CN112052542B - Intelligent design method and system for ultrasonic rolling amplitude transformer for blade surface strengthening - Google Patents
Intelligent design method and system for ultrasonic rolling amplitude transformer for blade surface strengthening Download PDFInfo
- Publication number
- CN112052542B CN112052542B CN202011101699.2A CN202011101699A CN112052542B CN 112052542 B CN112052542 B CN 112052542B CN 202011101699 A CN202011101699 A CN 202011101699A CN 112052542 B CN112052542 B CN 112052542B
- Authority
- CN
- China
- Prior art keywords
- amplitude transformer
- analysis
- horn
- initial
- unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/26—Composites
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/20—Design reuse, reusability analysis or reusability optimisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Geometry (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Apparatuses For Generation Of Mechanical Vibrations (AREA)
Abstract
The invention relates to an intelligent design method and system for an ultrasonic rolling amplitude transformer for strengthening the surface of a blade, wherein the method comprises the following steps: s1: determining target performance parameters, material parameters and structural parameters of the horn and randomly generating an initial horn; s2: establishing a finite element model for the initial horn; s3: performing modal analysis and harmonic response analysis on the finite element model, and extracting a result of the modal analysis and harmonic response analysis; s4: and carrying out optimization solution on the initial amplitude transformer according to the results of the modal analysis and the harmonic response analysis. According to the intelligent design method and system for the ultrasonic rolling amplitude transformer for blade surface reinforcement, a group of initial amplitude transformers are randomly generated according to target performance parameters, material parameters and structural parameters, finite element modeling and analysis are carried out on the initial amplitude transformers, an optimization algorithm is adopted to carry out optimization iteration on the result of finite element analysis, and the optimal solution under the constraint condition is obtained, so that the calculation is convenient, and the error is small.
Description
Technical Field
The invention relates to the field of ultrasonic rolling reinforcement of material surfaces, in particular to an intelligent design method and system of an ultrasonic rolling amplitude transformer for reinforcing the surfaces of blades.
Background
Surface ultrasonic rolling is a novel surface strengthening technology combining ultrasonic vibration and static load rolling. The method generates larger plastic deformation on the surface of the material through high-energy high-speed high-frequency vibration, and introduces beneficial residual compressive stress, thereby strengthening the processed surface. The ultrasonic surface rolling process is carried out on the blade and the like, so that the service life of the blade can be greatly prolonged, and huge economic and social benefits are generated. The traditional ultrasonic rolling technology is mostly applied to parts such as thin-wall parts and slender rod parts, and is also applied to complex scenes such as engine blade surfaces in recent years. The main components of the ultrasonic processing device comprise an amplitude transformer, a transducer, an ultrasonic generator and the like.
The horn is a critical component in the ultrasonic roll-processing head. The ultrasonic amplitude transformer has the main functions of amplifying mass point displacement or speed in mechanical vibration and acting as an energy collector in ultrasonic processing. Ultrasonic energy from the transducer is focused over a relatively small area via the horn and transmitted to the tool head. And secondly, the ultrasonic amplitude transformer performs impedance matching between the transducer and the acoustic load, so that ultrasonic energy of the transducer is efficiently transmitted to the load, and the function of a mechanical impedance converter is realized.
In order to cope with the diversified processing demands, the design of the horn is very important. The conventional analytical method designs the amplitude transformer, and solves the analytical expression of each performance parameter by using the wave equation and the boundary condition. Other methods are equivalent circuit methods, substitution methods, transmission matrix methods, and the like. The methods have large calculated amount and low design precision, and the finite element method can effectively solve the problems. The finite element analysis software is used for intuitively carrying out modal analysis and parameter solving on the ultrasonic amplitude transformer. But still have the following disadvantages: (1) time and labor are wasted. In the horn design process, the solution to the parameters may not meet the design requirements. To arrive at a satisfactory horn, the designer often performs extensive modeling and finite element analysis for different structural parameters. (2) there is a performance parameter error. The performance parameters and design requirements of the amplitude transformer obtained by manual modeling and finite element analysis often have certain errors, because the number of attempts is limited when the structural parameters of the amplitude transformer are designed manually, and better structural parameters of the amplitude transformer cannot be obtained.
Disclosure of Invention
The invention aims to provide an intelligent design method and system for an ultrasonic rolling amplitude transformer for blade surface reinforcement, which are used for solving the problems that finite element analysis design of the amplitude transformer is time-consuming and labor-consuming and performance parameter errors exist in the prior art.
The invention provides an intelligent design method of an ultrasonic rolling amplitude transformer for strengthening the surface of a blade, which comprises the following steps:
s1: determining target performance parameters, material parameters and structural parameters of the horn and randomly generating an initial horn;
s2: establishing a finite element model for the initial horn;
s3: performing modal analysis and harmonic response analysis on the finite element model, and extracting a result of the modal analysis and harmonic response analysis;
s4: and carrying out optimization solution on the initial amplitude transformer according to the results of the modal analysis and the harmonic response analysis.
Further, the target performance parameters include resonant frequency and amplification factor, the material parameters include density, elastic modulus, poisson's ratio, and ultrasonic wave velocity, and the structural parameters include major end radius, minor end radius, length, shape type, and constraints of the horn.
Further, step S2 includes:
s21: generating a parameter equation of the shape of the bus of the section of the initial amplitude transformer shaft;
s22: selecting key point coordinates of a bus of the initial luffing rod shaft section, and sequentially connecting the key points to form a shaft section;
s23: rotating and stretching the shaft section around the axis for 360 degrees to form a three-dimensional model;
s24: and determining the unit type and the unit size of the three-dimensional model, and performing self-adaptive grid division on the three-dimensional model.
Further, the method for selecting the key points in S22 includes:
-for stepped and conical horns, selecting the line segment end points as key points;
-for spline-curve horns, selecting spline-curve interpolation points as key points;
-for exponential and bessel curve horns, uniformly selecting points on 100-300 axial section buses as key points;
for a composite horn, the keypoints are the union of the keypoints of the different shaped horns.
Further, the unit type of the three-dimensional model is determined in S24 by determining whether there is a discontinuity in the derivative value at the section bus to determine whether there is a section discontinuity in the three-dimensional model:
-if there is a discontinuity in the derivative value at the axis cross section generatrix, there is a cross section discontinuity, the cell type is a tetrahedral cell;
-if there is no discontinuity of the derivative values at the axis section generatrix, there is no abrupt section change, the cell type is hexahedral.
Further, the unit size of the three-dimensional model is determined in S24 as a unit size according to the value of the small end radius r of the horn:
-when r.ltoreq.3 mm, the cell size is set to 0.2mm;
-when 3mm < r.ltoreq.5 mm, the cell size is set to 0.4mm;
when r > 5mm, the cell size is set to 0.5mm.
Further, the step S3 specifically includes:
s31: setting the frequency and the order of modal analysis according to the target performance parameters to finish modal analysis;
s32: setting the frequency of harmonic response analysis according to the target performance parameters, and completing the harmonic response analysis;
s33: extracting the natural frequency of the initial amplitude transformer from the modal analysis result, and extracting the amplification factor M and the rest node position x of the initial amplitude transformer from the harmonic response analysis result s 。
Further, the method for extracting the amplification factor M in S33 includes:
s331: firstly, selecting all nodes of the large end face of an initial amplitude transformer under natural frequency, calculating average amplitude X of all nodes of the large end face of the initial amplitude transformer, and judging that the amplitude transformer is not longitudinally vibrated under the mode if X is less than 0.001mm, otherwise, judging that the amplitude transformer is longitudinally vibrated under the mode;
s332: then extracting the amplitudes of all the nodes of the large end face and the small end face of the longitudinal vibration amplitude transformer, respectively calculating the average amplitude BIG_AVE of all the nodes of the large end face and the average amplitude SMA_AVE of all the nodes of the small end face, obtaining an amplification factor M through calculation,
further, the step S33 extracts a stationary node position x s The method of (1) is as follows:
extracting all nodes on the central connecting line of the origin of the longitudinal vibration amplitude transformer and the small end face, wherein the Z-axis coordinate value of the ith node is Z1, the node amplitude value is ZU1, the Z-axis coordinate value of the (i+1) th node is Z2, the node amplitude value is ZU2, and for different nodes i, if ZU1 and ZU2 are less than or equal to 0, calculating to obtain the position x of the static node s Wherein i=0, 1 … N-1, N is the number of all nodes on the central connecting line of the origin of the longitudinal vibration amplitude transformer and the small end face,
further, step S4 includes:
s41: calculating a performance parameter evaluation function according to the results of the modal analysis and the harmonic response analysis;
s42: judging the performance parameter evaluation function, and if the performance parameter evaluation function meets the design requirement, outputting the optimized resonant frequency, amplification coefficient and static node position; if the design requirements are not met, executing step S43;
s43: and generating a new-generation-shaped horn according to the structural parameters of the horn, and re-executing the steps S2-S4.
In another aspect, the invention provides an intelligent design system for an ultrasonic rolling horn for strengthening the surface of a blade, comprising:
-a parameter input module for randomly generating an initial horn based on the user-entered target performance parameters, material parameters, and structural parameters;
-a finite element modeling module for building a finite element model of the initial horn;
-an analysis module for performing a modal analysis and harmonic response analysis on the finite element model and extracting the results of the modal analysis and harmonic response analysis;
an optimal design module for optimally solving the initial horn based on the results of the modal analysis and the harmonic response analysis;
-a user interface module for integrating the parameter input module, the finite element modeling module, the analysis module and the optimization design module.
Further, the finite element modeling module includes:
-a busbar generation unit for generating a parametric equation for the initial horn shaft cross-sectional busbar shape;
the section generating unit is used for selecting key point coordinates of a cross section bus of the amplitude transformer shaft, and sequentially connecting the key points to form a shaft section;
-a rotational stretching unit for rotationally stretching the shaft section 360 ° about an axis to form a three-dimensional model;
-a meshing unit for meshing the three-dimensional model.
Further, the analysis module includes:
-a modal analysis unit for performing modal analysis for setting a frequency and an order of modal analysis according to the target performance parameter;
-a harmonic response analysis unit for setting a frequency of a harmonic response analysis according to the target performance parameter, completing the harmonic response analysis;
-a parameterized translation unit for implementing parameterized operations;
-a performance parameter extraction unit for extracting the natural frequency of the initial horn from the modal analysis results, and extracting the amplification factor and the resting node position of the initial horn from the harmonic response analysis results.
Further, the optimization design module includes:
-an evaluation function calculation unit for calculating a performance parameter evaluation function from the results of the modal analysis and the harmonic response analysis;
-a judging unit for judging the value of the performance parameter evaluation function, and outputting the optimized resonant frequency, amplification factor and rest node position if the design requirement is satisfied; if the design requirement is not met, generating a new-generation-shaped amplitude transformer, returning the new-generation-shaped amplitude transformer to the finite element modeling module, and carrying out modeling, analysis and optimal design again.
Further, the user interface module includes:
-a user interface unit for enabling human-machine interaction;
-a finite element batch unit for invoking a finite element modeling module and an analysis module for automatic modeling and analysis;
-an optimization tool unit for invoking an optimization design module;
-a temporary file cleaning unit for cleaning the temporary file obtained by the analysis module;
-an axial section generatrix drawing unit for drawing a curve of the axial section generatrix of the horn after optimization iterations.
The intelligent design method and system for the ultrasonic rolling amplitude transformer for blade surface reinforcement provided by the invention utilize a computer program to transfer data with finite element software, extract finite element analysis results and process data, and optimize and iterate the data obtained by finite element analysis by using an optimization algorithm to obtain an optimal solution in a boundary condition and constraint range. The user only needs to give out the material parameters and the design requirements of the amplitude transformer, and the amplitude transformer with the best performance meeting the requirements can be automatically designed by the system, so that the time and the labor are saved, and the error is small.
Drawings
FIG. 1 is a flow chart of an intelligent design method for an ultrasonic roll-down horn for blade surface strengthening in accordance with one embodiment of the present invention;
FIG. 2 is a flowchart of step S2 of an intelligent design method for an ultrasonic roll-down horn for blade surface strengthening according to an embodiment of the present invention;
FIG. 3 is a flowchart of step S3 of an intelligent design method for an ultrasonic roll-down horn for blade surface strengthening according to an embodiment of the present invention;
FIG. 4 is a flowchart of step S4 of an intelligent design method for an ultrasonic roll-down horn for blade surface strengthening according to an embodiment of the present invention;
FIG. 5 is a flowchart of a general meshing process for an ultrasonic rolled horn in accordance with one embodiment of the present invention;
FIG. 6 is a flowchart of a method for frequency extraction and longitudinal vibration determination of an ultrasonic rolled horn in accordance with one embodiment of the present invention;
FIG. 7 is a flowchart of a method and apparatus for determining the node and amplification factor of an ultrasonic roll-on horn according to one embodiment of the present invention;
FIG. 8 is a schematic diagram of an intelligent design system for an ultrasonic roll-down horn for blade surface strengthening in accordance with another embodiment of the present invention;
FIG. 9 is a schematic diagram of a user interface unit according to another embodiment of the present invention;
FIGS. 10 a-10 c are graphs of the cross-sectional busbar effects of the horn shaft for the first iteration, tenth iteration, and fifty iteration, respectively.
Detailed Description
Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Example 1
As shown in FIG. 1, the invention provides an intelligent design method for an ultrasonic rolling amplitude transformer for strengthening the surface of a blade, which comprises the following steps:
s1: target performance parameters, material parameters, and structural parameters of the horn are determined and an initial horn is randomly generated.
The target performance parameter includes the resonant frequency f of the horn r The amplification factor M can be determined according to the working requirements of ultrasonic processing; the material parameters comprise the ultrasonic wave speed c, the density rho, the elastic modulus E and the Poisson ratio sigma of the material adopted by the amplitude transformer, the material of the amplitude transformer can be selected according to the needs, and after a specific material is selected, the density rho, the elastic modulus E and the Poisson ratio sigma as well as the ultrasonic wave speed c can be determined.
The structural parameters of the amplitude transformer comprise the following two parts: the first variable parameter is a value range of a coordinate point for controlling the shape of the bus bar of the section of the amplitude transformer shaft. The Bezier curve amplitude transformer is the range of values of the Bezier control point coordinates. The range of step length values is the range of step length values for the stepped horn. For an exponential horn, the large end radius is often the variable parameter. Secondly, fixed parameters including the shape type and the big end half of the amplitude transformerDiameter R, small end radius R, and horn length L. Among them, the shape types of the commonly used amplitude transformer are Bessel amplitude transformer, stepped amplitude transformer, exponential amplitude transformer, etc.; the large end radius R depends on the ultrasonic wavelength lambda, wherein the ultrasonic wavelength can be obtained according to lambda=c/f (f is ultrasonic frequency), the large end radius R is less than or equal to lambda/4, and meanwhile, the large end radius R needs to be matched with an ultrasonic transducer; the small end radius r depends on the size of the processing head of ultrasonic rolling, the small end radius r is larger than or equal to the size of the processing head, if a higher amplification factor M is needed, the small end radius r is as small as possible, and the reverse is true; the horn length may take the form of an integer multiple of half wavelength, i.e., l=kλ/2 (where k is an integer). The shape of the generatrix of the horn shaft cross section is determined by the structural parameters, and if the parameters are determined, the shape of the generatrix of the horn shaft cross section is determined uniquely. Finally, 30 groups of variable parameters { a } are randomly generated in the value range n ,b n ,c n … } (n.ltoreq.30), which together with other parameters entered by the user into the system, produce 30 horns of the same shape type.
S2: a finite element model is built for the initial horn.
As shown in fig. 2, step S2 specifically includes the following steps:
s21: generating a parametric equation for the initial horn shaft cross-sectional busbar shape. And taking the center of the large end face of the amplitude transformer as an origin, the large end face as an xy plane, and the length L direction of the amplitude transformer as a z direction, and establishing a parameter equation.
S22: and selecting key point coordinates of the bus of the initial luffing rod shaft section, and sequentially connecting the key points to form the shaft section. The key points are selected in different modes according to the shapes of the amplitude transformers, so that modeling requirements of different amplitude transformers are met. The transformation rules from horn shape to key point are: the stepped and conical amplitude transformer is characterized in that the end points of line segments are selected as key points; a spline curve amplitude transformer, wherein spline curve interpolation points are selected as key points; and uniformly selecting the key point coordinates of 100-300, preferably 200, axial section buses of the exponential-shaped and Bessel curve-shaped amplitude transformers. For a composite horn, the key points are the union of the key points of the horn combinations of the different shapes. After the key points are selected, the original points (0, 0), the small end face centers (0, L) and the key points are sequentially connected to form the section of the amplitude transformer shaft.
S23: and (3) rotating and stretching the shaft section for 360 degrees around the axis, namely the connecting line of the origin and the center of the small end surface, so as to form a three-dimensional model of the initial amplitude transformer.
S24: and performing self-adaptive meshing on the three-dimensional model of the initial amplitude transformer. In order to divide the grids of the amplitude transformer with different shapes and sizes, the types or sizes of the units are required to be changed according to the radius r of the small end and whether the sections are suddenly changed or not. Therefore, a general rule flow of the grid division of the amplitude transformer is provided to avoid the situation of error, too-dense or too-sparse grid division. The specific rule flow is shown in fig. 5. For example, judging whether the section is suddenly changed by judging whether the section bus has a discontinuous position of a derivative value, if the section bus has a discontinuous position of a derivative value, the section is suddenly changed, the grid division is performed by adopting tetrahedral units, and if the section bus does not have a discontinuous position of a derivative value, the grid division is performed by adopting hexahedral units; and then determining the cell size according to the value of the small end radius r, wherein when r is less than or equal to 3mm, the cell size is set to be 0.2mm, when r is less than or equal to 3mm and less than or equal to 5mm, the cell size is set to be 0.4mm, and when r is more than or equal to 5mm, the cell size is set to be 0.5mm. After the unit types and the unit sizes are set, the automatic grid division can be performed.
The steps S21 to S24 can be converted into ANSYS Parametric Design Language language (APDL), and automated modeling and mesh division are realized by APDL. APDL is a parameterized design language, which provides convenience for simulation analysis, optimal design and self-adaptive grid division. The specific conversion method is as follows: using MPDATA, the EX field sets the elastic modulus; MPDATA, PRXY field sets Poisson's ratio; MPDATA, DENS field sets material density; establishing 200 key points in the step S22 by using a K field; connecting an origin (0, 0) and a small end face center (0, L) with the 200 key points by using an L field; merging the line segments using LCOMB fields; connecting the lines into facets using the AL field, thereby forming a horn shaft cross section; rotating the shaft section about the axis using the VROTAT field to form a rotating body; setting the type of the division unit cell by using the ET field; setting the size of the dividing cell using the ESIZE field; the meshing of the ultrasonic horn parameterized model is achieved using fields such as VSWEEP. To replace manual operations, APDL files are created using Python scripting language. The specific establishment method is as follows: establishing a parameter equation in the step S21 in the script, and calculating the coordinate position of each key point; and establishing a logic sequence used by each field in the amplitude transformer model according to APDL language, and arranging and generating APDL language files capable of automatically carrying out parametric modeling and grid division in ANSYS by utilizing a file reading and writing function.
Through the steps, the establishment of the finite element model of the initial amplitude transformer is completed. The initial horns are typically a group of 30 initial horns, so that when a finite model is built, each initial horn needs to be modeled in a parameterized manner, and the modeling method is performed automatically by the method described in steps S21 to S24 and by the method for building an APDL file using the Python scripting language as described above.
S3: and carrying out modal analysis and harmonic response analysis on the finite element model, and extracting the results of the modal analysis and harmonic response analysis.
As shown in fig. 3, the step S3 specifically includes the following steps:
s31: and setting the frequency and the order of the modal analysis according to the target performance parameters to finish the modal analysis. Wherein the frequency of the modal analysis is based on the resonance frequency f in the target performance parameter determined in step S1 r Determining that it belongs to f r In the range of + -5 kHz, the order of the modal analysis may be set to 15 orders. After the setting, the modal analysis can be performed.
S32: and setting the frequency of the harmonic response analysis according to the target performance parameters, and completing the harmonic response analysis. Wherein the frequency { f of the harmonic response analysis e The resonance frequency f in the target performance parameter determined in step S1 r Determining { f }, where e }＝{f e |f r ± (100·k), k=1, 2, …,20}. After the setting, the harmonic response analysis can be performed.
S33: extracting natural frequency f of initial amplitude transformer from modal analysis result n (n.ltoreq.15), the amplification M of the extracted initial horn and the resting node position x from the harmonic response analysis results s . Where n represents the modal analysis order, comparing and extracting f n Is closest to f r Person f nc . And judge the frequency as f nc Whether the mode of (2) is longitudinal vibration. Judgment f nc As shown in FIG. 6, the method of judging whether the amplitude transformer is longitudinally vibrating in the mode is that all nodes of the large end face under the frequency are selected, then the average amplitude X of the nodes of the large end face is calculated, if X is smaller than 0.001mm, the amplitude transformer is judged to be longitudinally vibrating in the mode, otherwise, the amplitude transformer is judged to be longitudinally vibrating in the mode. In general, the longitudinal vibration amplitude transformer has better effect, and only the longitudinal vibration amplitude transformer is discussed in the embodiment of the invention, namely, only the longitudinal vibration amplitude transformer meets the design requirement of the embodiment of the invention. Simultaneously extracting the amplitudes of all nodes of the large end face and the small end face of the longitudinal vibration amplitude transformer, respectively calculating the average amplitude BIG_AVE of all nodes of the large end face and the average amplitude SMA_AVE of all nodes of the small end face, wherein the ratio of the average amplitude BIG_AVE to the average amplitude SMA_AVE is the amplification factorExtracting the node on the connecting line of the origin and the center of the small end face, wherein the node with the amplitude closest to 0 is the stationary node position x of the amplitude transformer s . Selecting all nodes on a Z axis, wherein the Z axis coordinate value of the ith node is Z1, the node amplitude value is ZU1, the Z axis coordinate value of the (i+1) th node is Z2, and the node amplitude value is ZU2. For different nodes i, when ZU1 and ZU2 are different in number, the wave node +.>Determining the amplification factor M and the node point x s The specific method steps of (a) are shown in fig. 7, wherein the amplitude is in mm.
S4: and carrying out optimization solution on the initial amplitude transformer according to the results of the modal analysis and the harmonic response analysis.
After modal analysis and harmonic response analysis are completed, the shape of the initial amplitude transformer can be optimally designed by adopting a differential evolution algorithm to obtain an optimal solution, and the optimization can be realized by calling an evolution algorithm toolbox Geatpy of an open source. Geatpy is a high performance utility type evolutionary algorithm tool kit that provides a library function for important operations in many implemented evolutionary algorithms. The differential evolution algorithm control parameters include: population size (NP), mutation scaling factor (F), crossover probability (CR), and maximum algebra of evolution (MAXGEN). In a possible embodiment, np=30, f=0.5, cr=0.5, maxgen=100.
As shown in fig. 4, step S4 specifically includes the following steps:
s41: and calculating a performance parameter evaluation function according to the results of the modal analysis and the harmonic response analysis.
Specifically, the performance parameter evaluation function is calculated by the resonance frequency, the amplification factor, and the rest node position. The performance parameter evaluation function is given by the algorithm tool box, and is different due to the fact that different algorithms are adopted, and the invention is not limited to the fact that the performance parameter evaluation function is given by the algorithm tool box. Here, 30 initial horns are simultaneously calculated in synchronization.
S42: judging the performance parameter evaluation function, and if the performance parameter evaluation function meets the design requirement, outputting the optimized resonant frequency, amplification coefficient and static node position; if the design requirements are not met, step S43 is performed.
The performance parameters of the 30 initial horns were evaluated. Calculating a performance evaluation function corresponding to the amplitude transformer according to the performance parametersThe above formula shows that: the closer the resonance frequency is to the target resonance frequency, the closer the amplification factor is to the target amplification factor, and the more excellent the performance of the ultrasonic horn. Leaving the smaller (more superior and smaller) performance evaluation function value, and removing the larger performance evaluation function value, wherein the rules of reservation and removal are determined by the Geatpy toolbox along with the running progress. The algorithm in the kit will generate a set of 30 new horns, based on the variable parameters of the remaining horns, to participate in the next iteration. And then re-executing the finite element modeling module and the analysis module until a certain number of iterations, such as 100 times, are completed, or the performance parameter evaluation function value is minimum, completing the optimization design, and outputting the variable parameter with the minimum performance parameter evaluation function value. When the variable parameter is determined, the shape of the optimal horn is determined.
S43: and generating a new-generation-shaped horn according to the structural parameters of the horn, and re-executing the steps S2-S4.
According to the intelligent design method of the ultrasonic rolling amplitude transformer for blade surface strengthening, provided by the invention, a group of initial amplitude transformers are randomly generated according to target performance parameters, material parameters and structural parameters, and finite element modeling and analysis are carried out on the initial amplitude transformers. By using the self-adaptive grid division method, the conditions of too slow calculation, poor precision, division errors and the like caused by unified grid division can be avoided. And the APDL language is organized by using the Python script, so that automatic parametric modeling and analysis are realized, and the performance parameters of the amplitude transformer are obtained by adopting a self-adaptive amplification factor and wave node point extraction method, so that the labor cost is saved, and the operation threshold is reduced. And the optimization algorithm is adopted to carry out optimization iteration on the finite element analysis result, so that the optimal solution under the constraint condition is obtained, the calculation is convenient, and the error is small.
Example two
As shown in fig. 8, the present embodiment provides an intelligent design system for an ultrasonic roll-on horn for surface strengthening of a blade, which can implement the intelligent design method for an ultrasonic roll-on horn for surface strengthening of a blade according to the first embodiment, comprising: a parameter input module 1, a finite element modeling module 2, an analysis module 3, an optimal design module 4 and a user interface module 5.
The parameter input module 1 is used for randomly generating an initial horn according to target performance parameters, material parameters and structural parameters input by a user.
The target performance parameters, material parameters and structural parameters are the same as those of the first embodiment, and will not be described again here. The system is internally provided with material parameters of common materials such as aluminum alloy and the like for the amplitude transformer, and a user can select the material parameters according to the needs. The system of the embodiment can comprise a man-machine interaction interface through which a user can input or select various parameters according to the needs, so that the system is more convenient to use.
The finite element modeling module 2 is configured to build a finite element model of the initial horn.
The finite element modeling module 2 specifically includes: a busbar generating unit 21, a section generating unit 22, a rotary stretching unit 23, and a mesh dividing unit 24.
The busbar generating unit 21 is used for generating a parameter equation of the busbar shape of the section of the initial amplitude transformer shaft; the section generating unit 22 is used for selecting key point coordinates of a cross section bus of the amplitude transformer shaft, and sequentially connecting the key points to form a shaft section; the rotary stretching unit 23 is used for rotationally stretching the shaft section around the axis for 360 degrees to form a three-dimensional model; the mesh dividing unit 24 is used for mesh dividing the three-dimensional model. The modeling method of the finite element modeling module 2 is specifically described in embodiment one.
The analysis module 3 is used for carrying out modal analysis and harmonic response analysis on the finite element model and extracting the results of the modal analysis and harmonic response analysis.
The analysis module 3 specifically includes a modal analysis unit 31, a harmonic response analysis unit 32, a parameterized translation unit 33, and a performance parameter extraction unit 34.
Wherein, the modal analysis unit 31 is configured to set a frequency and an order of modal analysis according to the target performance parameter, so as to complete modal analysis; the harmonic response analysis unit 32 is configured to set a frequency of harmonic response analysis according to the target performance parameter, and complete the harmonic response analysis; the parameterization translation unit 33 is used for realizing parameterization operation; the performance parameter extraction unit 34 is configured to extract the natural frequency of the initial horn from the modal analysis results, and extract the amplification factor and the stationary node position of the initial horn from the harmonic response analysis results. The specific analysis method is as described in example one.
The finite element modeling module 2 and the analysis module 3 may be ANSYS simulation software. The ANSYS software is current general finite element analysis software, has powerful functions and simple and convenient operation, a user can conveniently construct a finite element model and carry out simulation analysis on the finite element model, and the user can organize ANSYS commands by using APDL and write parameterized user programs, so that the whole process of finite element modeling and analysis is realized, the operation is convenient, and the basis for realizing optimal design is also realized. For example, the parameterized translation unit 34 may be configured to automatically translate the horn busbar shape into modeling, analysis, and batch processes of the APDL command stream and to automatically execute.
In the system of this embodiment, both finite element modeling and analysis processes may be implemented by using APDL command streams, which respectively construct APDL command streams for generating a bus, generating a section, rotating and stretching, meshing, modal analysis, harmonic response analysis, and extracting performance parameters, and call ANSYS batch processing mode, complete the overall process of modeling and analysis, and store the required performance parameters such as natural frequency, amplification factor, and static node position of the initial horn in a data temporary file for the optimal design module 4 to take.
The optimal design module 4 is used for optimally solving the initial amplitude transformer according to the results of the modal analysis and the harmonic response analysis.
The optimum design module 4 includes an evaluation function calculation unit 41 and a judgment unit 42. Wherein the evaluation function calculation unit 41 is used for calculating a performance parameter evaluation function according to the results of the modal analysis and the harmonic response analysis; the judging unit 42 is configured to judge the value of the performance parameter evaluation function, and if the design requirement is met, output the optimized resonant frequency, amplification factor and rest node position; if the design requirement is not met, generating a new-generation-shaped amplitude transformer, returning the new-generation-shaped amplitude transformer to the finite element modeling module, and carrying out modeling, analysis and optimal design again. The specific optimization method is as described in the first embodiment.
The user interface module 5 is used for integrating the modules 1-4, and can enable the parameter input module 1 to intuitively interact with a user, enable the finite element modeling module 2 and the analysis module 3 to break away from the finite element software operation, and transfer data between the analysis module 3 and the optimal design module 4.
The user interface module 5 includes a user interface unit 51, a finite element batch processing unit 52, an optimization tool unit 53, a temporary file cleaning unit 54, and an axis section bus drawing unit 55. The user interface unit 51 is shown in fig. 9, and the system user types the parameters of the horn to be designed in the user interface, and then clicks the "smart design" button, so that the content that the user types can be transferred to the parameter input module 1, and the APDL code file for modeling and analysis is generated and stored in the temporary folder. The finite element batch processing unit 52 will automatically call the ANSYS batch processing mode, execute the APDL code file in the background, and finally obtain various performance parameters of the amplitude transformer output by the analysis module 3, and store the parameters in a temporary data file. The optimization tool unit 53 reads the temporary data file generated by the analysis module 3 and is responsible for implementing the optimization algorithm of the optimization design module 4. The temporary file cleaning unit 54 automatically deletes the temporary file for finite element analysis, the temporary file for APDL code and the temporary file for performance parameter after each finite element analysis, so as to save hard disk resources and avoid affecting the operation of the next finite element analysis and optimization iteration. The shaft section bus drawing unit 55 draws curves of 30 groups of horn shaft section buses in each iteration in a cartesian coordinate system to display the progress of the optimal design and prompt the system design effect.
According to the existing optimization algorithm, an optimization tool is independently developed, and matched software is designed by combining a user interface. The design work can be separated from finite element software, and one key is finished in a user interface, so that the operation of a designer is reduced, the technical threshold of design is reduced, and the design speed of the amplitude transformer is quickened for the designer according to different ultrasonic processing working conditions.
The application of the present system will be described in detail below by way of two examples.
Application one
One application of the present system is to automatically calculate horn performance parameters for a given material and size. At this time, the parameter input module 1, the finite element modeling module 2 and the analysis module 3 of the application system are needed, if the user needs to calculate the performance parameter of a given Bezier curve amplitude transformer, and the material is aluminum alloy, the user needs to input the material parameter of the aluminum alloy material, such as the elastic modulus E=76 GPa, the Poisson ratio sigma=0.33, and the density ρ=2.7 kg/m, into the system 3 And the like, simultaneously setting ultrasonic frequency (such as 20 kHz) and Bezier curve shape parameters, including a large end radius R, a small end radius R and a luffing rod length L. Wherein the large end radius R depends on the ultrasonic wavelength λ, which is 260mm according to the formula λ=c/f=260 mm, R is less than or equal to λ/4=65, and the large end radius r=50 mm is taken according to the ultrasonic transducer size. The radius r of the small end is half according to the ultrasonic rolling cutter headThe diameter is 5mm, and the radius r of the small end is=5mm. The resonance length of the horn was l=λ/2=130 mm using a half wavelength. The given bessel control points are (98, 27), (17, 15). The parameters are input into a parameter input module 1, the system automatically calls a finite element modeling module 2 and an analysis module 3, and after the operation is finished, the system outputs the performance data of a given amplitude transformer: resonant frequency f r Amplification factor M and rest node position x s 。
Application two
Another application of the present system is to automatically design a horn for a given constraint, such as an elongate bezier curve horn intended by a user to reduce the size of the ultrasonic roll-processing head. All modules of the application system are needed at this point. The parameters entered here are the same as in application one, except that the determined Bessel control point is changed to 4 constraints (x 1 ,y 1 ) And (x) 2 ,y 2 ). The user inputs the parameters and the constraint conditions into the parameter input module 1, and the system firstly generates 30 random amplitude transformers meeting the constraint conditions, namely, randomly generates Bezier curve control points (x 1 ,y 1 ) And (x) 2 ,y 2 ) The method comprises the steps of carrying out a first treatment on the surface of the Then the system calls the finite element modeling module 2 to complete the establishment of 30 times of finite element models; next to the system call analysis module 3, the set of 30 horn performance parameters are calculated and extracted and passed to the optimal design module 4. And the optimal design module 4 calculates the quality degree of the set of amplitude transformers according to the received performance parameters, and if the design target is reached, the optimal design module finishes the design and outputs the optimal design result to the user. If the design target is not met, the horns with better evaluation functions are reserved, the inferior horns are abandoned, and another group of 30 horns are regenerated according to the selection result. And putting into a finite element modeling and simulation analysis module again to perform iterative operation. After 50 iterations of the calculation, 1500 amplitude transformers are calculated, the time is 23 hours, and finally the slender Bezier curve amplitude transformer meeting the design requirement is calculated. 10 a-10 c are axial cross-sectional generatrices of each generation of horn in an iterative process, with radial abscissa and axial ordinate. It prompts the progress and effect of the optimal design, and the original 30 shapesThe amplitude transformers with the same parameters and random parameters tend to be consistent after iteration. Fig. 10a: performing first iteration; fig. 10b: a tenth iteration; fig. 10c: fifty iterations. Units: mm.
The intelligent design system for the ultrasonic rolling amplitude transformer for blade surface strengthening provided by the embodiment of the invention utilizes a computer program to transfer data with finite element software, extracts a finite element analysis result and processes the data, and uses an optimization algorithm to optimize and iterate the data obtained by the finite element analysis to obtain an optimal solution in a boundary condition and constraint range. The user only needs to give out the material parameters and the design requirements of the amplitude transformer, and the amplitude transformer with the best performance meeting the requirements can be automatically designed by the system, so that the time and the labor are saved, and the error is small.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and various modifications can be made to the above-described embodiment of the present invention. All simple, equivalent changes and modifications made in accordance with the claims and the specification of this application fall within the scope of the patent claims. The present invention is not described in detail in the conventional art.
Claims (8)
1. An intelligent design method for an ultrasonic rolling amplitude transformer for strengthening the surface of a blade is characterized by comprising the following steps:
s1: determining target performance parameters, material parameters and structural parameters of the horn and randomly generating an initial horn;
s2: establishing a finite element model for the initial horn; the step S2 comprises the following steps:
s21: generating a parameter equation of the shape of the bus of the section of the initial amplitude transformer shaft;
s22: selecting key point coordinates of a bus of the initial luffing rod shaft section, and sequentially connecting the key points to form a shaft section;
s23: rotating and stretching the shaft section around the axis for 360 degrees to form a three-dimensional model;
s24: determining the unit type and the unit size of the three-dimensional model, and performing self-adaptive grid division on the three-dimensional model;
the method for selecting the key points in the S22 comprises the following steps:
for stepped and conical amplitude transformers, selecting line segment endpoints as key points;
for a spline curve amplitude transformer, selecting spline curve interpolation points as key points;
for exponential and Bessel curve-shaped amplitude transformers, uniformly selecting points on 100-300 axis section buses as key points;
for a composite horn, the keypoints are the union of the keypoints of the horns of different shapes;
s3: performing modal analysis and harmonic response analysis on the finite element model, and extracting a result of the modal analysis and harmonic response analysis;
the step S3 specifically comprises the following steps:
s31: setting the frequency and the order of modal analysis according to the target performance parameters to finish modal analysis;
s32: setting the frequency of harmonic response analysis according to the target performance parameters, and completing the harmonic response analysis;
s33: extracting the natural frequency of the initial amplitude transformer from the modal analysis result, and extracting the amplification factor M and the rest node position x of the initial amplitude transformer from the harmonic response analysis result s ；
The method for extracting the amplification factor M in S33 is as follows:
s331: firstly, selecting all nodes of the large end face of an initial amplitude transformer under natural frequency, calculating average amplitude X of all nodes of the large end face of the initial amplitude transformer, and judging that the amplitude transformer is not longitudinally vibrated under the mode if X is less than 0.001mm, otherwise, judging that the amplitude transformer is longitudinally vibrated under the mode;
s332: then extracting the amplitudes of all the nodes of the large end face and the small end face of the longitudinal vibration amplitude transformer, respectively calculating the average amplitude BIG_AVE of all the nodes of the large end face and the average amplitude SMA_AVE of all the nodes of the small end face, obtaining an amplification factor M through calculation,
the stationary node position x is extracted in S33 s The method of (1) is as follows:
extracting all nodes on the central connecting line of the origin of the longitudinal vibration amplitude transformer and the small end face, wherein the Z-axis coordinate value of the ith node is Z1, the node amplitude value is ZU1, the Z-axis coordinate value of the (i+1) th node is Z2, the node amplitude value is ZU2, and for different nodes i, if ZU1 and ZU2 are less than or equal to 0, calculating to obtain the position x of the static node s Wherein i=0, 1 … N-1, N is the number of all nodes on the central connecting line of the origin of the longitudinal vibration amplitude transformer and the small end face,
s4: and carrying out optimization solution on the initial amplitude transformer according to the results of the modal analysis and the harmonic response analysis.
2. The intelligent design method for an ultrasonic roll-down horn for blade surface strengthening as recited in claim 1, wherein the target performance parameters include resonant frequency and amplification factor, the material parameters include density, elastic modulus, poisson's ratio and ultrasonic wave velocity, and the structural parameters include major end radius, minor end radius, length, shape type and constraints of the horn.
3. The intelligent design method for an ultrasonic rolling horn for strengthening a blade surface according to claim 1, wherein the determining in S24 is performed by determining whether the three-dimensional model has a sudden change in cross section by determining whether there is a discontinuity in a derivative value at a cross-section bus:
-if there is a discontinuity in the derivative value at the axis cross section generatrix, there is a cross section discontinuity, the cell type is a tetrahedral cell;
-if there is no discontinuity of the derivative values at the axis section generatrix, there is no abrupt section change, the cell type is hexahedral.
4. The intelligent design method for an ultrasonic roll-down horn for blade surface strengthening as recited in claim 1, wherein the determining the cell size of the three-dimensional model in S24 is determining the cell size according to the value of the small end radius r of the horn:
-when r.ltoreq.3 mm, the cell size is set to 0.2mm;
-when 3mm < r.ltoreq.5 mm, the cell size is set to 0.4mm;
when r > 5mm, the cell size is set to 0.5mm.
5. The intelligent design method for an ultrasonic roll-down horn for blade surface strengthening as recited in claim 1, wherein step S4 comprises:
s41: calculating a performance parameter evaluation function according to the results of the modal analysis and the harmonic response analysis;
s42: judging the performance parameter evaluation function, and if the performance parameter evaluation function meets the design requirement, outputting the optimized resonant frequency, amplification coefficient and static node position; if the design requirements are not met, executing step S43;
s43: and generating a new-generation-shaped horn according to the structural parameters of the horn, and re-executing the steps S2-S4.
6. An ultrasonic roll extrusion amplitude transformer intelligent design system for blade surface strengthening, which is characterized by comprising:
-a parameter input module for randomly generating an initial horn based on the user-entered target performance parameters, material parameters, and structural parameters;
-a finite element modeling module for building a finite element model of the initial horn; the finite element modeling module includes: the generating unit of the busbar, is used for generating the parameter equation of the cross-section busbar shape of the initial amplitude transformer shaft; the section generating unit is used for selecting key point coordinates of a cross section bus of the amplitude transformer shaft, and sequentially connecting the key points to form a shaft section; the rotary stretching unit is used for rotationally stretching the shaft section around the axis for 360 degrees to form a three-dimensional model; the grid dividing unit is used for dividing the three-dimensional model into grids; the method for selecting the key points by the section generating unit comprises the following steps: for stepped and conical amplitudesThe rod is used for selecting line segment endpoints as key points; for a spline curve amplitude transformer, selecting spline curve interpolation points as key points; for exponential and Bessel curve-shaped amplitude transformers, uniformly selecting points on 100-300 axis section buses as key points; for a composite horn, the keypoints are the union of the keypoints of the horns of different shapes; -an analysis module for performing a modal analysis and harmonic response analysis on the finite element model and extracting the results of the modal analysis and harmonic response analysis; the analysis module comprises: the modal analysis unit is used for setting the frequency and the order of modal analysis according to the target performance parameters and completing modal analysis; the harmonic response analysis unit is used for setting the frequency of harmonic response analysis according to the target performance parameter and completing the harmonic response analysis; the parameterized translation unit is used for realizing parameterized operation; a performance parameter extraction unit for extracting the natural frequency of the initial amplitude transformer from the modal analysis result, and extracting the amplification factor M and the rest node position x of the initial amplitude transformer from the harmonic response analysis result s The method comprises the steps of carrying out a first treatment on the surface of the The method for extracting the amplification factor M by the performance parameter extracting unit comprises the following steps: firstly, selecting all nodes of the large end face of an initial amplitude transformer under natural frequency, calculating average amplitude X of all nodes of the large end face of the initial amplitude transformer, and judging that the amplitude transformer is not longitudinally vibrated under the mode if X is less than 0.001mm, otherwise, judging that the amplitude transformer is longitudinally vibrated under the mode; then extracting the amplitudes of all the nodes of the large end face and the small end face of the longitudinal vibration amplitude transformer, respectively calculating the average amplitude BIG_AVE of all the nodes of the large end face and the average amplitude SMA_AVE of all the nodes of the small end face, obtaining an amplification factor M through calculation,the performance parameter extraction unit extracts a stationary node position x s The method of (1) is as follows: extracting all nodes on the central connecting line of the origin of the longitudinal vibration amplitude transformer and the small end face, wherein the Z-axis coordinate value of the ith node is Z1, the node amplitude value is ZU1, the Z-axis coordinate value of the (i+1) th node is Z2, the node amplitude value is ZU2, and for different nodes i, if ZU1 and ZU2 are less than or equal to 0, calculating to obtain the position x of the static node s WhereinI=0, 1 … N-1, N is the number of all nodes on the central connecting line of the origin of the longitudinal vibration amplitude transformer and the small end face,
an optimal design module for optimally solving the initial horn based on the results of the modal analysis and the harmonic response analysis;
-a user interface module for integrating the parameter input module, the finite element modeling module, the analysis module and the optimization design module.
7. The intelligent design system for an ultrasonic roll-on horn for blade surface strengthening of claim 6, wherein the optimal design module comprises:
-an evaluation function calculation unit for calculating a performance parameter evaluation function from the results of the modal analysis and the harmonic response analysis;
-a judging unit for judging the value of the performance parameter evaluation function, and outputting the optimized resonant frequency, amplification factor and rest node position if the design requirement is satisfied; if the design requirement is not met, generating a new-generation-shaped amplitude transformer, returning the new-generation-shaped amplitude transformer to the finite element modeling module, and carrying out modeling, analysis and optimal design again.
8. The intelligent design system for an ultrasonic roll-on horn for blade surface strengthening of claim 6, wherein the user interface module comprises:
-a user interface unit for enabling human-machine interaction;
-a finite element batch unit for invoking a finite element modeling module and an analysis module for automatic modeling and analysis;
-an optimization tool unit for invoking an optimization design module;
-a temporary file cleaning unit for cleaning the temporary file obtained by the analysis module;
-an axial section generatrix drawing unit for drawing a curve of the axial section generatrix of the horn after optimization iterations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011101699.2A CN112052542B (en) | 2020-10-15 | 2020-10-15 | Intelligent design method and system for ultrasonic rolling amplitude transformer for blade surface strengthening |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011101699.2A CN112052542B (en) | 2020-10-15 | 2020-10-15 | Intelligent design method and system for ultrasonic rolling amplitude transformer for blade surface strengthening |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112052542A CN112052542A (en) | 2020-12-08 |
| CN112052542B true CN112052542B (en) | 2023-10-13 |
Family
ID=73605739
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011101699.2A Active CN112052542B (en) | 2020-10-15 | 2020-10-15 | Intelligent design method and system for ultrasonic rolling amplitude transformer for blade surface strengthening |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112052542B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115121771B (en) * | 2022-07-27 | 2023-06-09 | 西北工业大学 | Intelligent ultrasonic continuous casting method and measuring and controlling device for metal section |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106682292A (en) * | 2016-12-14 | 2017-05-17 | 西安交通大学 | Blade root structure optimization method of dimensionality reduction simulated annealing algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101510229B (en) * | 2009-03-20 | 2011-09-21 | 西安电子科技大学 | Electronic apparatus cabinet optimum structure design method based on electricity, machine and thermal three-field coupling |
-
2020
- 2020-10-15 CN CN202011101699.2A patent/CN112052542B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106682292A (en) * | 2016-12-14 | 2017-05-17 | 西安交通大学 | Blade root structure optimization method of dimensionality reduction simulated annealing algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 基于多目标遗传算法的贝塞尔超声变幅杆优化设计方法研究;纪华伟;赵双双;胡小平;;中国机械工程(13);全文 * |
| 轴孔超声冲击强化及有限元模拟研究;唐亮;刘忠伟;王文健;颜力;朱有利;;表面技术(08);全文 * |
| 采用复合贝塞尔曲线的超声变幅杆优化设计;刘方晨;卢全国;刘新怀;赵冉;;南昌工程学院学报(04);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112052542A (en) | 2020-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Buonamici et al. | Generative design: an explorative study | |
| JP4381743B2 (en) | Method and program for generating volume data from boundary representation data | |
| CN111523270A (en) | Improved continuum structure topology optimization post-processing method | |
| CN108416158A (en) | A kind of composite material lathe lathe bed optimization of inside structure design method | |
| CN112052542B (en) | Intelligent design method and system for ultrasonic rolling amplitude transformer for blade surface strengthening | |
| CN113657009B (en) | Finite element model optimization method, device and equipment for rigging product | |
| WO2024036982A1 (en) | Three-dimensional modeling system and modeling method based on multi-modal fusion | |
| CN103106313B (en) | Roll consequent order reconstructing method | |
| Hao et al. | Progressive optimization of complex shells with cutouts using a smart design domain method | |
| CN111079326A (en) | Two-dimensional anisotropic grid unit measurement tensor field smoothing method | |
| Aranburu et al. | Reusability and flexibility in parametric surface-based models: a review of modelling strategies | |
| CN114200889A (en) | Milling parameter optimization method based on deep reinforcement learning | |
| Mayer et al. | Feature-Based Reconstruction of Non-Beam-Like Topology Optimization Design Proposals in Boundary-Representation | |
| EP3910602B1 (en) | Method for preserving shapes in solid model when distributing material during topological optimization with distance fields | |
| CN113011014B (en) | Composite material layering optimization method and system | |
| CN114925578A (en) | Automatic optimization method for concrete-filled circular steel tube short column based on particle swarm algorithm and ABAQUS | |
| Loos et al. | Structurally informed decision-making by means of Data Visualisations during the Conceptual Design Phase | |
| CN109210001B (en) | A kind of design method of superplastic forming hollow twist blade | |
| CN112560311A (en) | Method for quickly and automatically extracting structure information and analyzing and optimizing structure | |
| Su | The application of intelligent manufacturing technology in cnc tools design and machining | |
| CN114706349B (en) | Numerical control machining method, device and equipment for curved surface part and readable storage medium | |
| Cao et al. | A deep learning model for online prediction of in-process dynamic characteristics of thin-walled complex blade machining | |
| Belal et al. | Reverse engineering of a physical windmill sail and manufacturing the curving sail die in CAD/CAM system | |
| CN114896699B (en) | Multidisciplinary optimization design method for centripetal turbine impeller in aero-engine | |
| CN116679617B (en) | Automatic generation method for complex curved surface numerical control machining process planning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |