Traveling wave magnetic field assisted flash welding method and equipment for rare earth alloyed wheel steel

Abstract

The invention discloses a traveling wave magnetic field assisted flash welding method and equipment for rare earth alloyed wheel steel, and the method adopts a traveling wave magnetic field assisted flash welding process for the rare earth alloyed wheel steel meeting the automobile lightweight requirement, and enables a traveling wave magnetic field to continuously act on a welding area in the full stages of preheating, flashing and upsetting. Wherein the rare earth elements comprise cerium (Ce) and the like, and the components of the wheel steel are optimized through microalloying treatment; the temperature is accurately controlled to be 500-650 DEG C in the preheating stage, a traveling wave magnetic field of 10-30 mT and 50-100 Hz and specific welding parameters are adopted in the flash stage, the upsetting distance is controlled to be 2-5 mm and the upsetting pressure is controlled to be 6-8 MPa in the upsetting stage, heat transfer, metal flow and rare earth distribution are improved by means of the magnetic field, grains are refined, and defects are reduced. Meanwhile, the magnetic field and welding parameters are optimized through machine learning, and the purpose of minimizing the grain size is achieved. The method can remarkably improve the metallurgical bonding strength and the structure performance of the weld joint, and is suitable for efficient and high-quality welding of the rare earth alloying wheel steel.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of automobile parts manufacturing and welding, and particularly relates to a rare earth alloyed wheel performance improvement method based on a traveling wave magnetic field assisted flash welding technology and equipment. BACKGROUND

[0002] With the increasing demand for automobile lightweighting and high performance, the performance improvement of wheel steel has become a key in the field of automobile manufacturing. As an effective material modification method, rare earth alloying technology can significantly improve the corrosion resistance, fatigue resistance and welding performance of steel. However, the traditional welding process still has problems such as unstable welding joint performance and low fatigue life when welding rare earth alloyed wheels, which seriously restricts the wide application of rare earth alloyed wheel steel. As a new type of auxiliary technology, traveling wave magnetic field has gradually attracted attention in the welding field in recent years. By controlling the magnetic field strength and frequency, it can optimize the heat input and microstructure evolution in the welding process, thereby improving the quality and performance of the welded joint. Combining the traveling wave magnetic field assisted technology with the welding process of rare earth alloyed wheel steel is expected to solve the shortcomings of the existing technology and provide more reliable technical support for automobile lightweighting and high performance, but there are few reports on this aspect and few industrial applications, which need to be solved urgently. SUMMARY

[0003] In view of the above problems, the application discloses a rare earth alloyed wheel steel traveling wave magnetic field assisted flash welding method and equipment. The application of the traveling wave magnetic field in the flash welding process of the rare earth alloyed wheel steel can significantly improve the welding quality. Through the action of Lorentz force, the traveling wave magnetic field can accelerate the heating process of the welding zone, uniformly distribute the heat, promote the flow of the molten pool and the refinement of the microstructure, and reduce the generation of welding defects. These effects collectively improve the performance and reliability of the welded joint.

[0004] In order to achieve the above purpose, the application includes the following technical solutions:

[0005] A rare earth alloyed wheel steel traveling wave magnetic field assisted flash welding method, comprising the following steps:

[0006] (1) Rare earth element micro-alloying treatment is performed on the wheel steel, and the rare earth element includes cerium Ce;

[0007] (2) In the preheating stage, the flash stage and the upset stage of the flash welding process, the traveling wave magnetic field is continuously applied to the welding area, and the magnetic field direction of the traveling wave magnetic field is set to be perpendicular to the weld extension direction;

[0008] (3) In the preheating stage, the traveling wave magnetic field is started synchronously with the flash welder, and the heat in the welding area is uniformly distributed by adjusting the magnetic field strength and frequency, and the preheating temperature is accurately controlled in the range of 500-650℃;

[0009] (4) In the flash stage, the flash allowance is controlled to be 5-10mm, the flash rate is 4-6mm / s, the flash output power is 150-200kW, and at the same time, the traveling wave magnetic field with a magnetic field strength of 10-30mT and a frequency of 50-100Hz continuously acts on the welding area, and the electromagnetic stirring effect is used to promote metal flow and mixing;

[0010] (5) In the upset stage, the upset distance is controlled to be 2-5mm, the upset pressure is 6-8MPa, and the traveling wave magnetic field is kept on while the upset force is applied, and the electromagnetic force is used to refine the grains;

[0011] (6) A genetic algorithm (preferably ga function) with the optimization goal of minimizing the weld grain size is used to match and optimize the strength, frequency of the traveling wave magnetic field and the flash welding parameters.

[0012] Further, in the above method, the process of matching and optimizing in step (6) using a genetic algorithm specifically includes:

[0013] (61) Analyze the electromagnetic characteristics of the wheel rim material under the action of the traveling wave magnetic field;

[0014] (62) Establish a correlation model between the magnetic field strength and frequency parameters of the traveling wave magnetic field and the current, voltage, and speed parameters of the flash welding;

[0015] (63) Use a genetic algorithm or particle swarm optimization algorithm for multi-objective optimization to find the best parameter combination that can simultaneously meet the uniformity of the weld structure and the mechanical performance target;

[0016] (64) Verify the optimization results through actual welding experiments, and adjust the parameters of the correlation model and the optimization algorithm according to the verification results.

[0017] Further, in the above method, in steps (62) and (63), the optimization process takes a grain size prediction model based on machine learning as the objective function, which is represented as:

[0018] y = 20-0.1x1-15x2+0.02x3+0.005x4-0.3x5+2x6-0.01x7+0.2x8+1.5x9+0.001x1x2+3ε where y is the model-predicted grain size in microns (μm);

[0019] x1, x2, x3 are traveling wave magnetic field parameters, respectively representing magnetic field frequency (unit: Hz), magnetic field strength (unit: T) and magnetic field phase difference (unit: °);

[0020] x4, x5, x6, x7, x8, x9 are welding process parameters, respectively representing welding current (unit: A), welding voltage (unit: V), welding speed (unit: mm / s), preheating temperature (unit: ℃), cooling rate (unit: ℃ / s) and welding time (unit: s);

[0021] Term 0.001x1x2 is an interaction term between magnetic field frequency and magnetic field strength;

[0022] 3ε is a random noise term, wherein ε follows a standard normal distribution ε~N(0,1), and is ensured to be greater than or equal to 5 μm by constraint. The specific code is shown in Appendix 1, and the calculation result is shown in Figure 4 and Figure 5 .

[0023] Further, in the above method, after the welding process is completed, the weld is also subjected to integrated post-welding treatment, which includes the following in sequence:

[0024] (7) An oxidized skin formed on the surface of the weld is removed by scraping with a slag planing mechanism;

[0025] (8) A rolling pressure of 0.5 MPa to 2 MPa is applied to the surface of the weld after the slag has been removed by the rolling mechanism to improve the surface flatness thereof;

[0026] (9) The weld after rolling is subjected to controllable cooling by a water cooling system, and the cooling rate is controlled between 5 ℃ / s to 20 ℃ / s to optimize the microstructure performance of the weld area.

[0027] Further, in the above method, the method further includes a step of testing and verifying the performance of the joint welded according to the optimized parameters, and the performance testing and verification includes:

[0028] (10) Fatigue life testing is performed by using a high-frequency fatigue testing machine, and the number of cycles to fracture is recorded;

[0029] (11) Microstructure analysis of the weld and heat-affected zone is performed by using a metallographic microscope and a scanning electron microscope, including grain size measurement and defect observation;

[0030] (12) Room temperature tensile testing is performed by using a universal testing machine to determine the tensile strength, and hardness testing is performed by using a Vickers or Rockwell hardness tester.

[0031] The application also discloses a traveling wave magnetic field assisted flash welding equipment for implementing the above method, which comprises:

[0032] I. A flash butt welding main machine, which is provided with a clamp for clamping a rare earth alloyed wheel steel wheel rim blank and an electrode system for providing a welding current;

[0033] II. A traveling wave magnetic field generating device, which is composed of a magnetic field coil, a variable frequency power supply and a control system, the magnetic field coil is arranged around the outside of the welding area and can generate a traveling wave magnetic field with a strength of 5mT to 50mT and a frequency of 20Hz to 200Hz continuously adjustable;

[0034] III. A synchronous control module, which is electrically connected with the control systems of the flash butt welding main machine and the traveling wave magnetic field generating device respectively, for receiving welding process signals and realizing linkage adjustment and precise matching of welding parameters and magnetic field parameters in the preheating, flash and upset stages;

[0035] IV. A temperature monitoring unit and a magnetic field detection unit, the temperature monitoring unit is used for real-time detection and feedback of the temperature value of the welding area, the magnetic field detection unit is used for real-time detection and feedback of the magnetic field strength value of the welding area, and both are signal connected with the control system to constitute a closed loop control to dynamically adjust the welding current, the upsetting force and the magnetic field parameters;

[0036] V. A post-welding treatment device, which is integrated with a slag planing mechanism, a rolling mechanism and a cooling system, the slag planing mechanism is used for automatically removing the oxide skin on the surface of the weld, the rolling mechanism can apply a pressure of 0.5MPa to 2MPa to the surface of the weld to improve the flatness, and the cooling system is water-cooled and can control the cooling rate of the weld to be 5℃ / s to 20℃ / s.

[0037] Further, in the above equipment, the magnetic field coil of the II. traveling wave magnetic field generating device is arranged to simultaneously apply a traveling wave magnetic field to the upper and lower surfaces of the welded joint.

[0038] Further, in the above equipment, the III. synchronous control module is configured to: synchronously start the traveling wave magnetic field in the preheating stage to promote uniform heating; maintain the magnetic field action in the flash stage to strengthen metal stirring; and keep the magnetic field in the upset stage to assist grain refinement.

[0039] Further, in the above equipment, the cooling system in the V. post-welding treatment device adopts a closed-loop water cooling mode, and the cooling rate of 5℃ / s to 20℃ / s is accurately controlled by adjusting the water flow and temperature.

[0040] Further, in the above equipment, the rolling mechanism in the V. post-welding treatment device adopts a high-precision roller driven by a servo motor, which can implement constant pressure rolling processing on the weld.

[0041] Compared with the prior art, the present application has the following outstanding beneficial effects:

[0042] 1. Enhancing the fatigue life and fatigue resistance of welded joints

[0043] Through the traveling wave magnetic field assisted welding process, the fatigue life of the welded joint is significantly improved, and the fatigue resistance is obviously improved. Under the same load conditions, the fatigue crack propagation rate of the welded joint is significantly reduced, and the service life is greatly extended.

[0044] 2. Optimizing the microstructure of the welded joint

[0045] The traveling wave magnetic field can refine the grain structure of the welded joint, making the microstructure more uniform and dense. The refined grains can improve the strength and toughness of the material, reduce the occurrence of welding defects, and thus improve the overall performance of the welded joint.

[0046] 3. Achieving efficient and stable welding of rare earth alloyed wheel steel

[0047] The invention realizes efficient and stable welding of rare earth alloyed wheel steel by optimizing the welding process and parameters. The welding process is more controllable, and the welding quality is more stable, providing reliable technical support for the light weight and high performance of automobiles. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The overall flowchart of the method described in the invention;

[0049] Figure 2 The result diagram of the welding parameter optimization of the invention

[0050] Figure 3 The flowchart of data arrangement and preprocessing in the establishment of the quantitative relationship model of the invention;

[0051] Figure 4 Parameter exploration analysis of the invention Figure 1 ;

[0052] Figure 5 Parameter exploration analysis of the invention Figure 2 ;

[0053] Figure 6 The schematic diagram of the relationship model between the welding parameters and the performance of the welded joint in the embodiment of the invention;

[0054] Figure 7 Microstructure analysis in the embodiment of the invention Figure 1 ;

[0055] Figure 8 Microstructure analysis in the embodiment of the invention Figure 2 .。 DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0057] A method for flash welding of a rare earth alloyed wheel steel with the assistance of a traveling wave magnetic field, as shown in the accompanying drawings, comprises the following steps: Figure 3

[0058] 1. Design of rare earth alloyed wheel steel

[0059] 11) Micro-alloying treatment with rare earth elements (cerium, Ce) to optimize the chemical composition of the steel. By precisely controlling the amount of rare earth elements added, the corrosion resistance and fatigue resistance of the steel are improved, allowing it to maintain good overall performance during welding.

[0060] 12) Design a reasonable composition system of rare earth alloyed wheel steel to ensure that it meets the demand for lightweight vehicles while having excellent welding performance and use performance

[0061] 2. Traveling wave magnetic field assisted welding process

[0062] 21) Introduce a traveling wave magnetic field device during flash welding, and adjust the magnetic field strength and frequency to optimize the welding heat input. The traveling wave magnetic field can produce a dynamic thermal effect in the welding area, making the temperature distribution of the welded joint more uniform and reducing local overheating.

[0063] 22) Utilize the traveling wave magnetic field to refine the grain structure of the welded joint, suppress joint softening, and reduce the defect rate of the welded joint. Refining the grains can improve the strength and toughness of the material, while reducing defects such as pores and cracks during welding, thereby improving the overall performance of the welded joint.

[0064] 23) The traveling wave magnetic field cooperates with the flash welding process to produce different effects during the heating and preheating stages:

[0065] a. Heating stage: During the heating stage, the Lorentz force generated by the traveling wave magnetic field can accelerate the movement of electrons, thereby increasing the temperature rise rate of the welding zone. The direction of the Lorentz force is perpendicular to the direction of the current and magnetic field, which can cause the direction of electron movement in the welding zone to change, increasing the kinetic energy of the electrons, thereby accelerating the heating process of the welding zone.

[0066] b. Preheating stage: During the preheating stage, the Lorentz force generated by the traveling wave magnetic field helps to evenly distribute heat and reduce the temperature gradient in the welding zone. Through the action of the Lorentz force, the heat in the welding zone can be evenly distributed, avoiding local overheating, thereby improving the welding quality.​

[0067] c. Effect of traveling magnetic field on welding process: Lorentz force can affect the fluid flow in the molten pool, promoting the homogenization of the molten pool. In the molten pool, Lorentz force can cause stirring of the melt, helping the formation of the weld and the refinement of the structure.

[0068] d. Inhibition of welding defects: Lorentz force can break the balance of pores and inclusions, promoting their expulsion, thereby reducing welding defects. Through the action of Lorentz force, the formation of pores and inclusions during welding can be reduced, improving the quality of the welded joint.

[0069] e. Traveling magnetic field is generated by alternating current, whose magnetic field strength and direction change with time and propagate in space. This magnetic field can interact with the current in the welding process, generating Lorentz force, thereby affecting the welding process.

[0070] 3. Welding parameter optimization

[0071] 31) The orthogonal experiment method is used to optimize the flash welding parameters, including flash current, upset current, workpiece gap, etc. Through a large amount of experimental data and statistical analysis, the best combination of welding parameters is determined to ensure the uniformity of the microstructure and mechanical properties of the welded joint. The process parameter optimization function is first based on the grain refinement degree of rare earth elements and magnetic field, the model considers the effect of magnetic field and rare earth on grain refinement, which is shown as follows:

[0072] Grain refinement degree model

[0073]

[0074] Where: G0 and G represent the grain size of the weld and heat-affected zone without and with magnetic field, respectively, t s is the solidification time, ρ is the metal density, α is the material constant (0.12-0.18 for rare earth microalloyed zone), μ is the metal viscosity, F L represents the Lorentz force generated by the traveling magnetic field, t s represents the magnetic field action time.

[0075] At the same time, the material machine learning algorithm is used to predict the grain refinement degree and optimize the process, and the specific implementation process is as follows:

[0076] First, 200 groups of simulation data containing traveling magnetic field parameters (frequency, intensity, phase difference) and welding process parameters (current, voltage, etc.) are generated, and the correlation between feature distribution and grain size is analyzed through visualization; then the data is standardized for preprocessing, and linear regression, support vector machine, random forest and neural network models are trained, and the RMSE, R 2Then, the genetic algorithm is used to optimize the process parameters, and the model validity is verified through visualization methods such as feature importance and residual analysis. Finally, the grain size under the new process parameter combination is predicted based on the optimal model to provide data support for welding process optimization. Figure 4 Indicates.

[0077] 32) Secondly, the process parameter optimization function also considers a multi-objective function with tensile strength as an indicator, while adding constraints such as spatter and thermal balance during the welding process.

[0078] 33) A quantitative relationship model between welding parameters and weld joint performance was established to provide a theoretical basis for adjusting welding parameters in actual production. The model established a multi-objective function, including strength, toughness, and microstructure inheritance during welding. The microstructure inheritance term includes the negative effect of grain size and the magnetic field temperature control effect. The details are as follows:

[0079] Using nonlinear response surface equation and microstructure characteristic transfer function:

[0080] Y i =α0+∑α k X k +∑β kl X k X l +γ.Φ micro

[0081] Performance index Φ micro =δ1.G -1 +δ2.U RE .exp(-λ.ΔT)

[0082] Y in the model i Target performance, X k Standardized process parameters (magnetic field strength, frequency, speed, etc.), ΔT high temperature residence time controlled by magnetic field (calibrated by electromagnetic coupling simulation), Φ micro Microstructure transfer item, grain size G, rare earth uniformity U RE ,α0、α k ,γ,δ1,λ are constants.

[0083] 34) Flash Current: Initially set the current range based on the resistivity and cross-sectional area of ​​the wheel rim material. Low current may result in insufficient heating, while high current may cause overheating and material damage.

[0084] 35) Upsetting Current: The upsetting current affects the final upsetting effect of the weld zone. Excessively high current may cause excessive material flow, while too low current may result in incomplete weld penetration. The current range should be set based on the material's yield strength and the expected upsetting force.

[0085] 36) Workpiece clearance: The clearance affects the stability of the flash process. A small clearance can result in insufficient flash, while a large clearance increases energy consumption. Depending on the rim size and accuracy requirements, set the clearance range to 0.5-2.0 mm.

[0086] 37) Traveling wave magnetic field parameters: Including magnetic field strength and frequency. The magnetic field strength affects the size of the Lorentz force, and the frequency affects the rate of change of the magnetic field lines. According to the welding equipment capacity and material response, set the magnetic field strength range to 0.1-0.5 T, and the frequency range to 50-200 Hz.

[0087] 4. Quantitative relationship model establishment

[0088] 41) Data organization and preprocessing: The data organization and preprocessing process is shown in Figure 5 , which organizes the collected experimental data. The data includes material composition, initial grain, welding current, upset rate, upset amount, cooling rate, joint mechanical properties, and calculates the average performance index under each factor level combination. Remove abnormal data, and standardize or normalize the data to eliminate the influence of dimension.

[0089] 42) Model construction method selection: According to the data characteristics and research objectives, select the appropriate modeling method. For performance indicators with strong linear relationship, use multivariate linear regression model; for indicators with significant nonlinear relationship, use nonlinear modeling methods such as neural network or support vector machine. According to the influence relationship curves of material composition, initial grain, welding current, upset rate, upset amount, cooling rate, magnetic field strength and frequency on joint mechanical properties, establish machine learning models for process optimization and joint performance. In the model, the joint hardness and tensile strength, impact toughness are taken as the objective function, and the main effect phase (material composition, initial grain, welding current, upset rate, upset amount, cooling rate, magnetic field strength and frequency) and the interactive electromagnetic coupling phase are introduced.

[0090] 43) Model training and verification: Part of the data is used for model training to adjust the model parameters to improve the fitting accuracy. Use the remaining data for model verification to evaluate the prediction ability and generalization ability of the model. For example, for the linear regression model of weld hardness and welding parameters, the model quality can be evaluated by R 2 value and significance test; for neural network model, the pros and cons of the model can be judged by comparing the errors of training set and test set.

[0091] 44) Model optimization and improvement: According to the model verification results, optimize the model. For linear models, you can add interaction terms or quadratic terms to improve the fitting effect; for nonlinear models, you can adjust network structure, learning rate and other parameters. Through repeated iteration, the final quantitative relationship model is obtained.

[0092] 5. Traveling wave magnetic field and welding parameter matching optimization

[0093] 51) Material properties: In-depth study of the electromagnetic properties of wheel rim materials under the action of traveling wave magnetic field, including resistivity, permeability, etc. Through material composition analysis and microstructure observation, understand the phase transition behavior and dynamic recrystallization characteristics of materials under different temperature and magnetic field conditions.

[0094] 52) Magnetic field effect and welding parameter correlation: Analyze the influence mechanism of traveling wave magnetic field on welding process, such as the effect of Lorentz force on molten pool flow and heat distribution, the role of Joule heat on welding zone heating, etc. Establish the correlation model between magnetic field effect and welding parameters, for example, through numerical simulation and experimental verification, determine the quantitative relationship between Lorentz force and flash current, magnetic field strength and frequency.

[0095] 53) Application of multi-objective optimization method: Take the uniformity of the microstructure and mechanical properties of the welded joint as the optimization objective, and consider the influence of traveling wave magnetic field parameters and welding parameters. Use multi-objective optimization algorithms such as genetic algorithm, particle swarm optimization algorithm, etc. to find the best parameter combination that meets the multi-objective constraints. In the optimization process, balance the conflict between different objectives, such as the balance between hardness and toughness, the balance between welding efficiency and quality.

[0096] 54) Experimental verification and feedback adjustment: According to the parameter combination obtained by optimization, carry out experimental verification, and compare the actual welding joint performance with the model prediction value. If there is a deviation, analyze the reason and adjust the model and optimization algorithm parameters, and re-optimize and verify. Through multiple iterations, finally determine the best matching welding parameters with the traveling wave magnetic field, and realize the overall improvement of the performance of the welded joint.

[0097] Example 1

[0098] (I) Preparation of rare earth alloyed wheel steel

[0099] 1. Raw material selection

[0100] Select high-quality 490CL steel as raw material.

[0101] 2. Rare earth element addition

[0102] During the smelting process of the steel, add an appropriate amount of rare earth element cerium (Ce) according to the designed composition system, such as 20ppm-300ppm. By precisely controlling the addition amount, ensure the uniform distribution of rare earth elements in the steel.

[0103] (II) Traveling wave magnetic field assisted welding process

[0104] 1. Installation of traveling wave magnetic field device

[0105] Install the traveling wave magnetic field generator on the flash welding equipment, ensure its synchronous operation with the welding equipment. The traveling wave magnetic field generator can generate a traveling wave magnetic field with a specific intensity and frequency, covering the welding area.

[0106] 2. Magnetic field parameter adjustment

[0107] According to the material properties and size of the welded workpiece, adjust the intensity and frequency of the traveling wave magnetic field. Through experiments and data analysis, determine the optimal combination of magnetic field parameters, low frequency (10-50 Hz) for deep penetration for welds with thickness greater than 10 mm, and high frequency (100-200 Hz) for surface layer action for welds with thickness less than 10 mm. The magnetic field is usually in the range of 0.1-0.8T.

[0108] 3. Welding process control

[0109] During the flash welding process, turn on the traveling wave magnetic field device, and monitor the temperature, current and other parameters in real time. Through precise control of welding parameters, ensure the quality and performance of the welded joint.

[0110] (Three) Welding parameter optimization

[0111] 1. Orthogonal experimental design

[0112] Design an orthogonal experiment scheme, select flash current, upset current, workpiece gap and other welding parameters as experimental factors, and determine reasonable experimental levels and combinations.

[0113] The orthogonal experiment table is shown in Table 1, and the parameter exploration analysis is shown in Figure 6 and Evaluation index .

[0114] Table 1 Orthogonal experiment table

[0115]

[0116] 2. Experimental implementation and data collection

[0117] According to the orthogonal experiment scheme, carry out welding experiment, collect the mechanical properties, microstructure and other data of the welded joint. Through a large number of experimental data, establish the relationship model between welding parameters and welded joint performance, as shown in Definition and significance .

[0118] 3. Data analysis and optimization

[0119] Statistical analysis of experimental data to determine the optimal combination of welding parameters. Through the optimized welding parameters, the uniformity of the microstructure and mechanical properties of the welded joint can be ensured, the welding quality can be improved, and the specific indicators are as follows Table 2.

[0120] Table 2 Data analysis index

[0121] Optimization goal Tensile strength Maximum stress of joint resistance to tensile fracture 530 MPa Fatigue life Number of fractures under cyclic load Average grain size Average size of microstructure grains, affecting material toughness 10 7 secondary 10-15 μm Defect rate Proportion of defects such as pores and cracks in the weld Figure 7 Figure 8 2％

[0122] (iv) Performance Testing and Validation

[0123] 1. Fatigue Life Test

[0124] Fatigue life test is conducted on the welded joints using a rotary bending fatigue testing machine to simulate the load conditions under actual use. By comparing the fatigue life under different welding processes, the effectiveness of the traveling wave magnetic field assisted welding technology is verified.

[0125] 2. Microstructure Analysis

[0126] Using metallographic analysis technology, the microstructure of the welded joints is observed and analyzed. Through scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) and other means, the influence mechanism of the traveling wave magnetic field on the microstructure of the welded joints is studied. The microstructure analysis is shown in ​ and ​ .

[0127] 3. Mechanical Property Test

[0128] Tensile, hardness and other mechanical property tests are conducted on the welded joints to evaluate their strength, toughness and hardness and other indicators. By comparing the mechanical properties under different welding processes, the superiority of the traveling wave magnetic field assisted welding technology is verified.

[0129] The above are only a few preferred embodiments of the present application, which are described in more detail and in detail, but should not be construed as limiting the scope of the present patent. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application.

[0130] Appendix 1

[0131] Machine Learning Analysis of the Influence of Traveling Wave Magnetic Field on Grain Size of Flash Butt Welded Joints

[0132]

[0133]

[0134]

[0135]

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Claims

1. A traveling wave magnetic field assisted flash welding method for rare earth alloyed wheel steel, characterized in that: The following steps are involved: (1) performing a rare earth element microalloying treatment on the wheel steel, wherein the rare earth element includes cerium Ce; (2) During the preheating stage, flash stage, and upsetting stage of the flash welding process, a traveling wave magnetic field is continuously applied to the welding area, and the magnetic field direction of the traveling wave magnetic field is set to be perpendicular to the extension direction of the weld; (3) During the preheating stage, the traveling wave magnetic field and the flash welder are started synchronously, and the heat in the welding area is evenly distributed by adjusting the magnetic field intensity and frequency, and the preheating temperature is accurately controlled within the range of 500°C to 650°C; (4) During the flashing stage, the flash retention is controlled to be 5 mm to 10 mm, the flash rate is controlled to be 4 mm / s to 6 mm / s, and the flash output power is controlled to be 150 kW to 200 kW. At the same time, a traveling wave magnetic field with a magnetic field strength of 10 mT to 30 mT and a frequency of 50 Hz to 100 Hz is continuously applied to the welding area, and its electromagnetic stirring effect is utilized to promote metal flow and mixing; (5) During the upsetting stage, the upsetting distance is controlled to be 2 mm to 5 mm, the upsetting pressure is controlled to be 6 MPa to 8 MPa, and the traveling wave magnetic field is maintained while applying the upsetting force to refine the grains by electromagnetic force; (6) A genetic algorithm with the optimization goal of minimizing the weld grain size is used to match and optimize the intensity, frequency and flash welding parameters of the traveling wave magnetic field.

2. The method according to claim 1, characterized in that The process of using genetic algorithm to perform matching optimization in step (6) specifically includes: (61) Analyze the electromagnetic characteristics of wheel rim materials under the action of traveling wave magnetic field; (62) Establish a correlation model between the magnetic field intensity and frequency parameters of the traveling wave magnetic field and the current, voltage, and speed parameters of flash welding; (63) Genetic algorithm or particle swarm optimization algorithm is used for multi-objective optimization to find the best parameter combination that can simultaneously meet the weld microstructure uniformity and mechanical properties goals; (64) The optimization results are verified through actual welding experiments, and the parameters of the association model and optimization algorithm are adjusted based on the feedback of the verification results.

3. The method according to claim 2, characterized in that In steps (62) and (63), the optimization process uses a machine learning-based grain size prediction model as the objective function, which is expressed as: y=20-0.1x1-15x2+0.02x3+0.005x4-0.3x5+2x6-0.01x7+0.2x8+1.5x9+0.001x1x2+3ε, where y is the grain size predicted by the model, in micrometers (μm); x1, x2, and x3 are the traveling wave magnetic field parameters, representing the magnetic field frequency (unit: Hz), magnetic field intensity (unit: T), and magnetic field phase difference (unit: °), respectively; x4, x5, x6, x7, x8, and x9 are welding process parameters, representing welding current (unit: A), welding voltage (unit: V), welding speed (unit: mm / s), preheating temperature (unit: °C), cooling rate (unit: °C / s), and welding time (unit: s), respectively; The term 0.001x1x2 is the interaction term between the magnetic field frequency and the magnetic field strength; 3ε is a random noise term, where ε obeys the standard normal distribution ε~N(0,1), and the grain size prediction value y≥5μm is ensured by constraints.

4. The method according to claim 1, wherein After the welding process is completed, the weld is subjected to an integrated post-weld treatment, which includes: (7) Use a slag planing mechanism to scrape and remove the oxide scale formed on the weld surface; (8) Use a rolling mechanism to apply a rolling pressure of 0.5 MPa to 2 MPa to the slag-planed weld surface to improve its surface flatness; (9) A water cooling system is used to controllably cool the weld after rolling, and the cooling rate is controlled between 5°C / s and 20°C / s to optimize the microstructural properties of the weld area.

5. The method according to claim 1, wherein The method further includes the step of performing performance testing and verification on the joint obtained by welding according to the optimized parameters, wherein the performance testing and verification includes: (10) Use a high-frequency fatigue testing machine to conduct fatigue life testing and record the number of cycles to fracture; (11) Microstructural analysis of welds and heat-affected zones using metallographic microscopes and scanning electron microscopes, including grain size measurement and defect observation; (12) A room temperature tensile test was performed using a universal testing machine to determine the tensile strength, and a Vickers or Rockwell hardness tester was used to perform the hardness test.

6. A traveling wave magnetic field assisted flash welding equipment for implementing the method according to any one of claims 1 to 5, characterized in that: The equipment includes: I. A flash welding machine equipped with a clamp for clamping the rare earth alloyed wheel steel rim blank and an electrode system for providing welding current; II. A traveling wave magnetic field generator, which consists of a magnetic field coil, a variable frequency power supply, and a control system. The magnetic field coil is arranged around the outside of the welding area and can generate a continuously adjustable traveling wave magnetic field with an intensity between 5mT and 50mT and a frequency between 20Hz and 200Hz; III. A synchronization control module, which is electrically connected to the control systems of the flash welding host and the traveling wave magnetic field generating device, and is used to receive welding progress signals and realize the linkage adjustment and precise matching of welding parameters and magnetic field parameters in the three stages of preheating, flash welding, and upsetting; IV. A temperature monitoring unit and a magnetic field detection unit. The temperature monitoring unit is used to detect and provide feedback on the temperature value of the welding area in real time. The magnetic field detection unit is used to detect and provide feedback on the magnetic field intensity value of the welding area in real time. Both are connected to the control system signal to form a closed-loop control to dynamically adjust the welding current, upsetting force and magnetic field parameters. V. Post-weld processing device, which integrates a slag planing mechanism, a rolling mechanism and a cooling system. The slag planing mechanism is used to automatically remove the oxide scale on the weld surface. The rolling mechanism can apply a pressure of 0.5MPa to 2MPa to the weld surface to improve the flatness. The cooling system is water-cooled and can control the cooling rate of the weld to 5℃ / s to 20℃ / s.

7. The apparatus according to claim 6, characterized in that The magnetic field coils of the traveling wave magnetic field generating device II are arranged to apply a traveling wave magnetic field simultaneously on the upper and lower surfaces of the welding joint.

8. The apparatus according to claim 6, characterized in that The III. synchronization control module is configured to: synchronously start the traveling wave magnetic field in the preheating stage to promote uniform heating; maintain the magnetic field effect in the flash stage to enhance metal stirring; and maintain the magnetic field in the upsetting stage to assist grain refinement.

9. The apparatus according to claim 6, characterized in that The cooling system in the V. post-weld processing device adopts a closed-loop water cooling method, and the cooling rate of 5°C / s to 20°C / s is accurately controlled by adjusting the water flow and temperature.

10. The apparatus according to claim 6, characterized in that The rolling mechanism in the V-type post-weld processing device adopts a high-precision roller driven by a servo motor, which can perform a rolling process on the weld with constant pressure.

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