Forward and reverse optimization design method for steel heat treatment process

Abstract

The invention discloses a forward and reverse optimization design method for a steel heat treatment process. The forward and reverse optimization design method comprises the following steps: step 1, establishing a steel model; acquiring a temperature field of the steel model based on an actual heat treatment process; based on the cooling mode, obtaining a cooling curve of the steel model; 2, based on the cooling curve and the temperature field, the microstructure of the steel model after heat treatment is obtained through calculation; 3, mechanical property calculation of the steel model is conducted according to the microstructure, and forward optimization of the steel heat treatment process is achieved; meanwhile, according to the requirements of the mechanical property of the steel model, the microstructure and the heat treatment process of the steel model are reversely determined, and reverse optimization of the steel heat treatment process is achieved. According to the method, the influence rule of the heat treatment process parameters on the mechanical property can be positively calculated, and the heat treatment process parameters can be reversely and optimally designed based on the mechanical property.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of steel manufacturing, and particularly relates to a forward and reverse optimization design method of a steel heat treatment process. BACKGROUND

[0002] Heat treatment of steel can effectively improve the comprehensive mechanical properties of the steel. However, the heat treatment process includes several types such as normalizing, annealing, quenching, and tempering, and each type of heat treatment method includes multiple process parameters such as heating temperature, holding time, and cooling medium. Therefore, how to comprehensively consider the influence of multiple process parameters on mechanical properties and then optimize the heat treatment process based on the mechanical property indicators is one of the current problems in the optimization design of the heat treatment process.

[0003] For the influence of the heat treatment process parameters on the tensile and fatigue properties, the experimental method is currently used for research. A large amount of experimental work needs to be completed. Secondly, based on the tensile and fatigue property indicators, the commonly used method for optimizing the heat treatment process parameters is the orthogonal test method. This method considers the influence of multiple factors on the experimental results based on the orthogonal table to determine the interaction between the factors and the optimal combination of the factors. However, the optimal value obtained by this method is limited to a certain combination in the experiment, and the optimization result cannot exceed the range of the selected level. In addition, this method increases the number of experiments and cannot provide clear directionality, so the experiment still has a groping nature and the result is not accurate. SUMMARY

[0004] The purpose of the present application is to provide a forward and reverse optimization design method of a steel heat treatment process. The present application not only can calculate the influence of the heat treatment process parameters on the yield strength and fatigue limit strength in a forward direction, but also can optimize the heat treatment process parameters based on the mechanical properties in a reverse direction.

[0005] The technical solution of the present application is a forward and reverse optimization design method of a steel heat treatment process, which comprises the following steps:

[0006] Step 1: Establish a steel model; based on the actual heat treatment process, obtain the temperature field of the steel model; based on the oil quenching cooling method, obtain the cooling curve of the steel model;

[0007] Step 2: Based on the cooling curve and the temperature field, calculate the microstructure of the steel model after heat treatment;

[0008] Step 3: Calculate the mechanical properties of the steel model according to the microstructure to realize forward optimization of the steel heat treatment process; at the same time, determine the microstructure and the heat treatment process of the steel model in a reverse direction according to the requirements of the mechanical properties of the steel model to realize reverse optimization of the steel heat treatment process.

[0009] The steel heat treatment process forward and reverse optimization design method, in step one, the actual heat treatment process includes two heating stages: the first current stage, room temperature to 700-900 DEG C, heating duration 10-60s; the second current stage, 700-900 DEG C to set 900-1000 DEG C, heating duration 10-30s.

[0010] The steel heat treatment process forward and reverse optimization design method, the first current stage, room temperature to 800 DEG C, heating duration 30s; the second current stage, 800 DEG C to set 940 DEG C, heating duration 20s.

[0011] The steel heat treatment process forward and reverse optimization design method, in the actual heat treatment process, the boundary conditions of the steel model, the ambient temperature is 20-35 DEG C, the control range is the outer wall of the steel model; the initial temperature is 20-35 DEG C, the control range is the whole steel model; the potential of one end of the steel model is 0, the control range is the cross section of one end of the steel model; the other end of the steel model is electrified, the control range is the cross section of the other end of the steel model.

[0012] The steel heat treatment process forward and reverse optimization design method, in step two, the microstructure of the steel model after heat treatment is calculated by JmatPro software.

[0013] The steel heat treatment process forward and reverse optimization design method, in step three, the corresponding microstructure is selected according to the change of microstructure to calculate the mechanical properties of the steel model.

[0014] The steel heat treatment process forward and reverse optimization design method, in step three, the mechanical properties include yield strength and fatigue limit strength; the microstructure is grain size.

[0015] The steel heat treatment process forward and reverse optimization design method, the calculation formula of the relationship between the grain size and the yield strength is:

[0016]

[0017] Where, σ y is the yield strength, σ0 and k y are constants, d is the grain size, and if the steel model is martensitic or bainitic steel, d represents the width of martensitic or bainitic lath.

[0018] The steel heat treatment process forward and reverse optimization design method, the calculation formula of the fatigue limit strength is:

[0019] σ up = 0.478 × (σy+ σ rmax ) + 1.363 (dr / 1000000) -1 / 2 -894

[0020] where σ up is the fatigue limit, σ y is the yield strength, σ rmax is the maximum compressive residual stress, d r is the average grain size.

[0021] The aforementioned steel heat treatment process forward and reverse optimization design method, according to the requirement of the steel model to the fatigue limit strength, uses the fatigue strength calculation formula to inversely deduce the requirement of the steel model yield strength, and then uses the yield strength formula to inversely calculate the grain size of the microstructure of the steel model; based on the grain size, the temperature and time are regulated in the Jmatpro software, and the optimized temperature window is inversely obtained, and then the Abaqus software is used to inversely calculate the temperature field, to determine the optimal heat treatment process parameter window, to realize the reverse optimization of the steel heat treatment process.

[0022] Compared with the prior art, the present application has the following beneficial effects:

[0023] The present application can accurately simulate the heat treatment process of the steel by establishing a steel model and combining the actual heat treatment process, thereby providing accurate data basis for subsequent optimization design. The heat treatment process parameters optimized by the present application can improve the production efficiency of the steel, while ensuring the quality stability of the product, reducing the scrap rate and reducing the production cost. The present application reduces energy consumption and material waste by optimizing the heat treatment process parameters, which meets the concept of green manufacturing and sustainable development. In addition, the present application can comprehensively evaluate the comprehensive mechanical properties of the steel model by calculating the yield strength and fatigue limit strength, thereby providing a scientific basis for the reliability and durability of the product. The present application ensures the accuracy and practicability of the calculation model by testing on the MTS-809 dynamic fatigue machine and comparing the test results with the calculation results. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is the grain size calculation result graph of the present application;

[0025] Figure 2 is the yield strength calculation result comparison graph of the present application and actual test result. DETAILED DESCRIPTION

[0026] The present application will be further described below in conjunction with the drawings and examples, but it is not limited as a basis for the present application.

[0027] Example: A steel heat treatment process forward and reverse optimization design method, comprising the following steps:

[0028] Step one: Establish steel model; based on the actual heat treatment process, obtain the temperature field of the steel model; based on the cooling mode of the medium as oil (i.e. oil quenching, which is a method of rapidly cooling high-temperature metal materials), obtain the cooling curve of the steel model;

[0029] In this embodiment, the steel model is a hollow rod single component, and the model is mainly used to simulate the temperature distribution and history of the steel at different positions under the condition of current heating. The density adopts the corresponding data in the original model, and the other data is collected from the network. Based on the actual electric heating process, the temperature distribution of the steel at each position is calculated. Table 1 is the input current value when calculating the temperature field. The temperature field distribution cloud diagram is calculated, and the x, y and z coordinates in the diagram and the temperature values corresponding to the coordinates are extracted.

[0030] Table 1 Temperature field calculation process current distribution data

[0031]

[0032] Based on the temperature field calculation process current distribution data in Table 1, the actual heat treatment process in this embodiment includes two heating stages: the first current stage (25-27A), room temperature to 700-900℃, heating time 10-60s; the second current stage (11-12A), 700-900℃ to set 900-1000℃, heating time 10-30s. Preferably, the first current stage, room temperature to 800℃, heating time 30s; the second current stage, 800℃ to set 940℃, heating time 20s. The ambient temperature in the boundary condition is 26℃; the control range is the outer wall of the steel model; the setting position is Interaction. The current model sets the Film coefficient to 0, which means that the heat exchange between the outside temperature and the component is not considered. If it needs to be considered, the filmcoefficient needs to be adjusted, but there is no physical parameter to refer to, and it needs to be adjusted by comparing with the experiment. The initial temperature of the component is 26℃; the control range is the entire steel model; the setting position is Load>Predefined Field. The potential of one end of the steel model is 0; the control range is the cross section of one end of the steel model; the setting position is Load>Boundary Condition. The other end is powered, the control range is the cross section of the other end of the steel model, and the setting position is Load>Load.

[0033] Step two: based on the cooling curve and the temperature field, the microstructure of the steel model after heat treatment is calculated;

[0034] In step two, the JmatPro software is used, the temperature field and the cooling curve of the steel model are input into the JmatPro software, the material type of the steel model to be loaded is selected, the chemical element composition of the steel model material is input, the mass and volume fractions are adjusted, the quenching process parameters are input, the phase composition and the component content are obtained, the influence of different quenching processes on the microstructure of the material is analyzed, the tempering process parameters are input, and the martensite content and the grain size are calculated. The quenching process parameters include the original grain size of the material, the highest quenching temperature, and the cooling rate; the tempering process parameters include the austenitizing temperature, the range of tempering temperature, the cooling rate, the tempering times, and the tempering time.

[0035] The specific use method of the Jmatpro software is as follows: click Materials types, select the material type to be loaded, and select General steel in this item. Open the dialog box in the lower figure, input the chemical element composition in the red box, and adjust the mass fraction and the volume fraction through the button in the upper left corner. The right side of the dialog box is the functions that can be realized. For example, select Phases and Properties corresponding to Solidification, enter the original grain size of the material, the highest quenching temperature, and the cooling rate, which can be a continuous cooling rate or actual measured data. Then click Start calculation. After the calculation is completed, the dialog box in the lower figure pops up, and the results to be seen can be selected in the lower left corner, including the phase composition, the component content, etc. This part of the result is used to analyze the influence of different quenching processes on the microstructure of the material, so as to judge the influence of the quenching process on the microstructure. Further select Martensite structure in Mechanical properties, and the dialog box in the lower figure pops up. In this dialog box, input the austenitizing temperature, the range of tempering temperature, the cooling rate, the tempering times, and the tempering time. Click Start calculation to calculate the martensite content. Select Reaustenitisation, input the heat treatment process parameters, and calculate to obtain the grain size shown in the lower figure. By analyzing the above calculation results, it is found that in this case, different welding process parameters have the most obvious influence on the grain size after austenitization. Therefore, the following calculation process will focus on the influence of the grain size on the mechanical properties of the material. For different materials, the microstructure after the final heat treatment may have a large change in the martensite content or the phase composition, so the microstructure result with a large change should be found specifically. Figure 1

[0036] ​Step three: according to the microstructure, the mechanical properties of the steel model are calculated to realize the forward optimization of the steel heat treatment process; at the same time, according to the requirements of the mechanical properties of the steel model, the microstructure and the heat treatment process of the steel model are determined reversely to realize the reverse optimization of the steel heat treatment process.

[0037] According to the change amount of the microstructure, the corresponding microstructure is selected for the mechanical property calculation of the steel model. According to step two, it can be known that the quenching process and the tempering process parameters mainly affect the austenite grain size after austenitizing. Since the austenite grain size after austenitizing has an impact on dislocation movement and slip, and further affects the mechanical properties of the material, including yield strength and fatigue limit strength; the microstructure is the grain size. Therefore, based on the Hall-Petch effect, the calculation formula of the relationship between the grain size and the yield strength can be obtained:

[0038]

[0039] Wherein, σ y is the yield strength, σ0and k y are constants, the relationship between the grain size and the yield strength measured by the test can determine the two constants, in this case, k y = 2.49, σ0= -631. d is the grain size, if the steel is martensitic or bainitic steel, d represents the martensitic or bainitic lath width; based on the obtained yield strength, the calculation formula of the fatigue limit strength is:

[0040] σ up = 0.478×(σ y + σ rmax )+1.363(d r / 1000000) -1 / 2 -894;

[0041] Wherein, σ up is the fatigue limit, σ y is the yield strength, σ rmax is the maximum compressive residual stress, d r is the average grain size. Based on the above formula, the fatigue limit strength can be calculated.

[0042] In this example, in order to verify the accuracy of the calculation results, the yield strength and fatigue limit strength test is carried out. The yield strength and fatigue limit strength test is carried out on the MTS-809 dynamic fatigue machine, and the yield strength test is carried out in accordance with the tensile test standard of ASTM-E8, and the yield strength test is carried out in two displacement control modes (0.8mm / min and 2mm / min), wherein 3% of the strain is used as the switching point of displacement control, and the strain is collected by using the MTS multi-gauge extensometer, and 3-5 tests are carried out for each heat treatment state, and then the average value is taken. The fatigue limit strength test is carried out in accordance with the fatigue test standard of ASME metal material.

[0043] In this embodiment, the yield strength and fatigue performance of different groups are verified, and the comparison between the predicted yield strength and the actual test yield strength is shown in the figure Figure 2 , and the prediction error is marked in the figure. In addition, the fatigue limit strength prediction value of one group is 737MPa, and the test result is 730MPa, and the fatigue limit strength prediction value of another group is 776MPa, and the test result is 770MPa.

[0044] In this embodiment, for the reverse optimization process, according to the requirement of the steel model on the fatigue limit strength, the fatigue strength calculation formula is used to inversely deduce the requirement of the steel model on the yield strength, and then the yield strength formula is used to calculate the grain size of the microstructure of the steel model, and based on the grain size, the temperature and time are controlled in the Jmatpro software to obtain the optimized temperature window, and then the temperature field is calculated through the Abaqus software to determine the optimal heat treatment process parameter window, and the optimization of the heat treatment process parameter is realized, and the reverse optimization of the steel heat treatment process is realized.

[0045] The present application can accurately simulate the heat treatment process of the steel by establishing a steel model and combining the actual heat treatment process, thereby providing accurate data basis for subsequent optimization design. The heat treatment process parameter optimized by the present application can improve the production efficiency of the steel, while ensuring the quality stability of the product, reducing the scrap rate and reducing the production cost. The present application reduces energy consumption and material waste by optimizing the heat treatment process parameter, which meets the concept of green manufacturing and sustainable development. In addition, the present application can comprehensively evaluate the comprehensive mechanical properties of the steel model by calculating the yield strength and fatigue limit strength, which provides a scientific basis for the reliability and durability of the product. The present application ensures the accuracy and practicability of the calculation model by carrying out actual test on the MTS-809 dynamic fatigue machine and comparing the test results with the calculation results.

[0046] The present application can not only calculate the influence of the heat treatment process parameter on the mechanical properties in a positive way, but also optimize the heat treatment process parameter based on the mechanical properties in a reverse way.

Claims

1. A method for forward and reverse optimization design of a steel heat treatment process, characterized in that, The method comprises the following steps: Step one: establishing a steel model; obtaining a temperature field of the steel model based on an actual heat treatment process; and obtaining a cooling curve of the steel model based on an oil quenching cooling mode; Step two: calculating a microstructure of the steel model after heat treatment based on the cooling curve and the temperature field; Step three: calculating mechanical properties of the steel model according to the microstructure to realize forward optimization of the steel heat treatment process; and inversely determining the microstructure and the heat treatment process of the steel model according to requirements of the mechanical properties of the steel model to realize reverse optimization of the steel heat treatment process.

2. The method according to claim 1, wherein the method is characterized in that: In step one, the actual heat treatment process comprises two heating stages: a first current stage, from room temperature to 700-900 DEG C, with a heating duration of 10-60 s; and a second current stage, from 700-900 DEG C to a set 900-1000 DEG C, with a heating duration of 10-30 s. In the first current stage, the temperature is raised from room temperature to 800 DEG C, with a heating duration of 30 s; and in the second current stage, the temperature is raised from 800 DEG C to a set 940 DEG C, with a heating duration of 20 s.

3. The method according to claim 2, wherein the method is characterized in that: In the actual heat treatment process, the ambient temperature in the boundary conditions of the steel model is 20-35 DEG C, and the control range is the outer wall of the steel model; the initial temperature is 20-35 DEG C, and the control range is the entire steel model; the electric potential at one end of the steel model is 0, and the control range is the cross section at one end of the steel model; and a current is applied to the other end of the steel model, and the control range is the cross section at the other end of the steel model.

4. The method according to claim 2 or 3, characterized in that: In step two, the microstructure of the steel model after heat treatment is calculated by using JmatPro software.

5. The method of claim 1, wherein the method is characterized by: In step three, the mechanical properties of the steel model are calculated according to the corresponding microstructure selected according to the change amount of the microstructure.

6. The method according to claim 5, wherein the method is characterized in that: In step three, the mechanical properties include yield strength and fatigue limit strength; and the microstructure is grain size.

7. The method according to claim 6, wherein the method is characterized in that: The calculation formula of the relationship between the grain size and the yield strength is:

8. The method according to claim 7, wherein the method is characterized in that: The calculation formula of the fatigue limit strength is: where σ y is the yield strength, σ0and k y are constants, and d is the grain size, where d represents the martensite or bainite lath width if the steel model is a martensitic or bainitic steel.

9. The method according to claim 7, wherein the method is characterized in that: In step three, the yield strength requirement of the steel model is inversely deduced by using the fatigue strength calculation formula according to the fatigue limit strength requirement of the steel model, and then the grain size of the microstructure of the steel model is inversely calculated by using the yield strength formula; σ up = 0.478 x (σ y + σ rmax ) + 1.363(d r / 1000000) -1 / 2 - 894 where σ up is the fatigue limit, σ y is the yield strength, σ rmax is the maximum compressive residual stress, d r is the average grain size.

10. The method of claim 7, wherein the method is characterized by: Based on the grain size, the temperature and time are regulated in the Jmatpro software to inversely obtain an optimized temperature window, and then the temperature field is inversely calculated by using the Abaqus software to determine an optimal heat treatment process parameter window, thereby realizing reverse optimization of the steel heat treatment process. ​

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