early stage of the heat treatment to ensure the accuracy of the simulation.

[0043] The heat treatment temperature field predicted by the method of the present invention can provide effective guidance for actual production, predict the temperature change of the heat-treated workpiece, accurately judge the time required for the workpiece to be heated to a specified temperature, and thus determine the time when the heat treatment is taken out of the furnace to ensure that the inside of the workpiece reaches the corresponding temperature and reduce unnecessary energy waste.

[0044] In a specific implementation, step S1 includes simplifying a three-dimensional model of heat treatment, wherein the three-dimensional model includes a workpiece, an auxiliary tool, and a heat treatment furnace.

[0045] Specifically, the auxiliary tools such as shims, the workpiece and the auxiliary tools in the present invention are modeled according to actual sizes, and the inner wall model of the heat treatment furnace is established according to the actual size, and the main structure of the workpiece and the size near the temperature measurement point must be guaranteed to be complete.

[0046] Because we mainly focus on the data of the temperature measurement points and the time to reach the temperature at the slowest heating position in the core, for large workpieces, the core heats up very slowly, and some small structures that are not of concern, such as small chamfers or small fillets, can be simplified and cancelled. The structure of the heat treatment furnace is complex, but the most important heat exchange is the radiation heat exchange between the inner wall of the furnace and the workpiece, so we only need to build a model of the inner wall surface. This can improve modeling efficiency, facilitate mesh division, reduce the number of meshes, and increase calculation speed without affecting the simulation results.

[0047] The main focus is on the data of the temperature measurement points and the temperature reaching time at the slowest heating position in the core. For large workpieces, the core heats up very slowly, and some small structures, small chamfers or small fillets that are not of concern can be appropriately simplified, which has little effect on the results.

[0048] In a specific embodiment, in step S2, the simulation parameters include material property parameters, interface heat transfer coefficient, workpiece surface blackness value, heat treatment furnace wall blackness value, workpiece convection heat transfer coefficient in the furnace and furnace temperature.

[0049] It should be noted that in the present invention, tetrahedral meshes are generally used for mesh division in the Mesh module. Workpieces and auxiliary tools (such as pads) are solid structures, and face meshes and body meshes need to be divided. Heat treatment furnaces are surface structures, and only face meshes need to be divided. The smoothness of the model surface needs to be ensured, but there should not be too many meshes. For most less complex models, the total number of meshes is generally within 1 million.

[0050] Generally speaking, when a workpiece is heated in a heat treatment furnace, there is convection heat exchange between the workpiece surface and the furnace gas, and radiation heat exchange between the workpiece surface and the heat treatment furnace wall. The degree of radiation heat exchange is related to the angle and distance, and the radiation heat exchange at different positions on the workpiece surface is different. When using ProCAST software for calculation, if you want to simulate the radiation heat exchange situation consistent with the actual situation, you need to establish a model of the heat treatment furnace and turn on the view factor calculation. When simulating, you need to set the blackness value of the workpiece surface and the heat treatment furnace wall surface.

[0051] At present, in the application of heat treatment simulation, in order to simplify the model, the convective heat transfer between the workpiece surface and the furnace gas and the radiation heat transfer between the workpiece surface and the heat treatment furnace wall may be simplified into a comprehensive heat transfer coefficient, that is, the value of the convective heat transfer coefficient is increased; or the blackness of the workpiece surface and the heat treatment furnace wall surface is integrated into a blackness value. The simplified model does not consider the influence of the viewing angle coefficient, that is, it is assumed that the heat transfer at any position on the workpiece surface is the same. For the heat treatment of a single small workpiece with a simple shape, the impact of the simplified model may be small, but for large workpieces, or when multiple workpieces are placed in a heat treatment furnace for heat treatment at the same time, the above simplification cannot be performed, and the viewing angle coefficient must be calculated, otherwise the deviation between the simulation and the actual situation will be large. Turning on the viewing angle coefficient calculation (setting View Factor to ON) in the present invention is more in line with the actual situation of large workpieces.

[0052] In a specific embodiment, the material physical property parameters include thermal conductivity, specific heat capacity and density.

[0053] The material properties (thermal conductivity, specific heat and density) required for heat treatment simulation are obtained in the following order. Specifically, if there are experimental values, choose experimental measurement. If not, choose JMatPro software calculation. If there is no JMatPro software, choose ProCAST software calculation. However, the accuracy is also reduced in order:

[0054] 1. Experimental determination. For product materials, the parameters obtained by experimental testing are the most accurate, and the testing method is a universal testing method.

[0055] 2. Calculation by JMatPro software. Use the ExtendedGeneral function of the Thermo-Physical Properties module for calculation. The heat treatment temperature needs to be set before calculation. The phase composition below the heat treatment temperature remains unchanged, and the phase composition above the heat treatment temperature is consistent with the equilibrium state. For steel materials, the heat treatment temperature needs to be lower than the austenitizing temperature, which is generally around 600°C. Setting it too high or too low will increase the error.

[0056] 3. ProCAST software calculation. Use the Lever calculation model in the material database to calculate the thermophysical parameters. The software has three thermophysical parameter calculation models: Lever, Scheil and Back Diffusion. The results calculated by the Lever model are closest to the actual results.

[0057] In a specific embodiment, the interface heat transfer coefficient is the heat transfer coefficient of the contact surface of components of different materials.

[0058] For general steel workpieces and heat treatment furnaces, the interface heat transfer coefficient between metals is 1000-5000W / m 2 / K, workpiece surface blackness 0.4~0.9, furnace wall blackness 0.5~0.9, workpiece convection heat transfer coefficient in the furnace 0~20W / m 2 / K. The blackness of the workpiece surface is related to its surface state. During the production process of the workpiece, its surface oxidation state and roughness will change. For example, the blackness is different when there is oxide scale and when there is no oxide scale. The blackness of the workpiece surface is a parameter that needs to be adjusted during heat treatment simulation. For different heat treatment processes, the applicable simulation parameters may change each time. The simulation parameters are determined based on past experience or a value is selected within the above range, and then modified based on the comparison of the simulation results with the actual measured results.

[0059] The initial simulation parameters in the present invention are as follows: the interface heat transfer coefficient between the workpiece and the metal pad is 2000 W / m 2 / K, the convection heat transfer coefficient of the workpiece in the furnace is 10W / m 2 / K, the furnace wall blackness is 0.7. Workpiece surface blackness: (1) The workpiece surface is not oxidized before heat treatment, the blackness is 0.5 when the surface roughness is low, and the blackness is 0.7 when the surface roughness is high; if the heat treatment temperature is higher than 500°C, the surface will be oxidized during the heat treatment process, and the simulated blackness value should change with the temperature, and the blackness becomes 0.8 when it is above 500°C. (2) The workpiece surface is oxidized before heat treatment, the blackness is 0.8 when the oxide scale is close to the surface, and the blackness is 0.6 when the oxide scale has shown signs of falling off.

[0060] In a specific embodiment, in step S4, the total time for which the workpiece is heated in the heat treatment furnace is greater than 5 days.

[0061] In a specific embodiment, in step S4, the temperature measurement data of each temperature measurement point includes the temperature of different heat treatment stages.

[0062] In a specific embodiment, the different heat treatment stages include a heat treatment temperature rising stage and a constant temperature stage.

[0063] Specifically, in step S4, since the workpiece cannot be destroyed, the extracted temperature value can only measure the temperature at the surface position.

[0064] It should be noted that the temperature measurement point needs to be away from corners and other locations where the temperature rises too quickly, and should be located as far as possible in the center of the open surface. For example, for cylindrical workpieces, it is most reasonable to place the temperature measurement point in the center of the side. The heat treatment furnace is a gas furnace. For a gas furnace, when measuring the temperature with a thermocouple, the placement of the thermocouple cannot be too close to the nozzle position. The high temperature near the nozzle will affect the result. For a gas furnace with a nozzle at the bottom of the side, the temperature measurement point should be in the middle or upper part. Considering the cost of the thermocouple, it is sufficient to obtain data from one or two temperature measurement points. Since the temperature measurement point of the present invention is selected in the uniform furnace temperature area, accuracy can be guaranteed by selecting 1 to 2 temperature measurement points.

[0065] In a specific implementation, the number of temperature values ​​compared in step S5 is at least 10.

[0066] In a specific implementation, step S5 specifically includes: comparing the temperature difference and the average temperature difference between the temperature value in the simulation result in step S3 and the extracted measured temperature value at at least 10 corresponding time points to determine whether the simulation result in step S3 is credible.

[0067] In a specific implementation, if the temperature differences are all less than 5% and the average temperature difference is less than 2%, it is determined that the simulation result of step S3 is credible, otherwise, the heat treatment simulation parameters of step S2 are readjusted until the simulation result of step S3 is credible.

[0068] Preferably, the heat treatment simulation parameters of step S2 are readjusted to include the blackness value of the workpiece surface and / or the blackness value of the heat treatment furnace wall.

[0069] Specifically, since the material parameters are obtained through experimental measurement or software calculation, no adjustment is required later; generally, there is not much contact between the workpiece and the auxiliary tool, and the influence of the interface heat transfer coefficient is small, and no adjustment is required. Usually, only the black value of the workpiece surface, the black value of the furnace wall, and the convection heat transfer coefficient need to be adjusted.

[0070] Simulation parameter correction process:

[0071] After the calculation using the initial simulation parameters is completed, the simulation and actual differences are compared in step S5. If the condition of "the temperature difference is less than 5% and the average temperature difference is less than 2%" is not met, the simulation parameters need to be adjusted in the following order. If some simulation parameters already have very clear values, keep them unchanged and only adjust other parameters.

[0072] 1) If the simulated average temperature difference is larger than the actual value, return to step S2 and reduce the workpiece surface blackness value by 0.1 and start again, otherwise increase it by 0.1 and start again until the error requirement is met. If the workpiece surface blackness value has been adjusted to the lower limit (0.4) or the upper limit (0.9), and still does not meet the error requirement, adjust the next simulation parameter.

[0073] 2) If the simulated average temperature difference is greater than the actual value, return to step S2 and reduce the furnace wall blackness value by 0.1 and start again, otherwise increase it by 0.1 and start again until the error requirement is met. If the furnace wall blackness value has been adjusted to the lower limit (0.5) or the upper limit (0.9), and still does not meet the error requirement, adjust the next simulation parameter.

[0074] 3) If the simulated average temperature difference is greater than the actual value, return to step S2 and reduce the convection heat transfer coefficient by 5 W / m 2 / K restart, otherwise increase 5W / m 2 / K and restart until the error requirement is met. If the convection heat transfer coefficient has been adjusted to the lower limit (5W / m 2 / K) or upper limit (20W / m 2 / K), if the error requirement is still not met, the parameter adjustment is terminated.

[0075] 4) In general, the error requirements can be met by adjusting the surface blackness of the workpiece. If the error requirements cannot be met after adjusting the above parameters, the set of parameters with the smallest error is selected as the final parameters.

[0076] The method of comparing the measured temperature value with the simulation result of the present invention is more accurate and simpler.

[0077] In a specific embodiment, the prediction method also includes S6: extracting the temperature-reaching time of the slowest heating point in the core of the workpiece in the credible simulation results. After reaching the required temperature, it can be taken out of the furnace for cooling. The temperature-reaching time is the heat treatment furnace exit time.

[0078] The technical solution of the present invention is further explained below in conjunction with specific embodiments.

[0079] Example 1

[0080] A method for predicting the heating time to temperature of a large workpiece during heat treatment based on ProCAST, wherein the large workpiece in this embodiment is four large rotors, specifically comprises the following steps:

[0081] S1: Use 3D modeling software to build a 3D model of workpiece heat treatment, the model includes four rotors 1, pads 2 and heat treatment furnace 3, such as Figure 2 As shown;

[0082] S2: Import the 3D model in x_t format into the finite element simulation software ProCAST, divide the grid, and set the heat treatment simulation parameters. Specifically, set the rotor and pad materials. The material parameters are thermal conductivity, specific heat capacity and density, which are all measured values; set the initial simulation parameters: (1) Set the interface heat transfer coefficient between the rotor and pad contact surface to 2000 W / m 2 / K; (2) The surface of the pad iron is severely oxidized and the oxide scale has partially fallen off, so the surface blackness of the pad iron is set to 0.6; (3) The surface of the rotor is oxidized, so the surface blackness of the rotor is set to 0.8; (4) The heat treatment furnace is a gas furnace, so the furnace wall blackness is set to 0.7; (5) The convection heat transfer coefficient of the workpiece in the heat treatment furnace is 10W / m 2 / K; (6) Set the furnace temperature according to the heat treatment temperature and the actual furnace temperature data. Start the view factor calculation.

[0083] S3: Use ProCAST software to simulate and calculate the temperature field of the workpiece during heat treatment to obtain the temperature field simulation results;

[0084] S4: Extract measured temperature values: The total heating time of the actual heat treatment process is about 7 days. The measured temperature data of the heat treatment in the first 100 hours are extracted for simulation comparison, such as Figure 3 As shown in the figure, it can be seen that the first 80 hours are the heating stage, and the temperature after 80 hours is the constant temperature stage. The extracted measured temperature includes the temperature values ​​of the heating stage and the constant temperature stage; one temperature measurement point is selected for the measured temperature value, and the specific location is as follows Figure 4 As shown;

[0085] S5: Corresponding to the measured temperature value, the temperature simulation result at the same temperature measurement point position is selected from the simulation results obtained in step S3, one temperature point is selected every 10 hours, and a total of 10 temperature points are selected. The temperature difference between the simulated temperature value and the measured temperature value at the same time point and the average value of the 10 temperature differences are compared. The comparison between the simulated temperature value and the measured temperature value in this embodiment is as follows: Figure 5 As shown, if the temperature differences are all less than 5% and the average temperature difference is less than 2%, it is judged that the simulation result of step S3 is credible, otherwise, the heat treatment simulation parameters of step S2 are readjusted until the simulation result of step S3 is credible;

[0086] Specific adjustment process: (1) When the initial simulation parameters are used for simulation, the average value of the simulated temperature difference is larger than the actual value. Return to step S2 and reduce the surface blackness value of the workpiece by 0.1, adjust it to 0.7 and start again. After the simulation is completed, compare it with the actual value. The average value of the simulated temperature difference is still larger than the actual value. (2) Return to step S2 and reduce the surface blackness value of the workpiece by another 0.1, adjust it to 0.6 and start again. After the simulation is completed, compare it. At this time, the average value of the simulated and actual temperature differences meets the requirements.

[0087] The final parameter settings in this example are as follows: material parameters are measured data; convection heat transfer coefficient is 10W / m 2 / K; the interface heat transfer coefficient between the workpiece and the pad is 2000W / m 2 / K; the blackness of the workpiece surface is 0.6; the blackness of the furnace wall is 0.7.

[0088] Using the heat treatment simulation results obtained in step S5, the temperature change in the subsequent heat treatment process is predicted, and the temperature reaching time of the slowest heating point in the core is extracted, such as Figure 6 As shown, the temperature reached the specified requirement at 154h, the heat treatment furnace exit time can be set at 154h, and no quality problems were found after subsequent flaw detection, indicating that the method of the present invention not only ensures the heat treatment quality of the product but also reduces energy consumption, and realizes accurate simulation and prediction of the heat treatment process.

[0089] The heat treatment temperature field predicted by the method of this embodiment can guide actual heat treatment production, predict the temperature change of the heat-treated workpiece, and accurately judge the time required for the workpiece to be heated to the specified temperature, thereby determining the time for the heat treatment to be taken out of the furnace to ensure that the inside of the workpiece reaches the corresponding temperature and reduce unnecessary energy waste.

[0090] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0091] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for predicting the heating time of large workpiece heat treatment based on ProCAST, characterized in that: The specific steps include: S1: Establish a three-dimensional model of workpiece heat treatment; S2: Divide the three-dimensional model into grids in ProCAST software, set the heat treatment simulation parameters, and start the view factor calculation; S3: Use ProCAST software to simulate and calculate the temperature field of the workpiece during heat treatment to obtain the temperature field simulation results; S4: Extracting the measured temperature value: When the workpiece is actually heat treated, select 1 to 2 temperature measurement positions for temperature measurement. After the workpiece is heated in the heat treatment furnace for ≥48h, extract the temperature value of the temperature measurement data; S5: Compare the temperature value in the simulation result of step S3 at the same position with the temperature value extracted in step S4 to determine whether the simulation result of step S3 is credible. If not, readjust the heat treatment simulation parameters of step S2 until the simulation result of step S3 is credible.

2. The method for predicting the heating time to temperature of a large workpiece during heat treatment based on ProCAST according to claim 1, characterized in that: Step S1 includes simplifying a three-dimensional model of heat treatment, wherein the three-dimensional model includes a workpiece, an auxiliary tool, and a heat treatment furnace.

3. The method for predicting the heating time to temperature of a large workpiece heat treatment based on ProCAST according to claim 1, characterized in that: In step S2, the simulation parameters include material property parameters, interface heat transfer coefficient, workpiece surface blackness value, heat treatment furnace wall blackness value, workpiece convection heat transfer coefficient in the furnace and furnace temperature.

4. The method for predicting the heating time to temperature of a large workpiece heat treatment based on ProCAST according to claim 3 is characterized in that: The material physical property parameters include thermal conductivity, specific heat capacity and density.

5. The method for predicting the heating time to temperature of a large workpiece heat treatment based on ProCAST according to claim 1, characterized in that: In step S4, the temperature measurement data of each temperature measurement point includes the temperature at different heat treatment stages.

6. The method for predicting the heating-to-temperature time of a large workpiece during heat treatment based on ProCAST according to claim 1, characterized in that: The number of temperature values ​​compared in step S5 is at least 10.

7. The method for predicting the heating-to-temperature time of a large workpiece during heat treatment based on ProCAST according to claim 6, characterized in that: Step S5 specifically includes: comparing the temperature difference and the average temperature difference between the temperature value in the simulation result in step S3 and the extracted measured temperature value at at least 10 corresponding time points, and judging whether the simulation result in step S3 is credible.

8. The method for predicting the heating-to-temperature time of a large workpiece during heat treatment based on ProCAST according to claim 7, characterized in that: If the temperature differences are all less than 5% and the average temperature difference is less than 2%, it is determined that the simulation result of step S3 is credible, otherwise, the heat treatment simulation parameters of step S2 are readjusted until the simulation result of step S3 is credible.

9. The method for predicting the heating-to-temperature time of a large workpiece during heat treatment based on ProCAST according to claim 8, characterized in that: The heat treatment simulation parameters of step S2 are readjusted to include the blackness value of the workpiece surface and / or the blackness value of the heat treatment furnace wall.

10. The method for predicting the heating-to-temperature time of a large workpiece heat treatment based on ProCAST according to claim 1, characterized in that: The prediction method further includes S6: extracting the temperature-reaching time of the slowest heating point in the core of the workpiece from the reliable simulation results. After reaching the required temperature, the workpiece can be taken out of the furnace for cooling. The temperature-reaching time is the heat treatment furnace-out time.

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