CN114091300A - A method for identifying dynamic characteristic parameters of rolling joint of ball screw feed system - Google Patents
A method for identifying dynamic characteristic parameters of rolling joint of ball screw feed system Download PDFInfo
- Publication number
- CN114091300A CN114091300A CN202111339195.9A CN202111339195A CN114091300A CN 114091300 A CN114091300 A CN 114091300A CN 202111339195 A CN202111339195 A CN 202111339195A CN 114091300 A CN114091300 A CN 114091300A
- Authority
- CN
- China
- Prior art keywords
- ball screw
- feeding system
- screw feeding
- model
- twin
- 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.)
- Granted
Links
Images
Classifications
-
- 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
- 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
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- 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
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Geometry (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Computer Hardware Design (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Transmission Devices (AREA)
Abstract
本发明公开了一种滚珠丝杠进给系统滚动结合部动态特性参数识别方法，包括：以滚珠丝杠进给系统为分析对象，建立其孪生机械模型；选用一系列弹性关节等效滚动结合部，建立可以同时考虑多个滚动结合部及其各个方向刚度、阻尼的滚珠丝杠进给系统孪生有限元模型；以待识别的动态特性参数作为输入、以系统前六阶固有频率作为输出，设计深度神经网络(DNN)模型，并加以训练，优选最佳网络层数，确定孪生数据模型并最终完成滚珠丝杠进给系统动力学数字孪生模型的建立；结合模态测试数据构建优化识别模型，采用粒子群算法进行求解从而识别各刚度、阻尼参数。根据本发明，提高了滚珠丝杠进给系统动力学建模精度、高效准确地识别各滚动结合部动态特性参数。
The invention discloses a method for identifying dynamic characteristic parameters of a rolling joint of a ball screw feeding system. , establish a twin finite element model of the ball screw feed system that can consider multiple rolling joints and their stiffness and damping in all directions at the same time; take the dynamic characteristic parameters to be identified as the input and the first six natural frequencies of the system as the output, design Deep neural network (DNN) model, and train it, optimize the optimal number of network layers, determine the twin data model, and finally complete the establishment of the ball screw feed system dynamics digital twin model; The particle swarm algorithm is used to solve the problem to identify the stiffness and damping parameters. According to the present invention, the dynamic modeling accuracy of the ball screw feeding system is improved, and the dynamic characteristic parameters of each rolling joint can be identified efficiently and accurately.
Description
Technical Field
The invention relates to the technical field of machine tool joint dynamic characteristic parameter identification, in particular to a method for identifying dynamic characteristic parameters of a rolling joint of a ball screw feeding system.
Background
The ball screw feeding system is widely applied to the numerical control machine tool by the advantages of high positioning precision, high transmission efficiency, high reliability, relatively low cost and the like. The system is formed by connecting parts of different types, such as a lead screw, a nut, a rolling guide rail, a bearing, a workbench, a lathe bed and the like. The rolling joint part is used as a movable joint part, the rolling joint part is large in quantity and relatively dispersed in distribution, the rigidity value of the rolling joint part is relatively small, and the rolling joint part has a particularly obvious influence on the dynamic performance of the system. Therefore, the method has important significance for evaluating the dynamic performance of the feeding system of the numerical control machine tool and improving the design level of the machine tool by accurately establishing a dynamic model of the ball screw feeding system and accurately identifying the dynamic characteristic parameters of the rolling joint part of the ball screw feeding system. Establishing an accurate kinetic model is a prerequisite for identifying dynamic characteristic parameters of the joint.
From the present research, the following problems still exist with respect to ball screw feed system dynamics modeling and its rolling joint dynamic characteristic parameter identification. First, many documents assume complete decoupling of the dynamic performance in different directions, followed by independent identification of the dynamic characteristic parameters of the rolling joint in a single direction. Although a plurality of rolling joints can be considered simultaneously during dynamic modeling, the coupling effect which is not negligible in the dynamic characteristics of the feeding system in different directions is ignored, and the dynamic performance of the whole system is influenced by the combined action of the plurality of rolling joints distributed in the feeding system and the dynamic characteristic parameters in different directions, so that the dynamic modeling has limitations, and the modeling precision and the parameter identification accuracy are restricted. Secondly, a large number of documents consider the combined action of dynamic characteristic parameters in all directions of the space, perform dynamic modeling for a single joint, and identify all stiffness and damping parameters. Although the combined action of a plurality of directional dynamic characteristic parameters is considered during dynamic modeling, the studied rolling joint part is taken out of the ball screw feeding system, the assembly state of a study object is changed, the study object comes in and goes out of the actual situation, and the modeling precision and the parameter identification accuracy are also limited.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for identifying the dynamic characteristic parameters of the rolling joint part of the ball screw feeding system, which improves the dynamic modeling precision of the ball screw feeding system and efficiently and accurately identifies the dynamic characteristic parameters of each rolling joint part. To achieve the above objects and other advantages in accordance with the present invention, there is provided a ball screw feed system rolling joint dynamic characteristic parameter identification method, comprising:
s1, constructing a twin mechanical model of the ball screw feeding system by taking a physical entity of the ball screw feeding system as an analysis object;
s2, importing the twin mechanical model constructed in the step S1 into finite element software, and constructing a dynamic twin finite element model of the ball screw feeding system;
s3, generating a large amount of modal analysis data through a twin finite element model and a parametric analysis tool, and generating dynamic twin data of the ball screw feeding system;
s4, designing a DNN model by taking twin data as a data set, training, determining the optimal network depth, constructing a dynamic twin data model of the ball screw feeding system, and completing the establishment of a dynamic digital twin model of the ball screw feeding system;
s5, carrying out modal test on the physical entity of the ball screw feeding system to obtain experimental modal data;
s6, establishing an optimization model for identifying dynamic characteristic parameters of a rolling combination part of the ball screw feeding system, and identifying all rigidity and damping parameters at the same time;
and S7, substituting the recognition result in the step S6 into the twin finite element model to solve modal data, comparing the modal data with an experimental result, and checking the correctness and the precision.
Preferably, in step S1, attribute information such as mechanical structure, geometric dimension, etc. of the physical entity of the ball screw feeding system is digitally mirrored, and a twin mechanical model of the system is created by using three-dimensional software.
Preferably, in step S2, the twin mechanical model is imported into a multi-body dynamics module of a finite element software COMSOL Multiphysics, a series of matched elastic joints are selected according to actual conditions to be equivalent to each rolling joint, total stiffness and total damping in x, y and z directions of each joint are respectively defined, and material properties, constraint conditions and mesh division are further set, so as to establish a ball screw feeding system dynamics twin finite element model that can simultaneously consider a plurality of rolling joints and dynamic characteristic parameters in different directions thereof.
Preferably, in step S3, according to the hertzian contact theory and the literature investigation, the value ranges of the stiffness and damping parameters in different directions of each rolling joint portion are determined, random, uniform and disordered sampling is performed in a value space, 6000 groups of dynamic characteristic parameter sample points to be identified are obtained, and the dynamic characteristic parameters are used as input items of finite element analysis; by utilizing a parametric scanning function of finite element software COMSOL Multiphysics, input parameters are automatically extracted group by group and substituted into a finite element model for modal analysis, 6000 groups of inherent frequencies of the first six orders of the ball screw feeding system under different corresponding input conditions are solved and used as output items of the finite element analysis, and therefore twin data of the ball screw feeding system digital twin kinetic model are generated.
Preferably, in step S4, the dynamic characteristic parameters of the rolling joint to be identified are used as input, the first six-order natural frequency of the ball screw feeding system is used as output, and DNN models with 2-10 hidden layers are respectively designed; training the constructed DNN model by taking the twin data generated in the step S3 as a data set, so as to determine the optimal number of hidden layers and further complete the establishment of a dynamic twin data model of the ball screw feeding system; and completing the establishment of a dynamic digital twinning model of the ball screw feeding system by fusing a twinning mechanical model, a twinning finite element model and a twinning data model.
Preferably, in step S5, the LMS test. lab vibration noise test system is used to perform a modal test on the ball screw feeding system by a single-point excitation and multi-point vibration pickup method, so as to obtain an experimental modal result of the system.
Preferably, in step S6, the stiffness and damping parameters to be identified are used as design variables, the value ranges of the dynamic characteristic parameters are used as constraint conditions, an optimization objective function is constructed by combining the natural frequency experimental values measured by modal testing and the natural frequency predicted values driven by the digital twin dynamics model, an optimization model for identifying the dynamic characteristic parameters of the rolling joint of the ball screw feeding system is established, a particle swarm optimization algorithm is used for solving, and the stiffness and damping parameters of all the rolling joints in different directions are identified at the same time.
Preferably, in step S7, the optimally identified stiffness and damping parameters are substituted into the twin finite element model for modal analysis, so as to obtain the first six-order natural frequency of the ball screw feeding system based on the identified parameters, and the first six-order natural frequency is compared with the corresponding order experiment results, so as to calculate the relative error, thereby checking the accuracy and precision of the method of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
(1) a series of contact characteristics of equivalent rolling joint parts of elastic joints with different direction rigidity and damping can be considered at the same time, a finite element model of the ball screw feeding system in an assembly state is established, and the influence of multi-direction dynamic characteristic parameters of multiple joint parts on the dynamic characteristic of the feeding system is comprehensively considered.
(2) A digital twin dynamics model capable of faithfully mirroring the ball screw feeding system is established by means of three-dimensional mechanical modeling, parametric design, finite element simulation, DNN and the like, digital twin is applied to dynamic modeling of a machine tool joint and dynamic characteristic parameter identification, and dynamic modeling precision and parameter identification accuracy are improved.
(3) The method is not only suitable for the rolling joint part of the ball screw, but also suitable for solving the dynamics problem of a complex mechanical system with multiple joint parts and multi-directional dynamic characteristic parameters, and has high technical popularization value.
Drawings
FIG. 1 is a block diagram of a method for identifying dynamic characteristic parameters of a rolling joint of a ball screw feed system according to the present invention;
FIG. 2 is a twin mechanical model of a ball screw feed system rolling joint dynamic characteristic parameter identification method according to the present invention;
FIG. 3 is a diagram of a series of elastic joint equivalent rolling joints which can simultaneously consider stiffness and damping in different directions according to the dynamic characteristic parameter identification method of the rolling joints of the ball screw feeding system of the present invention;
FIG. 4 is a twin finite element model of a method for identifying dynamic characteristic parameters of a rolling contact portion of a ball screw feed system according to the present invention;
FIG. 5 is a performance diagram of DNN models of different depths according to a method for identifying dynamic characteristic parameters of a rolling joint of a ball screw feed system;
FIG. 6 is a modal test equivalent three-dimensional model and a test point distribution diagram of the method for identifying the dynamic characteristic parameters of the rolling joint of the ball screw feeding system according to the invention;
FIG. 7 is a graph of the optimization convergence of particle swarm optimization algorithm for the dynamic characteristic parameter identification method of the rolling joint of the ball screw feeding system according to the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 7, a method for identifying a dynamic characteristic parameter of a rolling contact portion of a ball screw feed system includes: s1, constructing a twin mechanical model of the ball screw feeding system by taking a physical entity of the ball screw feeding system as an analysis object;
s2, importing the twin mechanical model constructed in the step S1 into finite element software, and constructing a dynamic twin finite element model of the ball screw feeding system;
s3, generating a large amount of modal analysis data through a twin finite element model and a parametric analysis tool, and generating dynamic twin data of the ball screw feeding system;
s4, designing a DNN model by taking twin data as a data set, training, determining the optimal network depth, constructing a dynamic twin data model of the ball screw feeding system, and completing the establishment of a dynamic digital twin model of the ball screw feeding system;
s5, carrying out modal test on the physical entity of the ball screw feeding system to obtain experimental modal data;
s6, establishing an optimization model for identifying dynamic characteristic parameters of a rolling combination part of the ball screw feeding system, and identifying all rigidity and damping parameters at the same time;
and S7, substituting the recognition result in the step S6 into the twin finite element model to solve modal data, comparing the modal data with an experimental result, and checking the correctness and the precision.
Further, in step S1, attribute information such as mechanical structure, geometric dimension, and the like of the physical entity of the ball screw feeding system is digitally mirrored, and a twin mechanical model of the system is established by using three-dimensional software.
Further, in the step S2, the twin mechanical model is imported into a multi-body dynamics module of the finite element software COMSOL Multiphysics, a series of matched elastic joints are selected to be equivalent to each rolling joint according to actual conditions, total stiffness and total damping in x, y and z directions of each joint are respectively defined, and material properties, constraint conditions and mesh division are further set, so that a ball screw feeding system dynamics twin finite element model which can simultaneously consider a plurality of rolling joints and dynamic characteristic parameters in different directions thereof is established.
Further, in step S3, according to the hertzian contact theory and the literature investigation, determining the value ranges of the stiffness and damping parameters of each rolling joint in different directions, randomly, uniformly and disorderly sampling in a value space, obtaining 6000 groups of dynamic characteristic parameter sample points to be identified, and using the dynamic characteristic parameters as input items of finite element analysis; by utilizing a parametric scanning function of finite element software COMSOL Multiphysics, input parameters are automatically extracted group by group and substituted into a finite element model for modal analysis, 6000 groups of inherent frequencies of the first six orders of the ball screw feeding system under different corresponding input conditions are solved and used as output items of the finite element analysis, and therefore twin data of the ball screw feeding system digital twin kinetic model are generated.
Further, in the step S4, the dynamic characteristic parameters of the rolling joint to be identified are used as input, the first six-order natural frequency of the ball screw feeding system is used as output, and DNN models with 2-10 hidden layers are respectively designed; training the constructed DNN model by taking the twin data generated in the step S3 as a data set, so as to determine the optimal number of hidden layers and further complete the establishment of a dynamic twin data model of the ball screw feeding system; and completing the establishment of a dynamic digital twinning model of the ball screw feeding system by fusing a twinning mechanical model, a twinning finite element model and a twinning data model.
Furthermore, in step S5, a LMS test.lab vibration noise test system is used to perform a modal test on the ball screw feeding system by a single-point excitation and multi-point vibration pickup method, so as to obtain an experimental modal result of the system.
Further, in step S6, the stiffness and damping parameters to be identified are used as design variables, the value ranges of the dynamic characteristic parameters are used as constraint conditions, an optimization objective function is constructed by combining the natural frequency experimental values measured by modal testing and the natural frequency predicted values driven by the digital twin dynamic model, an optimization model for identifying the dynamic characteristic parameters of the rolling joint of the ball screw feeding system is established, a particle swarm optimization algorithm is used for solving, and the stiffness and damping parameters of all the rolling joints in different directions are identified at the same time.
Further, in the step S7, the optimally identified stiffness and damping parameters are substituted into the twin finite element model for modal analysis, the first six-order natural frequency of the ball screw feeding system based on the identified parameters is obtained, and is compared with the corresponding order experiment results, and the relative error is calculated, so as to check the accuracy and precision of the method of the present invention.
Example 1
S1: a self-designed and manufactured ball screw feeding system is used as an analysis object, attribute information such as a mechanical structure, a geometric dimension and the like of a physical entity of the ball screw feeding system is mirrored, and a twin mechanical model of the ball screw feeding system is established by utilizing SolidWorks and is shown in figure 2.
S2: and (3) importing the twin mechanical model into a multi-body dynamic module of finite element software COMSOL Multiphysics, adopting a series of predefined elastic joints to be equivalent to each rolling joint part, and defining the total rigidity and the total damping of each elastic joint in each direction under a Cartesian coordinate system. The transmission direction of the ball screw feeding system is defined as an x direction, the supporting direction is defined as a y direction, and the direction perpendicular to both the x direction and the y direction is defined as a z direction. As shown in FIG. 3, the screw-nut equivalent screw joint in the software is selected, and the dynamic characteristic parameters of each direction are defined as
And
selecting hinge pair joint equivalent fixationAn end bearing joint part, the dynamic characteristic parameters of each direction of which are defined as
And
selecting the equivalent simply supported end bearing joint part of the cylindrical auxiliary joint, and defining the dynamic characteristic parameters of each direction as
And
selecting a prism joint equivalent rolling guide rail joint part, and defining dynamic characteristic parameters of each direction as
And
the screw rod is hardly loaded in the x direction of the simple support end, the bearing can play a small amount along the direction, so that the deformation and thermal deformation of the screw rod caused by temperature change are released, and the rigidity and damping of the joint part in the x direction can be regarded as 0, namely the rigidity and damping of the joint part in the x direction are regarded as 0
The displacement of the rolling guide rail pair in the x direction is mainly that the nut drives the workbench to do linear feed motion, the joint part of the rolling guide rail is hardly loaded in the direction, and the rigidity and the damping of the joint part in the x direction can be regarded as 0, namely
And the lead screw is used as a revolving body, rotates around the x axis and is mainly acted by the pre-tightening force of the nut in the axial direction (x direction), and the lead screw-nut joint part, the fixed end joint part and the simple end bearing joint part are hardly influenced by the gravity of the workbench, so that the rolling joint of the three revolving kinematic pairs can be consideredThe contact characteristics of the joint in both radial directions (y-direction and z-direction) of the feed system are comparable, i.e.
And then defining material properties, setting constraint conditions and dividing proper grids so as to establish a twin finite element model of the ball screw feeding system, as shown in fig. 4.
S3: according to the Hertz contact theory and literature research, the value ranges of the stiffness and the damping parameters of different directions of each rolling joint part of the ball screw feeding system are determined as shown in the table 1.
TABLE 1 rolling combination part dynamic characteristic parameter value range in each direction
And randomly, uniformly and disorderly sampling in the range to generate 6000 groups of dynamic characteristic parameter sample points to be identified as shown in the formula (1), and taking the dynamic characteristic parameters as input items of finite element analysis.
By using the parameterized scanning function of finite element software COMSOLMUTIPhysics, input parameters are automatically extracted group by group and substituted into a finite element model for modal analysis, and 6000 groups of inherent frequencies of the ball screw feeding system under different input conditions shown in a formula (2) are solved to serve as output items of finite element analysis. Thereby generating twin data of the digital twin dynamics model of the ball screw feed system.
(f'1i,f'2i,f'3i,f'4i,f'5i,f'6i)i＝1,2,…,6000 (2)
S4: with the rolling joint dynamic characteristic parameter variable to be identified
For the first six natural frequencies (f) of the input, ball screw feed system1,f2,f3,f4,f5,f6) And respectively designing a DNN model with 2-10 hidden layers for output, wherein the input layer of the DNN model comprises 14 neurons, and the output layer comprises 6 neurons. Setting the number of the neurons of each layer to be 11, selecting 'Sigmod' as an excitation function of a hidden layer and 'Linear' as an excitation function of an output layer, setting the learning rate to be 0.01 and the minimum gradient to be 1 multiplied by 10-20. Taking global Mean Square Error (MSE) as a loss function, the calculation formula is shown as formula (3):
wherein ([ k, c)]) For input layer variables, N is the number of sample groups, fjThe actual value in the network training process, namely the j-th order natural frequency of the ball screw feeding system solved through finite element analysis,
the predicted value in the network training, namely the j-th order natural frequency of the ball screw feeding system predicted by the DNN model, is smaller, the smaller the MSE value is, the closer the predicted value is to the true value, and the more the model is converged.
Training the constructed DNN model by using the twin data generated in the step S3 as a data set, and setting a training cut-off condition to be 1 × 10 of the minimum global mean square error-8Or the maximum number of iterations is 2000, when one of the two conditions is met and the loss function converges, the DNN training is considered complete and valid.
Taking a data set of 500 groups of non-training sample points as a test set, and calculating the average absolute Percentage Error (MAPE) of the output result of the test set by using a trained DNN model with 2-10 hidden layers, wherein the calculation formula is shown as the formula (4):
the natural frequency is used as an evaluation index to reflect the closeness degree of a predicted value of the natural frequency and a finite element value in a test set, and the smaller the index value is, the predicted natural frequency
And finite element calculation result fijThe closer the model is, the better the model performance and the higher the prediction accuracy. The test set MAPE results obtained by the DNN models with different depths are shown in FIG. 5, and from the analysis results, the optimal number of hidden layer layers is 6, and finally the number of layer-by-layer neurons of the DNN model is determined to be 14-11-11-11-11-11-11-11-6.
S5: and performing modal test on the ball screw feeding system by using the LMS (least mean square) through a single-point excitation and multipoint vibration pickup method to obtain an experimental modal result of the system. Firstly, establishing an equivalent three-dimensional model as shown in fig. 6 in LMS modal analysis software, arranging 33 measuring points in one-to-one correspondence with positions in a real object, wherein the measuring point No. 33 at the center position of the surface of the workbench is set as an excitation point. This point was hammered with a Kistler model 9724a200 hammer to excite the system. And then picking up vibration signals of the point in the x, y and z directions respectively by using a BK4525B type three-way acceleration sensor arranged at the corresponding measuring point, transmitting the vibration signals to an LMS data acquisition device, and analyzing and processing experimental data by LMS modal analysis software. After the vibration signals of all the measuring points are collected, the LMS analyzes and calculates the natural frequency of the ball screw feeding system, as shown in Table 2.
TABLE 2 Experimental values of the first six-step natural frequency of a ball screw feed system
| Order of the order | Natural frequency experiment value (Hz) |
| 1 | 59.63 |
| 2 | 88.47 |
| 3 | 178.11 |
| 4 | 237.17 |
| 5 | 279.95 |
| 6 | 383.59 |
S6: the stiffness and damping parameters to be identified are used as design variables, the value range of each dynamic characteristic parameter is used as a constraint condition, and the experimental value f of the first six-order natural frequency measured by combining modal testing is usedjNatural frequency prediction value driven by digital twin dynamic model
And (5) constructing an optimization objective function, and establishing an optimization model of the dynamic characteristic parameter identification of the rolling joint of the ball screw feeding system as shown in the formula (5).
The above equation is solved by a particle swarm optimization algorithm to identify the dynamic characteristic parameters of each rolling joint in each direction, the convergence curve is shown in fig. 7, and the identification result is shown in table 3.
TABLE 3 optimized recognition results
S7: and substituting the optimally identified rigidity and damping parameters into a twin finite element model in the digital twin dynamic model, carrying out modal analysis on the twin finite element model, obtaining the first six-order natural frequency of the ball screw feeding system based on the identified parameters, and comparing the six-order natural frequency with the experimental values step by step, wherein the comparison result is shown in table 4.
TABLE 4 comparison of natural frequency finite element calculated values and experimental values for ball screw feed systems based on identified dynamic characteristic parameters
| Order of the order | Experimental values (Hz) | Finite element value (Hz) based on identified parameters | Relative error (%) |
| 1 | 59.63 | 59.55 | -0.13 |
| 2 | 88.47 | 89.49 | 1.15 |
| 3 | 178.11 | 179.30 | 0.67 |
| 4 | 237.17 | 237.06 | -0.05 |
| 5 | 279.95 | 279.46 | -0.18 |
| 6 | 383.59 | 394.72 | 2.90 |
It can be seen from the above table that the relative error values of the natural frequency solved based on the identified dynamic characteristic parameters and the natural frequency measured by the modal test are not more than 3%, so that high precision is achieved, and the correctness and the effectiveness of the method are verified.
The number of devices and the scale of the processes described herein are intended to simplify the description of the invention, and applications, modifications and variations of the invention will be apparent to those skilled in the art.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (8)
1. A method for identifying dynamic characteristic parameters of a rolling joint of a ball screw feeding system is characterized by comprising the following steps:
s1, constructing a twin mechanical model of the ball screw feeding system by taking a physical entity of the ball screw feeding system as an analysis object;
s2, importing the twin mechanical model constructed in the step S1 into finite element software, and constructing a dynamic twin finite element model of the ball screw feeding system;
s3, generating a large amount of modal analysis data through a twin finite element model and a parametric analysis tool, and generating dynamic twin data of the ball screw feeding system;
s4, designing a DNN model by taking twin data as a data set, training, determining the optimal network depth, constructing a dynamic twin data model of the ball screw feeding system, and completing the establishment of a dynamic digital twin model of the ball screw feeding system;
s5, carrying out modal test on the physical entity of the ball screw feeding system to obtain experimental modal data;
s6, establishing an optimization model for identifying dynamic characteristic parameters of a rolling combination part of the ball screw feeding system, and identifying all rigidity and damping parameters at the same time;
and S7, substituting the recognition result in the step S6 into the twin finite element model to solve modal data, comparing the modal data with an experimental result, and checking the correctness and the precision.
2. The method as claimed in claim 1, wherein the step S1 is performed by digitally mirroring the physical entity of the ball screw feeding system with the mechanical structure and geometric dimension of the physical entity, and using three-dimensional software to create a twin mechanical model of the physical entity.
3. The method for identifying the dynamic characteristic parameters of the rolling joints of the ball screw feeding system as claimed in claim 1, wherein in step S2, the twin mechanical model is introduced into a multi-body dynamic module of finite element software COMSOL Multiphysics, a series of matched elastic joints are selected according to actual conditions to be equivalent to each rolling joint, the total stiffness and the total damping in x, y and z directions of each joint are respectively defined, and material properties, constraint conditions and meshes are further set, so as to establish the ball screw feeding system dynamic twin finite element model which can simultaneously consider a plurality of rolling joints and dynamic characteristic parameters in different directions thereof.
4. The method for identifying the dynamic characteristic parameters of the rolling joint of the ball screw feeding system according to claim 1, wherein in step S3, the value ranges of the stiffness and damping parameters of the rolling joints in different directions are determined according to the hertzian contact theory and the literature research condition, and random, uniform and disordered sampling is performed in the value space to obtain 6000 groups of sample points of the dynamic characteristic parameters to be identified, and the dynamic characteristic parameters are used as input items of finite element analysis; by utilizing a parametric scanning function of finite element software COMSOL Multiphysics, input parameters are automatically extracted group by group and substituted into a finite element model for modal analysis, 6000 groups of inherent frequencies of the first six orders of the ball screw feeding system under different corresponding input conditions are solved and used as output items of the finite element analysis, and therefore twin data of the ball screw feeding system digital twin kinetic model are generated.
5. The method for identifying the dynamic characteristic parameters of the rolling joint of the ball screw feeding system according to claim 1, wherein in step S4, the dynamic characteristic parameters of the rolling joint to be identified are used as input, the first six-order natural frequency of the ball screw feeding system is used as output, and DNN models with 2-10 hidden layers are respectively designed; training the constructed DNN model by taking the twin data generated in the step S3 as a data set, so as to determine the optimal number of hidden layers and further complete the establishment of a dynamic twin data model of the ball screw feeding system; and completing the establishment of a dynamic digital twinning model of the ball screw feeding system by fusing a twinning mechanical model, a twinning finite element model and a twinning data model.
6. The method for identifying the dynamic characteristic parameters of the rolling joint of the ball screw feeding system according to claim 1, wherein in step S5, the LMS test.lab vibration noise test system is used to perform a modal test on the ball screw feeding system by a single-point excitation and multi-point vibration pickup method, so as to obtain experimental modal results of the system.
7. The method for identifying the dynamic characteristic parameters of the rolling joint of the ball screw feeding system according to claim 1, wherein in step S6, stiffness and damping parameters to be identified are used as design variables, the value range of each dynamic characteristic parameter is used as a constraint condition, an optimization objective function is constructed by combining an experimental value of natural frequency measured by modal testing and a predicted value of natural frequency driven by a digital twin dynamics model, an optimization model for identifying the dynamic characteristic parameters of the rolling joint of the ball screw feeding system is established, a particle swarm optimization algorithm is used for solving, and the stiffness and damping parameters of all the rolling joints in different directions are identified at the same time.
8. The method as claimed in claim 1, wherein the stiffness and damping parameters are inputted into the twin finite element model for modal analysis in step S7, and the first six-order natural frequency of the ball screw feeding system based on the identified parameters is obtained and compared with the experimental results of the corresponding order to calculate the relative error, thereby checking the accuracy and precision of the method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111339195.9A CN114091300B (en) | 2021-11-12 | 2021-11-12 | A method for identifying dynamic characteristic parameters of rolling joint of ball screw feed system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111339195.9A CN114091300B (en) | 2021-11-12 | 2021-11-12 | A method for identifying dynamic characteristic parameters of rolling joint of ball screw feed system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114091300A true CN114091300A (en) | 2022-02-25 |
| CN114091300B CN114091300B (en) | 2025-03-14 |
Family
ID=80300240
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111339195.9A Active CN114091300B (en) | 2021-11-12 | 2021-11-12 | A method for identifying dynamic characteristic parameters of rolling joint of ball screw feed system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114091300B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114707428A (en) * | 2022-06-01 | 2022-07-05 | 中科航迈数控软件(深圳)有限公司 | Method, device, terminal and storage medium for simulating unobservable links of numerical control machine tool |
| CN114722660A (en) * | 2022-03-22 | 2022-07-08 | 西北工业大学 | Analysis method of rigid-flexible coupling dynamic contact characteristics of planetary roller screw |
| CN114999596A (en) * | 2022-05-31 | 2022-09-02 | 上海理工大学 | A method for identifying parameters of virtual material layers in sliding joints |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120128524A1 (en) * | 2010-11-22 | 2012-05-24 | Chun Young Soo | Steel wire rod having excellent cold heading quality and hydrogen delayed fracture resistance, method of manufacturing the same, and mehod of manufacturing bolt using the same |
| CN112414708A (en) * | 2020-11-16 | 2021-02-26 | 兰州理工大学 | A method for identifying parameters of axial dynamic characteristics of ball screw feed system |
| CN112800574A (en) * | 2019-11-13 | 2021-05-14 | 罗伯特·博世有限公司 | Method for generating a digital twin of a cartesian movement system |
| CN112904805A (en) * | 2021-01-15 | 2021-06-04 | 天津商业大学 | Digital twin modeling and virtual-real synchronization method for multi-axis numerical control machine tool |
-
2021
- 2021-11-12 CN CN202111339195.9A patent/CN114091300B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120128524A1 (en) * | 2010-11-22 | 2012-05-24 | Chun Young Soo | Steel wire rod having excellent cold heading quality and hydrogen delayed fracture resistance, method of manufacturing the same, and mehod of manufacturing bolt using the same |
| CN112800574A (en) * | 2019-11-13 | 2021-05-14 | 罗伯特·博世有限公司 | Method for generating a digital twin of a cartesian movement system |
| CN112414708A (en) * | 2020-11-16 | 2021-02-26 | 兰州理工大学 | A method for identifying parameters of axial dynamic characteristics of ball screw feed system |
| CN112904805A (en) * | 2021-01-15 | 2021-06-04 | 天津商业大学 | Digital twin modeling and virtual-real synchronization method for multi-axis numerical control machine tool |
Non-Patent Citations (1)
| Title |
|---|
| 朱坚民 等: "滚珠丝杠进给系统滚动结合部径向动态特征参数辨识", 中国机械工程, vol. 29, no. 4, 28 February 2018 (2018-02-28), pages 441 - 449 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114722660A (en) * | 2022-03-22 | 2022-07-08 | 西北工业大学 | Analysis method of rigid-flexible coupling dynamic contact characteristics of planetary roller screw |
| CN114999596A (en) * | 2022-05-31 | 2022-09-02 | 上海理工大学 | A method for identifying parameters of virtual material layers in sliding joints |
| CN114707428A (en) * | 2022-06-01 | 2022-07-05 | 中科航迈数控软件(深圳)有限公司 | Method, device, terminal and storage medium for simulating unobservable links of numerical control machine tool |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114091300B (en) | 2025-03-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114091300B (en) | A method for identifying dynamic characteristic parameters of rolling joint of ball screw feed system | |
| Bahloul et al. | A study on optimal design of process parameters in single point incremental forming of sheet metal by combining Box–Behnken design of experiments, response surface methods and genetic algorithms | |
| Zhu et al. | Robust machine tool thermal error modeling through thermal mode concept | |
| CN111368466B (en) | Mechanical vibration prediction method based on frequency response function parameter correction | |
| CN112926152A (en) | Accurate control and optimization method for clamping force of thin-walled part driven by digital twin | |
| Zheng et al. | Distributed Dynamic load identification on irregular planar structures using subregion interpolation | |
| Wang et al. | Crack parameters identification based on a kriging surrogate model for operating rotors | |
| Zhang et al. | Dynamic parameters identification of rolling joints based on the digital twin dynamic model of an assembled ball screw feed system | |
| CN119337503A (en) | Production process optimization method, system, device and storage medium for automobile parts | |
| CN114970028A (en) | A digital twin-driven manufacturing equipment research and development method and system | |
| CN110362951A (en) | Ball-screw Drive Systems engaging portion virtual materials model parameter acquisition methods | |
| Zeng et al. | Milling surface roughness prediction method based on spatiotemporal ensemble learning | |
| Possenti et al. | Detection of changes in dynamic characteristics of composite structures using Kriging metamodeling procedure: Experimental and computational analysis | |
| Yun et al. | Research on identification of unbalance parameters of rotor with multi-plane using improved particle swarm optimization | |
| CN113849901B (en) | Improved self-adaptive optimization method and system for contact heat exchange coefficient identification | |
| CN112414708A (en) | A method for identifying parameters of axial dynamic characteristics of ball screw feed system | |
| Liu et al. | Data-driven thermal error modeling based on a novel method of temperature measuring point selection | |
| CN103323278A (en) | Slide guide rail system virtual material layer parameter identification method | |
| Wang et al. | Application of genetic programming in the identification of tool wear | |
| Abdelwahab | Surface roughness modeling and prediction based on vibration signal analysis and machining parameters in milling of aluminum by artificial neural network | |
| Jin et al. | Wavelet neural network based on NARMA-L2 model for prediction of thermal characteristics in a feed system | |
| CN110705180B (en) | Nonlinear autoregressive neural network machine tool thermal error modeling method with external input | |
| He et al. | A novel geometric nonlinear reduced order modeling method using multi-fidelity surrogate for real-time structural analysis | |
| CN114999596A (en) | A method for identifying parameters of virtual material layers in sliding joints | |
| Du et al. | Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm |
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 |