Analysis method for rolling forming process of large special-shaped ring piece

Abstract

The invention relates to the technical field of ring piece rolling, and discloses a large special-shaped ring piece rolling forming process analysis method which comprises the following steps: (1) forge piece and blank design: designing different types of blanks; (2) material characteristic modeling: establishing a material model meeting rolling conditions; (3) finite element model establishment: establishing a geometric model of a rolling process by utilizing finite element analysis software; (4) rolling process scheme design: designing a plurality of rolling process schemes; (5) process simulation and defect prediction: analyzing strain, stress, temperature field and metal flow conditions in the simulated rolling process, and analyzing defect generation conditions; and (6) real-time monitoring and feedback control: monitoring changes of stress, strain and temperature in real time, and automatically adjusting process parameters. And through dynamic material characteristic modeling, a self-adaptive grid division technology and multi-physical field coupling analysis, the analysis accuracy of the rolling process is improved, so that defect prediction is more accurate, and the quality of finished products is improved.

Description

Technical Field

[0001] The invention relates to the technical field of ring rolling, in particular to a method for analyzing the rolling forming process of large-scale special-shaped rings. Background Art

[0002] The rolling forming process of large special-shaped rings is a process that utilizes the plastic deformation characteristics of metal materials to process metals into ring-shaped or special-shaped workpieces of specific shapes and sizes through rolling equipment. This process is widely used in industries such as aerospace, energy, chemical industry and machinery manufacturing. It is mainly used to produce products such as turbine rings, flanges and pipe connectors. The process flow usually includes preparation, heating, rolling, cooling and post-processing of raw materials. The heating process can improve the plasticity of the material and facilitate rolling. The process has the characteristics of high efficiency, strong adaptability and high material utilization rate, and can ensure that the finished product has excellent mechanical properties. With the development of materials science and automation technology, the rolling forming process of large special-shaped rings will continue to improve to meet more complex industrial needs. In order to improve product quality and production efficiency, as well as reduce defect rates and production costs, its process is often analyzed.

[0003] Traditional rolling analysis methods usually use static material property models, which fail to effectively reflect the true behavior of materials under different temperatures and stress states. The rheological properties and strength of materials will change under high or low temperature conditions, which directly affects the accuracy of the rolling process and the quality of the final product. Therefore, the lack of support for dynamic material property modeling leads to inaccurate analysis of the rolling process and difficulty in predicting potential defects. Moreover, in complex deformation areas, traditional meshing methods often cannot achieve sufficient detail, resulting in a decrease in calculation accuracy. The presence of complex geometric shapes and deformation bands makes the adaptability of the mesh a key issue. The lack of support for adaptive meshing technology makes the stress and temperature field calculations in key areas inaccurate. , which in turn affects the overall analysis effect of the rolling process; and the rolling forming of large special-shaped rings involves the interaction of multiple physical fields such as temperature, stress and material flow. Traditional analysis methods usually treat these factors separately and cannot fully consider the coupling relationship between them. This lack makes the understanding of thermodynamic behavior and material flow in the forming process not in-depth enough, resulting in increased difficulty in defect prediction and finished product quality control; in addition, potential defects in the rolling process often gradually appear in the production process, and there is a lack of real-time monitoring and feedback control methods, which leads to the impact on the finished product when defects are discovered. Traditional process optimization often relies on experience and historical data, and fails to make full use of real-time data for dynamic adjustment, thereby failing to effectively reduce the defect rate. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for analyzing the rolling forming process of large-scale special-shaped rings, which solves the problem of inaccurate analysis of the rolling process.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: A large-scale special-shaped ring rolling forming process analysis method comprises the following steps:

[0006] (1) Forging and blank design: Design different types of blanks based on the geometric shape and size requirements of large special-shaped rings;

[0007] (2) Material property modeling: Through experiments, the stress-strain curve, grain evolution and other data of the target material at high temperature are obtained, and a material model that meets the rolling conditions is established;

[0008] (3) Finite element model establishment: Based on the material property modeling results, the geometric model of the rolling process is established using finite element analysis software;

[0009] (4) Rolling process design: design a variety of rolling process schemes based on the characteristics of forgings;

[0010] (5) Process simulation and defect prediction: Use finite element analysis to simulate the strain, stress, temperature field and metal flow during the rolling process and analyze the defect generation;

[0011] (6) Real-time monitoring and feedback control: During the actual rolling process, the changes in stress, strain and temperature are monitored in real time through an intelligent feedback control system, and the process parameters are automatically adjusted.

[0012] Preferably, in (1), the blank forms include rectangular blanks and step-shaped blanks, and a preliminary design is performed based on the blank volume, wall thickness and diameter requirements.

[0013] Preferably, in (2), the model takes into account the heat treatment behavior and dynamic recrystallization characteristics of the material. The dynamic recrystallization behavior of the material is dynamically adjusted by combining the high-temperature stress-strain curve measured experimentally with finite element simulation to match the actual stress and strain state in the rolling process.

[0014] Preferably, in (3), the geometric model includes:

[0015] a. Three-dimensional geometric model of rolling equipment and blank, defining the dimensions of the equipment and blank;

[0016] b. Material flow, heat conduction, friction and boundary conditions, using distributed computing and adaptive meshing technology to refine the mesh of local complex deformation areas to ensure simulation accuracy.

[0017] Preferably, in said (4), the scheme includes radial rolling and radial-axial rolling, and said (4) further includes the following steps:

[0018] a. Optimize and adjust according to different core roll feed speed, drive roll speed, rolling pressure and temperature parameters;

[0019] b. Use intelligent optimization technology based on genetic algorithm or particle swarm optimization algorithm to iteratively simulate process parameters and select the optimal process combination;

[0020] The optimization is calculated using the following formula:

[0021] Rolling force calculation formula:

[0022] F=k·σ s ·A

[0023] Among them, F is the rolling force, k is the stress concentration factor of the material, σ s is the yield strength of the material, A is the deformation cross-sectional area;

[0024] The relationship between the core roller feed speed and deformation rate is as follows:

[0025]

[0026] Among them, V f is the core roller feed speed, V r is the driving roller speed, D r is the core roller diameter, D f is the outer diameter of the workpiece.

[0027] Preferably, in (5), analyzing defect generation conditions includes:

[0028] a. Simulate and analyze possible insufficient filling, cracks and surface folding;

[0029] b. Combine the multi-physics simulation results to adjust the process parameters to reduce and eliminate defects;

[0030] The crack is predicted using the following formula:

[0031]

[0032] Among them, ∈ max is the maximum strain, σ t is the local tensile stress, E is the Young's modulus of the material, α is the linear expansion coefficient of the material, and ΔT is the temperature gradient.

[0033] Preferably, in (5), the strain field analysis determines the area of ​​uneven metal flow through finite element simulation and identifies the stress concentration area.

[0034] Preferably, in (6), the intelligent feedback control system uses a multi-point sensor network to collect and feedback real-time data of the temperature field, strain field and rolling force field during the rolling process, which specifically includes the following steps:

[0035] a. Temperature field monitoring: Use infrared sensors to monitor the temperature of key deformation areas in real time, and adjust the power of the heating equipment through feedback;

[0036] b. Strain field monitoring: Use strain gauges or fiber optic sensors to monitor changes in local stress and strain:

[0037] c. Feedback control algorithm: Based on the machine learning algorithm, the system automatically adjusts the core roll feed speed and rolling temperature when stress or temperature anomalies are detected.

[0038] Preferably, in (6), the intelligent feedback control system further includes a data analysis module, which performs comprehensive analysis on historical process data and real-time monitoring data, specifically including the following steps:

[0039] a. Data collection and processing: Real-time collection of temperature, stress and strain data during the rolling process, combined with historical rolling data to form a data set;

[0040] b. Abnormal pattern recognition: Use machine learning algorithms to analyze data sets, identify pattern differences between normal and abnormal processes, and predict potential process anomalies and defects;

[0041] c. Generation of optimization suggestions: Generate process optimization suggestions based on the identified abnormal patterns, automatically adjust rolling parameters and feed back to the control system.

[0042] Preferably, in (6), the automatic adjustment of the process parameters is specifically to adjust the rolling parameters, which is controlled by the following formula:

[0043]

[0044] Among them, T opt is the optimized rolling temperature, T ref is the reference temperature, Δσ is the stress change, and Δ∈ is the strain change.

[0045] The present invention provides a method for analyzing the rolling forming process of large-sized special-shaped rings. It has the following beneficial effects:

[0046] 1. The present invention improves the accuracy of analysis of the rolling process through dynamic material property modeling, adaptive meshing technology and multi-physics field coupling analysis. Dynamic material property modeling takes into account the real behavior of the material under different temperature and stress states. Adaptive meshing technology ensures high-precision calculation in complex deformation areas, while multi-physics field coupling analysis integrates multiple factors such as temperature, stress and material flow. This series of innovations makes defect prediction more accurate and improves the quality of finished products.

[0047] 2. The present invention realizes effective prediction of potential defects in the rolling process through real-time monitoring and feedback control, combined with data-driven process optimization and abnormal pattern recognition. Through real-time data collection and analysis, the system can quickly identify abnormal situations and adjust process parameters in time to prevent the occurrence of defects. This enhanced defect prediction capability greatly reduces the defect rate that may occur in the production process and ensures the consistency and stability of the forming quality.

[0048] 3. The present invention can adapt to different rolling conditions and material properties in real time by introducing an intelligent feedback control system. This flexibility enables the analysis method to quickly respond to and optimize process parameters in a changing production environment, thereby improving overall production efficiency. Whether under high temperature, low temperature or dynamically changing conditions, the system can perform accurate analysis and adjustment, ensuring the stability and adaptability of the process. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Embodiment 1:

[0052] Please see attached Figure 1 The embodiment of the present invention provides a large-scale special-shaped ring rolling forming process analysis method, comprising the following steps:

[0053] (1) Forging and blank design: Design different types of blanks based on the geometric shape and size requirements of large special-shaped rings;

[0054] (2) Material property modeling: Through experiments, the stress-strain curve, grain evolution and other data of the target material at high temperature are obtained, and a material model that meets the rolling conditions is established;

[0055] (3) Finite element model establishment: Based on the material property modeling results, the geometric model of the rolling process is established using finite element analysis software;

[0056] (4) Rolling process design: design a variety of rolling process schemes based on the characteristics of forgings;

[0057] (5) Process simulation and defect prediction: Use finite element analysis to simulate the strain, stress, temperature field and metal flow during the rolling process and analyze the defect generation;

[0058] (6) Real-time monitoring and feedback control: During the actual rolling process, the changes in stress, strain and temperature are monitored in real time through an intelligent feedback control system, and process parameters are automatically adjusted;

[0059] In (1), the blank forms include rectangular blanks and step-shaped blanks, and preliminary design is performed based on the blank volume, wall thickness and diameter requirements;

[0060] In (2), the model takes into account the heat treatment behavior and dynamic recrystallization characteristics of the material. The dynamic recrystallization behavior of the material is dynamically adjusted to match the actual stress and strain state in the rolling process by combining the high-temperature stress-strain curve measured experimentally with finite element simulation;

[0061] In (3), the geometric model includes:

[0062] a. Three-dimensional geometric model of rolling equipment and blank, defining the dimensions of the equipment and blank;

[0063] b. Material flow, heat conduction, friction and boundary conditions, using distributed computing and adaptive meshing technology to refine the meshing of local complex deformation areas to ensure simulation accuracy;

[0064] In (4), the scheme includes radial rolling and radial-axial rolling, and (4) further includes the following steps:

[0065] a. Optimize and adjust according to different core roll feed speed, drive roll speed, rolling pressure and temperature parameters;

[0066] b. Use intelligent optimization technology based on genetic algorithm or particle swarm optimization algorithm to iteratively simulate process parameters and select the optimal process combination;

[0067] The optimization is calculated using the following formula:

[0068] Rolling force calculation formula:

[0069] F=k·σ s ·A

[0070] Among them, F is the rolling force, k is the stress concentration factor of the material, σ sis the yield strength of the material, A is the deformation cross-sectional area;

[0071] The relationship between the core roller feed speed and deformation rate is as follows:

[0072]

[0073] Among them, V f is the core roller feed speed, V r is the driving roller speed, D r is the core roller diameter, D f is the outer diameter of the workpiece;

[0074] In (5), the defect generation analysis includes:

[0075] a. Simulate and analyze possible insufficient filling, cracks and surface folding;

[0076] b. Combine the multi-physics field simulation results to adjust the process parameters to reduce and eliminate defects;

[0077] The cracks are predicted using the following formula:

[0078]

[0079] Among them, ∈ max is the maximum strain, σ t is the local tensile stress, E is the Young's modulus of the material, α is the linear expansion coefficient of the material, and ΔT is the temperature gradient;

[0080] (5), strain field analysis determines the areas of uneven metal flow and identifies stress concentration areas through finite element simulation;

[0081] (6), the intelligent feedback control system uses a multi-point sensor network to collect and feedback real-time data of the temperature field, strain field and rolling force field during the rolling process, which specifically includes the following steps:

[0082] a. Temperature field monitoring: Use infrared sensors to monitor the temperature of key deformation areas in real time, and adjust the power of the heating equipment through feedback;

[0083] b. Strain field monitoring: Use strain gauges or fiber optic sensors to monitor changes in local stress and strain;

[0084] c. Feedback control algorithm: Based on the machine learning algorithm, the system automatically adjusts the core roll feed speed and rolling temperature when stress or temperature anomalies are detected;

[0085] (6), the intelligent feedback control system also includes a data analysis module, which performs comprehensive analysis through historical process data and real-time monitoring data, specifically including the following steps:

[0086] a. Data collection and processing: Real-time collection of temperature, stress and strain data during the rolling process, combined with historical rolling data to form a data set;

[0087] b. Abnormal pattern recognition: Use machine learning algorithms to analyze data sets, identify pattern differences between normal and abnormal processes, and predict potential process anomalies and defects;

[0088] c. Generation of optimization suggestions: Generate process optimization suggestions based on the identified abnormal patterns, automatically adjust rolling parameters and feed back to the control system;

[0089] (6), the automatic adjustment of process parameters is specifically to adjust the rolling parameters, which is controlled by the following formula:

[0090]

[0091] Among them, T opt is the optimized rolling temperature, T ref is the reference temperature, Δσ is the stress change, and Δ∈ is the strain change.

[0092] Example 2: Simplified analysis method

[0093] step:

[0094] Blank design: Only rectangular blanks are designed, and the design of complex step shapes is not considered.

[0095] Material Property Modeling: Standard material properties were used and no high temperature dynamic testing was performed.

[0096] Finite element model building: The geometric model is built, but it lacks detailed modeling of friction and heat transfer.

[0097] Process design: Fixed process parameters were used without any optimization.

[0098] Process simulation and defect prediction: Finite element analysis was performed, but the impact of metal flow was not fully analyzed.

[0099] Real-time monitoring: Only one-time monitoring is performed and process parameters cannot be adjusted dynamically.

[0100] Summarize:

[0101] The second embodiment simplifies the process analysis and reduces the implementation complexity, but because it does not consider dynamic factors and defect prediction, it may cause fluctuations in forming quality and lack of adaptability. This shortcoming highlights the advantages of the first embodiment in terms of dynamic monitoring and real-time feedback.

[0102] Example 3: Analysis method under high temperature conditions

[0103] step:

[0104] Blank design: Design complex special-shaped step-shaped blanks, suitable for high temperature rolling.

[0105] Material property modeling: Obtain the stress-strain characteristics of materials at high temperatures and establish a dynamic model.

[0106] Finite element model establishment: Establish a geometric model of high temperature rolling and pay attention to the temperature effect of material flow.

[0107] Process design: Set process parameters under high temperature, especially the temperature control of the core roller.

[0108] Process simulation and defect prediction: Simulate the rolling process under high temperature conditions and evaluate defect generation.

[0109] Real-time monitoring: monitors temperature and strain changes, but fails to fully integrate actual rolling conditions for dynamic adjustments.

[0110] Summarize:

[0111] The third embodiment focuses on rolling analysis under high temperature conditions and can handle complex blank designs, but it is deficient in real-time monitoring and feedback, especially in dynamically adjusting process parameters, which limits the stability of the overall forming. This deficiency further highlights the advantages of the first embodiment in feedback control and real-time monitoring.

[0112] Example 4: Analysis method under low temperature conditions

[0113] step:

[0114] Blank Design: Simple rectangular blank design, suitable for low temperature rolling.

[0115] Material property modeling: Uses room temperature data and does not consider material behavior at low temperatures.

[0116] Finite element model establishment: The geometric model was established, but it failed to accurately reflect the material properties under low temperature conditions.

[0117] Process design: fixed rolling parameters, not optimized for low temperatures.

[0118] Process simulation and defect prediction: Simulating the low-temperature rolling process fails to fully consider the strain and flow conditions.

[0119] Real-time monitoring: Performs initial monitoring but fails to make feedback adjustments based on process data.

[0120] Summarize:

[0121] The fourth embodiment focuses on low temperature rolling. Although it is simple and easy to implement, it fails to fully consider the impact of low temperature on material properties, resulting in the failure to effectively control the defects that may occur during the forming process. This deficiency further highlights the advantages of the first embodiment in material property modeling and real-time feedback control.

[0122] Example 5: Dynamic process parameter adjustment analysis method

[0123] step:

[0124] Blank Design: Design complex, shaped blanks, taking into account multiple rolling paths.

[0125] Material property modeling: Obtain multiple material properties through experiments and build detailed models.

[0126] Finite element model building: Create detailed geometric models, focusing on flow and stress distribution.

[0127] Process design: Dynamically set rolling conditions and make adjustments based on real-time data.

[0128] Process simulation and defect prediction: Perform dynamic simulation to predict defects under different parameters.

[0129] Real-time monitoring and feedback: Real-time monitoring through sensors and algorithms, and dynamic adjustment of process parameters.

[0130] Summarize:

[0131] Embodiment 5 enhances the flexible response capability to the production process by dynamically adjusting the process parameters. However, due to its complex implementation and high requirements on equipment, it increases the difficulty and cost of implementation. In comparison, Embodiment 1 provides a more stable and reliable process solution.

[0132] Summary:

[0133] Through the above five embodiments, we can see the applicability and advantages of the large-scale special-shaped ring rolling forming process analysis method in different process analysis methods. Embodiment 1, as the technical solution of the present invention, covers a comprehensive process analysis method with high stability and reliability.

[0134] The second embodiment provides a simplified analysis method, highlighting the advantage of the first embodiment in terms of dynamic adaptability.

[0135] The third and fourth embodiments focus on the rolling analysis under high temperature and low temperature conditions respectively, demonstrating the limitations that may occur under extreme working conditions and emphasizing the superiority of the first embodiment in terms of wide applicability.

[0136] The fifth embodiment demonstrates the innovation of dynamic process parameter adjustment, but its implementation complexity is relatively high, which further highlights the advantages of the first embodiment in terms of operability and stability.

[0137] Test experiment:

[0138] Purpose:

[0139] The applicability and effects of the process analysis methods of the five embodiments in the rolling process of large special-shaped rings are compared, including forming quality, parameter optimization capability and defect prediction accuracy.

[0140] Experimental design and process:

[0141] Experimental process:

[0142] Sample preparation: Different blanks are designed according to each embodiment, and the same type of material (such as alloy steel) is selected.

[0143] Experimental parameter setting: setting the analysis parameters required for each embodiment, including rolling temperature, speed, pressure, etc.

[0144] Example 1 (standard analytical method):

[0145] Steps: Conduct full process analysis according to the technical solution steps, perform material property modeling, finite element simulation and defect prediction, and record the data of each step.

[0146] Embodiment 2 (simplified analysis method):

[0147] Steps: Only simple geometric model establishment and process parameter setting are performed, and defect analysis results are recorded.

[0148] Embodiment 3 (high temperature analysis method):

[0149] Steps: Establish material model under high temperature conditions, perform finite element analysis under dynamic temperature, and record defect generation.

[0150] Embodiment 4 (low temperature analysis method):

[0151] Procedure: Perform material modeling and simulation at cryogenic conditions, document the results, and focus on cracks and defects.

[0152] Embodiment 5 (dynamic parameter adjustment analysis method):

[0153] Steps: Monitor parameter changes in real time, make dynamic process adjustments, and record the effects and defects after each adjustment.

[0154] Experimental data and test results:

[0155] Experimental data table:

[0156]

[0157] Test result analysis:

[0158] Example 1 (standard analytical method):

[0159] Effect of the method: The material model is highly accurate, and cracks and underfilling are both zero, which reflects good process control capabilities and defect prediction accuracy. The number of optimization parameter adjustments is moderate, showing the effectiveness of the overall method.

[0160] Embodiment 2 (simplified analysis method):

[0161] Effect of the method: The material model accuracy and strain prediction accuracy are low, and a certain number of cracks and insufficient filling appear, which reflects the limitations of the method under dynamic conditions and fails to fully capture the effects of complex deformation.

[0162] Embodiment 3 (high temperature analysis method):

[0163] Method effect: The material model under high temperature rolling has high accuracy, the strain prediction accuracy is good, no cracks and insufficient filling occur, showing the superior performance of material flow under high temperature conditions, and the overall method performs well.

[0164] Embodiment 4 (low temperature analysis method):

[0165] Method effect: Since the influence of low temperature on material properties is not considered, cracks and insufficient filling are obvious, and the prediction accuracy is low, which reflects the shortcomings of the method under special conditions.

[0166] Embodiment 5 (dynamic parameter adjustment analysis method):

[0167] Effect of the method: Through dynamic monitoring and adjustment, the material model of the method is highly accurate and the strain prediction accuracy is good. Although there are slight cracks and insufficient filling, the number of overall optimization parameter adjustments increases, which reflects the ability to flexibly cope with complex working conditions.

[0168] Summary:

[0169] Through the above experiments, it can be seen that the applicability and effect differences of the process analysis methods of each embodiment in the rolling of large special-shaped rings. Embodiment 1, as a technical solution of the present invention, shows excellent process stability and product quality. The simplified solution of embodiment 2 does not perform well under dynamic conditions and cannot accurately predict defects; the high-temperature solution of embodiment 3 has outstanding performance, but the low-temperature embodiment shows a greater risk of defects; embodiment 5 achieves flexibility through dynamic adjustment, but the complexity and number of adjustments are high.

[0170] Comparative experimental design:

[0171] Experiment 1: Comparison of traditional process analysis methods in existing technologies

[0172] Existing technical solutions:

[0173] Process analysis method: using traditional empirical formulas and static analysis, relying only on material properties and simple geometric models, lacks comprehensive consideration of the impact of multiple physical fields.

[0174] Analysis tools: Traditional finite element analysis software cannot provide real-time feedback and adjust parameters, and does not take dynamic changes into account.

[0175] Technical solution of the present invention:

[0176] Process analysis method: Combining material property modeling, finite element model building, multi-physics field coupling and dynamic feedback control, it is possible to adjust and optimize process parameters in real time.

[0177] Experimental process:

[0178] Prepare special-shaped ring blanks of the same size.

[0179] The existing technology and the technical solution of the present invention are respectively used for analysis, and the required parameters are recorded.

[0180] A comprehensive process analysis and defect prediction are carried out for each experimental rolling process.

[0181] Experimental data table:

[0182]

[0183]

[0184] Result analysis:

[0185] Forming quality: The embodiments of the present invention perform well in strain prediction and defect control, without cracks and insufficient filling, thereby improving the forming quality.

[0186] Analysis efficiency: Existing technologies rely on static analysis and lack real-time feedback, resulting in low strain prediction accuracy.

[0187] Experiment 2: Comparison of process analysis methods based on temperature effects

[0188] Existing technical solutions:

[0189] Process analysis method: Fixed temperature is used for process analysis, the impact of temperature changes on material properties is not considered, and there is a lack of simulation of dynamic temperature fields.

[0190] Material selection: Standard alloy steel is used, without high temperature performance verification.

[0191] Technical solution of the present invention:

[0192] Process analysis method: Dynamic temperature analysis, combining material heat treatment behavior and dynamic recrystallization characteristics, real-time simulation of the impact of temperature field on the rolling process.

[0193] Experimental process:

[0194] Prepare special-shaped ring blanks of the same size and set fixed temperature and dynamic temperature respectively.

[0195] The temperature analysis of the rolling process is carried out using the analysis methods of the prior art and the present invention respectively.

[0196] Record stress, strain, temperature and defect generation during rolling.

[0197] Experimental data table:

[0198]

[0199] Result analysis:

[0200] Forming quality: The embodiments of the present invention reduce the occurrence of cracks and insufficient filling under dynamic temperature analysis, reflecting the effective control of temperature effects.

[0201] Prediction accuracy: The existing technology does not take dynamic temperature changes into consideration, resulting in low strain prediction accuracy, which affects the forming quality.

[0202] Experiment 3: Comparative experiment of multi-physics field coupling analysis methods

[0203] Existing technical solutions:

[0204] Process analysis method: Use a single physical field (such as stress field or temperature field) for analysis and ignore the interaction between multiple physical fields.

[0205] Analysis tools: Traditional finite element software cannot comprehensively consider the coupled effects of material flow, temperature and stress.

[0206] Technical solution of the present invention:

[0207] Process analysis method: Through multi-physical field coupling analysis, the mutual influence of stress field, temperature field and material flow field is simulated at the same time to achieve comprehensive process analysis.

[0208] Experimental process:

[0209] Special-shaped ring blanks of the same size are prepared, and rolling simulation is performed using the prior art and the multi-physical field analysis method of the present invention respectively.

[0210] Record parameters including stress, temperature, flow behavior and defect generation.

[0211] Compare and analyze the effects and prediction accuracy of different methods.

[0212] Experimental data table:

[0213]

[0214] Result analysis:

[0215] Forming quality: Through multi-physical field coupling analysis, the embodiments of the present invention did not have cracks and insufficient filling, demonstrating excellent control over the rolling process.

[0216] Prediction accuracy: The existing technology only considers a single physical field, resulting in low prediction accuracy, which affects the process effect.

[0217] Summarize:

[0218] Through the above three comparative experiments, it can be clearly demonstrated that the large-scale special-shaped ring rolling forming process analysis method of the present invention has obvious advantages over the prior art:

[0219] Improved analysis accuracy: The dynamic analysis method of the present invention is superior to existing technologies in terms of strain prediction, crack control and underfilling, ensuring the production of high-quality finished products.

[0220] Enhanced adaptability: Through real-time monitoring and dynamic adjustment of parameters, it can better adapt to complex rolling conditions and improve production efficiency.

[0221] Multi-physics coupling: The multi-physics coupling analysis method in the embodiment effectively integrates the effects of stress, temperature and material flow, making the process analysis more comprehensive and accurate.

[0222] These experimental results show that the method of the present invention has higher reliability and adaptability in practical applications, and provides an important basis for the selection of rolling process for large special-shaped rings.

[0223] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The rolling forming process analysis method for large special-shaped rings is characterized by: The following steps are involved: (1) Forging and blank design: Design different types of blanks based on the geometric shape and size requirements of large special-shaped rings; (2) Material property modeling: Through experiments, the stress-strain curve, grain evolution and other data of the target material at high temperature are obtained, and a material model that meets the rolling conditions is established; (3) Finite element model establishment: Based on the material property modeling results, the geometric model of the rolling process is established using finite element analysis software; (4) Rolling process design: design a variety of rolling process schemes based on the characteristics of forgings; (5) Process simulation and defect prediction: Use finite element analysis to simulate the strain, stress, temperature field and metal flow during the rolling process and analyze the defect generation; (6) Real-time monitoring and feedback control: During the actual rolling process, the changes in stress, strain and temperature are monitored in real time through an intelligent feedback control system, and the process parameters are automatically adjusted.

2. The large-sized special-shaped ring rolling forming process analysis method according to claim 1 is characterized in that: In (1), the blank forms include rectangular blanks and step-shaped blanks, and preliminary design is performed based on the blank volume, wall thickness and diameter requirements.

3. The large-sized special-shaped ring rolling forming process analysis method according to claim 1 is characterized in that: In (2), the model takes into account the heat treatment behavior and dynamic recrystallization characteristics of the material. The dynamic recrystallization behavior of the material is dynamically adjusted by combining the high-temperature stress-strain curve measured experimentally with finite element simulation to match the actual stress and strain state in the rolling process.

4. The large-sized special-shaped ring rolling forming process analysis method according to claim 1 is characterized in that: In (3), the geometric model includes: a. Three-dimensional geometric model of rolling equipment and blank, defining the dimensions of the equipment and blank; b. Material flow, heat conduction, friction and boundary conditions, using distributed computing and adaptive meshing technology to refine the mesh of local complex deformation areas to ensure simulation accuracy.

5. The large-sized special-shaped ring rolling forming process analysis method according to claim 1 is characterized in that: In said (4), the scheme includes radial rolling and radial-axial rolling, and said (4) further includes the following steps: a. Optimize and adjust according to different core roll feed speed, drive roll speed, rolling pressure and temperature parameters; b. Use intelligent optimization technology based on genetic algorithm or particle swarm optimization algorithm to iteratively simulate process parameters and select the optimal process combination; The optimization is calculated using the following formula: Rolling force calculation formula: F=k·σ s·A Among them, F is the rolling force, k is the stress concentration factor of the material, σ s is the yield strength of the material, A is the deformation cross-sectional area; The relationship between the core roller feed speed and deformation rate is as follows: Among them, V f is the core roller feed speed, V r is the driving roller speed, D r is the core roller diameter, D f is the outer diameter of the workpiece.

6. The large-sized special-shaped ring rolling forming process analysis method according to claim 1 is characterized in that: In (5), analyzing the defect generation situation includes: a. Simulate and analyze possible insufficient filling, cracks and surface folding; b. Combine the multi-physics simulation results to adjust the process parameters to reduce and eliminate defects; The crack is predicted using the following formula: Among them, ∈ max is the maximum strain, σ t is the local tensile stress, E is the Young's modulus of the material, α is the linear expansion coefficient of the material, and ΔT is the temperature gradient.

7. The large-sized special-shaped ring rolling forming process analysis method according to claim 1 is characterized in that: In (5), the strain field analysis determines the area of ​​uneven metal flow through finite element simulation and identifies the stress concentration area.

8. The large-sized special-shaped ring rolling forming process analysis method according to claim 1 is characterized in that: In (6), the intelligent feedback control system uses a multi-point sensor network to collect and feedback real-time data of the temperature field, strain field and rolling force field during the rolling process, which specifically includes the following steps: a. Temperature field monitoring: Use infrared sensors to monitor the temperature of key deformation areas in real time, and adjust the power of the heating equipment through feedback; b. Strain field monitoring: Use strain gauges or fiber optic sensors to monitor changes in local stress and strain; c. Feedback control algorithm: Based on the machine learning algorithm, the system automatically adjusts the core roll feed speed and rolling temperature when stress or temperature anomalies are detected.

9. The large-sized special-shaped ring rolling forming process analysis method according to claim 1 is characterized in that: In (6), the intelligent feedback control system further includes a data analysis module, which performs comprehensive analysis on historical process data and real-time monitoring data, specifically including the following steps: a. Data collection and processing: Real-time collection of temperature, stress and strain data during the rolling process, combined with historical rolling data to form a data set; b. Abnormal pattern recognition: Use machine learning algorithms to analyze data sets, identify pattern differences between normal and abnormal processes, and predict potential process anomalies and defects; c. Generation of optimization suggestions: Generate process optimization suggestions based on the identified abnormal patterns, automatically adjust rolling parameters and feed back to the control system.

10. The large-sized special-shaped ring rolling forming process analysis method according to claim 1, characterized in that: In (6), the automatic adjustment of the process parameters is specifically to adjust the rolling parameters, which are controlled by the following formula: Among them, T opt is the optimized rolling temperature, T ref is the reference temperature, Δσ is the stress change, and Δ∈ is the strain change.

Images