Optimization method and system for thermal vibration and bulging coordinated control stress of hollow shaft component

Abstract

The invention relates to the technical field of component processing, and discloses an optimization method and system for thermal vibration and bulging coordinated control stress of a hollow shaft component, and the method comprises the following steps: collecting the material components, sample shape and size, surface state and stress measurement direction of a molded hollow shaft SiCf / TC17 composite component, building a simulation model of the initial state of the component, and establishing a simulation model of the initial state of the component; the data are input into a simulation model, and the thermal aging technology, the vibration aging technology and the thermal bulging machining composite regulation and control residual stress can be combined through the digital twinning technology. That is to say, the machining process of the components is established in a virtual space through a digital model, the actual machining process is simulated, meanwhile, various conditions occurring in the machining process can be predicted, a database in the machining process is established, and the components which are not machined can be predicted through the digital twinborn model by storing existing data. And the quality and the machining efficiency of machined components are improved.

Description

Technical Field

[0001] The invention relates to the technical field of component processing, and in particular to an optimization method and system for coordinated stress control of thermal vibration and bulging of hollow shaft components. Background Art

[0002] In recent years, with the increasing application of hollow shaft components in various industries and higher and higher performance requirements, it is extremely important to study the manufacturing and forming of hollow shaft components. However, the problems of hollow shaft components are also obvious. They are prone to plastic instability and loss of overall roundness in the later stage of the processing process. Studies have shown that components are prone to deformation during processing and use mainly due to residual stress. In order to effectively control and reduce deformation, it is necessary to reduce residual stress as much as possible and make the residual stress as uniform as possible. Commonly used methods for reducing residual stress include "thermal aging" and "vibration aging". Thermal aging will coarsen the grains of the ring and reduce the strength. The vibration aging method can reduce the residual stress on the end face of the component, but both have their own defects. In addition, the bulging method can also be used to regulate the residual stress of hollow shaft components. The bulging method can reduce the average residual stress of the component, so that the uniformity of the residual stress of the component is improved, but the improvement of the uneven distribution of the overall residual stress of the component is not obvious;

[0003] As the thrust-to-weight ratio of aircraft engines continues to increase, the single-stage load continues to increase, the stress level of components is getting higher and higher, and the working conditions are becoming more severe. It is necessary to seek new materials that can meet the more stringent requirements of the engine. f / TC17 has great application potential due to its excellent room temperature and high temperature strength, modulus, low density, excellent creep resistance and fatigue performance. f / TC17 is usually prepared by foil pressing, fiber-matrix coating, etc. However, due to the f / TC17 has a high strength, and its residual stress control has limitations such as difficulty in precise control and uneven control area. In addition, the method of coordinated control of residual stress by thermal vibration and bulging has many control parameters, and the control accuracy is difficult to guarantee. In addition, the range of action on large parts is small and the accuracy is not high. For high-strength composite materials, residual stress control is difficult and the accuracy is low. Therefore, in order to solve the above problems, it is necessary to have an optimization method and system for coordinated control of stress by thermal vibration and bulging of hollow shaft components. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides an optimization method and system for coordinated stress control during thermal vibration and bulging of hollow shaft components, which solves the problems existing in the above-mentioned background technology.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: A method for optimizing the coordinated control of stress of thermal vibration and bulging of hollow shaft components, comprising the following steps:

[0008] S1. Collect hollow shaft SiC f / TC17 composite components after forming, the material composition, sample shape and size, surface state and stress measurement direction of the component, establish a simulation model of the initial state of the component, and input the above data into the simulation model;

[0009] S2. According to the initial simulation model, a digital twin model with initial residual stress is established; the digital twin model data is collected from actual operation, production and experiments; the model data includes: initial residual stress, laboratory temperature, thermal aging system, vibration aging system and bulging system model data;

[0010] S3. Based on the digital twin model, the components are thermally aged. The process simulation mainly considers the thermal and mechanical properties of the material; the properties include the mechanical properties of the material itself, vibration frequency, aging temperature, insulation temperature, cooling method and clamping method;

[0011] S4, in hollow shaft SiC f During the thermal aging process of the TC17 composite component, hydraulic bulging and vibration aging are carried out simultaneously; the finished workpiece is obtained, and the various parameters of the processing process and the residual stress value are recorded; the processing parameters include heating speed, heating temperature, cooling method, excitation frequency, excitation time, amplitude, bulging rate and bulging force;

[0012] S5. Adjust the errors that occur during the operation of the digital twin model based on data analysis methods, and f / TC17 The residual stress value and bulging coefficient detected after actual processing of composite components are matched to correct the error;

[0013] S6. Compare the digital twin model with the actual hollow shaft SiC f / TC17 composite components, hollow shaft SiC under different conditions f / TC17 composite components thermal vibration, bulging coordinated control residual stress prediction, get the prediction results;

[0014] S7, hollow shaft SiC according to knowledge base technology f / TC17 composite component residual stress value and thermal expansion coefficient are updated in real time to evaluate the prediction results and optimize the digital twin model;

[0015] S8. Based on the optimized digital twin model, the thermal aging parameters, vibration aging parameters and bulging parameters are predicted and optimized through the improved butterfly optimization algorithm. The parameters that can be optimized include heating speed, heating temperature, cooling method, excitation frequency, excitation time, vibration shape, bulging rate and bulging force.

[0016] Furthermore, the model established based on digital twins in S3 can simulate various model situations in different states and conditions in the virtual space, which can improve the authenticity of the model.

[0017] Furthermore, in S5, the hollow shaft SiC f / TC17 Intelligent optimization of residual stress value and thermal expansion coefficient of composite components. The objective system cannot be fully observed through simulation alone. It is also necessary to improve the authenticity of the system by establishing a knowledge set and update various data of the digital twin system in real time. By improving fault diagnosis, accident simulation, production indicators, operating procedures and other contents, the system's risk resistance can be improved and the accuracy of prediction and inspection can be optimized.

[0018] Furthermore, the butterfly optimization algorithm in S8 is inspired by the foraging and mating behavior of butterflies, which receive / sense and analyze odors in the air to determine the potential direction of food sources / mating partners. The BOA algorithm has few adjustment parameters, simple principles, and is easy to implement, but it will fall into local optimality and has the disadvantage of slow convergence speed in later iterations.

[0019] Furthermore, the particle swarm algorithm in S8 can update the global search and local search methods of the butterfly optimization algorithm, so that the iterative descent speed of the butterfly optimization algorithm is better than the original butterfly algorithm, the convergence performance is better than BOA, and the search performance is better.

[0020] Furthermore, the application steps of the algorithm in S8 are: S8.1, randomly generate and initialize butterfly population, S8.2, calculate the fitness values ​​of all butterflies, S8.3, select the optimal position, and select the nectar source through comparison in S8.2, S8.4, determine the butterfly search stage and update the butterfly position, S8.5, optimize the butterfly search weight through particle swarm optimization, update the butterfly search method, and update the optimal value, S8.6, update the position and speed of the particles, calculate the fitness value, and output the butterfly position, S8.7, repeat the above steps to determine whether the optimal butterfly position is reached, S8.8, update the best position of the butterfly.

[0021] According to another aspect of the present invention, an optimization system for coordinated stress control of thermal vibration and bulging of hollow shaft components is provided, comprising a heating tube, a thermal aging control system, a power supply, a heating furnace, a workbench, two excitation heads, two valves, a bulging and frequency control system, a pressing block and a fixture base, wherein two clamps are fixedly arranged on the top of the fixture base, and the tops of the two clamps are fixedly clamped with a workpiece, one side of the two valves is provided with a connecting ring, and the two valves are respectively fixedly connected to an excitation head through the connecting ring, and the hydraulic bulging system drives the hydraulic transmission to the pressing block through the bulging and frequency control system to realize the thermal bulging process, and an excitation head is arranged on one side of each valve so as to excite the component while bulging; during the entire thermal bulging process, the bulging control system in the bulging and frequency control system controls the bulging speed and holding time of the thermal bulging; the excitation time and excitation frequency are guaranteed by the frequency control system in the bulging and frequency control system; and the bulging temperature is controlled by the thermal aging control system.

[0022] (III) Beneficial effects

[0023] The present invention provides an optimization method and system for controlling the stress of the hollow shaft components by thermal vibration and bulging coordination, which has the following beneficial effects:

[0024] Through digital twin technology, thermal aging technology, vibration aging technology and thermal expansion processing composite control residual stress can be combined. That is, their processing process can be established in virtual space through digital models to simulate the actual processing process. At the same time, various situations that may occur during the processing can be predicted and a database of the processing process can be established. For example, the processing temperature, excitation frequency, expansion coefficient, etc. can be recorded. By saving the existing data, the digital twin model can be used to predict the components that have not yet been processed, thereby improving the quality and processing efficiency of the processed components.

[0025] By combining bulging technology with thermal vibration aging technology, the residual stress control of components is integrated and uniform, which improves the processing quality and accuracy of components. The residual stress of components can also be predicted through the digital twin model. Based on the digital twin model, the process parameters of the component processing process are optimized through intelligent algorithms, which solves the problem that the existing technology is difficult to control the residual stress of high-strength composite materials, and has great economic value.

[0026] Hollow shaft SiC f / TC17 Intelligent optimization of residual stress value and thermal expansion coefficient of composite components. It is impossible to fully observe the objective system through simulation alone. It is also necessary to improve the authenticity of the system by establishing a knowledge set and update various data of the digital twin system in real time. By improving fault diagnosis, accident simulation, production indicators, operation procedures and other contents, the system's risk resistance ability is improved and the accuracy of prediction and inspection is optimized;

[0027] The butterfly optimization algorithm is inspired by the foraging and mating behavior of butterflies. Butterflies receive / perceive and analyze odors in the air to determine the potential direction of food sources / mating partners. The BOA algorithm has few adjustment parameters, simple principles, and is easy to implement, but it will fall into local optimality and have slow convergence speed in later iterations. The particle swarm algorithm can update the global search and local search methods of the butterfly optimization algorithm, making the iterative descent speed of the butterfly optimization algorithm better than the original butterfly algorithm, and the convergence performance is better than BOA, with better search performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A process flow chart of an optimization method and system for coordinating and controlling stresses of hollow shaft components during thermal vibration and bulging;

[0029] Figure 2 Flow chart of the optimization method for coordinated control of stress of thermal vibration and bulging of hollow shaft components and the algorithm in the system;

[0030] Figure 3 The present invention provides an optimization method for coordinated stress control of thermal vibration and bulging of hollow shaft components and a schematic diagram of the structure of the hydraulic bulging excitation system in the system.

[0031] In the figure: 1. Heating tube; 2. Thermal aging control system; 3. Power supply; 4. Heating furnace; 5. Workbench; 6. Fixture; 7. Workpiece; 8. Excitation head; 9. Valve; 10. Expansion and frequency control system; 11. Press block; 12. Connecting ring; 13. Fixture base. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0033] The present invention provides a technical solution: a method for optimizing the coordinated control of stress of thermal vibration and bulging of hollow shaft components, comprising the following steps:

[0034] S1. Collect hollow shaft SiC f / TC17 composite components after forming, the material composition, sample shape and size, surface state and stress measurement direction of the component are used to establish a simulation model of the initial state of the component, and the above data are input into the simulation model.

[0035] S2. Based on the initial simulation model, a digital twin model with initial residual stress is established; the digital twin model data is collected from actual operation, production and experiments; the model data includes: initial residual stress, laboratory temperature, thermal aging system, vibration aging system and bulging system model data.

[0036] S3. Based on the digital twin model, thermal aging is performed on the components. The process simulation mainly considers the thermal and mechanical properties of the material; the performance includes the mechanical properties of the material itself, vibration frequency, aging temperature, insulation temperature, cooling method and clamping method. The model established based on the digital twin can simulate various model situations in different states and conditions in the virtual space, which can improve the realism of the model.

[0037] S4, in hollow shaft SiC f During the thermal aging process of the TC17 composite component, hydraulic bulging and vibration aging are carried out simultaneously; the finished workpiece is obtained, and the various parameters of the processing process and the residual stress value are recorded; the processing parameters include heating rate, heating temperature, cooling method, excitation frequency, excitation time, amplitude, bulging rate and bulging force.

[0038] S5. Adjust the errors that occur during the operation of the digital twin model based on data analysis methods, and f / TC17 The residual stress value and bulging coefficient of the composite component after actual processing are matched, the error is corrected, and the hollow shaft SiC is matched according to the knowledge set. f / TC17 Intelligent optimization of residual stress value and thermal expansion coefficient of composite components. The objective system cannot be fully observed through simulation alone. It is also necessary to improve the authenticity of the system by establishing a knowledge set and update various data of the digital twin system in real time. By improving fault diagnosis, accident simulation, production indicators, operating procedures and other contents, the system's risk resistance can be improved and the accuracy of prediction and inspection can be optimized.

[0039] S6. Compare the digital twin model with the actual hollow shaft SiC f / TC17 composite components, hollow shaft SiC under different conditions f / TC17 composite components thermal vibration and bulging coordinated control residual stress is predicted and the prediction results are obtained.

[0040] S7, hollow shaft SiC according to knowledge base technology f / TC17 composite components’ residual stress values ​​and thermal expansion coefficients are updated in real time to evaluate prediction results and optimize the digital twin model.

[0041] S8. Based on the optimized digital twin model, the improved butterfly optimization algorithm is used to predict and optimize the thermal aging parameters, vibration aging parameters and bulging parameters. The optimizable parameters include heating speed, heating temperature, cooling method, excitation frequency, excitation time, vibration shape, bulging rate and bulging force. The particle swarm algorithm can update the global search and local search methods of the butterfly optimization algorithm to make the iterative descent speed of the butterfly optimization algorithm better than the original butterfly algorithm, and the convergence performance is better than BOA, with better search performance. The application steps of the algorithm are: S8.1. Randomly generate and initialize the butterfly population, S8.2. Calculate the fitness values ​​of all butterflies, S8.3. Select the optimal position, and select the nectar source through comparison in S8.2, S8.4. Determine the butterfly search stage and update the butterfly position, S8.5. Optimize the butterfly search weight through particle swarm, update the butterfly search method, and update the optimal value, S8.6. Update the position and speed of the particles, calculate the fitness value, and output the butterfly position, S8.7. Repeat the above steps to determine whether the optimal butterfly position is reached, S8.8. Update the best position of the butterfly.

[0042] The method also provides an optimization system for the coordinated control of stress of thermal vibration and bulging of hollow shaft components. The system includes a heating tube 1, a thermal aging control system 2, a power supply 3, a heating furnace 4, a workbench 5, two excitation heads 8, two valves 9, a bulging and frequency control system 10, a pressing block 11 and a fixture base 13. Two clamps 6 are fixedly arranged on the top of the fixture base 13. The tops of the two clamps 6 are fixedly clamped with a workpiece 7. A connecting ring 12 is arranged on one side of the two valves 9. The two valves 9 are respectively fixedly connected to an excitation head 8 through the connecting ring 12. The hydraulic bulging system drives the hydraulic transmission to the pressing block 11 through the bulging and frequency control system 10 to realize the thermal bulging process. The excitation head 8 is arranged on one side of each valve 9 so as to excite the component while bulging; during the entire thermal bulging process, the bulging control system in the bulging and frequency control system 10 controls the bulging speed and holding time of the thermal bulging; the excitation time and excitation frequency are guaranteed by the frequency control system in the bulging and frequency control system 10; and the bulging temperature is controlled by the thermal aging control system 2.

[0043] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device.

[0044] 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. An optimization method for controlling the stress of hollow shaft components by thermal vibration and bulging coordination, characterized in that: The following steps are involved: S1. Collect hollow shaft SiC f / TC17 composite components after forming, the material composition, sample shape and size, surface state and stress measurement direction of the component, establish a simulation model of the initial state of the component, and input the above data into the simulation model; S2. According to the initial simulation model, a digital twin model with initial residual stress is established; the digital twin model data is collected from actual operation, production and experiments; the model data includes: initial residual stress, laboratory temperature, thermal aging system, vibration aging system and bulging system model data; S3. Based on the digital twin model, the components are thermally aged. The process simulation mainly considers the thermal and mechanical properties of the material; the properties include the mechanical properties of the material itself, vibration frequency, aging temperature, insulation temperature, cooling method and clamping method; S4, in hollow shaft SiC f During the thermal aging process of the TC17 composite component, hydraulic bulging and vibration aging are carried out simultaneously; the finished workpiece is obtained, and the various parameters of the processing process and the residual stress value are recorded; the processing parameters include heating speed, heating temperature, cooling method, excitation frequency, excitation time, amplitude, bulging rate and bulging force; S5. Adjust the errors that occur during the operation of the digital twin model based on data analysis methods, and f / TC17 The residual stress value and bulging coefficient detected after actual processing of composite components are matched to correct the error; S6. Compare the digital twin model with the actual hollow shaft SiC f / TC17 composite components, hollow shaft SiC under different conditions f / TC17 composite components thermal vibration, bulging coordinated control residual stress prediction to obtain the prediction results; S7, hollow shaft SiC according to knowledge base technology f / TC17 composite component residual stress value and thermal expansion coefficient are updated in real time to evaluate the prediction results and optimize the digital twin model; S8. Based on the optimized digital twin model, the thermal aging parameters, vibration aging parameters and bulging parameters are predicted and optimized through the improved butterfly optimization algorithm. The parameters that can be optimized include heating speed, heating temperature, cooling method, excitation frequency, excitation time, vibration shape, bulging rate and bulging force.

2. The optimization method for controlling the stress of the hollow shaft components by thermal vibration and bulging coordination according to claim 1 is characterized in that: The model established based on digital twins in S3 can simulate various model situations in different states and conditions in the virtual space, which can improve the authenticity of the model.

3. The optimization method for controlling the stress of the hollow shaft components by thermal vibration and bulging coordination according to claim 1 is characterized in that: In S5, according to the knowledge set, the hollow shaft SiC f / TC17 Intelligent optimization of residual stress value and thermal expansion coefficient of composite components. The objective system cannot be fully observed through simulation alone. It is also necessary to improve the authenticity of the system by establishing a knowledge set and update various data of the digital twin system in real time. By improving fault diagnosis, accident simulation, production indicators, operating procedures and other contents, the system's risk resistance can be improved and the accuracy of prediction and inspection can be optimized.

4. The optimization method for controlling the stress of the hollow shaft components by thermal vibration and bulging coordination according to claim 1 is characterized in that: The butterfly optimization algorithm in S8 is inspired by the foraging and mating behavior of butterflies, which receive / sense and analyze odors in the air to determine the potential direction of food sources / mating partners. The BOA algorithm has few adjustment parameters, simple principles, and is easy to implement, but it will fall into local optimality and has the disadvantage of slow convergence speed in later iterations.

5. The optimization method for controlling the stress of the hollow shaft components by thermal vibration and bulging coordination according to claim 4 is characterized in that: The particle swarm algorithm in S8 can update the global search and local search methods of the butterfly optimization algorithm, so that the iterative descent speed of the butterfly optimization algorithm is better than the original butterfly algorithm, the convergence performance is better than BOA, and the search performance is better.

6. The optimization method for controlling the stress of the hollow shaft components by thermal vibration and bulging coordination according to claim 5 is characterized in that: The application steps of the algorithm in S8 are: S8.1, randomly generate and initialize butterfly population, S8.2, calculate the fitness values ​​of all butterflies, S8.3, select the optimal position, and select the nectar source through comparison in S8.2, S8.4, determine the butterfly search stage and update the butterfly position, S8.5, optimize the butterfly search weight through particle swarm optimization, update the butterfly search method, and update the optimal value, S8.6, update the position and speed of particles, calculate the fitness value, and output the butterfly position, S8.7, repeat the above steps to determine whether the optimal butterfly position is reached, S8.8, update the best butterfly position.

7. An optimization system for controlling the stress of hollow shaft components by thermal vibration and bulging coordination, comprising the optimization method for controlling the stress of hollow shaft components by thermal vibration and bulging coordination according to any one of claims 1 to 6, characterized in that: The invention comprises a heating tube (1), a heat aging control system (2), a power supply (3), a heating furnace (4), a workbench (5), two vibration heads (8), two valves (9), a bulging and frequency control system (10), a pressing block (11) and a clamp base (13), wherein two clamps (6) are fixedly arranged on the top of the clamp base (13), and the tops of the two clamps (6) are fixedly clamped with a workpiece (7), and one side of the two valves (9) is provided with a connecting ring (12), and the two valves (9) are respectively connected to an vibration head through the connecting ring (12). (8) is fixedly connected, the hydraulic bulging system drives the hydraulic transmission to the pressing block (11) through the bulging and frequency control system (10) to realize the thermal bulging process, and an exciting head (8) is arranged on one side of each valve (9) so as to excite the component while bulging; during the whole thermal bulging process, the bulging control system in the bulging and frequency control system (10) controls the bulging speed and holding time of the thermal bulging; the excitation time and the excitation frequency are guaranteed by the frequency control system in the bulging and frequency control system (10); and the bulging temperature is controlled by the thermal aging control system (2).

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