A surface integrity constrained aero-engine blade grinding and polishing process optimization method and system

Abstract

The present invention belongs to but is not limited to the field of aviation technology, and discloses a method and system for optimizing the grinding and polishing process of aero-engine blades with surface integrity constraints, and establishes a material removal model for the blades based on an orthogonal center combination experimental data set. The model input and output of machine learning are set and the data is preprocessed, and the surface integrity of the grinding and polishing is predicted by the machine learning XGBoost algorithm. According to the MSPSO particle swarm multi-objective optimization algorithm, multiple sets of solutions for the optimization variables are obtained. Based on the multiple sets of solutions for the optimization variables, the priority solutions for each optimization objective and the global optimal solution that meets all constraints and optimization objectives are screened out. The present invention establishes a material removal model for the blade and calibrates the material removal coefficient by analyzing the grinding and polishing experimental data set. This model can quantitatively describe the influence of each process parameter on the material removal depth, and provides theoretical support for the quantitative and precise grinding and polishing of aero-engine blades.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of aviation technology, and in particular relates to a method and system for optimizing the grinding and polishing process of an aero-engine blade with surface integrity constraints. Background Art

[0002] As the core component of advanced aero-engines, the machining accuracy and surface quality of blades directly determine the performance and service life of the entire engine. However, due to the characteristics of aero-engine blades such as difficult-to-machine materials, complex surfaces, and high contour accuracy requirements, it is often difficult to ensure their surface quality and material removal accuracy after grinding and polishing. At present, the contour accuracy and surface quality of blades after grinding and polishing mainly depend on manual grinding and polishing technology, which makes it difficult to accurately control the blade grinding and polishing allowance and surface integrity based on experience. As a result, there are problems such as large quality differences, poor surface consistency, and blade roughness and residual stress that cannot meet the requirements. The surface integrity and material removal of blades during grinding and polishing are affected by many parameters such as contact force, feed speed, tool speed, abrasive grain size, tool curvature, and workpiece curvature. The planning of machining parameters has a great influence on the workpiece surface integrity, material removal rate, quality consistency, and contour removal accuracy.

[0003] In view of the above analysis, the technical problems that need to be solved urgently in the existing technology are: the process planning lacks consideration of the integrity of the grinding and polishing surface and the accuracy of material removal, making it difficult to ensure the quality and stability of the processed surface. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides a surface integrity constrained aero-engine blade grinding and polishing process optimization method and system. On the basis of ensuring the contour accuracy of the aero-engine blade, the residual stress and surface roughness in the blade grinding and polishing surface integrity are optimized to ensure the processing quality and stability.

[0005] The present invention is implemented as follows: a surface integrity constrained aero-engine blade grinding and polishing process optimization method, comprising:

[0006] S1: Based on the orthogonal central composite test (CCD), an experimental data set is designed to obtain the relationship between grinding and polishing process parameters and surface integrity and material removal depth.

[0007] S2: Establish a material removal model for the blade based on the grinding and polishing experimental data set.

[0008] S3: Set the input and output of the machine learning model: Set the normal contact force F n , tool speed V s , robot tangential feed speed V f , tool curvature radius R1, workpiece curvature radius R2, and abrasive grain size P are used as model feature inputs, and surface roughness and residual stress are used as model outputs.

[0009] S4: Preprocess the data: randomly divide the grinding and polishing experiment sample data into a training set of 90% samples and a test set of 10% samples. At the same time, use Max-Min normalization to perform linear transformation on the original data, normalize it to [0,1], and ensure the original data structure.

[0010] S5: Predicting grinding and polishing surface integrity using machine learning XGBoost algorithm.

[0011] S6: Obtain multiple sets of solutions for the optimization variables according to the MSPSO particle swarm multi-objective optimization algorithm. Continuously update the positions of the swarm particles, and then continuously search for the optimal solution.

[0012] S7: Set the optimization target as the surface roughness and residual stress of the workpiece after grinding and polishing. is the surface roughness, is the surface residual stress.

[0013]

[0014] S8: Set the process parameters and material removal depth constraints, including the normal contact force F n , tool speed V s , robot tangential feed speed V f The limited range of each process parameter and the material removal depth constraint h.

[0015]

[0016] S9: Based on the multiple sets of solutions for the optimization variables, the priority solutions for each optimization objective are screened out, a Pareto frontier surface diagram is obtained, and a global optimal solution that meets all constraints and optimization objectives is selected.

[0017] Furthermore, the grinding and polishing process parameters in S1 include normal contact force, feed rate, tool rotation speed, abrasive grain size, tool curvature, and workpiece curvature, and the experimental measurement results include surface roughness, residual stress, and material removal depth.

[0018] Furthermore, the material removal model in S2 is:

[0019]

[0020] Where h is the removal depth, k h The material removal coefficient is calibrated by experiment, V s is the speed of the sanding machine, V f is the robot tangential feed speed, F n is the normal contact force, R1 is the curvature radius of the tool belt machine contact wheel, and R2 is the curvature radius of the workpiece contact point.

[0021] Furthermore, S5 specifically includes:

[0022] Objective function:

[0023]

[0024] in For training loss functions, common ones are square loss and logistic regression loss. is the sum of the complexity of all k regression trees, which serves as the regularization term in the objective function. The tree model trained in the tth iteration is f t ,but:

[0025]

[0026] In formula (13), only the t-th tree is an unknown variable. The structure of the first t-1 trees is determined, and the complexity and are known, which are replaced by constants. The loss function is expanded by the second-order Taylor to obtain:

[0027]

[0028] Substituting Equation (15) into Equation (14) and removing the constant term, we obtain the simplified objective function:

[0029]

[0030] Furthermore, S6 specifically includes:

[0031] Typically, velocity updates are performed before position updates, and the update formulas for both are as follows:

[0032]

[0033] Among them, N * is the population size; N D is the dimension of the search space, that is, the number of variables; k is the current iteration number; I max is the maximum number of iterations; w is the inertia weight; is the velocity of particle p of dimension q in the kth iteration; c1 is the cognitive acceleration coefficient; c2 is the social acceleration coefficient; r1 and r2 are numbers generated randomly and uniformly in [0,1]; is the position value of particle p in dimension q in the kth iteration; is the value of the individual best position of particle p in dimension q until the kth iteration; is the value of the position of the global best particle in q dimensions until the kth iteration.

[0034] Furthermore, the MOPSO optimization algorithm process is as follows:

[0035] (a): Set the initial parameters of the MOPSO multi-objective particle swarm optimization algorithm, including the number of particles, particle search range, learning factor, inertia weight, and roulette wheel selection operator;

[0036] (b): Evaluate particle fitness, obtain the initial non-dominated solution, update the individual optimal solution, and determine the global optimal solution;

[0037] (c): Update particle position and velocity;

[0038] (d): Determine whether to perform mutation based on demand;

[0039] (e): Calculate the function value of each optimization objective and update pbest;

[0040] (f): Update external files and select the global optimal one;

[0041] (g): Determine the termination condition.

[0042] Another object of the present invention is to provide a surface integrity constrained aero-engine blade grinding and polishing process optimization system for realizing the surface integrity constrained aero-engine blade grinding and polishing process optimization method, comprising:

[0043] Dataset acquisition module: Based on the orthogonal central composite test CCD, the experimental data set between grinding and polishing process parameters and surface integrity and material removal depth is designed and acquired.

[0044] Material removal model building module: establishes the material removal model of the blade based on the grinding and polishing experiment data set.

[0045] Model input and output setting module: Set the model input and output of machine learning: set the normal contact force F n , tool speed V s , robot tangential feed speed V f , tool curvature radius R1, workpiece curvature radius R2, and abrasive grain size P are used as model feature inputs, and surface roughness and residual stress are used as model outputs.

[0046] Data preprocessing module: Preprocess the data: randomly divide the grinding and polishing experiment sample data into a training set of 90% samples and a test set of 10% samples. At the same time, use Max-Min standardization to perform linear transformation on the original data, normalize it to [0,1], and ensure the original data structure.

[0047] Integrity prediction module: Predicts the integrity of the polished surface through the machine learning XGBoost algorithm.

[0048] Optimal solution search module: According to the MSPSO particle swarm multi-objective optimization algorithm, multiple sets of solutions for the optimization variables are obtained, the positions of the swarm particles are continuously updated, and the optimal solution is continuously searched.

[0049] Optimization target setting module: Set the optimization target as the surface roughness and residual stress of the workpiece after grinding and polishing.

[0050]

[0051] Constraint setting module: Set constraints for process parameters and material removal depth, including normal contact force F n , tool speed V s , robot tangential feed speed V f The limited range of each process parameter and the material removal depth constraint h.

[0052]

[0053] Global optimal solution selection module: Based on the multiple sets of solutions for the optimization variables, the priority solutions for each optimization objective are screened out, a Pareto frontier surface diagram is obtained, and the global optimal solution that meets all constraints and optimization objectives is selected.

[0054] Another object of the present invention is to provide a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the surface integrity constrained aero-engine blade grinding and polishing process optimization method.

[0055] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the surface integrity constrained aero-engine blade grinding and polishing process optimization method.

[0056] Another object of the present invention is to provide an information data processing terminal, which includes the surface integrity constrained aero-engine blade grinding and polishing process optimization system.

[0057] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0058] First, the advantages and positive effects of the technical solution to be protected by the present invention are:

[0059] (1) Based on the orthogonal central composite CCD test, an experimental data set was designed to obtain the relationship between grinding and polishing process parameters, surface integrity, and material removal depth. A material removal model for the blade was established based on the grinding and polishing experimental data set, and the material removal coefficient was calibrated to determine the influence of each parameter on the material removal depth. This ensured the quantitative removal of material and the blade profile accuracy during the grinding and polishing process.

[0060] (2) According to the machine learning XGBoost algorithm, the normal contact force F n , tool speed V s , robot tangential feed speed V f , tool curvature radius R1, workpiece curvature radius R2, and abrasive grain size P were used as model feature inputs, while surface roughness and residual stress were used as model outputs. Data preprocessing and data partitioning were performed to explore and analyze the influence of various grinding and polishing process parameters on surface integrity, and an accurate surface integrity prediction model was established.

[0061] (3) Taking into account the limited range of each processing parameter and the material removal constraint, the optimal surface integrity was taken as the optimization goal. The roughness and residual stress in the surface integrity were simultaneously optimized according to the MSPSO particle swarm multi-objective optimization algorithm, and the optimal grinding and polishing process parameters were obtained.

[0062] (4) The technical solution of the present invention is applicable to, but not limited to, the grinding and polishing of curved workpieces such as aircraft engine blades, and has strong adaptability and scalability. While ensuring quantitative material removal, process parameters are optimized with the goal of achieving optimal surface integrity, ensuring the contour accuracy, surface quality, and consistency of aircraft engine blades.

[0063] Second, as auxiliary evidence for the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0064] (1) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0065] 1. By analyzing the grinding and polishing experimental data set, a blade material removal model was established and the material removal coefficient was calibrated. This model can quantitatively describe the impact of various process parameters on the material removal depth, providing theoretical support for quantitative and precise grinding and polishing of aeroengine blades.

[0066] 2. The machine learning algorithm XGBoost was used to predict the surface integrity of the polished surface, specifically the surface roughness and residual stress. This enabled accurate prediction of the surface integrity of the robot-polished surface.

[0067] 3. Optimization of grinding and polishing process parameters was achieved through the MSPSO particle swarm multi-objective optimization algorithm, particularly for the simultaneous optimization of surface roughness and residual stress. This multi-objective optimization method has important application value in the grinding and polishing of aeroengine blades, filling the gaps in existing multi-objective optimization techniques.

[0068] 4. Adaptability and Scalability: The technical solution of this invention is not only applicable to the grinding and polishing of aircraft engine blades, but can also be extended to the grinding and polishing needs of other curved workpieces, demonstrating its strong adaptability. This provides a new solution for grinding and polishing in related industries and expands the scope of application of the technology.

[0069] 5. Improved Processing Quality and Stability: By optimizing grinding and polishing process parameters, we ensure the contour accuracy, surface quality, and consistency of aero-engine blades, significantly improving processing quality and stability. This achievement has important practical and economic value in the field of aero-engine manufacturing.

[0070] (2) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never been able to solve successfully:

[0071] 1. Balancing surface integrity and material removal accuracy: In the grinding and polishing of aeroengine blades, optimizing surface integrity (residual stress and surface roughness) while ensuring material removal accuracy has always been a challenge within the industry. Traditional grinding and polishing processes often struggle to simultaneously meet these two requirements, resulting in inconsistent machining quality. This invention successfully achieves this balance by establishing a material removal model and optimizing grinding and polishing process parameters.

[0072] 2. Previous grinding and polishing process optimization relied heavily on experience and trial-and-error, which was inefficient and difficult to achieve. By introducing machine learning and multi-objective optimization algorithms, this invention provides an efficient and scientific process optimization method that enables precise control of various parameters in the grinding and polishing process, significantly improving processing efficiency and quality.

[0073] 3. The Challenge of Surface Integrity Control: Surface integrity significantly impacts the performance and lifespan of aeroengine blades, but effectively controlling surface integrity during the grinding and polishing process has always been a technical challenge. This invention, through optimization of grinding and polishing process parameters, successfully achieves effective control of surface integrity, ensuring the long-term stability and reliability of aeroengine blades.

[0074] 4. Adaptability and Scalability: The technical solution of this invention not only solves the challenges in the grinding and polishing of aircraft engine blades, but also possesses strong adaptability and scalability, making it widely applicable to the grinding and polishing of other curved workpieces. This feature provides a new solution for the related grinding and polishing fields, overcoming the limitations of previous technologies in terms of scope of application.

[0075] Third, the present invention uses an orthogonal central composite design (CCD) to obtain an experimental dataset of grinding and polishing process parameters (such as normal contact force, feed rate, tool speed, tool curvature, workpiece curvature, and abrasive grit size), surface integrity (such as surface roughness and residual stress), and material removal depth. Based on this experimental data, a material removal model is established to describe the relationship between material removal depth and grinding and polishing parameters. This model comprehensively considers factors such as tool curvature, workpiece curvature, and normal contact force, providing a data foundation for process optimization.

[0076] A surface integrity prediction model was established using the machine learning XGBoost algorithm. The model takes normal contact force, tool speed, and feed rate as inputs, and outputs surface roughness and residual stress. After preprocessing, the data was normalized and randomly divided into training and test sets. A second-order Taylor expansion optimization of the surface integrity was performed using the XGBoost algorithm to generate a high-precision prediction model. This model can quickly predict surface quality under different grinding and polishing parameters, laying the foundation for process optimization.

[0077] The MSPSO (Multi-Objective Particle Swarm Optimization) algorithm is used to optimize process parameters, setting the optimization objectives to minimize surface roughness and maximize residual compressive stress. Constraints are also set for process parameters (such as normal contact force and tool speed) and material removal depth. The algorithm updates the velocity and position of the particle swarm to find the Pareto frontier and selects the global optimal solution through non-dominated sorting and external file updates, ensuring that the optimized solution set meets the multi-objective constraints and achieves optimal surface quality.

[0078] By focusing on the optimized solutions selected by the Pareto frontier, weights are set according to different processing objectives, and the priority solution is flexibly selected. The optimization results show that the surface roughness is reduced and the absolute value of the residual compressive stress is increased, which significantly improves the surface quality of the blade. The technical solution of the present invention effectively solves the technical problem of the existing technology that it is difficult to balance surface integrity and processing efficiency. The particle swarm optimization algorithm is used to achieve precise optimization of complex process parameters, significantly improve processing efficiency and surface quality, and provide a new technical path for aero-engine blade processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a schematic diagram of the overall process of the surface integrity constrained aero-engine blade grinding and polishing process optimization method provided by an embodiment of the present invention;

[0080] Figure 2 This is a schematic diagram of an orthogonal experimental group based on an orthogonal central composite design provided by an embodiment of the present invention;

[0081] Figure 3 This is a flow chart of the MOPSO multi-objective optimization algorithm provided by an embodiment of the present invention;

[0082] Figure 4 A Pareto frontier graph obtained by the MOPSO multi-objective optimization algorithm provided in an embodiment of the present invention;

[0083] Figure 5 1. Schematic diagram of the cascade blades before and after polishing provided by an embodiment of the present invention;

[0084] Figure 6 1 is a schematic diagram of a finite element simulation of abrasive belt grinding and polishing provided by an embodiment of the present invention;

[0085] Figure 7 This is a structural diagram of an aero-engine blade grinding and polishing process optimization system with surface integrity constraints provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0087] Example 1: Optimization of the Grinding and Polishing Process for High-Temperature Blades of Aero-Engine

[0088] High-temperature blades in aircraft engines are subject to extremely high temperatures and high-speed airflow during operation. Their surface integrity is crucial to the performance and life of the engine. This invention is used to optimize the grinding and polishing process of blades to ensure that their surface quality meets high-performance requirements.

[0089] 1) Data acquisition: The material removal depth, surface roughness, and residual stress data under different normal contact forces, tool speeds, abrasive grain sizes, and workpiece curvatures are experimentally measured to construct a material removal model.

[0090] 2) Machine learning modeling: The XGBoost algorithm is used to establish a surface integrity prediction model to predict the surface roughness and residual stress under different grinding and polishing parameter combinations.

[0091] 3) Optimization calculation: The MSPSO algorithm is used to optimize the grinding and polishing process parameters, setting the goals of minimizing surface roughness and maximizing residual compressive stress.

[0092] 4) Application results: The optimal solution was screened through the Pareto frontier. The optimized process parameters reduced the blade surface roughness by 20% and increased the residual compressive stress by 30%, significantly improving the blade's fatigue resistance and antioxidant capacity.

[0093] Example 2: Improving the surface processing quality of gas turbine blades

[0094] Gas turbine blades have high requirements for surface integrity, and their surface roughness and residual stress directly affect turbine efficiency and service life. The present invention is applied to optimize the grinding and polishing process of gas turbine blades to improve turbine performance.

[0095] 1) Experimental design: Through orthogonal central composite design (CCD), surface integrity experimental data of gas turbine blades under different grinding and polishing parameters were collected.

[0096] 2) Material removal model construction: Combined with experimental data, a material removal model is established to analyze the influence of grinding and polishing parameters on material removal depth and surface integrity.

[0097] 3) Optimization and implementation: Use the MSPSO algorithm to optimize process parameters, set constraints on material removal depth and surface roughness, and screen out the global optimal solution.

[0098] 4) Optimization Effect: After optimization, the blade surface roughness was reduced by 15%, and the absolute value of residual compressive stress increased by 25%. The optimized process significantly improved the blade surface quality, making its performance more stable and reliable under high-temperature and high-pressure conditions.

[0099] Two embodiments demonstrate the application of the present invention in the grinding and polishing process of aircraft engine and gas turbine blades. By optimizing process parameters, both surface integrity and material removal are improved, providing an efficient and precise technical solution for the manufacture of high-performance blades.

[0100] The present invention is achieved by: a surface integrity and surface integrity constraint aero-engine blade grinding and polishing process optimization method as follows Figure 1 As shown, a blade material removal model is established based on an orthogonal central combination experimental data set. The machine learning model input and output are set and the data is preprocessed. The grinding and polishing surface integrity is predicted using the machine learning XGBoost algorithm. Multiple sets of solutions for the optimization variables are obtained using the MSPSO particle swarm optimization algorithm. Based on the multiple sets of solutions for the optimization variables, prioritized solutions for each optimization objective are screened, as well as a global optimal solution that meets all constraints and optimization objectives.

[0101] S1.1: Based on the orthogonal central composite test (CCD), design and obtain the experimental data set between the grinding and polishing process parameters and the surface integrity and material removal depth. Figure 2 As shown in the figure, the grinding and polishing process parameters include normal contact force, feed speed, tool rotation speed, abrasive grain size, tool curvature and workpiece curvature, and the experimental measurement results include surface roughness, residual stress and material removal depth.

[0102] S1.2: Establish a material removal model for the blade based on the grinding and polishing experimental data set.

[0103]

[0104] Where h is the removal depth, k h The material removal coefficient is calibrated by experiment, V s is the speed of the sanding machine, V f is the robot tangential feed speed, F n is the normal contact force, R1 is the curvature radius of the tool belt machine contact wheel, and R2 is the curvature radius of the workpiece contact point.

[0105] S2.1: Set the input and output of the machine learning model. n , tool speed V s , robot tangential feed speed V f , tool curvature radius R1, workpiece curvature radius R2, and abrasive grain size P are used as model feature inputs, and surface roughness and residual stress are used as model outputs.

[0106] S2.2: Preprocess the data. Randomly divide the grinding and polishing test sample data into a training set of 90% samples and a test set of 10% samples. At the same time, use Max-Min normalization to perform linear transformation on the original data and normalize it to [0, 1], while maintaining the original data structure.

[0107] S2.3: Predict grinding and polishing surface integrity using machine learning XGBoost algorithm.

[0108] Objective function:

[0109]

[0110] in For training loss functions, common ones are square loss and logistic regression loss. is the sum of the complexity of all k regression trees, which serves as the regularization term in the objective function. The tree model trained in the tth iteration is f t ,but:

[0111]

[0112]

[0113] In formula (13), only the t-th tree is an unknown variable. The structure of the first t-1 trees is determined, and the complexity and are known, which are replaced by constants. The loss function is expanded by the second-order Taylor to obtain:

[0114]

[0115] Substituting equation (15) into equation (14) and removing the constant term, we obtain the simplified objective function:

[0116]

[0117] S3.1: Based on the MSPSO particle swarm multi-objective optimization algorithm, multiple sets of solutions for the optimization variables are obtained. The positions of the swarm particles are continuously updated, and the optimal solution is continuously searched. Usually, the speed update is performed before the position update. The update formulas of the two are as follows:

[0118]

[0119] Among them, N * is the population size; N D is the dimension of the search space, that is, the number of variables; k is the current iteration number; I max is the maximum number of iterations; w is the inertia weight; is the velocity of particle p of dimension q in the kth iteration; c1 is the cognitive acceleration coefficient; c2 is the social acceleration coefficient; r1 and r2 are numbers generated randomly and uniformly in [0,1]; is the position value of particle p in dimension q in the kth iteration; is the value of the individual best position of particle p in dimension q until the kth iteration; is the value of the position of the global best particle in q dimensions until the kth iteration.

[0120] like Figure 3 As shown in Figure 2, the MOPSO optimization algorithm process is as follows:

[0121] (a): Set the initial parameters of the MOPSO multi-objective particle swarm optimization algorithm, including the number of particles, particle search range, learning factor, inertia weight, and roulette wheel selection operator;

[0122] (b): Evaluate particle fitness, obtain the initial non-dominated solution, update the individual optimal solution, and determine the global optimal solution;

[0123] (c): Update particle position and velocity;

[0124] (d): Determine whether to perform mutation based on demand;

[0125] (e): Calculate the function value of each optimization objective and update pbest;

[0126] (f): Update external files and select the global optimal one;

[0127] (g): Determine the termination condition.

[0128] S3.2: Set the optimization objectives as the surface roughness and residual stress of the workpiece after grinding and polishing.

[0129]

[0130] S3.3: Set process parameters and material removal depth constraints, including the normal contact force F n , tool speed V s , robot tangential feed speed V f The limited range of each process parameter and the material removal depth constraint h.

[0131]

[0132] S3.4: Based on the multiple solutions of the optimization variables, the priority solutions of each optimization objective are screened out, and the following are obtained: Figure 4 The Pareto frontier surface diagram shown selects the global optimal solution that meets all constraints and optimization objectives. In this embodiment, the lower the roughness in the surface integrity and the larger the absolute value of the residual compressive stress, the better the surface quality of the blade after grinding and polishing. The embodiment of the present invention uses a multi-objective particle swarm optimization algorithm to calculate the optimal solution set for surface integrity. Based on this optimal solution set, different weights can be set for different optimization objectives to address the different requirements for each optimization objective in the actual processing process. The optimization results obtained using the particle swarm optimization algorithm are as follows:

[0133]

[0134]

[0135] like Figure 5 The figure shows the effect of blade grinding and polishing before and after. From the perspective of the integrity of the grinding and polishing surface, the process parameters obtained by the multi-objective optimization method of the embodiment of the present invention are compared with the original empirical process parameters. On the basis of ensuring the same material removal depth, the optimized grinding and polishing process parameters reduce the roughness of the workpiece surface and increase the absolute value of the residual compressive stress, thereby improving the surface integrity of the blade after grinding and polishing, and are more conducive to improving the service performance and fatigue life of the blade.

[0136] like Figure 7 As shown in the figure, the surface integrity constrained aero-engine blade grinding and polishing process optimization system includes:

[0137] Dataset acquisition module: Based on the orthogonal central composite test CCD, it designs and acquires the experimental data set between the grinding and polishing process parameters and the surface integrity and material removal depth;

[0138] Material removal model building module: builds the blade material removal model based on the grinding and polishing experiment data set;

[0139] Model input and output setting module: Set the model input and output of machine learning: set the normal contact force F n , tool speed V s , robot tangential feed speed V f, tool curvature radius R1, workpiece curvature radius R2, and abrasive grain size P are used as model feature inputs, and surface roughness and residual stress are used as model outputs;

[0140] Data preprocessing module: Preprocess the data by randomly dividing the grinding and polishing experiment sample data into a training set of 90% samples and a test set of 10% samples. At the same time, use Max-Min normalization to perform linear transformation on the original data, normalize it to [0,1], and ensure the original data structure;

[0141] Integrity prediction module: predicts the integrity of the grinding and polishing surface through the machine learning XGBoost algorithm;

[0142] Optimal solution search module: According to the MSPSO particle swarm multi-objective optimization algorithm, multiple sets of solutions for the optimization variables are obtained, the positions of the swarm particles are continuously updated, and the optimal solution is continuously searched;

[0143] Optimization target setting module: Set the optimization target as the surface roughness and residual stress of the workpiece after grinding and polishing;

[0144]

[0145] Constraint setting module: Set constraints for process parameters and material removal depth, including normal contact force F n , tool speed V s , robot tangential feed speed V f The limited range of each process parameter and the material removal depth constraint h;

[0146]

[0147] Global optimal solution selection module: Based on the multiple sets of solutions for the optimization variables, the priority solutions for each optimization objective are screened out, a Pareto frontier surface diagram is obtained, and the global optimal solution that meets all constraints and optimization objectives is selected.

[0148] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a method for optimizing the grinding and polishing process of an aero-engine blade with surface integrity constraints.

[0149] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a method for optimizing the grinding and polishing process of an aero-engine blade with surface integrity constraints.

[0150] An application embodiment of the present invention provides an information data processing terminal, which includes an aero-engine blade grinding and polishing process optimization system with surface integrity constraints.

[0151] Aircraft engine blades operate in high-temperature, high-pressure environments, requiring excellent fatigue resistance and surface roughness. During the aircraft engine manufacturing process, the grinding and polishing process plays a crucial role in influencing the surface integrity of the blades. Reasonable process parameters can effectively improve the surface quality of the blades and extend their service life. To this end, this example optimizes the parameters of the grinding and polishing process for aircraft engine blades.

[0152] Application Example 1: Optimization of Grinding and Polishing Process Parameters for Cascaded Blades of Aero-Engine

[0153] Cascaded blades in aircraft engines play a crucial role in guiding and compressing airflow. Their surface quality directly impacts engine performance and efficiency. The surface integrity-constrained polishing optimization method for aircraft engine blades proposed in this paper can improve the surface quality and performance of polished blades.

[0154] 1) Based on the orthogonal central composite test (CCD), an experimental data set was designed to obtain the relationship between grinding and polishing process parameters and surface integrity and material removal depth.

[0155] 2) Establish a material removal model for the blade based on the grinding and polishing experimental data set.

[0156] 3) Set the model input and output for machine learning.

[0157] 4) Predict the surface integrity of blade grinding and polishing through machine learning XGBoost algorithm.

[0158] 5) According to the MSPSO particle swarm multi-objective optimization algorithm, multiple groups of solutions of the optimization variables are obtained.

[0159] 6) Set the optimization target as the surface roughness and residual stress of the workpiece after grinding and polishing.

[0160] 7) Set constraints on process parameters and material removal depth.

[0161] 8) Based on the multiple sets of solutions for the optimization variables, the priority solutions for each optimization objective are screened out, a Pareto frontier surface diagram is obtained, and a global optimal solution that meets all constraints and optimization objectives is selected.

[0162] Application Example 2: Optimization of Grinding and Polishing Process Parameters for Aero-Engine Blades

[0163] After milling, the surface quality and roughness of aeroengine blades still fail to meet manufacturing precision requirements. To improve surface integrity, the blades of the aeroengine blades require grinding and polishing. The surface integrity-constrained grinding and polishing process optimization method of the present invention can improve the surface quality and performance of blades after grinding and polishing.

[0164] 1) Based on the orthogonal central composite test (CCD), an experimental data set was designed to obtain the relationship between grinding and polishing process parameters and surface integrity and material removal depth.

[0165] 2) Establish a material removal model for the blade based on the grinding and polishing experimental data set.

[0166] 3) Set the model input and output for machine learning.

[0167] 4) Predict the surface integrity of blade grinding and polishing through machine learning XGBoost algorithm.

[0168] 5) According to the MSPSO particle swarm multi-objective optimization algorithm, multiple groups of solutions of the optimization variables are obtained.

[0169] 6) Set the optimization target as the surface roughness and residual stress of the workpiece after grinding and polishing.

[0170] 7) Set constraints on process parameters and material removal depth.

[0171] 8) Based on the multiple sets of solutions for the optimization variables, the priority solutions for each optimization objective are screened out, a Pareto frontier surface diagram is obtained, and a global optimal solution that meets all constraints and optimization objectives is selected.

[0172] like Figure 5 As shown in the figure, the surface finish of the blade can be significantly improved by comparing the surface before and after polishing. Figure 6 The figure shows the finite element simulation results of the surface integrity of belt grinding and polishing. By comparing the empirical parameter group with the optimized parameter group under constraints, it can be seen that, while ensuring consistent material removal, the roughness of the grinding and polishing surface integrity of the optimized parameter group selected in this example is lower and the absolute value of the residual compressive stress is larger, indicating that the surface quality of the blade after grinding and polishing is better, and it has better fatigue life and performance.

[0173] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0174] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A surface integrity constrained aero-engine blade grinding and polishing process optimization method, characterized in that: include: S1: Based on the orthogonal central composite test (CCD), we designed and acquired the experimental data sets on the relationship between grinding and polishing process parameters and surface integrity and material removal depth. S2: Establishing a material removal model for the blade based on the grinding and polishing experimental data set; S3: Set the input and output of the machine learning model: Set the normal contact force F n , tool speed V s , robot tangential feed speed V f , tool curvature radius R1, workpiece curvature radius R2, and abrasive grain size P are used as model feature inputs, and surface roughness and residual stress are used as model outputs; S4: Data preprocessing: Randomly divide the grinding and polishing experiment sample data into a training set of 90% samples and a test set of 10% samples. At the same time, use Max-Min normalization to perform linear transformation on the original data, normalize it to [0,1], and ensure the original data structure; S5: Predicting grinding and polishing surface integrity using machine learning XGBoost algorithm; S6: Based on the MSPSO particle swarm multi-objective optimization algorithm, multiple sets of solutions for optimization variables are obtained; the positions of the swarm particles are continuously updated, and the optimal solution is continuously searched; S7: Setting the optimization target as the surface roughness and residual stress of the workpiece after grinding and polishing; S8: Set the process parameters and material removal depth constraints, including the normal contact force F n , tool speed V s , robot tangential feed speed V f The limited range of each process parameter and the material removal depth constraint h; S9: Based on the multiple sets of solutions for the optimization variables, the priority solutions for each optimization objective are screened out, a Pareto frontier surface diagram is obtained, and a global optimal solution that meets all constraints and optimization objectives is selected.

2. The surface integrity constrained aero-engine blade grinding and polishing process optimization method according to claim 1, characterized in that: The grinding and polishing process parameters in S1 include normal contact force, feed rate, tool rotation speed, abrasive grain size, tool curvature and workpiece curvature. The experimental measurement results include surface roughness, residual stress and material removal depth.

3. The surface integrity constrained aero-engine blade grinding and polishing process optimization method according to claim 1, characterized in that: The material removal model in S2 is: Where h is the removal depth, k h The material removal coefficient is calibrated by experiment, V s is the speed of the sanding machine, V f is the robot tangential feed speed, F n is the normal contact force, R1 is the curvature radius of the tool belt machine contact wheel, and R2 is the curvature radius of the workpiece contact point.

4. The surface integrity constrained aero-engine blade grinding and polishing process optimization method according to claim 1, characterized in that: S5 specifically includes: Objective function: in For training loss functions, common ones are square loss and logistic regression loss. is the sum of the complexity of all k regression trees, which serves as the regularization term in the objective function; the tree model trained in the tth iteration is f t ,but: In formula (13), only the t-th tree is an unknown variable. The structures of the first t-1 trees are determined, and the complexity and are known, which are replaced by constants. The loss function is expanded by the second-order Taylor to obtain: Substituting equation (15) into equation (14) and removing the constant term, we obtain the simplified objective function:

5. The surface integrity constrained aero-engine blade grinding and polishing process optimization method according to claim 1, characterized in that: S6 specifically includes: Typically, velocity updates are performed before position updates, and the update formulas for both are as follows: Among them, N * is the population size; N D is the dimension of the search space, that is, the number of variables; k is the current iteration number; I max is the maximum number of iterations; w is the inertia weight; is the velocity of particle p of dimension q in the kth iteration; c1 is the cognitive acceleration coefficient; c2 is the social acceleration coefficient; r1 and r2 are numbers generated randomly and uniformly in [0,1]; is the position value of particle p in dimension q in the kth iteration; is the value of the individual best position of particle p in dimension q until the kth iteration; is the value of the position of the global best particle in q dimensions until the kth iteration.

6. The surface integrity constrained aero-engine blade grinding and polishing process optimization method according to claim 1, characterized in that: The MOPSO optimization algorithm process is as follows: (a): Set the initial parameters of the MOPSO multi-objective particle swarm optimization algorithm, including the number of particles, particle search range, learning factor, inertia weight, and roulette wheel selection operator; (b): Evaluate particle fitness, obtain the initial non-dominated solution, update the individual optimal solution, and determine the global optimal solution; (c): Update particle position and velocity; (d): Determine whether to perform mutation based on demand; (e): Calculate the function value of each optimization objective and update pbest; (f): Update external files and select the global optimal one; (g): Determine the termination condition.

7. A surface integrity constrained aero-engine blade grinding and polishing process optimization system that implements the surface integrity constrained aero-engine blade grinding and polishing process optimization method according to any one of claims 1 to 6, characterized in that: include: Dataset acquisition module: Based on the orthogonal central composite test CCD, it designs and acquires the experimental data set between the grinding and polishing process parameters and the surface integrity and material removal depth; Material removal model building module: builds the blade material removal model based on the grinding and polishing experiment data set; Model input and output setting module: Set the model input and output of machine learning: set the normal contact force F n , tool speed V s , robot tangential feed speed V f , tool curvature radius R1, workpiece curvature radius R2, and abrasive grain size P are used as model feature inputs, and surface roughness and residual stress are used as model outputs; Data preprocessing module: Preprocess the data by randomly dividing the grinding and polishing experiment sample data into a training set of 90% samples and a test set of 10% samples. At the same time, use Max-Min normalization to perform linear transformation on the original data, normalize it to [0,1], and ensure the original data structure; Integrity prediction module: predicts the integrity of the grinding and polishing surface through the machine learning XGBoost algorithm; Optimal solution search module: Based on the MSPSO particle swarm multi-objective optimization algorithm, multiple sets of solutions for optimization variables are obtained, the positions of swarm particles are continuously updated, and the optimal solution is continuously searched; Optimization target setting module: Set the optimization target as the surface roughness and residual stress of the workpiece after grinding and polishing; Constraint setting module: Set constraints for process parameters and material removal depth, including normal contact force F n , tool speed V s , robot tangential feed speed V f The limited range of each process parameter and the material removal depth constraint h; Global optimal solution selection module: Based on the multiple sets of solutions for the optimization variables, the priority solutions for each optimization objective are screened out, a Pareto frontier surface diagram is obtained, and the global optimal solution that meets all constraints and optimization objectives is selected.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the surface integrity constrained aero-engine blade grinding and polishing process optimization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the steps of the surface integrity constrained aero-engine blade grinding and polishing process optimization method according to any one of claims 1 to 6.

10. An information data processing terminal, comprising the surface integrity constrained aero-engine blade grinding and polishing process optimization system according to claim 7.

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