Method and equipment for predicting depth of effective hardened layer of carburized and quenched gear

Abstract

The invention discloses a carburizing and quenching gear effective hardened layer depth prediction method and equipment, and the method comprises the following steps: S1, carrying out the thermal compression test of a gear material at different temperatures and different strain rates through a Gleeble thermal simulation testing machine, and obtaining a thermal compression test result; s2, performing mathematical statistics on material constants of a fitting gear based on a thermal compression experiment result, and establishing an Arrhenius constitutive model of the gear after material strain compensation; s3, establishing a gear carburizing and quenching model, and simulating a gear surface heat treatment process based on a three-dimensional finite element algorithm; s4, hardness field output of numerical simulation is modified according to the hardness weighting function, a hardness field result is extracted from a calculation output result, and the effective hardened layer depth of the gear under the technological condition is determined according to the hardness field result; and S5, establishing a process database by using the effective hardened layer depth sets corresponding to different process parameters, and forming a mapping relationship between the process parameters and the hardened layer depths.

Description

Technical Field

[0001] The present application relates to the technical field of carburized layer prediction, and in particular to a method and device for predicting the effective hardened layer depth of a carburized and quenched gear. Background Art

[0002] Carburizing and quenching is a heat treatment process commonly used to improve the surface performance of gear parts. It is usually divided into two stages: carburizing and quenching. First, the carbon concentration distribution that meets the use requirements is obtained by controlling the carburizing process, and then the gear parts are matched with the quenching process to obtain the characteristics of hard surface and tough inside. The comprehensive mechanical properties such as wear resistance and toughness of the gears can be significantly improved to meet the use requirements under various load changes. Although gear parts with good performance can be obtained through experiments, a set of carburizing and quenching processes are time-consuming and labor-intensive in the actual production process, and the test parameters are adjusted more by experience. It is difficult to test the laws of important parameters such as the depth of the carburized layer after surface heat treatment. If the laws of changes in relevant parameters can be mastered, it will be helpful to guide related experiments to obtain the desired results more efficiently.

[0003] With the continuous development of computer numerical simulation technology, finite element simulation technology can largely replace experiments to obtain some data with reference value. In recent years, according to the principle of carburizing and quenching process, corresponding mathematical models are established for each stage in the heat treatment process and corresponding parameters are set. Finite element analysis and other means are used to analyze the actual gear surface heat treatment related problems, and the carburizing and quenching hardness, tissue content, stress and other output results are comprehensively calculated. However, in current research, many studies set the carbon diffusion coefficient and surface heat transfer coefficient in the carburizing and quenching stage as constant values, and the solution process of the hardness field lacks a corresponding weighted calculation model, and the overall calculation result data lacks reference value. At the same time, with the help of tools such as BP neural network, the output results of numerical simulation need to be repeatedly trained and tested to establish the mapping relationship between carburizing and quenching process parameters and the effective hardening layer depth of gears to achieve the corresponding prediction function. In short, the current technology has the problem of difficulty in testing the laws of important parameters such as carburizing layer depth. Summary of the invention

[0004] The present application provides a method and device for predicting the effective hardened layer depth of carburized and quenched gears to solve the above-mentioned problems.

[0005] On the one hand, the present application provides a method for predicting the effective hardened layer depth of a carburized and quenched gear, the method comprising the following steps:

[0006] Step S1: using a Gleeble thermal simulation tester to perform a thermal compression test on the gear material at different temperatures and different strain rates to obtain thermal compression test results;

[0007] Step S2: Based on the thermal compression test results, mathematically and statistically fit the material constants of the gear, and establish an Arrhenius constitutive model of the gear material after strain compensation;

[0008] Step S3: establishing a gear carburizing and quenching model, and simulating the gear surface heat treatment process based on a three-dimensional finite element algorithm;

[0009] Step S4: modifying the hardness field output of the numerical simulation according to the hardness weighting function, extracting the hardness field result from the calculated output result, and determining the effective hardened layer depth of the gear under the process condition;

[0010] Step S5: using the effective hardened layer depth set corresponding to different process parameters, a process database is established, and a mapping relationship between process parameters and hardened layer depth is formed.

[0011] In one implementation of the present application, in step S1, a Gleeble thermal simulation tester is used to perform a thermal compression test on the gear material at different temperatures and different strain rates.

[0012] In one implementation of the present application, step S2 specifically includes:

[0013] Step S21: Determine the material constant function of the gear as follows:

[0014]

[0015] In the formula, ε represents the strain;

[0016] Step S22: Establish the Arrenhnius constitutive model of the gear material as follows:

[0017]

[0018]

[0019] In the formula, Z represents the ZenerHo-llomon parameter, Q represents the thermal deformation activation energy, T represents the temperature, and R represents the gas constant taking a fixed value;

[0020] Step S23: The Arrhenius constitutive model of the gear material is optimized based on strain compensation as follows:

[0021]

[0022] In the formula, α, n, Q, are all material constants, and ε represents the strain.

[0023] In one implementation of the present application, step S3 specifically includes:

[0024] Step S31: Establish a complete gear 3D model, and considering the actual situation of the finite element model, select a 1 / 8 gear model and import it into a commercial finite element simulation software;

[0025] Step S32: setting gear material properties;

[0026] Step S33: Setting the parameters of the heat treatment process according to the actual process conditions, wherein the carburizing stage includes the setting of the carbon diffusion coefficient and the carbon transfer coefficient, and the formulas are as follows:

[0027]

[0028]

[0029] Where E is the activation energy of the reaction, R is the gas constant which takes a fixed value, and T is the temperature.

[0030] In one implementation of the present application, in step S4, the process of modifying the hardness field output of the numerical simulation according to the hardness weighting function specifically includes: using the custom data result function of SIMUFACT FORMING to define the hardness field of martensite, bainite, ferrite-pearlite and the hardness formula of austenite structure respectively, and determining the weighting function of the hardness field as the output result of the hardness value, and the hardness output formula is as follows:

[0031] HVC="HVF-P"*(CVFFERR+CVFPERL)+"HVB-2"*CVFBAIN

[0032] +"HVM3"*CVFMART-HRCRA*CVFAUST

[0033] Wherein, HVF-P represents the Vickers hardness of ferrite-pearlite structure, CVFFERR+CVFPERL represents the ferrite-pearlite structure content, HVB-2 represents the Vickers hardness field of bainite structure, CVFBAIN represents the bainite structure content, HVM3 represents the Vickers hardness of martensite structure, and CVFMART represents the martensite structure content.

[0034] In one implementation of the present application, in step S4, the process of extracting the hardness field result from the calculation output result and using it to determine the effective hardened layer depth of the gear under the process conditions specifically includes:

[0035] The hardness value at the defined tracking point is output, and HV550 is used as the effective hardened layer boundary. The effective hardened layer depth of the gear under the process parameters is determined by interpolation.

[0036] In one implementation of the present application, step S5 specifically includes:

[0037] For carburizing and quenching heat treatment under different process parameters, computer finite element numerical simulation calculations are performed to obtain the corresponding relationship between process parameters and effective hardened layer depth;

[0038] The neural network is used to train and test the above-mentioned hardened layer depth, and then the corresponding effective hardened layer depth can be predicted according to the process parameters.

[0039] In one implementation of the present application, the Gleeble thermal simulator device adopts a single-pass high-temperature compression test, the compression method is unidirectional compression, the deformation temperature is 850°C, 900°C, 950°C, 1000°C, and the strain rate is 0.01s -1 , 0.1s -1 , 1s -1 , 10s -1 , the compression is set to 60%.

[0040] In one implementation of the present application, the material properties include Young's modulus, density, Poisson's ratio and Arrhenius constitutive equation.

[0041] The present application also provides a device for predicting the effective hardened layer depth of a carburized and quenched gear, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can complete a method for predicting the effective hardened layer depth of a carburized and quenched gear.

[0042] The present application provides a method and device for predicting the effective hardened layer depth of carburized and quenched gears, and provides a method for predicting the effective hardened layer depth of carburized and quenched gears. A constitutive model of the gear material and a surface heat transfer coefficient model of the corresponding quenching oil are established through hot compression tests and quenching oil cooling characteristic curves, which are used as material property parameter settings and heat exchange parameter inputs respectively. A hardness weighted function is established based on a function model of different tissues and carbon contents. The hardness field weighted function is input into a finite element numerical simulation model, and a gear carburizing and quenching process model consistent with actual test conditions is established. A process database for surface heat treatment processes is then established with the help of a neural network, thereby achieving the effect of predicting the effective hardened layer depth of the gear. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0044] Figure 1A flow chart of a method for predicting the effective hardened layer depth of a carburized and quenched gear provided in an embodiment of the present application;

[0045] Figure 2 A material constant curve that is fitted for the unidirectional thermal compression test provided in the embodiments of the present application;

[0046] Figure 3 Arrhenius constitutive equation curve of gear material based on strain compensation provided in the embodiment of the present application;

[0047] Figure 4 A flow chart of establishing a gear carburizing and quenching model provided in an embodiment of the present application;

[0048] Figure 5 A gear carburizing and quenching heat treatment model provided in an embodiment of the present application;

[0049] Figure 6 The carbon content gradient distribution and hardness field distribution cloud diagram from the gear surface to the core calculated by finite element software provided in the embodiment of the present application;

[0050] Figure 7 The embodiment of the present application provides an effective hardened layer depth map of the gear under the process conditions by extracting the hardness field output values ​​corresponding to all tracking points;

[0051] Figure 8 A schematic diagram of establishing a process parameter-effective hardened layer depth mapping relationship using a neural network provided in an embodiment of the present application;

[0052] Fig. 9 A graph showing the neural network training and prediction results provided in the embodiments of the present application;

[0053] Fig.10 A schematic diagram of a device for predicting the effective hardened layer depth of a carburized and quenched gear provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0055] The embodiment of the present application provides a method and device for predicting the effective hardened layer depth of a carburized and quenched gear. The technical solution proposed in the embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0056] Figure 1 A flow chart of a method for predicting the effective hardened layer depth of a carburized and quenched gear provided in an embodiment of the present application. Figure 1 As shown, the method mainly includes the following steps:

[0057] Step S1: using a Gleeble thermal simulation tester to perform thermal compression tests on the gear material at different temperatures and different strain rates to obtain thermal compression test results.

[0058] Step S2: Based on the thermal compression test results, mathematically and statistically fit the material constants of the gear, and establish an Arrhenius constitutive model of the gear material after strain compensation.

[0059] Use the statistical software origin to collect data and use mathematical statistics to fit the material constants of the gears. The material constant fitting is as follows: Figure 2 As shown, the material constant function of the gear is as follows:

[0060]

[0061] In the formula, ε represents the strain variable.

[0062] The Arrhenius constitutive model of gear materials is as follows:

[0063]

[0064]

[0065] In the formula, Z represents the Zener Hollomon parameter, Q represents the thermal deformation activation energy, T represents the temperature, and R represents the gas constant, which takes a fixed value. The Arrhenius constitutive equation curve of gear materials based on strain compensation is as follows: Figure 3 shown.

[0066] Step S3: Establish a gear carburizing and quenching model, and simulate the gear surface heat treatment process based on a three-dimensional finite element algorithm.

[0067] In the embodiment of the present application, the flowchart of establishing the gear carburizing and quenching model is as follows Figure 4 Step S3 specifically includes: Step S31, establishing a complete gear three-dimensional model, considering the actual situation of the finite element model, selecting a 1 / 8 gear model and importing it into a commercial finite element simulation software; the gear carburizing and quenching heat treatment model is as follows: Figure 5 shown.

[0068] Step S32, setting gear material properties: parameters include Young's modulus, density, Poisson's ratio and Arrhenius constitutive equation;

[0069] Step S33, setting the parameters of the heat treatment process according to the actual process conditions, wherein the carburizing stage includes the setting of the carbon diffusion coefficient and the carbon transfer coefficient, and the formulas are as follows:

[0070]

[0071]

[0072] Where E is the activation energy of the reaction, R is the gas constant which takes a fixed value, and T is the temperature.

[0073] Step S4: modify the hardness field output of the numerical simulation according to the hardness weighting function, extract the hardness field result from the calculated output result, and use it to determine the effective hardened layer depth of the gear under the process conditions.

[0074] In the embodiment of the present application, step S4 includes: using the custom data result function of SIMUFACT FORMING to define the hardness field of martensite, bainite, and ferrite-pearlite respectively. The hardness formula of austenite structure, and determining the weighted function of the hardness field as the output result of the hardness value. The hardness field distribution cloud diagram is as follows Figure 6 As shown, the hardness output formula is as follows:

[0075] HVC="HVF-P"*(CVFFERR+CVFPERL)+"HVB-2"*CVFBAIN

[0076] +"HVM3"*CVFMART-HRCRA*CVFAUST; where HVF-P represents the Vickers hardness of ferrite-pearlite structure, CVFFERR+CVFPERL represents the ferrite-pearlite structure content, HVB-2 represents the Vickers hardness field of bainite structure, CVFBAIN represents the bainite structure content, HVM3 represents the Vickers hardness of martensite structure and CVFMART represents the martensite structure content.

[0077] Step S5: using the effective hardened layer depth set corresponding to different process parameters, a process database is established, and a mapping relationship between process parameters and hardened layer depth is formed.

[0078] In the embodiment of the present application, the step S5 specifically includes: outputting the hardness value at the defined tracking point, taking HV550 as the effective hardened layer boundary, and interpolating to determine the effective hardened layer depth of the gear under the process parameters. Outputting the hardness value at the defined tracking point, taking HV550 as the effective hardened layer boundary, and interpolating to determine the effective hardened layer depth of the gear under the process parameters. Figure 7 As shown, this is the effective hardened layer depth diagram corresponding to the finite element numerical simulation of gear carburizing and quenching.

[0079] like Figure 8As shown, a schematic diagram of the process parameter-effective hardened layer depth mapping relationship established using a neural network. For carburizing and quenching heat treatments under different process parameters, computer finite element numerical simulation calculations were performed to obtain the corresponding relationship between process parameters and effective hardened layer depth.

[0080] Furthermore, for carburizing and quenching heat treatment under different process parameters, computer finite element numerical simulation calculations were performed to obtain the corresponding relationship between process parameters and effective hardened layer depth; Fig. 9 The figure shows the training and prediction results of the neural network. The neural network is used to train and test the above hardened layer depth, and then the corresponding effective hardened layer depth can be predicted according to the process parameters.

[0081] In an embodiment of the present application, a unidirectional compression test is carried out on the gear material using a thermal simulation testing machine, and the material constants are fitted under different conditions to establish the Arrhenius constitutive equation of the gear material. The influence of different strain amounts on the rheological stress during the compression deformation process is fully considered, and an Arrhenius constitutive equation under strain compensation conditions is established, which can accurately characterize the mechanical properties of the material.

[0082] The embodiment of the present application determines the hardness field distribution model of a single tissue based on the function model of different tissues and carbon contents, and establishes a hardness weighted function. The hardness field weighted function is input into the finite element numerical simulation model to establish a gear carburizing and quenching process model consistent with the actual test conditions. By comparing the finite element numerical simulation with the test results, it can be seen that the finite element model proposed in the present application has high reliability and can characterize the properties of the gear after carburizing and quenching treatment, such as the depth of the carburized layer.

[0083] The embodiment of the present application uses a neural network to train and test the mapping relationship between process parameters and effective hardening layer depth, and realizes the function of predicting the corresponding effective hardening layer depth based on known process parameters. Compared with experiments, it saves time and economic costs.

[0084] The above is a method for predicting the effective hardened layer depth of a carburized and quenched gear provided in an embodiment of the present application. Based on the same inventive concept, an embodiment of the present application also provides a device for predicting the effective hardened layer depth of a carburized and quenched gear. Fig.10 A schematic diagram of a carburized and quenched gear effective hardened layer depth prediction device provided in an embodiment of the present application, such as Fig.10 As shown, the device mainly includes: at least one processor 1001; and a memory 1002 that is communicatively connected to the at least one processor; wherein the memory 1002 stores instructions that can be executed by the at least one processor 1001, and the instructions are executed by the at least one processor 1001 so that the at least one processor 1001 can complete the aforementioned method for predicting the effective hardened layer depth of a carburized and quenched gear.

[0085] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0086] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0087] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for predicting the effective hardened layer depth of carburized and quenched gears, characterized in that: The method comprises the following steps: Step S1: using a Gleeble thermal simulation tester to perform a thermal compression test on the gear material at different temperatures and different strain rates to obtain thermal compression test results; Step S2: Based on the thermal compression test results, mathematically and statistically fit the material constants of the gear, and establish an Arrhenius constitutive model of the gear material after strain compensation; Step S3: establishing a gear carburizing and quenching model, and simulating the gear surface heat treatment process based on a three-dimensional finite element algorithm; Step S4: modifying the hardness field output of the numerical simulation according to the hardness weighting function, extracting the hardness field result from the calculated output result, and determining the effective hardened layer depth of the gear under the process condition; Step S5: using the effective hardened layer depth set corresponding to different process parameters, a process database is established, and a mapping relationship between process parameters and hardened layer depth is formed.

2. A method for predicting the effective hardened layer depth of a carburized and quenched gear according to claim 1, characterized in that: In the step S1, a Gleeble thermal simulation tester is used to perform a thermal compression test on the gear material at different temperatures and different strain rates.

3. The method for predicting the effective hardened layer depth of a carburized and quenched gear according to claim 1, characterized in that: The step S2 specifically includes: Step S21: Determine the material constant function of the gear as follows: In the formula, ε represents the strain; Step S22: Establish the Arrenhnius constitutive model of the gear material as follows: In the formula, Z represents the ZenerHo-llomon parameter, Q represents the thermal deformation activation energy, T represents the temperature, and R represents the gas constant taking a fixed value; Step S23: The Arrhenius constitutive model of the gear material is optimized based on strain compensation as follows: In the formula, α, n, Q, are all material constants, and ε represents the strain.

4. The method for predicting the effective hardened layer depth of a carburized and quenched gear according to claim 1, characterized in that: The step S3 specifically includes: Step S31: Establish a complete gear 3D model, and considering the actual situation of the finite element model, select a 1 / 8 gear model and import it into a commercial finite element simulation software; Step S32: setting gear material properties; Step S33: Setting the parameters of the heat treatment process according to the actual process conditions, wherein the carburizing stage includes the setting of the carbon diffusion coefficient and the carbon transfer coefficient, and the formulas are as follows: D(T,M,C)=[0.146-0.036C*(1-1.075Cr)+∑k1M] Where E is the activation energy of the reaction, R is the gas constant which takes a fixed value, and T is the temperature.

5. The method for predicting the effective hardened layer depth of a carburized and quenched gear according to claim 1, characterized in that: In step S4, the process of modifying the hardness field output of the numerical simulation according to the hardness weighting function specifically includes: using the custom data result function of SIMUFACT FORMING to define the hardness field of martensite, bainite, ferrite-pearlite and the hardness formula of austenite structure respectively, and determining the weighting function of the hardness field as the output result of the hardness value. The hardness output formula is as follows: HVC="HVF-P"*(CVFFERR+CVFPERL)+"HVB-2"*CVFBAIN +"HVM3"*CVFMART-HRCRA*CVFAUST Wherein, HVF-P represents the Vickers hardness of ferrite-pearlite structure, CVFFERR+CVFPERL represents the ferrite-pearlite structure content, HVB-2 represents the Vickers hardness field of bainite structure, CVFBAIN represents the bainite structure content, HVM3 represents the Vickers hardness of martensite structure, and CVFMART represents the martensite structure content.

6. A method for predicting the effective hardened layer depth of a carburized and quenched gear according to claim 1, characterized in that: In step S4, the process of extracting the hardness field result from the calculation output result and using it to determine the effective hardened layer depth of the gear under the process conditions specifically includes: The hardness value at the defined tracking point is output, and HV550 is used as the effective hardened layer boundary. The effective hardened layer depth of the gear under the process parameters is determined by interpolation.

7. The method for predicting the effective hardened layer depth of a carburized and quenched gear according to claim 1, characterized in that: The step S5 specifically includes: For carburizing and quenching heat treatment under different process parameters, computer finite element numerical simulation calculations are performed to obtain the corresponding relationship between process parameters and effective hardened layer depth; The neural network is used to train and test the above-mentioned hardened layer depth, and then the corresponding effective hardened layer depth can be predicted according to the process parameters.

8. The method for predicting the effective hardened layer depth of a carburized and quenched gear according to claim 1, characterized in that: The Gleeble thermal simulator device adopts a single-pass high-temperature compression test, the compression method is unidirectional compression, the deformation temperature is 850°C, 900°C, 950°C, 1000°C, and the strain rate is 0.01s -1 , 0.1s -1 , 1s -1 , 10s -1 , the compression is set to 60%.

9. A method for predicting the effective hardened layer depth of a carburized and quenched gear according to claim 4, characterized in that: The material properties include Young's modulus, density, Poisson's ratio and Arrhenius constitutive equation.

10. A device for predicting the effective hardened layer depth of carburized and quenched gears, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can complete the method for predicting the effective hardened layer depth of a carburized and quenched gear as described in any one of claims 1-9.

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