Granular medium flow field regulation and control method for blisk spiral vibration composite polishing and grinding

Abstract

The invention belongs to the technical field of barreling finishing machining of blisk parts, and aims to solve the problems that air inlet and exhaust edges of blisk parts are over-polished, blade roots are under-polished, and machining is not uniform. The invention provides a particle medium flow field regulation and control method for blisk spiral vibration composite polishing and grinding. A multi-target evaluation model of a blisk wear depth average value and a variable coefficient is constructed based on an entropy weight method and a subjective assignment method; optimal process parameters are selected through orthogonal simulation of container size parameters and motion parameters; a container wall configuration optimization method is provided for non-uniformity of the area from the blade root to the blade tip, and optimization design is conducted on container wall configuration through a response curved surface, a neural network, a genetic algorithm and the like; in order to solve the problem of excessive polishing of air inlet and exhaust edges, a blade expansion configuration regulation and control method is provided, and the machining uniformity and consistency of the blisk are comprehensively achieved. According to the invention, high surface integrity and uniform consistency polishing and grinding of the blisk are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of tumbling and finishing processing of integral blade disk parts, and particularly relates to a method for controlling the flow field of a granular medium in tumbling and vibrating composite polishing of an integral blade disk. Background Art

[0002] The blisk is a core component of an aero-engine. It is very easy to fail due to long-term service in extreme environments such as high temperature, high pressure, high speed and alternating loads. Studies have shown that the surface integrity and machining uniformity of the blisk significantly affect the service performance and service life of the aero-engine. After CNC milling, the surface roughness of the blisk is Ra The value is large, and it is necessary to use various polishing techniques to make the surface roughness without destroying its surface accuracy. Ra The value dropped to below 0.4μm. However, due to the difficulties in polishing such as the structure, material and processing requirements of the integral blade disk, it is difficult to meet the high surface integrity requirements of the integral blade disk.

[0003] At present, vibration roller grinding and finishing are used to polish the integral blade disc at home and abroad. However, there are problems such as over-polishing of the intake and exhaust edges, under-polishing of the blade roots, and uneven processing. Some areas still need to be manually polished. The manual polishing effect is unstable and random, and it will affect the health of the operator.

[0004] The paper "Simulation Analysis of Particle Mechanical Behavior During Rotation-Assisted Horizontal Vibration Polishing of Integral Blades" proposed a rotary-assisted horizontal vibration tumbling finishing method. However, due to the complex structural characteristics of the blisk, the blade surface forces and processing effects exhibit significant variations in strength, making it difficult to meet the machining requirements of the blisk. Therefore, based on the rotary-assisted horizontal vibration polishing method, the present invention proposes a particle medium flow field control method for rotary vibration composite polishing of the blisk, addressing the issues of over-polishing at the intake and exhaust edges, under-polishing at the blade root, and uneven machining of the blisk surface. Summary of the Invention

[0005] In order to solve at least one of the above-mentioned technical problems existing in the prior art, the present invention provides a method for controlling the flow field of granular media in integral blade disk vortex vibration composite polishing.

[0006] The present invention is implemented by the following technical solution: a method for controlling the flow field of a granular medium by integral blade disc vortex vibration composite polishing, comprising the following steps: S1: Construct an EDEM discrete element simulation model for the integrated blade disc gyratory vibration composite polishing; S2: Determine the evaluation index of the integral blade vibration composite polishing and build a multi-objective evaluation model; S3: According to the structural characteristics of the overall blade disk, the container size parameters and container motion parameters, the left and right wall configurations of the container, and the blade expansion configuration are used to actively control the flow field of the granular medium; three groups of simulations and multi-objective evaluations are performed in sequence, wherein the first, second, and third groups of simulations and multi-objective evaluations are used to obtain the optimal combination of container size parameters and container motion parameters, the optimal combination of left and right wall configuration parameters, and the optimal expansion disk thickness of the blade expansion configuration; before the second and third groups of simulations and multi-objective evaluations are performed, the simulation and multi-objective evaluation results of the previous group must be subjected to EDEM discrete element simulation to determine the overall blade disk processing effect; if the simulation and multi-objective evaluation results of the first group meet the requirements of the additional If the processing requirements are met, the control scheme is determined to be the optimal container size parameters and container motion parameters, otherwise the second set of simulations and multi-objective evaluations are performed; if the second set of simulations and multi-objective evaluation results meet the processing requirements, the control scheme is determined to be the optimal container size parameters and container motion parameters and the optimal container left and right wall configuration thickness and height parameters, otherwise the third set of simulations and multi-objective evaluations are performed; if the third set of simulations and multi-objective evaluation results meet the processing requirements, the control scheme is determined to be the optimal container size parameters and container motion parameters, the optimal container left and right wall configuration thickness and height parameters and the optimal expansion disk thickness parameters of the blade expansion configuration, otherwise step S3 is repeated until the processing requirements are met.

[0007] Preferably, step S3 includes: S31: Determine the container size parameter range and container motion parameter range based on the structural characteristics of the integral blade disk, and perform orthogonal simulation; S32: Perform multi-objective evaluation based on the orthogonal simulation results and the multi-objective evaluation model to obtain the optimal combination of container size parameters and container motion parameters; S33: Simulating the optimal combination of container size parameters and container motion parameters based on the EDEM discrete element simulation model to determine whether the overall blade disk processing effect meets the processing requirements; if it does not meet the processing requirements, proceeding to step S34, regulating the container wall configuration based on the optimal combination of container size parameters and container motion parameters; if it meets the processing requirements, determining the regulation scheme as the optimal container size parameters and container motion parameters; S34: Based on the structural characteristics of the integral blade disk, determine the parameter ranges of the thickness and height of the left and right walls of the container and perform response surface simulation. S35: Construct a neural network and genetic algorithm coupling model, and determine the optimal combination of container left and right wall configuration parameters based on the neural network and genetic algorithm coupling model, response surface simulation results, and a multi-objective evaluation model; S36: Simulating the optimal combination of left and right container wall configuration parameters based on the EDEM discrete element simulation model to determine whether the overall blade disk processing effect meets the processing requirements; if it does not meet the processing requirements, proceeding to step S37, performing blade expansion configuration control based on the optimal combination of left and right container wall configuration parameters; if it meets the processing requirements, determining the control scheme as the optimal container size parameters and container motion parameters, as well as the optimal left and right container wall configuration thickness and height parameters; S37: Based on the structural characteristics of the integral blade disk, determine the thickness range of the extended disk in the blade extended configuration and perform single factor simulation; S38: Based on the results of the single-factor simulation and the multi-objective evaluation model, a multi-objective evaluation is performed to obtain the optimal expansion disk thickness of the blade expansion configuration; S39: Based on the EDEM discrete element simulation model, the thickness of the expansion disk of the optimal blade expansion configuration is simulated to determine whether the overall blade disk processing effect meets the processing requirements; if it does not meet the processing requirements, repeat steps S31 to S39 until the processing requirements are met; if it meets the processing requirements, determine the control scheme as the optimal container size parameters and container motion parameters, the optimal container left and right wall configuration thickness and height parameters, and the optimal blade expansion configuration expansion disk thickness parameters.

[0008] Preferably, the Hertz-Mindlin model and the Archard wear model are selected as the basic models of the EDEM discrete element simulation model for the integral blade vibration composite polishing; At the same time, the data blocks are divided according to the structural characteristics of the integral blade disk, and the data blocks are evenly distributed at equal intervals along the blade chord length direction and the blade body length direction on the blade back surface and blade basin surface of the integral blade disk.

[0009] Preferably, the evaluation indicators of the integral blade disc cyclonic vibration composite polishing include the average value and coefficient of variation of the wear depth of the blade back surface and the average value and coefficient of variation of the wear depth of the blade basin surface, and a multi-objective evaluation model is constructed based on the entropy weight method and the subjective assignment method.

[0010] Preferably, the expression of the multi-objective evaluation model is: Where: It is a multi-objective evaluation model; and are the mean value and coefficient of variation of the wear depth of the blade back surface after normalization and data translation; and are the mean value and coefficient of variation of the wear depth of the blade basin profile after normalization and data translation; and are the mean value and weight coefficient of the wear depth of the blade back surface respectively; and are the mean value of the wear depth of the blade basin profile and the weight coefficient of the coefficient of variation; and are the subjective values ​​of the mean value and coefficient of variation of the wear depth of the blade back surface, and are the subjective values ​​of the mean value and coefficient of variation of the wear depth of the blade basin surface, and .

[0011] Preferably, in step S31, the maximum profile diameter of the given blisk is , the width of the overall blade disk is The container size parameter range determined by the maximum profile diameter and width of the blisk includes: Container inner wall diameter Parameter range: 1.05 ~1.25 , and the container inner wall width Parameter range: 2.50 ~3.50 ; The parameter range of the container's vibration frequency is: 20Hz~30Hz, the parameter range of the container's vibration amplitude is: 5.469mm~2.431mm, and the parameter range of the container's rotation parameter is: 5.0rpm~15.0rpm.

[0012] Preferably, the orthogonal simulation results include values ​​of evaluation indicators corresponding to different combinations of container size parameters and container motion parameters. Based on the orthogonal simulation results and a multi-objective evaluation model, a multi-objective evaluation value corresponding to the orthogonal simulation results is obtained, thereby obtaining an optimal combination of container size parameters and container motion parameters. Based on the EDEM discrete element simulation model, the optimal combination of container size parameters and container motion parameters is simulated, and the wear depth characteristics of the blade back surface and blade basin surface corresponding to the optimal combination of container size parameters and container motion parameters are extracted to determine whether the processing requirements are met.

[0013] Preferably, in step 34, according to the structural characteristics of the integral blade disk, the container wall configuration adopts a quadratic function curve around X The curved surface configuration formed by the axis rotation, where the expression of the quadratic function curve is: Where: and are the input and output values ​​of the quadratic function curve respectively; and are the characteristic parameters of the quadratic function curve respectively; among them, Used to reflect the thickness of the container wall , Used to reflect the height of the container wall configuration ; The range of configuration parameters of the left and right walls of the container determined based on the structural characteristics of the integral blade disk includes: The parameter range of the thickness of the left wall of the container is: 12mm~20mm, and the parameter range of the height of the left wall of the container is: 132mm~148mm; the parameter range of the thickness of the right wall of the container is: 4mm~12mm, and the parameter range of the height of the right wall of the container is: 140mm~156mm.

[0014] Preferably, the results of the response surface simulation include values ​​of evaluation indicators corresponding to different combinations of left and right container wall configuration parameters; and the step of determining the optimal combination of left and right container wall configuration parameters based on the neural network and genetic algorithm coupling model, the results of the response surface simulation, and the multi-objective evaluation model includes: S351: performing a multi-objective evaluation based on the response surface simulation results and the multi-objective evaluation model, and obtaining a multi-objective evaluation value corresponding to the response surface simulation results; S352: Using the thickness and height of the left and right walls of the container as input and the multi-objective evaluation value as output, a database of processing effects of the blisk is constructed; S353: Constructing the initial neural network structure, optimizing the neural network weights and thresholds based on the genetic algorithm, optimizing the number of hidden layer nodes and the number of hidden layers of the neural network, and obtaining a neural network and genetic algorithm coupling model; S354: Evaluate the blisk surface machining effect based on the blisk machining effect database and the neural network and genetic algorithm coupling model, and select the combination of the thickness and height of the container left and right wall configurations with the highest multi-objective evaluation value as the optimal container left and right wall configuration parameter combination; Based on the EDEM discrete element simulation model, the optimal combination of the left and right wall configuration parameters of the container is simulated, and the wear depth characteristics of the blade back surface and blade basin surface corresponding to the optimal combination of the left and right wall configuration parameters of the container are extracted to determine whether the processing requirements are met.

[0015] Preferably, the values ​​of the expansion disk thickness of the blade expansion configuration include: 2mm, 4mm, 6mm, 8mm and 10mm; the results of the single-factor simulation include the values ​​of the evaluation indicators corresponding to different expansion disk thicknesses; based on the results of the single-factor simulation and the multi-objective evaluation model, the multi-objective evaluation values ​​corresponding to the results of the single-factor simulation are obtained, and then the optimal expansion disk thickness of the blade expansion configuration is obtained to be 8mm; based on the EDEM discrete element simulation model, the optimal expansion disk thickness of the blade expansion configuration is simulated, and the wear depth characteristics of the blade back surface and the blade basin surface corresponding to the optimal expansion disk thickness of the blade expansion configuration are extracted to determine whether the processing requirements are met.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention improves the processing uniformity of the entire blade disk by regulating the container size parameters and configuration parameters. A multi-objective evaluation model for the average wear depth and coefficient of variation of the entire blade disk is constructed based on the entropy weight method and subjective assignment method. The optimal process parameters are selected through orthogonal simulation of the container size parameters and motion parameters. A container wall configuration optimization method is proposed to address the unevenness of the root to tip area, and the container wall configuration is optimized and designed through response surface analysis, neural networks, genetic algorithms, etc. In order to solve the problem of over-throw of the intake and exhaust edges, a blade expansion configuration control method is proposed to comprehensively achieve uniform processing consistency of the entire blade disk, so that the surface roughness of the blade back and blade basin of the entire blade disk is reduced to 0.2~0.4μm, significantly improving the surface integrity of the entire blade disk. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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.

[0018] Figure 1 This is a flow chart of a method for controlling the flow field of granular media in integral blade-disk vortex vibration composite polishing; Figure 2 This is a schematic diagram of the division of the blisk profile data blocks; Figure 3 It is the main effect analysis diagram of multi-objective evaluation values; Figure 4 This is a schematic diagram of the principle of the container wall configuration control method; Figure 5 It is the mean absolute percentage error diagram between the predicted value and the actual value under different numbers of hidden layer nodes; Figure 6 is the mean absolute percentage error graph between the predicted value and the actual value under the number of nodes in the first hidden layer; Figure 7 This is a schematic diagram of the principle of the blade expansion configuration control method; Figure 8 It is the wear depth characteristics and multi-objective evaluation values ​​corresponding to different blade expansion configuration parameters. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention are clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other implementations derived by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0020] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention. It should be noted that in this specification, relational terms such as first and second are only used to distinguish one entity from several other entities, and do not necessarily require or imply any actual relationship or order between these entities.

[0021] The present invention provides an embodiment: like Figure 1 As shown, a method for controlling the flow field of a granular medium by integral blade disc vortex vibration composite polishing includes the following steps: S1: Construct an EDEM discrete element simulation model for the integrated blade disk's gyro-vibration composite polishing and divide the data blocks according to the structural characteristics of the integrated blade disk.

[0022] Specifically, the Hertz-Mindlin model and the Archard wear model are selected as the basic models of the EDEM discrete element simulation model of the integral blade vibration composite polishing; the Hertz-Mindlin model is a contact model in the EDEM discrete element simulation; the Archard wear model is a wear model.

[0023] The steps of dividing the data blocks according to the structural characteristics of the integral blade disk are as follows: on the blade back surface and blade basin surface of the integral blade disk, 7 data blocks and 9 data blocks are evenly distributed along the blade chord length direction and the blade body length direction at equal intervals, and a total of 63 data blocks are generated for one surface, such as Figure 2 shown.

[0024] S2: Determine the evaluation index for the integral blade disc gyratory vibration composite polishing. The steps of constructing a multi-objective evaluation model based on the entropy weight method and the subjective assignment method include: S201: Determine the evaluation index of the integral blade disc cyclo-vibration composite polishing. The evaluation index of the integral blade disc cyclo-vibration composite polishing includes the average value of the wear depth of the blade back surface. and coefficient of variation And the average wear depth of the blade basin profile and coefficient of variation , a total of 4 evaluation indicators.

[0025] Coefficient of variation of wear depth The calculation formula is as follows: Where: is the coefficient of variation of wear depth; is the standard deviation; is the average value.

[0026] S202: For including An evaluation matrix is ​​established for a multi-objective evaluation problem with four evaluation indicators and four examples. The average value and coefficient of variation of the wear depth of the blade back surface and the average value and coefficient of variation of the wear depth of the blade basin surface are normalized and data shifted. The calculation formulas are as follows: Where: It is the average value of the wear depth of the blade back surface and the average value of the wear depth of the blade basin surface after normalization and data conversion; It is the coefficient of variation of the wear depth of the blade back surface and the blade basin surface after normalization and data conversion; and Respectively The maximum and minimum values ​​of the evaluation indicators; are the mean and coefficient of variation of the wear depth of the blade back surface and blade basin surface.

[0027] S203: Based on the normalization and data conversion results, the entropy weight method is used to calculate the proportion and entropy value of the evaluation indicators in the instance, and the objective weight of each evaluation indicator is calculated. and are the mean value and weight coefficient of the wear depth of the blade back surface respectively; and are the average value of the wear depth of the blade basin profile and the weight coefficient of the coefficient of variation, respectively.

[0028] S204: Settings and are the subjective values ​​of the mean value and coefficient of variation of the wear depth of the blade back surface, and are the subjective values ​​of the mean value and coefficient of variation of the wear depth of the blade basin surface, and .

[0029] Then the expression of the multi-objective evaluation model of the integral blade vibration composite polishing process is obtained as follows: Where: It is a multi-objective evaluation model; and are the mean value and coefficient of variation of the wear depth of the blade back surface after normalization and data translation; and are the mean value and coefficient of variation of the wear depth of the blade basin profile after normalization and data translation; and are the mean value and weight coefficient of the wear depth of the blade back surface respectively; and are the mean value of the wear depth of the blade basin profile and the weight coefficient of the coefficient of variation; and are the subjective values ​​of the mean value and coefficient of variation of the wear depth of the blade back surface, and are the subjective values ​​of the mean value and coefficient of variation of the wear depth of the blade basin surface, and .

[0030] S3: According to the structural characteristics of the overall blade disk, the container size parameters and container motion parameters, the left and right wall configurations of the container, and the blade expansion configuration are used to actively control the flow field of the granular medium; three groups of simulations and multi-objective evaluations are performed in sequence, wherein the first, second, and third groups of simulations and multi-objective evaluations are used to obtain the optimal combination of container size parameters and container motion parameters, the optimal combination of left and right wall configuration parameters, and the optimal expansion disk thickness of the blade expansion configuration; before the second and third groups of simulations and multi-objective evaluations are performed, the simulation and multi-objective evaluation results of the previous group must be subjected to EDEM discrete element simulation to determine the overall blade disk processing effect; if the simulation and multi-objective evaluation results of the first group meet the requirements of the additional If the processing requirements are met, the control scheme is determined to be the optimal container size parameters and container motion parameters, otherwise the second set of simulations and multi-objective evaluations are performed; if the second set of simulations and multi-objective evaluation results meet the processing requirements, the control scheme is determined to be the optimal container size parameters and container motion parameters and the optimal container left and right wall configuration thickness and height parameters, otherwise the third set of simulations and multi-objective evaluations are performed; if the third set of simulations and multi-objective evaluation results meet the processing requirements, the control scheme is determined to be the optimal container size parameters and container motion parameters, the optimal container left and right wall configuration thickness and height parameters and the optimal expansion disk thickness parameters of the blade expansion configuration, otherwise step S3 is repeated until the processing requirements are met.

[0031] S31: Based on the structural characteristics of the integral blade disk, determine the container size parameter range and the container motion parameter range, and perform orthogonal simulation.

[0032] In step S31, the maximum profile diameter of the given blisk is , the width of the overall blade disk is The container size parameter range determined by the maximum profile diameter and width of the blisk includes: Container inner wall diameter Parameter range: 1.05 ~1.25 , and the container inner wall width Parameter range: 2.50 ~3.50 The maximum profile diameter of the blisk studied in this embodiment is 250mm, and the width of the blisk is 46mm. Therefore, the inner wall diameter of the container The parameter range of the container is 262.5mm~312.5mm, and the parameter range of the container inner wall thickness is 115mm~161mm.

[0033] The container's vibration frequency parameter range is 20 Hz to 30 Hz, the container's vibration amplitude parameter range is 5.469 mm to 2.431 mm, and the container's rotation parameter range is 5.0 rpm to 15.0 rpm. Furthermore, the particle diameter is set to 4 mm, and the fill level is 70%. The simulation time is the time required for the container to rotate once. When the container's rotation parameter range is 5.0 rpm to 15.0 rpm, the corresponding simulation time parameter range is 12 seconds to 4 seconds.

[0034] A four-factor, five-level orthogonal simulation scheme was designed based on the parameter range. The results of the orthogonal simulation include the corresponding evaluation index values ​​for different combinations of vessel size parameters and vessel motion parameters, as shown in Table 1. It should be noted that the wear depth data per unit time for different parameter combinations should be used when analyzing the average wear depth. Furthermore, since the vessel size parameters and vessel motion parameters did not effectively improve the machining results of the inlet and exhaust edges of the integral blade disk, only the blade airfoil area was considered when optimizing the vessel size parameters and vessel motion parameters.

[0035] Table 1 Four-factor five-level orthogonal simulation scheme and results S32: Perform multi-objective evaluation based on the orthogonal simulation results and the multi-objective evaluation model to obtain the optimal combination of container size parameters and container motion parameters.

[0036] Based on the results of orthogonal simulation and the multi-objective evaluation model, the multi-objective evaluation values ​​corresponding to the results of orthogonal simulation were obtained, with a total of 25 examples. According to the entropy weight method, the objective weight coefficients of different evaluation indicators were calculated. The weight coefficients of the average value and variation coefficient of the wear depth of the blade back surface were 0.3558 and 0.2226 respectively, and the weight coefficients of the average value and variation coefficient of the wear depth of the blade basin surface were 0.3178 and 0.1038 respectively; in view of the fact that the average value of the wear depth is given priority when optimizing the container size parameters and container motion parameters, the average value of the wear depth is set. ; Finally, the corresponding multi-objective evaluation value is calculated according to the multi-objective evaluation formula.

[0037] Perform main effect analysis on orthogonal simulation results, such as Figure 3 As shown in the figure, as the container inner wall width and container inner wall diameter increase, the multi-objective evaluation value increases, with increases of 0.054 and 0.036 respectively; however, as the container vibration frequency increases, the multi-objective evaluation value gradually decreases, with a decrease of 0.078; as the container rotation parameter increases, the coefficient of variation of the wear depth of the blade back surface and the coefficient of variation of the wear depth of the blade basin surface fluctuate within a certain range. Based on the main effect diagram, the optimal combination of container size parameters and container motion parameters is selected. The optimal combination of container size parameters and container motion parameters is 161mm in width, 312.5mm in diameter, 20Hz in vibration frequency, 5.469mm in vibration amplitude, and 10rpm in rotation parameter.

[0038] S33: Simulating the optimal combination of container size parameters and container motion parameters based on the EDEM discrete element simulation model to determine whether the overall blade disk processing effect meets the processing requirements; if it does not meet the processing requirements, proceeding to step S34, regulating the container wall configuration based on the optimal combination of container size parameters and container motion parameters; if it meets the processing requirements, determining the regulation scheme as the optimal container size parameters and container motion parameters; Specifically, based on the EDEM discrete element simulation model, the optimal combination of container size parameters and container motion parameters is simulated, and the wear depth characteristics of the blade back surface and blade basin surface corresponding to the optimal combination of container size parameters and container motion parameters are extracted to determine whether the overall blade disk processing effect meets the processing requirements.

[0039] In this embodiment, the calculated coefficient of variation RSD values ​​of the wear depth of the blade area of ​​the blade back surface and the blade basin surface are 0.306 and 0.460 respectively, but the over-throw phenomenon of the intake and exhaust edges still exists, which does not meet the processing requirements.

[0040] S34: Based on the structural characteristics of the integral blade disk, determine the range of configuration parameters of the left and right walls of the container and perform response surface simulation.

[0041] like Figure 4 As shown, optimization was performed using a vessel wall configuration control method. The parameter ranges for the left and right vessel wall configurations were determined based on the dimensions of the blisk, vessel, and granular media. To ensure granular media flowability, an appropriate distance between the vessel wall and the blisk impeller (25 mm was selected in this example) was maintained. The vessel wall thickness was set at intervals of two granular media diameters, ranging from 0 mm to 32 mm. To enhance the granular media's effect on the blade root area, the vessel wall height was set higher than the impeller diameter. Therefore, the minimum vessel wall heights on the left and right sides were greater than 48 mm and 64 mm, respectively, while the maximum heights on both sides were 156 mm. The vessel wall heights were set at intervals of four granular media diameters, ranging from 60 mm to 156 mm and from 76 mm to 156 mm, respectively.

[0042] The range of configuration parameters of the left and right walls of the container determined based on the structural characteristics of the integral blade disk includes: The parameter range of the thickness of the left wall of the container is: 12mm~20mm, and the parameter range of the height of the left wall of the container is: 132mm~148mm; the parameter range of the thickness of the right wall of the container is: 4mm~12mm, and the parameter range of the height of the right wall of the container is: 140mm~156mm.

[0043] A four-factor, three-level response surface simulation scheme was designed based on the parameter range. The mean and coefficient of variation of the wear depth of the blade back surface and the blade basin surface corresponding to different combinations of container left and right wall configuration parameters were extracted, as shown in Table 2. Furthermore, since the container wall configuration did not effectively improve the machining effect of the inlet and exhaust edges of the integral blade disk, only the blade airfoil area was considered.

[0044] Table 2 Four-factor three-level response surface simulation scheme and results According to the structural characteristics of the integral blade disk, the container wall configuration adopts a quadratic function curve around X The curved surface configuration formed by the axis rotation, where the expression of the quadratic function curve is: Where: and are the input and output values ​​of the quadratic function curve respectively; and are the characteristic parameters of the quadratic function curve respectively; among them, Used to reflect the thickness of the container wall , Used to reflect the height of the container wall configuration ; S35: Constructing a neural network and genetic algorithm coupling model, and determining the optimal combination of left and right container wall configuration parameters based on the neural network and genetic algorithm coupling model, the response surface simulation results, and the multi-objective evaluation model includes the following steps: S351: Based on the results of response surface simulation and the multi-objective evaluation model, a multi-objective evaluation was performed to obtain the multi-objective evaluation values ​​corresponding to the results of response surface simulation. A total of 31 cases were obtained. The weight coefficients of different evaluation indicators were calculated according to the entropy weight method. The weight coefficients of the average value and variation coefficient of the wear depth of the blade back surface were 0.2694 and 0.1920 respectively, and the weight coefficients of the average value and variation coefficient of the wear depth of the blade basin surface were 0.2700 and 0.2686 respectively. In view of the configuration of the left and right container walls, the variation coefficient of the wear depth was given priority, so the ; Finally, the multi-objective evaluation value is calculated according to the multi-objective evaluation formula.

[0045] S352: Using the thickness and height of the left and right walls of the container as input and the multi-objective evaluation value as output, a database of the machining effect of the blisk is constructed; S353: In the vessel wall configuration optimization, the input layer has four nodes and the output layer has one node. Based on empirical evidence, the initial neural network structure is constructed using the feedforwardnet function in MATLAB software to determine the initial BP neural network topology. The initial network is a single hidden layer neural network. The number of hidden layer nodes is determined to be in the range [3, 13]. The initial hidden layer node number is selected as 5, and the initial structure of the neural network is 4-5-1. The feedforwardnet function is a MATLAB function used to create feedforward neural networks.

[0046] Optimize the weights and thresholds of the neural network based on the genetic algorithm: set the training set to 80% of the database samples (a total of 25 samples), and the validation set to the remaining 20% ​​of the database samples (select the validation set to be the data of the 5th, 10th, 15th, 20th, 25th and 30th groups). Set the transfer function between the input layer and the hidden layer to the Logsig function, the transfer function between the hidden layer and the output layer to the Purelin function, and the neural network training function to the trainlm function. Construct the initial neural network based on this. Extract the weights and thresholds of each neuron in the initial neural network to form the genetic algorithm gene matrix. Set the initial population of the genetic algorithm to 50, the number of iterations to 1000, the crossover probability to 0.1, the mutation probability to 0.08, and set the fitness function to the mean absolute percentage error between the predicted value and the actual value. MAPE , and perform genetic algorithm optimization. The Logsig function is a logarithmic sigmoid activation function, the Purelin function is a linear activation function, and the trainlm function is an optimization algorithm.

[0047] According to the range of the number of hidden layer nodes, iterative training is performed for different numbers of hidden layer nodes, and the mean absolute percentage error between the predicted value and the actual value under different numbers of hidden layer nodes is calculated. MAPE ,like Figure 5 As shown in the figure, the optimal number of hidden layer nodes is finally determined to be 10, and the neural network with a structure of 4-10-1 is obtained.

[0048] Hidden layer optimization: Set the number of hidden layers to 2, with 1 node in the first hidden layer and 9 nodes in the second hidden layer. Train the neural network based on this and record the mean absolute percentage error between the predicted value and the actual value. MAPE The number of nodes in the first hidden layer is increased successively until the total number of nodes in the hidden layer is reached, and the mean absolute percentage error under different hidden layer allocations is compared. MAPE , determine the optimal number of nodes in the first hidden layer.

[0049] Determine whether the number of nodes in the second hidden layer is greater than or equal to 2. If the condition is met, increase the number of hidden layers to ensure that the optimal number of nodes in the first hidden layer remains unchanged. Allocate the remaining number of hidden layer nodes and use the above method to determine the optimal number of nodes in the second hidden layer. Repeat the above steps until the number of hidden layer nodes is less than 2 and no more allocation is possible. The average absolute percentage error between the predicted value and the actual value under the number of nodes in the first hidden layer is as follows: Figure 6 As shown in the figure, the optimal neural network structure is finally determined to be 4-8-2-1.

[0050] A neural network relationship model between the characteristic parameters of the vessel wall configuration and multi-objective evaluation values ​​was constructed using a genetic algorithm and optimization of the number of hidden layer nodes and layers. The mean square error, mean absolute percentage error, mean absolute error, and root mean square error of the prediction results were calculated, as shown in Table 3. When the neural network structure is 4-8-2-1, all evaluation indicators of the prediction results are minimized, meeting the accuracy requirements, and a coupled neural network and genetic algorithm model is obtained.

[0051] Table 3 Error comparison of different neural network structures S354: The left and right wall configuration parameters of the container are input as genes to be optimized into the neural network and genetic algorithm coupling model, and the multi-objective evaluation value is used to evaluate the overall blade disk surface processing effect corresponding to different combinations of the left and right wall configuration parameters of the container. The larger the multi-objective evaluation value, the better. The combination of the left and right wall configuration thickness and the left and right wall configuration height of the container with the highest multi-objective evaluation value is selected as the optimal combination of the left and right wall configuration parameters of the container. In the optimal combination of the left and right wall configuration parameters of the container, the left wall configuration thickness of the container is 12 mm, the left wall configuration height of the container is 138 mm, the right wall configuration thickness of the container is 7 mm, and the right wall configuration height of the container is 148 mm.

[0052] S36: Using the EDEM discrete element model, the optimal combination of left and right container wall configuration parameters is simulated. The wear depth characteristics of the blade back and blade basin corresponding to this optimal combination of left and right container wall configuration parameters are extracted to determine whether the processing requirements are met. If not, step S37 is performed to control the blade expansion configuration based on the optimal combination of left and right container wall configuration parameters. If the processing requirements are met, the control scheme is determined to be the optimal container size parameters, container motion parameters, and left and right container wall configuration thickness and height parameters.

[0053] The calculated coefficients of variation of the wear depth of the blade area on the blade back surface and the blade basin surface are 0.261 and 0.400, respectively, which are further improved compared with the optimal container parameters. However, the over-throw phenomenon of the intake and exhaust edges still exists, which does not meet the processing requirements. Therefore, the blade expansion configuration control method is used for optimization, such as Figure 7 shown.

[0054] S37: Based on the structural characteristics of the integral blade disk, the thickness of the blade extension configuration is determined and a single-factor simulation is performed. Multiple values ​​for the blade extension thickness can be set. To optimize the blade extension configuration parameters, blade extension configuration schemes with thicknesses of 2mm, 4mm, 6mm, 8mm, and 10mm are designed.

[0055] The blisk studied in this example has 19 blades, meaning the blade extension structure also has 19 curved surfaces, fixed to the extension disc. The diameters of the left and right extension discs correspond to the minimum and maximum diameters of the blisk body, respectively, to ensure that the movement of the granular medium at the root of the flow channel is not affected. The curved surface characteristics of the blade extension structure should be similar to those of the blisk, with good connection to the intake and exhaust edges. Therefore, the curved surface characteristics of the blade extension structure are related to the thickness of the extension disc. Furthermore, while ensuring improved processing uniformity of the blisk, the exchangeability and flowability of the granular medium should be maintained as much as possible.

[0056] S38: The results of the single-factor simulation include the values ​​of the evaluation indicators corresponding to different expansion disk thicknesses; based on the results of the single-factor simulation and the multi-objective evaluation model, the multi-objective evaluation values ​​corresponding to the results of the single-factor simulation are obtained, and then the expansion disk thickness of the optimal blade expansion configuration is obtained to be 8mm; there are a total of 5 instances in this step. Since the blade expansion configuration effectively improves the over-throw problem of the intake and exhaust edges, all data blocks are considered when calculating the average value and coefficient of variation.

[0057] According to the entropy weight method, the weight coefficients of different evaluation indicators are calculated. The weight coefficients of the average value and variation coefficient of the wear depth of the blade back surface are 0.2360 and 0.2854, respectively. The weight coefficients of the average value and variation coefficient of the wear depth of the blade basin surface are 0.2545 and 0.2241, respectively. In view of the fact that the variation coefficient of the wear depth is given priority when optimizing the blade expansion configuration, the weight coefficient of the wear depth is set to ; Finally, the multi-objective evaluation value is calculated according to the multi-objective evaluation formula. Figure 8 Shown are the wear depth and multi-objective evaluation values ​​for different blade expansion configuration parameters.

[0058] It can be seen that the machining effect is optimal when the expansion disk thickness is 8 mm. At this time, the coefficients of variation of the wear depth of the blade back surface and the blade basin surface are 0.310 and 0.290, respectively. Compared with the optimized container wall configuration, the coefficients of variation are reduced by 77.47% and 45.88%, respectively.

[0059] S39: Based on the EDEM discrete element simulation model, the thickness of the expansion disk of the optimal blade expansion configuration is simulated. The wear depth characteristics of the blade back surface and blade basin surface corresponding to the expansion disk thickness of the optimal blade expansion configuration are extracted. The over-throw phenomenon in the intake and exhaust edge areas is alleviated, the coefficient of variation is significantly reduced, the uniformity is improved, and the processing requirements are met.

[0060] The grinding experiment was carried out on the integral blade disc specimen after grinding, and the grinding time was 2.5 hours. The surface roughness of the blade back and blade basin Ra The values ​​are reduced from 0.939μm and 0.918μm to 0.259μm and 0.252μm, and the polishing uniformity is good. The blade back and blade basin surface are SD The values ​​are 0.018μm and 0.012μm respectively, RSD The values ​​are 0.068 and 0.048 respectively, which meet the requirements for blisk polishing.

[0061] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for controlling the flow field of granular media in integral blade vortex vibration composite polishing, characterized in that: The following steps are involved: S1: Construct an EDEM discrete element simulation model for the integrated blade disc gyratory vibration composite polishing; S2: Determine the evaluation index of the integral blade vibration composite polishing and build a multi-objective evaluation model; S3: According to the structural characteristics of the overall blade disk, the container size parameters and container motion parameters, the left and right wall configurations of the container, and the blade expansion configuration are used to actively control the flow field of the granular medium; three groups of simulations and multi-objective evaluations are performed in sequence, wherein the first, second, and third groups of simulations and multi-objective evaluations are used to obtain the optimal combination of container size parameters and container motion parameters, the optimal combination of left and right wall configuration parameters, and the optimal expansion disk thickness of the blade expansion configuration; before the second and third groups of simulations and multi-objective evaluations are performed, the simulation and multi-objective evaluation results of the previous group must be subjected to EDEM discrete element simulation to determine the overall blade disk processing effect; if the simulation and multi-objective evaluation results of the first group meet the requirements of the additional If the processing requirements are met, the control scheme is determined to be the optimal container size parameters and container motion parameters, otherwise the second set of simulations and multi-objective evaluations are performed; if the second set of simulations and multi-objective evaluation results meet the processing requirements, the control scheme is determined to be the optimal container size parameters and container motion parameters and the optimal container left and right wall configuration thickness and height parameters, otherwise the third set of simulations and multi-objective evaluations are performed; if the third set of simulations and multi-objective evaluation results meet the processing requirements, the control scheme is determined to be the optimal container size parameters and container motion parameters, the optimal container left and right wall configuration thickness and height parameters and the optimal expansion disk thickness parameters of the blade expansion configuration, otherwise step S3 is repeated until the processing requirements are met.

2. The method for controlling the flow field of granular media in the integral bladed disc vortex vibration composite polishing according to claim 1, characterized in that: Step S3 includes: S31: Determine the container size parameter range and container motion parameter range based on the structural characteristics of the integral blade disk, and perform orthogonal simulation; S32: Perform multi-objective evaluation based on the orthogonal simulation results and the multi-objective evaluation model to obtain the optimal combination of container size parameters and container motion parameters; S33: Simulating the optimal combination of container size parameters and container motion parameters based on the EDEM discrete element simulation model to determine whether the overall blade disk processing effect meets the processing requirements; if it does not meet the processing requirements, proceeding to step S34, regulating the container wall configuration based on the optimal combination of container size parameters and container motion parameters; if it meets the processing requirements, determining the regulation scheme as the optimal container size parameters and container motion parameters; S34: Based on the structural characteristics of the integral blade disk, determine the parameter ranges of the thickness and height of the left and right walls of the container and perform response surface simulation. S35: Construct a neural network and genetic algorithm coupling model, and determine the optimal combination of container left and right wall configuration parameters based on the neural network and genetic algorithm coupling model, response surface simulation results, and a multi-objective evaluation model; S36: Simulating the optimal combination of left and right container wall configuration parameters based on the EDEM discrete element simulation model to determine whether the overall blade disk processing effect meets the processing requirements; if it does not meet the processing requirements, proceeding to step S37, performing blade expansion configuration control based on the optimal combination of left and right container wall configuration parameters; if it meets the processing requirements, determining the control scheme as the optimal container size parameters and container motion parameters, as well as the optimal left and right container wall configuration thickness and height parameters; S37: Based on the structural characteristics of the integral blade disk, determine the thickness range of the extended disk in the blade extended configuration and perform single factor simulation; S38: Based on the results of the single-factor simulation and the multi-objective evaluation model, a multi-objective evaluation is performed to obtain the optimal expansion disk thickness of the blade expansion configuration; S39: Based on the EDEM discrete element simulation model, the thickness of the expansion disk of the optimal blade expansion configuration is simulated to determine whether the overall blade disk processing effect meets the processing requirements; if it does not meet the processing requirements, repeat steps S31 to S39 until the processing requirements are met; if it meets the processing requirements, determine the control scheme as the optimal container size parameters and container motion parameters, the optimal container left and right wall configuration thickness and height parameters, and the optimal blade expansion configuration expansion disk thickness parameters.

3. The method for controlling the flow field of granular media in the integral bladed disc vortex vibration composite polishing according to claim 2, characterized in that: The Hertz-Mindlin model and Archard wear model are selected as the basic models of the EDEM discrete element simulation model for the integral blade vibration composite polishing; At the same time, the data blocks are divided according to the structural characteristics of the integral blade disk, and the data blocks are evenly distributed at equal intervals along the blade chord length direction and the blade body length direction on the blade back surface and blade basin surface of the integral blade disk.

4. The method for controlling the flow field of granular media in the integral bladed disc vortex vibration composite polishing according to claim 3, characterized in that: The evaluation indicators of the integral blade disc cyclo-vibration composite polishing include the mean value and coefficient of variation of the wear depth of the blade back surface and the mean value and coefficient of variation of the wear depth of the blade basin surface. A multi-objective evaluation model is constructed based on the entropy weight method and the subjective assignment method.

5. The method for controlling the flow field of granular media in the integral bladed disc vortex vibration composite polishing according to claim 4, characterized in that: The expression of the multi-objective evaluation model is: Where: It is a multi-objective evaluation model; and are the mean value and coefficient of variation of the wear depth of the blade back surface after normalization and data translation; and are the mean value and coefficient of variation of the wear depth of the blade basin profile after normalization and data translation; and are the mean value and weight coefficient of the wear depth of the blade back surface respectively; and are the mean value of the wear depth of the blade basin profile and the weight coefficient of the coefficient of variation; and are the subjective values ​​of the mean value and coefficient of variation of the wear depth of the blade back surface, and are the subjective values ​​of the mean value and coefficient of variation of the wear depth of the blade basin surface, and .

6. The method for controlling the flow field of granular media in the integral bladed disc vortex vibration composite polishing according to claim 4, characterized in that: In step S31, the maximum profile diameter of the given blisk is , the width of the entire blade is The container size parameter range determined by the maximum profile diameter and width of the blisk includes: Container inner wall diameter Parameter range: 1.05 ~1.25 , and the container inner wall width Parameter range: 2.50 ~3.50 ; The parameter range of the container's vibration frequency is: 20Hz~30Hz, the parameter range of the container's vibration amplitude is: 5.469mm~2.431mm, and the parameter range of the container's rotation parameter is: 5.0rpm~15.0rpm.

7. The method for controlling the flow field of granular media in the integral bladed disc vortex vibration composite polishing according to claim 4, characterized in that: The results of the orthogonal simulation include the values ​​of the evaluation indicators corresponding to different combinations of container size parameters and container motion parameters. Based on the results of the orthogonal simulation and the multi-objective evaluation model, the multi-objective evaluation values ​​corresponding to the results of the orthogonal simulation are obtained, and then the optimal combination of container size parameters and container motion parameters is obtained. Based on the EDEM discrete element simulation model, the optimal combination of container size parameters and container motion parameters is simulated, and the wear depth characteristics of the blade back surface and blade basin surface corresponding to the optimal combination of container size parameters and container motion parameters are extracted to determine whether the processing requirements are met.

8. The method for controlling the flow field of granular media in the integral bladed disc vortex vibration composite polishing according to claim 4, characterized in that: In step S34, according to the structural characteristics of the integral blade disk, the container wall configuration adopts a quadratic function curve to X The curved surface configuration formed by the axis rotation, where the expression of the quadratic function curve is: Where: and are the input and output values ​​of the quadratic function curve respectively; and are the characteristic parameters of the quadratic function curve respectively; among them, Used to reflect the thickness of the container wall , Used to reflect the height of the container wall configuration ; The range of configuration parameters of the left and right walls of the container determined based on the structural characteristics of the integral blade disk includes: The parameter range of the thickness of the left wall of the container is: 12mm~20mm, and the parameter range of the height of the left wall of the container is: 132mm~148mm; the parameter range of the thickness of the right wall of the container is: 4mm~12mm, and the parameter range of the height of the right wall of the container is: 140mm~156mm.

9. The method for controlling the flow field of granular media in the integral bladed disc vortex vibration composite polishing according to claim 4, characterized in that: The results of the response surface simulation include the values ​​of the evaluation index corresponding to different combinations of left and right container wall configuration parameters. The steps of determining the optimal combination of left and right container wall configuration parameters based on the neural network and genetic algorithm coupling model, the results of the response surface simulation, and the multi-objective evaluation model include: S351: performing a multi-objective evaluation based on the response surface simulation results and the multi-objective evaluation model, and obtaining a multi-objective evaluation value corresponding to the response surface simulation results; S352: Using the thickness and height of the left and right walls of the container as input and the multi-objective evaluation value as output, a database of processing effects of the blisk is constructed; S353: Constructing the initial neural network structure, optimizing the neural network weights and thresholds based on the genetic algorithm, optimizing the number of hidden layer nodes and the number of hidden layers of the neural network, and obtaining a neural network and genetic algorithm coupling model; S354: Evaluate the blisk surface machining effect based on the blisk machining effect database and the neural network and genetic algorithm coupling model, and select the combination of the thickness and height of the container left and right wall configurations with the highest multi-objective evaluation value as the optimal container left and right wall configuration parameter combination; Based on the EDEM discrete element simulation model, the optimal combination of the left and right wall configuration parameters of the container is simulated, and the wear depth characteristics of the blade back surface and blade basin surface corresponding to the optimal combination of the left and right wall configuration parameters of the container are extracted to determine whether the processing requirements are met.

10. The method for controlling the flow field of granular media in the integral bladed disc vortex vibration composite polishing according to claim 4, characterized in that: The values ​​of the expansion disk thickness of the blade expansion configuration include: 2mm, 4mm, 6mm, 8mm and 10mm; the results of the single-factor simulation include the values ​​of the evaluation indicators corresponding to different expansion disk thicknesses; based on the results of the single-factor simulation and the multi-objective evaluation model, the multi-objective evaluation value corresponding to the result of the single-factor simulation is obtained, and then the optimal expansion disk thickness of the blade expansion configuration is obtained to be 8mm; based on the EDEM discrete element simulation model, the optimal expansion disk thickness of the blade expansion configuration is simulated, and the wear depth characteristics of the blade back surface and the blade basin surface corresponding to the optimal expansion disk thickness of the blade expansion configuration are extracted to determine whether the processing requirements are met.

Images