A granular medium flow field regulation method for integral blade disk spin vibration composite polishing and grinding

Abstract

The present application belongs to the technical field of whole-blade-disk parts rolling finishing, aiming to solve the problems of over-finishing of inlet and outlet edges, under-finishing of blade root and uneven finishing of whole-blade-disk parts. A kind of granular medium flow field regulation and control method for whole-blade-disk rotary vibration combined polishing is proposed, a multi-objective evaluation model of average value and variation coefficient of whole-blade-disk wear depth is constructed based on entropy weight method and subjective assignment method; the optimal process parameters are selected through orthogonal simulation of container size parameters and motion parameters; a container wall configuration optimization method is proposed for the unevenness of the blade root to the blade tip area, and the container wall configuration is optimized through response surface, neural network, genetic algorithm and other methods; to solve the problem of over-finishing of inlet and outlet edges, a blade expansion configuration regulation and control method is proposed, which comprehensively realizes the uniform consistency of whole-blade-disk finishing. The present application realizes the high surface integrity and uniform consistency of whole-blade-disk polishing.

Description

Technical Field

[0001] The present invention belongs to the technical field of tumbling and finishing processing of integral blade disk parts, and in particular 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:

[0007] S1: Construct an EDEM discrete element simulation model for the integrated blade disc gyratory vibration composite polishing;

[0008] S2: Determine the evaluation index of the integral blade vibration composite polishing and build a multi-objective evaluation model;

[0009] S3: According to the structural characteristics of the overall blisk, the container size parameters and the container motion parameters, the container left and right wall configurations, and the blade extension configuration are used to actively regulate the granular medium flow field; three groups of simulations and multi-objective evaluations are sequentially performed, wherein the first group, the second group, and the third group of simulations and multi-objective evaluations are used to obtain the optimal container size parameter and container motion parameter combination, the optimal container left and right wall configuration parameter combination, and the optimal blade extension configuration extension disc thickness; before the second group and the third group of simulations and multi-objective evaluations are performed, EDEM discrete element simulation of the simulation and multi-objective evaluation results of the previous group is required to determine the overall blisk machining effect; if the simulation and multi-objective evaluation results of the first group meet the machining requirements, the control scheme is determined as the optimal container size parameter and container motion parameter, otherwise the second group of simulation and multi-objective evaluation is performed; if the simulation and multi-objective evaluation results of the second group meet the machining requirements, the control scheme is determined as the optimal container size parameter and container motion parameter, and the optimal container left and right wall configuration thickness and height parameters, otherwise the third group of simulation and multi-objective evaluation is performed; if the simulation and multi-objective evaluation results of the third group meet the machining requirements, the control scheme is determined as the optimal container size parameter and container motion parameter, the optimal container left and right wall configuration thickness and height parameters, and the optimal blade extension configuration extension disc thickness parameter, otherwise step S3 is repeated until the machining requirements are met.

[0010] Preferably, step S3 comprises:

[0011] S31: According to the structural characteristics of the overall blisk, the container size parameter range and the container motion parameter range are determined, and orthogonal simulation is performed;

[0012] S32: Multi-objective evaluation is performed based on the results of the orthogonal simulation and the multi-objective evaluation model to obtain the optimal container size parameter and container motion parameter combination;

[0013] S33: The optimal container size parameter and container motion parameter combination is simulated based on the EDEM discrete element simulation model to determine whether the overall blisk machining effect meets the machining requirements; if not, step S34 is performed to regulate the container wall configuration based on the optimal container size parameter and container motion parameter combination; if yes, the control scheme is determined as the optimal container size parameter and container motion parameter;

[0014] S34: According to the structural characteristics of the overall blisk, the container left and right wall configuration thickness and height parameter range are determined, and response surface simulation is performed;

[0015] S35: A neural network and genetic algorithm coupling model is constructed, and the optimal container left and right wall configuration parameter combination is determined based on the neural network and genetic algorithm coupling model, the response surface simulation results, and the multi-objective evaluation model;

[0016] S36: Simulate the optimal container left and right wall configuration parameter combination based on the EDEM discrete element simulation model, and determine whether the overall blade machining effect meets the machining requirements; if not, proceed to step S37, and control the blade expansion configuration based on the optimal container left and right wall configuration parameter combination; if the machining requirements are met, the control scheme is determined as the optimal container size parameter and container motion parameter, and the optimal container left and right wall configuration thickness and height parameter;

[0017] S37: Determine the thickness range of the expansion disc in the blade expansion configuration according to the structural characteristics of the overall blade, and perform single-factor simulation;

[0018] S38: Perform multi-objective evaluation based on the results of single-factor simulation and the multi-objective evaluation model to obtain the optimal expansion disc thickness of the blade expansion configuration;

[0019] S39: Simulate the optimal expansion disc thickness of the blade expansion configuration based on the EDEM discrete element simulation model, and determine whether the overall blade machining effect meets the machining requirements; if not, repeat steps S31 to S39 until the machining requirements are met; if the machining requirements are met, the control scheme is determined as the optimal container size parameter and container motion parameter, the optimal container left and right wall configuration thickness and height parameter, and the optimal expansion disc thickness parameter of the blade expansion configuration.

[0020] Preferably, the Hertz-Mindlin model and the Archard wear model are selected as the basic model of the EDEM discrete element simulation model for the overall blade spin-vibration composite polishing;

[0021] Meanwhile, according to the structural characteristics of the overall blade, the data blocks are divided on the blade back surface and the blade basin surface, and the data blocks are evenly distributed along the chord length direction and the blade length direction.

[0022] Preferably, the evaluation indexes of the overall blade spin-vibration composite polishing include the average value and the coefficient of variation of the blade back surface wear depth, and the average value and the coefficient of variation of the blade basin surface wear depth, and a multi-objective evaluation model is constructed based on the entropy weight method and the subjective assignment method.

[0023] Preferably, the expression of the multi-objective evaluation model is:

[0024]

[0025] In the formula: is the multi-objective evaluation model; and are the average value and the coefficient of variation of the blade back surface wear depth after normalization and data translation, respectively; 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 .

[0026] Preferably, 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:

[0027] Container inner wall diameter Parameter range: 1.05 ~1.25 , and the width of the container inner wall 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.

[0028] 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.

[0029] 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.

[0030] 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:

[0031]

[0032] In the formula: and are input and output values of a quadratic function curve, respectively; and are characteristic parameters of the quadratic function curve; wherein, is used to reflect the thickness of the left and right container wall configurations , is used to reflect the height of the left and right container wall configurations ;

[0033] The parameter range of the left and right container wall configurations determined according to the structural characteristics of the overall blade disc includes:

[0034] The parameter range of the thickness of the left container wall configuration is 12mm-20mm, and the parameter range of the height of the left container wall configuration is 132mm-148mm; the parameter range of the thickness of the right container wall configuration is 4mm-12mm, and the parameter range of the height of the right container wall configuration is 140mm-156mm.

[0035] Preferably, the results of the response surface simulation include the values of the evaluation indexes corresponding to different combinations of the left and right container wall configuration parameters; the step of determining the optimal combination of the left and right container wall configuration parameters based on the coupling model of the neural network and the genetic algorithm, the results of the response surface simulation, and the multi-objective evaluation model includes:

[0036] S351: performing multi-objective evaluation based on the results of the response surface simulation and the multi-objective evaluation model to obtain the multi-objective evaluation values corresponding to the results of the response surface simulation;

[0037] S352: constructing an overall blade disc machining effect database by taking the thickness and height of the left and right container wall configurations as inputs and taking the multi-objective evaluation values as outputs;

[0038] S353: constructing an initial neural network structure, optimizing the weights and thresholds of the neural network based on the genetic algorithm, optimizing the number of hidden layer nodes and the number of hidden layers of the neural network, and obtaining the coupling model of the neural network and the genetic algorithm;

[0039] S354: evaluating the machining effect of the overall blade disc profile based on the overall blade disc machining effect database and the coupling model of the neural network and the genetic algorithm, and selecting the combination of the thickness and height of the left and right container wall configurations with the highest multi-objective evaluation value as the optimal combination of the left and right container wall configuration parameters;

[0040] Based on the EDEM discrete element simulation model, the optimal combination of the left and right container wall configuration parameters is simulated, the wear depth characteristics of the blade back profile and the blade basin profile corresponding to the optimal combination of the left and right container wall configuration parameters are extracted, and it is determined whether the machining requirements are met.

[0041] Preferably, the value of the thickness of the expansion disc of the blade expansion configuration comprises: 2mm, 4mm, 6mm, 8mm and 10mm; the result of the single-factor simulation comprises the value of the evaluation index corresponding to different thicknesses of the expansion disc; the multi-objective evaluation value corresponding to the result of the single-factor simulation is obtained based on the result of the single-factor simulation and the multi-objective evaluation model, and then the thickness of the expansion disc of the optimal blade expansion configuration is 8mm; the thickness of the expansion disc of the optimal blade expansion configuration is simulated based on the EDEM discrete element simulation model, the wear depth characteristics of the blade back surface and the blade basin surface corresponding to the thickness of the expansion disc of the optimal blade expansion configuration are extracted, and whether the machining requirement is met is judged.

[0042] Compared with the prior art, the beneficial effects of the present application are:

[0043] The present application improves the machining uniformity of the integral blade disc by regulating the container size parameters and the configuration parameters. A multi-objective evaluation model of the average value and the variation coefficient of the integral blade disc wear depth is constructed based on the entropy weight method and the subjective assignment method; the optimal process parameters are selected through the orthogonal simulation of the container size parameters and the motion parameters; a container wall configuration optimization method is proposed for the non-uniformity of the blade root to the blade tip region, and the container wall configuration is optimized through response surface, neural network, genetic algorithm and the like; in order to solve the over-throwing problem of the inlet and outlet edges, a blade expansion configuration regulation method is proposed, and the machining uniformity of the integral blade disc is comprehensively realized, so that the surface roughness of the blade back and the blade basin surface of the integral blade disc is reduced to 0.2-0.4μm, and the surface integrity of the integral blade disc is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0045] Figure 1 It is a flow chart of a particle medium flow field regulation method for integral blade disc spin-vibration composite polishing and grinding;

[0046] Figure 2 It is an integral blade disc surface data block division schematic diagram;

[0047] Figure 3 It is a main effect analysis diagram of the multi-objective evaluation value;

[0048] Figure 4 It is a schematic diagram of the principle of the container wall configuration regulation method;

[0049] Figure 5is a mean absolute percentage error graph of predicted values and actual values under different numbers of hidden layer nodes;

[0050] Figure 6 is a mean absolute percentage error graph of predicted values and actual values under the first number of hidden layer nodes;

[0051] Figure 7 is a schematic diagram of a blade expansion configuration control method;

[0052] Figure 8 is a wear depth characteristic and multi-objective evaluation value corresponding to different blade expansion configuration parameters. DETAILED DESCRIPTION

[0053] With reference to the accompanying drawings in the embodiments of the present application, the technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of the present application.

[0054] It should be understood that the structures, proportions, sizes, etc. shown in the drawings of the specification are only used to cooperate with the content disclosed in the specification, to be understood and read by those skilled in the art, and do not have technical substantive significance, and any modification of the structure, change of the proportion relationship, or adjustment of the size, without affecting the effects and purposes that can be achieved by the present application, should fall within the scope of the technical content disclosed by the present application. It should be noted that in the specification, relationship terms such as first and second are only used to distinguish one entity from another entity, and do not necessarily require or imply any actual relationship or order between the entities.

[0055] An embodiment of the present application is provided:

[0056] As shown in Figure 1 A particle medium flow field control method for integral blade spin-vibration composite polishing, comprising the following steps:

[0057] S1: Construct an EDEM discrete element simulation model for integral blade spin-vibration composite polishing, and divide data blocks according to the structural characteristics of the integral blade.

[0058] Specifically, the Hertz-Mindlin model and the Archard wear model are selected as the basic model of the EDEM discrete element simulation model for integral blade spin-vibration composite polishing; the Hertz-Mindlin model is a contact model in the EDEM discrete element simulation; and the Archard wear model is a wear model.

[0059] 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.

[0060] 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:

[0061] 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.

[0062] Coefficient of variation of wear depth The calculation formula is as follows:

[0063]

[0064] Where: is the coefficient of variation of wear depth; is the standard deviation; is the average value.

[0065] 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:

[0066]

[0067]

[0068] 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.

[0069] S203: According to the normalization processing and data conversion results, the proportion of evaluation indexes in instances, entropy values are calculated in turn by using entropy weight method, and objective weights of each evaluation index are calculated. Among them, and respectively are the weight coefficients of the average value and the coefficient of variation of the back surface profile wear depth; and respectively are the weight coefficients of the average value and the coefficient of variation of the back surface profile wear depth.

[0070] S204: Set and respectively are the subjective evaluation of the average value and the coefficient of variation of the back surface profile wear depth, and respectively are the subjective evaluation of the average value and the coefficient of variation of the back surface profile wear depth, and .

[0071] Further, the expression of the multi-objective evaluation model of the overall blisk spin-vibration composite polishing process is:

[0072]

[0073] In the formula: is a multi-objective evaluation model; and respectively are the average value and the coefficient of variation of the back surface profile wear depth after normalization processing and data translation; and respectively are the average value and the coefficient of variation of the back surface profile wear depth after normalization processing and data translation; and respectively are the weight coefficients of the average value and the coefficient of variation of the back surface profile wear depth; and respectively are the weight coefficients of the average value and the coefficient of variation of the back surface profile wear depth; and respectively are the subjective evaluation of the average value and the coefficient of variation of the back surface profile wear depth, and respectively are the subjective evaluation of the average value and the coefficient of variation of the back surface profile wear depth, and .

[0074] S3: According to the overall blisk structure characteristics, the container size parameters and container motion parameters, the container left and right wall configuration, and the blade extension configuration are used for active regulation of the granular medium flow field; three groups of simulation and multi-objective evaluation are sequentially performed, wherein the first group, the second group and the third group of simulation and multi-objective evaluation are used to obtain the optimal container size parameters and container motion parameter combination, the optimal container left and right wall configuration parameter combination, and the optimal blade extension configuration extension disc thickness; before the second group and the third group of simulation and multi-objective evaluation are performed, the simulation and multi-objective evaluation results of the previous group are required to be simulated by EDEM discrete element to determine the overall blisk machining effect; if the simulation and multi-objective evaluation results of the first group meet the machining requirements, the control scheme is determined as the optimal container size parameters and container motion parameters, otherwise the second group of simulation and multi-objective evaluation is performed; if the simulation and multi-objective evaluation results of the second group meet the machining requirements, the control scheme is determined as 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 group of simulation and multi-objective evaluation is performed; if the simulation and multi-objective evaluation results of the third group meet the machining requirements, the control scheme is determined 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 extension configuration extension disc thickness parameters, otherwise step S3 is repeated until the machining requirements are met.

[0075] S31: According to the structure characteristics of the overall blisk, the container size parameter range and the container motion parameter range are determined, and orthogonal simulation is performed.

[0076] In step S31, the maximum profile diameter of the overall blisk is given as , the width of the overall blisk is , and the container size parameter range determined according to the maximum profile diameter of the overall blisk and the width of the overall blisk includes:

[0077] the parameter range of the container inner wall diameter is 1.05 ~1.25 , and the parameter range of the container inner wall width is 2.50 ~3.50 ; the maximum profile diameter of the overall blisk researched in this embodiment is 250mm, and the width of the overall blisk is 46mm. Therefore, the parameter range of the container inner wall diameter is 262.5mm~312.5mm, and the parameter range of the container inner wall thickness is 115mm~161mm.

[0078] The parameter range of the vibration frequency of the container is 20 Hz-30 Hz, the parameter range of the vibration amplitude of the container is 5.469 mm-2.431 mm, and the parameter range of the rotation parameter of the container is 5.0 rpm-15.0 rpm. In addition, it is determined that the particle medium diameter is 4 mm, and the filling amount is 70%. The simulation time is the time required for one rotation of the container, and when the parameter range of the rotation parameter of the container is 5.0 rpm-15.0 rpm, the corresponding parameter range of the simulation time is 12 s-4 s.

[0079] A four-factor five-level orthogonal simulation scheme is designed according to the parameter range, and the results of the orthogonal simulation include the values of the evaluation indexes corresponding to different combinations of the container size parameters and the container motion parameters, as shown in Table 1. It should be noted that the average wear depth data per unit time under different parameter combinations should be used when analyzing the average wear depth. In addition, since the container size parameters and the container motion parameters do not effectively improve the overall blade disc inlet and outlet edge region machining effect, only the blade body region is considered when the container size parameters and the container motion parameters are optimized.

[0080] Table 1: Four-factor five-level orthogonal simulation scheme and results

[0081]

[0082] S32: Multi-objective evaluation based on the results of the orthogonal simulation and the multi-objective evaluation model, to obtain the optimal combination of the container size parameters and the container motion parameters.

[0083] 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, a total of 25 instances. According to the entropy weight method, the objective weight coefficients of different evaluation indexes are calculated, and the weight coefficients of the average and variation coefficient of the blade back surface wear depth are 0.3558 and 0.2226, respectively, and the weight coefficients of the average and variation coefficient of the blade basin surface wear depth are 0.3178 and 0.1038, respectively. In view of the optimization of the container size parameters and the container motion parameters, the average wear depth is given priority, so the weight coefficients are set as ; finally, the corresponding multi-objective evaluation values are calculated according to the multi-objective evaluation formula.

[0084] The main effect analysis of the orthogonal simulation results is as follows: Figure 3The multi-objective evaluation value increases by 0.054 and 0.036 respectively as the width of the inner wall of the container and the diameter of the inner wall of the container increase, gradually decreases by 0.078 as the vibration frequency of the container increases, and fluctuates within a certain range as the rotation parameter of the container increases. According to the main effect diagram, the optimal combination of the container size parameters and the container motion parameters is selected, and in the optimal combination of the container size parameters and the container motion parameters, the width of the inner wall of the container is 161 mm, the diameter of the inner wall of the container is 312.5 mm, the vibration frequency of the container is 20 Hz, the vibration amplitude of the container is 5.469 mm, and the rotation parameter of the container is 10 rpm.

[0085] S33: Simulate the optimal combination of the container size parameters and the container motion parameters based on the EDEM discrete element simulation model, and determine whether the overall blade machining effect meets the machining requirements; if not, perform step S34 to control the configuration of the container wall based on the optimal combination of the container size parameters and the container motion parameters; if the machining requirements are met, the control scheme is determined to be the optimal combination of the container size parameters and the container motion parameters.

[0086] Specifically, the optimal combination of the container size parameters and the container motion parameters is simulated based on the EDEM discrete element simulation model, the wear depth characteristics of the blade back surface and the blade basin surface corresponding to the optimal combination of the container size parameters and the container motion parameters are extracted, and it is determined whether the overall blade machining effect meets the machining requirements.

[0087] In this embodiment, it is calculated that the wear depth coefficient of variation RSD values of the blade body 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 inlet and outlet edges still exists, which does not meet the machining requirements.

[0088] S34: According to the structure characteristics of the overall blade, the configuration parameters range of the container wall is determined, and the response surface simulation is performed.

[0089] As shown in FIG. 6, the wear depth of the blade back surface and the blade basin surface is shown. Figure 4The optimization is performed by using the container wall configuration adjustment method. The parameter ranges of the left and right container wall configurations are determined according to the overall blade disc, the container and the particle medium size. In order to ensure the flowability of the particle medium, a proper distance (25 mm in this case) should be ensured between the container wall and the overall blade disc wheel disc. The thickness of the container wall configuration is set to 0 mm to 32 mm with two particle medium diameters as the interval. In order to enhance the effect of the particle medium on the blade root area, the height of the container wall configuration should be higher than the wheel disc diameter, so the minimum height of the left and right container wall configurations should be greater than 48 mm and 64 mm respectively, and the maximum height of the left and right container wall configurations is 156 mm. The height of the container wall configuration is set to 60 mm to 156 mm and 76 mm to 156 mm for the left and right container wall configurations respectively with four particle medium diameters as the interval.

[0090] The parameter ranges of the left and right container wall configurations determined according to the structural characteristics of the overall blade disc include:

[0091] The parameter range of the thickness of the left container wall configuration is 12 mm to 20 mm, and the parameter range of the height of the left container wall configuration is 132 mm to 148 mm. The parameter range of the thickness of the right container wall configuration is 4 mm to 12 mm, and the parameter range of the height of the right container wall configuration is 140 mm to 156 mm.

[0092] A four-factor three-level response surface simulation scheme is designed according to the parameter ranges, and the average value and coefficient of variation of the blade back surface wear depth and the average value and coefficient of variation of the blade basin surface wear depth corresponding to different combinations of the left and right container wall configuration parameters are extracted, as shown in Table 2. In addition, since the container wall configuration does not effectively improve the machining effect of the inlet and outlet edge regions of the overall blade disc, only the blade body region is considered.

[0093] Table 2 Four-factor three-level response surface simulation scheme and results

[0094]

[0095] According to the structural characteristics of the overall blade disc, the container wall configuration adopts a curved surface configuration formed by revolving a quadratic function curve around the X axis, wherein the expression of the quadratic function curve is:

[0096]

[0097] In the formula, the input and output values of the quadratic function curve are respectively: and The characteristic parameters of the quadratic function curve are respectively: and wherein, is used to reflect the thickness of the container wall configuration, ​ for reflecting the container wall configuration height ;

[0098] S35: The step of constructing a neural network and genetic algorithm coupling model and determining the optimal container left and right wall configuration parameter combination based on the neural network and genetic algorithm coupling model, the response surface simulation result and the multi-objective evaluation model comprises:

[0099] S351: Multi-objective evaluation is performed based on the response surface simulation result and the multi-objective evaluation model, and a multi-objective evaluation value corresponding to the response surface simulation result is obtained, and there are 31 instances. The weight coefficients of different evaluation indexes are calculated according to the entropy weight method, and the weight coefficients of the average value and the variation coefficient of the blade back surface wear depth are 0.2694 and 0.1920 respectively, and the weight coefficients of the average value and the variation coefficient of the blade back surface wear depth are 0.2700 and 0.2686 respectively; in view of the left and right container wall configuration, the variation coefficient of the wear depth is preferred, and therefore ; finally, the multi-objective evaluation value is calculated according to the multi-objective evaluation formula.

[0100] S352: The container left and right wall configuration thickness and height are input, and the multi-objective evaluation value is output to construct the overall blade disc machining effect database;

[0101] S353: In the container wall configuration optimization, the number of input layer nodes is 4, the number of output layer nodes is 1, and the initial neural network structure is constructed according to experience: the initial BP neural network topology structure is determined by using the feedforwardnet function in MATLAB software, the initial network is a single hidden layer neural network, the number of hidden layer nodes is determined to be in the range of [3, 13], the initial number of hidden layer nodes is selected to be 5, and the neural network with the initial structure of 4-5-1 is constructed. The feedforwardnet function is a function in MATLAB software for creating a feedforward neural network.

[0102] Optimizing neural network weights and thresholds based on genetic algorithm: setting the training set as 80% of the database samples (a total of 25 samples), and the verification set as the remaining 20% of the database samples (selecting the data of the 5th, 10th, 15th, 20th, 25th and 30th groups as the verification set). The transfer function between the input layer and the hidden layer is set as the Logsig function, the transfer function between the hidden layer and the output layer is set as the Purelin function, and the neural network training function is set as the trainlm function. Based on this, the initial neural network is constructed. The weights and thresholds of each neuron in the initial neural network are extracted to form a genetic algorithm gene matrix. The initial population of the genetic algorithm is set to 50, the number of iterations is set to 1000, the crossover probability is set to 0.1, the mutation probability is set to 0.08, and the fitness function is set as the average absolute percentage error between the predicted value and the actual value MAPE, genetic algorithm optimization is carried out. The Logsig function is a logarithmic S-shaped activation function, and the Purelin function is a linear activation function; the trainlm function is an optimization algorithm.

[0103] According to the range of hidden layer node number, different hidden layer node numbers are iteratively trained, and the average absolute percentage error of the predicted value and the actual value under different hidden layer node numbers is calculated MAPE , as shown in Figure 5 . Finally, the optimal hidden layer node number is determined to be 10, and the neural network structure is 4-10-1.

[0104] Hidden layer number optimization: Set the number of hidden layers to 2, with the first hidden layer node number being 1 and the second hidden layer node number being 9. Train the neural network on this basis and record the average absolute percentage error of the predicted value and the actual value MAPE . Increase the first hidden layer node number sequentially until the total number of hidden layer nodes is reached, compare the average absolute percentage error under different hidden layer distribution MAPE , and determine the optimal first hidden layer node number.

[0105] Determine whether the second hidden layer node number is greater than or equal to 2, if the condition is met, increase the number of hidden layers, ensure the optimal first hidden layer node number, and distribute the remaining hidden layer node number, determine the optimal second hidden layer node number using the above method. Repeat the above steps until the hidden layer node number is less than 2 and cannot be further distributed. The average absolute percentage error of the predicted value and the actual value under the first hidden layer node number is shown in Figure 6 . Finally, the optimal neural network structure is determined to be 4-8-2-1.

[0106] Through genetic algorithm, hidden layer node number and hidden layer number optimization, the neural network relationship model between container wall configuration feature parameters and multi-objective evaluation value is constructed, and the mean square error, average absolute percentage error, average absolute error and root mean square error of the prediction result are calculated, as shown in Table 3. When the structure of the neural network is 4-8-2-1, the different evaluation indexes of the prediction result all reach the minimum, meeting the accuracy requirement, and the coupling model of neural network and genetic algorithm is obtained.

[0107] Table 3 Error comparison of different neural network structures

[0108]

[0109] S354: input the container left and right wall configuration parameters as the genes to be optimized into the neural network and genetic algorithm coupling model, evaluate the overall blade profile machining effect corresponding to different combinations of container left and right wall configuration parameters by multi-objective evaluation value, and the larger the multi-objective evaluation value is, the better, select the combination of the container left and right wall configuration thickness and the container left and right wall configuration height with the highest multi-objective evaluation value as the optimal container left and right wall configuration parameter combination, and the optimal container left and right wall configuration parameter combination is that the container left wall configuration thickness is 12 mm, the container left wall configuration height is 138 mm, the container right wall configuration thickness is 7 mm, and the container right wall configuration height is 148 mm.

[0110] S36: simulate the optimal container left and right wall configuration parameter combination based on the EDEM discrete element simulation model, extract the wear depth characteristics of the blade back profile and the blade basin profile corresponding to the optimal container left and right wall configuration parameter combination, and determine whether the machining requirements are met. If the machining requirements are not met, perform step S37 to control the blade expansion configuration based on the optimal container left and right wall configuration parameter combination; if the machining requirements are met, determine that the control scheme is the optimal container size parameter and container motion parameter and container left and right wall configuration thickness and height parameter.

[0111] It is calculated that the variation coefficients of the wear depth of the blade body region of the blade back profile and the blade basin profile are 0.261 and 0.400, respectively, which are further improved compared to the optimal container parameters. However, the over-throw phenomenon of the inlet and outlet edges still exists, which does not meet the machining requirements. Therefore, the blade expansion configuration control method is used for optimization, as shown in Figure 7 .

[0112] S37: according to the structural characteristics of the overall blade disc, determine the expansion disc thickness of the blade expansion configuration, and perform single-factor simulation; the value of the expansion disc thickness is set to have multiple values; in order to optimize the blade expansion configuration parameters, blade expansion configuration schemes with thicknesses of 2 mm, 4 mm, 6 mm, 8 mm and 10 mm are designed.

[0113] The overall blade disc studied in this embodiment has 19 blades, i.e., the blade expansion configuration also has 19 curved surfaces, which are fixed on the expansion disc. The diameters of the left and right expansion discs correspond to the minimum diameter and the maximum diameter of the overall blade disc body, respectively, and the purpose is not to affect the movement of the granular medium at the root of the flow passage. The curved surface characteristics of the blade expansion configuration should be similar to those of the overall blade disc, and have good connection with the inlet and outlet edges, so the curved surface characteristics of the blade expansion configuration are related to the thickness of the expansion disc. In addition, while ensuring the improvement of the uniformity of the overall blade disc machining, the granular medium should have good exchangeability and flowability as much as possible.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 width of the container inner wall 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.

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