A multi-energy field control process method and system for improving the service life of complex aerospace components

Abstract

The present invention discloses a multi-energy field control process method and system for improving the service life of complex aerospace components, which belongs to the field of complex aerospace component manufacturing. The method includes: placing the complex aerospace component in a magnetic levitation polishing device that integrates magnetic levitation magnetic field, ultrasonic field, pressure field and temperature field, and using the synergistic effect of the composite energy field to perform surface treatment and stress relief optimization on the component, thereby significantly improving the stress relief effect and polishing uniformity, forming a surface layer structure with high density and smoothness, and effectively promoting the closure of internal microscopic defects; obtaining process parameters related to the magnetic levitation magnetic field, ultrasonic field, pressure field and temperature field, and constructing a latent space dimensionality reduction model based on a variational autoencoder after normalization preprocessing, extracting the nonlinear coupling relationship between complex process parameters, and dynamically adjusting key process parameters in combination with the Hippo optimization algorithm on the premise of significantly improving optimization efficiency, quickly and accurately obtaining the optimal process plan, and comprehensively improving the service performance of complex aerospace components.

Description

Technical Field

[0001] The present invention belongs to the technical field of complex aerospace component manufacturing, and specifically relates to a multi-energy field control process method and system for improving the service life of complex aerospace components. Background Art

[0002] Complex aerospace components are important structural parts used in aircraft and spacecraft in the aerospace field. They are characterized by complex structures, thin walls with weak rigidity, high performance requirements, and difficult manufacturing processes. Their manufacturing often uses lightweight alloys, composite materials, or high-temperature alloys, and is completed through advanced processes such as precision machining, additive manufacturing, or superplastic forming. They are widely used in key parts such as aircraft wings, spacecraft cabins, and engine blades. The design and manufacture of complex aerospace components directly affect the performance, reliability, and cost of aerospace products and are an important indicator of the level of aerospace technology. These components are usually designed with complex internal cavities and flow channels, such as the cooling channels in engine blades, the thermal system channels in spacecraft, and the fuel pipes inside aircraft wings. These designs not only help to reduce weight, but also improve efficiency or meet special functional requirements. At the same time, complex aerospace components often involve complex geometries and tiny support structures.

[0003] Traditional magnetic polishing technology can effectively improve the irregular micro-morphology (roughness) of the surface of ordinary components. However, the manufacturing requirements for complex aerospace components are stringent, and their internal defects and stress distribution are highly random and unpredictable. Traditional surface polishing technology is difficult to effectively remove surface and internal micro-defects and residual stresses of components at the same time, and cannot meet the comprehensive quality requirements of high precision and high performance. Compared with traditional magnetic polishing technology, magnetic levitation polishing technology uses the principle of magnetic levitation to make the component float in a container, so that the abrasive is more evenly and stably distributed around the workpiece; it has higher precision and stronger adaptability, can accurately control the polishing position and force, and can flexibly adjust parameters according to the shape, size and material of the workpiece, which is more advantageous for dealing with special shapes or complex curved workpieces; and the damage is smaller, the workpiece is suspended without direct mechanical contact, reducing the risk of wear and scratches, and producing a better flexible processing effect on the component surface, which is suitable for easily deformed, fragile or surface-sensitive workpieces.

[0004] However, magnetic levitation polishing technology has little effect on the microscopic defects and residual stresses within components. The presence of internal microscopic defects and residual stresses makes components prone to deformation, cracking and other failure phenomena during subsequent use, greatly shortening the service life of the components. In summary, to meet the stringent requirements of high reliability and long life for complex aerospace components, it is necessary to break through the limitations of traditional processes. Summary of the Invention

[0005] The present invention aims to solve the deficiencies of the prior art and provides the following solutions:

[0006] A multi-energy field control process method for improving the service life of complex aerospace components includes the following steps:

[0007] S1. Place the complex aerospace component to be processed in a magnetic levitation polishing machine for a multi-physics field coordinated grinding and polishing process using a magnetic field, ultrasound, pressure, and high temperature. The process parameters and component performance of the complex aerospace component after processing are collected and normalized to obtain normalized process parameters and component performance.

[0008] S2. Using a variational autoencoder to extract the latent space structure between the normalized process parameters, and mapping the high-dimensional parameters to a low-dimensional latent space to obtain low-dimensional latent variables, and using the low-dimensional latent variables to initialize the individual positions of the population in the Hippo optimization algorithm;

[0009] S3. Using the initialized Hippo optimization algorithm to search for the optimal parameter combination in the compression space, update the individual position, and construct a comprehensive objective function using the normalized process parameters as input and the component performance index as the objective function;

[0010] S4. Taking the comprehensive objective function as the target, calculating the function value using the process parameters, optimizing the iterative process parameter combination until the comprehensive objective function converges, extracting the function value and mapping it to component performance, and introducing the mapped component performance as a feedback control variable into the optimization iterative process;

[0011] S5. During each iteration of the optimization process in step S4, the process parameters are adjusted using the variational autoencoder and the Hippo optimization algorithm and the corresponding objective function value is calculated. If the change in the objective function value of this round is less than the threshold, the algorithm stops and the process parameters are optimized. Otherwise, the process proceeds to step S2.

[0012] S6. Repeat steps S2 to S5 until the global optimal solution of the process parameter combination is obtained.

[0013] Preferably, the method for performing multi-physical field coordinated grinding and polishing processing using the magnetic suspension polishing machine includes:

[0014] Ensure that the magnetic levitation polishing machine is in a closed state and the interior is clean and free of debris, place the complex aerospace component to be processed in the sealed box of the magnetic levitation polishing machine, close the box to ensure airtightness, then turn on the power and start the magnetic levitation polishing machine through the console;

[0015] The process parameters of the magnetic levitation magnetic field, ultrasonic field, pressure field and temperature field are set in the console, and then the AC magnetic coil generates the alternating magnetic levitation magnetic field, the bottom sound wave generating device generates the ultrasonic field, the pressurizing device generates the pressure field, and the heating device generates the temperature field;

[0016] The grinding tool generates relative motion with the surface of the complex aerospace component under the action of the magnetic levitation magnetic field, and micro-grinds and polishes the surface of the complex aerospace component through the coordinated action of the magnetic levitation magnetic field, the ultrasonic field, the pressure field and the temperature field.

[0017] Preferably, the normalized process parameters include: the magnetic field intensity H', the magnetic field frequency f of the magnetic suspension polishing machine m ', ultrasonic power W', ultrasonic frequency f u ', container pressure P', inner cavity heating temperature T', magnetic field action time t m ', Ultrasonic field action time t u ', pressure field action time t p ' and temperature field action time t t ';

[0018] The normalized component properties include: surface roughness R', residual stress σ', internal defect closure degree D' and fatigue life L'.

[0019] Preferably, the method for initializing the positions of individuals in the population in the Hippo optimization algorithm includes:

[0020] ,

[0021] Among them, X t Indicates the current individual's position, X rand represents a randomly selected individual in the population, X best represents the global optimal individual in the current population, R(τ)= σ(τ)·(X rand -Xτ) represents the random disturbance term, σ(τ) represents the disturbance factor that changes dynamically with time, r1, r2 and r3 represent random numbers between 0 and 1, LB represents the lower bound of the search space, UB represents the upper bound of the search space, q represents the random threshold of the control strategy, ω1(τ), ω2(τ), ω3(τ) and ω4(τ) represent dynamic weight adjustment factors, φ represents the nonlinear adjustment coefficient, ω i,min Represents the minimum value of weight, ω i,max represents the maximum value of the weight, τ represents the current number of iterations, represents the maximum number of iterations, α(τ) represents the first correction factor, α0 represents the first initial correction factor value, and γ represents the parameter that controls the decay speed of the first correction factor.

[0022] Preferably, the method for updating individual locations includes:

[0023] ,

[0024] Among them, X preyrepresents the current global optimal solution, r represents a random number between 0 and 1, E represents the escape energy, E0 represents the initial escape energy, δ represents the parameter that controls the nonlinear attenuation of the escape energy, Ψ(τ) represents the dynamically adjusted convergence factor, η(τ) represents the penalty term weight, and C penalty represents the penalty factor of constrained optimization, β(τ) represents the second correction factor, β0 represents the second initial correction factor value, and μ controls the parameter of the decay speed of the second correction factor.

[0025] Preferably, the comprehensive objective function includes:

[0026] ,

[0027] Among them, R' min Indicates the minimum value of surface roughness, R' max Indicates the maximum value of surface roughness, σ' min Represents the minimum value of residual stress, σ' max Indicates the maximum value of residual stress, D' min Indicates the minimum value of the internal defect closure degree, D' max Indicates the maximum value of the internal defect closure degree, L' min Indicates the minimum value of fatigue life, L' max represents the maximum value of fatigue life, w1, w2, w3 and w4 represent the weights of each objective function.

[0028] The present invention also provides a multi-energy field control process system for improving the service life of complex aerospace components. The system applies any of the above-mentioned methods and includes: a component processing module, an algorithm initialization module, a function construction module, an iterative optimization module, and a threshold comparison module;

[0029] The component processing module is used to place the aerospace complex component to be processed in a magnetic suspension polishing machine for magnetic field-ultrasound-pressure-high temperature multi-physical field coordinated grinding and polishing processing, and collect the process parameters and component performance of the processed aerospace complex component and perform normalization processing to obtain normalized process parameters and normalized component performance;

[0030] The algorithm initialization module uses a variational autoencoder to extract the latent space structure between the normalized process parameters, and maps the high-dimensional parameters to a low-dimensional latent space to obtain low-dimensional latent variables, and uses the low-dimensional latent variables to initialize the individual positions of the population in the Hippo optimization algorithm;

[0031] The function construction module uses the initialized Hippo optimization algorithm to search for the optimal parameter combination in the compression space, updates the individual positions, takes the normalized process parameters as input and the component performance index as the objective function, and constructs a comprehensive objective function;

[0032] The iterative optimization module takes the comprehensive objective function as a target, calculates a function value through the process parameters, optimizes the iterative process parameter combination until the comprehensive objective function converges, extracts the function value and maps it to component performance, and introduces the mapped component performance as a feedback control quantity into the optimization iteration process;

[0033] The threshold comparison module adjusts the process parameters and calculates the corresponding objective function value using the variational autoencoder and the Hippo optimization algorithm during each iteration of the optimization process of the iterative optimization module. If the change in the objective function value of this round is less than the threshold, the algorithm stops and the process parameters are optimized. Otherwise, the algorithm initialization module is restarted.

[0034] The workflow of the algorithm initialization module, the function construction module, the iterative optimization module and the threshold comparison module is repeated until a global optimal solution of the process parameter combination is obtained.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention solves the difficult problem that conventional polishing technology is difficult to quickly and effectively improve the service performance of complex aerospace components. The complex aerospace components are placed in a magnetic levitation polishing machine coupled with a magnetic levitation magnetic field, an ultrasonic field, a pressure field and a temperature field. The synergistic effect of multiple energy fields can significantly improve the surface roughness and smoothness of the components, effectively reduce and equalize the residual stress level, greatly eliminate various defects inside the material, and comprehensively improve the overall durability and service life of the components; the variational autoencoder and the Hippo optimization algorithm are combined to scientifically, quickly and accurately seek the best combination of process conditions, and finally provide a multi-energy field optimal synergistic control solution that comprehensively and deeply improves the surface quality, reliability and service life of complex aerospace components. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

[0039] Figure 2 This is a structural diagram of a magnetic suspension polishing machine according to an embodiment of the present invention;

[0040] Description of reference numerals:

[0041] 1. Pressurizing device; 2. Magnetic levitation polishing machine housing; 3. Multi-energy field sensor; 4. Control console; 5. AC magnetic coil; 6. Heating device; 7. Aerospace complex components; 8. Magnetic levitation generator; 9. Sound wave generator; 10. AC power supply; 11. Bracket. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] First, let’s introduce the technologies involved in this application:

[0045] Complex aerospace components involve core components widely used in aerospace vehicles. They usually have complex geometric shapes, thin walls with weak rigidity, lightweight, high strength, high precision, fatigue resistance, resistance to extreme environments, high reliability and multi-functional integration. Their manufacturing processes cover casting, forging, additive manufacturing, spinning, stretch bending, roll bending, explosive forming, CNC machine tool processing, special processing, welding, riveting, screw connection, superplastic forming-diffusion bonding, etc.

[0046] Magnetic levitation polishing technology: Magnetic levitation polishing technology uses the magnetic force generated by the magnetic levitation magnetic field to suspend the workpiece in the polishing space, and uses high-hardness, uniform-sized abrasives to move around the workpiece to achieve efficient and precise surface processing. Commonly used abrasives include aluminum oxide, silicon carbide, and diamond. By adjusting the magnetic field strength and frequency, the movement trajectory and speed of the abrasive can be precisely controlled to ensure that the abrasive evenly covers the surface of the workpiece, greatly improving the surface roughness accuracy. Since the workpiece is suspended without mechanical contact, it can effectively avoid scratches and deformation caused by traditional polishing. It is especially suitable for thin-walled, easily deformed, and complex aerospace components with high surface quality requirements. For components with complex curves or irregular geometries, such as aircraft engine blades, spacecraft cabins, etc., this technology can dynamically adjust the magnetic levitation magnetic field parameters according to the geometric characteristics, so that the abrasive acts precisely on the target part, significantly improving the surface quality and overall performance.

[0047] Magnetic aging treatment technology: Magnetic aging treatment technology is a technical means of using a magnetic field to treat materials to change properties such as residual stress. It relies on the interaction between the magnetic field and the material. When the material is in a magnetic field environment, the magnetic field affects the residual stress by affecting the movement of charged particles, dislocation behavior, magnetic domain structure, and atomic diffusion within the material. The alternating magnetic field will cause changes in the internal microstructure of complex components, causing relative microscopic movement between its molecules. This microscopic movement helps to break down the lattice defects, dislocations, and other uneven structures that may exist within the component, promoting the homogenization and stability of the microstructure. On the other hand, the periodic changes in the alternating magnetic field will produce a tiny magnetostrictive effect within the component, that is, the material undergoes a tiny deformation under the action of the magnetic levitation magnetic field. This deformation helps to improve the internal stress distribution of the material at the microscopic level and reduce stress concentration areas, thereby enhancing the overall mechanical properties and stability of the component and improving the polishing effect.

[0048] Ultrasonic aging technology: Ultrasonic aging technology utilizes ultrasonic energy to treat materials. The principle is that when ultrasonic waves propagate through a material, they induce internal vibrations and high-frequency alternating stresses. This activates dislocations, causing them to move and multiply, relaxing and redistributing residual stresses. This process also refines the microstructure, refines grains, increases grain boundaries, hinders dislocation movement, and enhances material strength and toughness. It can also repair or close microscopic defects, improving material density and integrity.

[0049] Pressure Densification Technology: This technology uses pressure to close microscopic defects within components. By installing a pressurizing device within the sealed container of a magnetic levitation polisher, controlled pressure is applied to the component, gradually closing microscopic defects within the material under the pressure, thereby increasing the material's density. This increased density results in a tighter atomic arrangement within the material, strengthening atomic bonds and significantly improving the material's continuity and overall mechanical properties (strength, hardness, and toughness). Simultaneously, the pressure evens out internal stress distribution, effectively alleviating localized stress concentrations and reducing the risk of deformation and cracking, thereby comprehensively enhancing the component's stability, reliability, and service life.

[0050] The ultrasonic field and pressure field act on the components. The ultrasonic equipment generates ultrasound that propagates in the components, causing high-frequency vibrations in the component medium, resulting in periodic tensile and compressive deformations in the internal microstructure of the material, breaking the stress concentration areas in the microstructure, and redistributing and releasing the residual stress, ultimately making the surface of the component smoother after polishing and achieving better polishing quality. At the same time, the pressure field changes the distance and arrangement between atoms. Under high-pressure conditions, microscopic pores, cracks and other defects inside complex aerospace components will close or deform under the action of pressure, reducing discontinuities in the microstructure, improving material density, and promoting the closure of internal defects.

[0051] Thermal aging technology: During the machining process, the complex component is first placed in a thermal field and heated to the desired temperature. The temperature is then maintained at this temperature for a period of time, known as a holding process. The basic operating principle is that the yield strength of a material is correlated with temperature. Specifically, as the temperature increases, the yield strength decreases accordingly. When the temperature rises to a certain level, the residual stress within the workpiece exceeds the material's yield limit. This causes localized plastic deformation within the workpiece, releasing the residual strain and reducing the residual stress. Furthermore, there is a significant synergistic effect between the thermal field and other energy fields. The thermal field can enhance the polishing effect of the magnetic levitation field. By intensifying atomic thermal motion, the polishing medium driven by the magnetic levitation field can more efficiently act on the component surface, precisely removing microscopic protrusions, filling depressions, and reducing surface roughness. The thermal field and ultrasonic field work together to accelerate atomic migration and rearrangement, promoting the relaxation and uniform distribution of residual stresses, thereby reducing the risk of component deformation and cracking. The thermal field can also help the pressure field close defects. On the one hand, it reduces the yield strength of the material, making the material around the defect easier to flow plastically under pressure. On the other hand, it enhances the diffusion capacity of atoms, uses dislocation movement to even out stress, avoids stress concentration, further promotes defect closure, and effectively improves the density of the material and the overall performance of the component. The temperature field has a thermal aging effect on complex components. For the internal microstructure of complex aerospace components, high temperature helps the diffusion and migration of atoms, allowing micro defects to recombine and repair, thereby promoting the closure and optimization of the microstructure. At the same time, the yield strength of the material is reduced in a high temperature environment, and the internal residual stress is released and redistributed, which effectively reduces stress concentration, improves the dimensional stability and fatigue resistance of the component, and further enhances the improvement effect of the component surface quality during the polishing process.

[0052] Example 1

[0053] In this embodiment, if Figure 1 As shown, a multi-energy field control process method for improving the service life of complex aerospace components includes the following steps:

[0054] S1. Place the complex aerospace component 7 to be processed in a magnetic levitation polishing machine for coordinated grinding and polishing using a multi-physical field of magnetic field, ultrasound, pressure and high temperature, and collect the process parameters and component performance of the processed complex aerospace component 7 and perform normalization processing to obtain normalized process parameters and normalized component performance.

[0055] In this embodiment, the complex aerospace component 7 to be processed is placed in a magnetic levitation polishing machine assisted by multi-energy field coupling. Figure 2As shown, a multi-energy field sensor 3 is arranged in the magnetic levitation polishing machine case 2, and the magnetic levitation polishing machine case 2 is installed on the bracket 11. The AC power supply 10 provides current to the equipment, and the AC magnetic coil 5 generates an alternating magnetic field. A magnetic levitation generating device 8 is arranged inside, and a sound wave generating device 9 is installed at the bottom of the equipment. A pressurizing device 1 and a heating device 6 are connected to the equipment to generate a pressure field and a temperature field inside the closed container. The entire machine is controlled and operated by the control console 4 to perform multi-energy field assisted polishing treatment on the complex aerospace components 7; it should be noted that the multi-energy field sensor 3 has a built-in dedicated physical field detection module, involving multi-physical field detection such as magnetic levitation magnetic field, ultrasonic field, pressure field and temperature field.

[0056] The steps for using the magnetic levitation polishing machine are as follows: first, ensure that the magnetic levitation polishing machine is in the closed state and the inside of the box is clean and free of debris, place the complex aerospace component 7 to be processed in the closed box of the magnetic levitation polishing machine, and after placement, close the box to ensure airtightness, then turn on the power, start the equipment through the console 4, set the process parameters related to the magnetic levitation magnetic field, ultrasonic field, pressure field and temperature field on the console 4, and the equipment starts to run; at this time, the AC magnetic coil 5 generates an alternating magnetic field, the bottom sound wave generating device 9 generates an ultrasonic field, the pressurizing device 1 and the heating device 6 generate a pressure field and a temperature field respectively, and the grinding tool generates relative motion with the surface of the component under the action of the magnetic levitation magnetic field. For movement, the abrasive performs micro-cutting and grinding on the surface of the component. After polishing is completed, the equipment is stopped through the console 4, and a period of time is waited to ensure that the internal environment is stable. Then the box is opened and the polished aerospace complex component 7 is taken out; through the synergistic effect of the magnetic levitation magnetic field, ultrasonic field, pressure field and temperature field, the surface quality of the material is effectively improved, the residual stress distribution is improved, and the internal micro defects are eliminated, and the overall comprehensive performance and service life of the aerospace complex component 7 are improved in all aspects and depth; based on precision detection and data analysis technology, the surface roughness R, residual stress σ, internal defect closure degree D, and fatigue life L of the aerospace complex component 7 after polishing are characterized.

[0057] In this embodiment, the magnetic field intensity H and magnetic field frequency f are extracted from the collected raw data. m , ultrasonic power W, ultrasonic frequency f u , container pressure P, inner cavity heating temperature T, recording magnetic field action time t m , ultrasonic field action time t u , pressure field action time t p , temperature field action time t t The surface roughness R, residual stress σ, internal defect closure degree D, and fatigue life L parameters are collected at the same time. The minimum-maximum normalization method is used to map the data to the [0,1] interval. The formula is as follows:

[0058] ,

[0059] Among them, x represents the original data, x=[H, f m , W, f u , P, T, t m , t u , t p , t t , R, σ, D, L],x min Represents the minimum value of the original data, x max Indicates the maximum value of the original data, x norm Represents normalized data. The normalized process parameters include: magnetic field intensity H', magnetic field frequency f of the magnetic suspension polishing machine m ', ultrasonic power W', ultrasonic frequency f u ', container pressure P', inner cavity heating temperature T', magnetic field action time t m ', Ultrasonic field action time t u ', pressure field action time t p ' and temperature field action time t t '; The normalized component properties include: surface roughness R', residual stress σ', internal defect closure degree D' and fatigue life L'.

[0060] S2. Use variational autoencoders to extract the latent space structure between normalized process parameters, and map high-dimensional parameters to low-dimensional latent space to obtain low-dimensional latent variables, which are then used to initialize the individual positions of the population in the Hippo optimization algorithm.

[0061] In this embodiment, a variational autoencoder (VAE) is used to extract the latent space structure between process parameters and map the high-dimensional parameter space to a low-dimensional latent space to reduce the optimization calculation complexity. The specific structure is as follows: VAE consists of an encoder, a decoder, and a latent variable generation network; the encoder maps the input process parameters to a low-dimensional latent space and obtains latent variables by learning the latent distribution; the decoder generates output data from the latent variables and reconstructs the input features; the model input layer contains the normalized magnetic field strength H' and magnetic field frequency f of the magnetic levitation polishing machine. m ', ultrasonic power W', ultrasonic frequency f u ', container pressure P', inner cavity heating temperature T', magnetic field action time t m ', Ultrasonic field action time t u ', pressure field action time t p ' and temperature field action time t t', the encoder network extracts the latent space features of the input parameters, and the mean and variance of the latent space are used to generate latent variables. The decoder generates output data based on the latent variables to evaluate the reconstruction effect of the original process parameters. VAE optimizes the network parameters by maximizing the similarity between the input and the reconstructed output, thereby effectively capturing the nonlinear relationship between the process parameters and providing a more accurate low-dimensional feature representation for the subsequent process parameter optimization based on the Hippo Optimization Algorithm (HHO). The low-dimensional latent variables extracted by the variational autoencoder (VAE) are used as the input of the Hippo Optimization Algorithm (HHO) to initialize the individual positions of the population. The potential nonlinear correlation between the process parameters is captured through the latent variables, reducing the complexity of the optimization calculation. In the exploration phase, the methods for initializing the individual positions of the population in the Hippo Optimization Algorithm include:

[0062] ,

[0063] Among them, X t Indicates the current individual's position, X rand represents a randomly selected individual in the population, X best represents the global optimal individual in the current population, R(τ)= σ(τ)·(X rand -Xτ) represents the random disturbance term, σ(τ) represents the disturbance factor that changes dynamically with time, r1, r2 and r3 represent random numbers between 0 and 1, LB represents the lower bound of the search space, UB represents the upper bound of the search space, q represents the random threshold of the control strategy, ω1(τ), ω2(τ), ω3(τ) and ω4(τ) represent dynamic weight adjustment factors, φ>1 represents the nonlinear adjustment coefficient, ω i,min Represents the minimum value of weight, ω i,max represents the maximum value of the weight, τ represents the current number of iterations, represents the maximum number of iterations, α(τ) represents the first correction factor, α0 represents the first initial correction factor value, and γ represents the parameter that controls the decay speed of the first correction factor.

[0064] S3. Use the initialized Hippo optimization algorithm to search for the optimal parameter combination in the compressed space, update the individual positions, take the normalized process parameters as input and the component performance index as the objective function, and construct a comprehensive objective function.

[0065] In the development phase, the method of updating individual positions of Hippopotamus is based on dynamic escape energy E, combined with prey position and constraint optimization mechanism. The methods include:

[0066] ,

[0067] Among them, X preyrepresents the current global optimal solution, r represents a random number between 0 and 1, E represents the escape energy, E0 represents the initial escape energy, δ>1 represents the parameter that controls the nonlinear attenuation of the escape energy, Ψ(τ) represents the dynamically adjusted convergence factor, η(τ) represents the penalty term weight, and C penalty represents the penalty factor for constrained optimization, defined as , g(X) represents the independent process parameter constraints, , λ represents the penalty coefficient, β(τ) represents the second correction factor, β0 represents the second initial correction factor value, and μ controls the parameter of the second correction factor attenuation rate. The algorithm uses an automatic adjustment mechanism to iteratively optimize based on performance feedback, taking normalized process parameters as input and component performance indicators as the objective function to construct a comprehensive objective function:

[0068] ,

[0069] Among them, R' min Indicates the minimum value of surface roughness, R' max Indicates the maximum value of surface roughness, σ' min Represents the minimum value of residual stress, σ' max Indicates the maximum value of residual stress, D' min Indicates the minimum value of the internal defect closure degree, D' max Indicates the maximum value of the internal defect closure degree, L' min Indicates the minimum value of fatigue life, L' max represents the maximum value of fatigue life, w1, w2, w3 and w4 represent the weights of each objective function.

[0070] S4. Taking the comprehensive objective function as the goal, calculate the function value through the process parameters, optimize the iterative process parameter combination until the comprehensive objective function converges, extract the function value and map it to component performance, and introduce the mapped component performance as a feedback control quantity into the optimization iterative process.

[0071] S5. During each iteration of the optimization process in step S4, the variational autoencoder and Hippo optimization algorithm are used to adjust the process parameters and calculate the corresponding objective function value. If the change in the objective function value of this round is less than the threshold, the algorithm stops and the process parameters are optimized. Otherwise, go to step S2.

[0072] In this embodiment, the threshold k is a dynamic adaptive parameter, and its initial value range is set to 0.0001 to 0.01. It is dynamically adjusted according to the convergence trend of the objective function value during the iteration process. When the relative error of the objective function value change in three consecutive iterations is less than k, the algorithm is determined to have converged and the optimization is stopped, and the current process parameter combination is output as the optimal solution.

[0073] The value of the threshold k is determined in the following way: (1) In the initial optimization stage, k is set to 0.01 to allow a larger range of search and quickly approach the global optimal area; (2) When the rate of decrease of the objective function value is lower than the preset threshold (the decrease is less than 5% every 10 iterations), k is adjusted to 0.001 to improve the local search accuracy; (3) If the objective function value does not change significantly after two consecutive adjustments (the change is less than 0.1%), k is further compressed to 0.0001 to ensure the convergence accuracy of the final solution; this dynamic adjustment mechanism combines the high-dimensional nonlinear characteristics of the multi-energy field coupled optimization of complex aerospace components7. Through the coordination of coarse adjustment and fine adjustment, it not only avoids premature convergence into the local optimum, but also reduces the redundant calculation amount, significantly improving the optimization efficiency and result reliability.

[0074] S6. Repeat steps S2 to S5 until the global optimal solution of the process parameter combination is obtained.

[0075] Example 2

[0076] In this embodiment, a multi-energy field control process system for improving the service life of complex aerospace components includes: a component processing module, an algorithm initialization module, a function construction module, an iterative optimization module and a threshold comparison module.

[0077] The component processing module is used to place the complex aerospace component 7 to be processed in a magnetic levitation polishing machine for magnetic field-ultrasound-pressure-high temperature multi-physical field coordinated grinding and polishing processing, and collect the process parameters and component performance of the processed complex aerospace component 7 and perform normalization processing to obtain normalized process parameters and normalized component performance.

[0078] The algorithm initialization module uses a variational autoencoder to extract the latent space structure between normalized process parameters, and maps high-dimensional parameters to a low-dimensional latent space to obtain low-dimensional latent variables, which are then used to initialize the individual positions of the population in the Hippo optimization algorithm.

[0079] The function construction module uses the initialized Hippo optimization algorithm to search for the optimal parameter combination in the compressed space, updates the individual positions, takes the normalized process parameters as input and the component performance indicators as the objective function, and constructs a comprehensive objective function.

[0080] The iterative optimization module takes the comprehensive objective function as the target, calculates the function value through the process parameters, optimizes the iterative process parameter combination until the comprehensive objective function converges, extracts the function value and maps it to the component performance, and introduces the mapped component performance as the feedback control quantity into the optimization iteration process.

[0081] During each iteration of the optimization process of the iterative optimization module, the threshold comparison module uses the variational autoencoder and the Hippo optimization algorithm to adjust the process parameters and calculate the corresponding objective function value. If the change in the objective function value of this round is less than the threshold, the algorithm stops and the process parameters are the optimized results. Otherwise, the algorithm initialization module is restarted.

[0082] The workflow of the algorithm initialization module, function construction module, iterative optimization module, and threshold comparison module is repeated until the global optimal solution of the process parameter combination is obtained.

[0083] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A multi-energy field control process method for improving the service life of complex aerospace components, characterized in that: The following steps are involved: S1. Place the complex aerospace component to be processed in a magnetic levitation polishing machine for a multi-physics field coordinated grinding and polishing process using a magnetic field, ultrasound, pressure, and high temperature. The process parameters and component performance of the complex aerospace component after processing are collected and normalized to obtain normalized process parameters and component performance. S2. Using a variational autoencoder to extract the latent space structure between the normalized process parameters, and mapping the high-dimensional parameters to a low-dimensional latent space to obtain low-dimensional latent variables, and using the low-dimensional latent variables to initialize the individual positions of the population in the Hippo optimization algorithm; S3. Using the initialized Hippo optimization algorithm to search for the optimal parameter combination in the compression space, update the individual position, and construct a comprehensive objective function using the normalized process parameters as input and the component performance index as the objective function; S4. Taking the comprehensive objective function as the target, calculating the function value using the process parameters, optimizing the iterative process parameter combination until the comprehensive objective function converges, extracting the function value and mapping it to component performance, and introducing the mapped component performance as a feedback control variable into the optimization iterative process; S5. During each iteration of the optimization process in step S4, the process parameters are adjusted using the variational autoencoder and the Hippo optimization algorithm and the corresponding objective function value is calculated. If the change in the objective function value of this round is less than the threshold, the algorithm stops and the process parameters are optimized. Otherwise, the process proceeds to step S2. S6. Repeat steps S2 to S5 until the global optimal solution of the process parameter combination is obtained.

2. According to claim 1, a multi-energy field control process method for improving the service life of complex aerospace components is characterized by: The method for performing multi-physical field coordinated grinding and polishing processing using the magnetic suspension polishing machine includes: Ensure that the magnetic levitation polishing machine is in a closed state and the interior is clean and free of debris, place the complex aerospace component to be processed in the sealed box of the magnetic levitation polishing machine, close the box to ensure airtightness, then turn on the power and start the magnetic levitation polishing machine through the console; The process parameters of the magnetic levitation magnetic field, ultrasonic field, pressure field and temperature field are set in the console, and then the AC magnetic coil generates the alternating magnetic levitation magnetic field, the bottom sound wave generating device generates the ultrasonic field, the pressurizing device generates the pressure field, and the heating device generates the temperature field; The grinding tool generates relative motion with the surface of the complex aerospace component under the action of the magnetic levitation magnetic field, and micro-grinds and polishes the surface of the complex aerospace component through the coordinated action of the magnetic levitation magnetic field, the ultrasonic field, the pressure field and the temperature field.

3. According to claim 1, a multi-energy field control process method for improving the service life of complex aerospace components is characterized in that: The normalized process parameters include: the magnetic field intensity H', the magnetic field frequency f of the magnetic suspension polishing machine m ', ultrasonic power W', ultrasonic frequency f u ', container pressure P', inner cavity heating temperature T', magnetic field action time t m ', ultrasonic field action time t u ', pressure field action time t p ' and temperature field action time t t '; The normalized component properties include: surface roughness R', residual stress σ', internal defect closure degree D' and fatigue life L'.

4. The multi-energy field control process method for improving the service life of complex aerospace components according to claim 1, characterized in that: Methods for initializing the positions of individuals in the Hippo optimization algorithm include: ， Among them, X τ Indicates the current individual's position, X rand represents a randomly selected individual in the population, X best represents the global optimal individual in the current population, R(τ)= σ(τ)·(X rand -X τ ) represents the random disturbance term, σ(τ) represents the disturbance factor that changes dynamically with time, r1, r2, and r3 represent random numbers between 0 and 1, LB represents the lower bound of the search space, UB represents the upper bound of the search space, q represents the random threshold of the control strategy, ω1(τ), ω2(τ), ω3(τ), and ω4(τ) represent dynamic weight adjustment factors, represents the nonlinear adjustment coefficient, ω i,min Represents the minimum value of weight, ω i,max represents the maximum value of the weight, τ represents the current number of iterations, represents the maximum number of iterations, α(τ) represents the first correction factor, α0 represents the first initial correction factor value, and γ represents the parameter that controls the decay speed of the first correction factor.

5. The multi-energy field control process method for improving the service life of complex aerospace components according to claim 4, characterized in that: Methods for updating individual locations include: ， Among them, X prey represents the current global optimal solution, r represents a random number between 0 and 1, E represents the escape energy, E0 represents the initial escape energy, δ represents the parameter that controls the nonlinear attenuation of the escape energy, Ψ(τ) represents the dynamically adjusted convergence factor, η(τ) represents the penalty term weight, and C penalty represents the penalty factor of constrained optimization, β(τ) represents the second correction factor, β0 represents the second initial correction factor value, and μ controls the parameter of the decay speed of the second correction factor.

6. The multi-energy field control process method for improving the service life of complex aerospace components according to claim 1, characterized in that: The comprehensive objective function includes: ， Among them, R' min Indicates the minimum value of surface roughness, R' max Indicates the maximum value of surface roughness, σ' min Represents the minimum value of residual stress, σ' max Indicates the maximum value of residual stress, D' min Indicates the minimum value of the internal defect closure degree, D' max Indicates the maximum value of the internal defect closure degree, L' min Indicates the minimum value of fatigue life, L' max represents the maximum value of fatigue life, w1, w2, w3 and w4 represent the weights of each objective function.

7. A multi-energy field control process system for improving the service life of complex aerospace components, wherein the system applies the method according to any one of claims 1 to 6, characterized in that: include: Component processing module, algorithm initialization module, function construction module, iterative optimization module and threshold comparison module; The component processing module is used to place the aerospace complex component to be processed in a magnetic suspension polishing machine for magnetic field-ultrasound-pressure-high temperature multi-physical field coordinated grinding and polishing processing, and collect the process parameters and component performance of the processed aerospace complex component and perform normalization processing to obtain normalized process parameters and normalized component performance; The algorithm initialization module uses a variational autoencoder to extract the latent space structure between the normalized process parameters, and maps the high-dimensional parameters to a low-dimensional latent space to obtain low-dimensional latent variables, and uses the low-dimensional latent variables to initialize the individual positions of the population in the Hippo optimization algorithm; The function construction module uses the initialized Hippo optimization algorithm to search for the optimal parameter combination in the compression space, updates the individual positions, takes the normalized process parameters as input and the component performance index as the objective function, and constructs a comprehensive objective function; The iterative optimization module takes the comprehensive objective function as a target, calculates a function value through the process parameters, optimizes the iterative process parameter combination until the comprehensive objective function converges, extracts the function value and maps it to component performance, and introduces the mapped component performance as a feedback control quantity into the optimization iteration process; The threshold comparison module adjusts the process parameters and calculates the corresponding objective function value using the variational autoencoder and the Hippo optimization algorithm during each iteration of the optimization process of the iterative optimization module. If the change in the objective function value of this round is less than the threshold, the algorithm stops and the process parameters are optimized. Otherwise, the algorithm initialization module is restarted. The workflow of the algorithm initialization module, the function construction module, the iterative optimization module and the threshold comparison module is repeated until a global optimal solution of the process parameter combination is obtained.

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