CN116150897A - Machine tool spindle performance evaluation method and system based on digital twin - Google Patents
Machine tool spindle performance evaluation method and system based on digital twin Download PDFInfo
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Abstract
The invention provides a method and a system for evaluating the performance of a machine tool spindle based on digital twin, which relate to the technical field of numerical control machine tools, and are characterized in that parameters of the machine tool spindle are firstly obtained, and a digital twin model of the machine tool spindle is constructed; establishing a digital twin model based on MCMC, and optimizing the acquired machine tool spindle parameters; packaging the constructed machine tool spindle digital twin model, reserving a data interface, and transmitting the acquired machine tool spindle information to the machine tool spindle digital twin model to realize real-time mapping of multiple dimensions; determining the weight of a machine tool spindle performance evaluation index by using a subjective and objective fusion mode; and (3) evaluating the comprehensive performance of the machine tool spindle to obtain a quantification result of the multi-dimensional index fusion evaluation of the machine tool spindle, establishing a machine tool spindle performance evaluation model based on a comprehensive index method by combining twin data and test data, and obtaining the quantification score by carrying out index transformation and weighting on index values of different dimensions. The invention improves the maintenance efficiency and the starting rate of the machine tool spindle.
Description
Technical Field
The invention relates to the technical field of numerical control machine tools, in particular to a machine tool spindle performance evaluation method and system based on digital twinning.
Background
A numerical control machine tool is a "machine tool" of the equipment manufacturing industry, and its processing level directly affects the level of the manufacturing industry. As a key functional component of the numerical control machine tool, the machining performance of the machine tool spindle directly affects the precision retention, stability and reliability of the numerical control machine tool. The main shaft of machine tool is a complex electromechanical product formed by coupling several subsystems, and is characterized by that it possesses the characteristics of multivariable, non-linear and strong coupling, etc.. In the working process of the numerical control machine tool, the quality of the assembly quality of the main shaft component and the whole machine tool of the numerical control machine tool directly influences the performance parameters such as vibration, temperature, rigidity and the like of the main shaft, thereby influencing the durability and the service life of the main shaft. Therefore, the performance of the machine tool main shaft is evaluated, the health state of the main shaft is monitored in real time, and the method has important significance for improving the machining efficiency of the numerical control machine tool and prolonging the service life of the machine tool.
To complete comprehensive, effective and accurate performance evaluation of a machine tool spindle, firstly, a machine tool spindle performance evaluation index system is established, evaluation items to be tested are defined, and an appropriate method is selected to collect index data. From the previous research, there are two main methods for obtaining the spindle performance index: the method based on experimental test and the method based on model simulation can be divided into single-domain model simulation and multi-domain model simulation, and the different methods have respective advantages and disadvantages as shown in table 1.
The test-based method is to perform test analysis by building a specific test bed, and in general, the test is most effective, but the performance detection of the spindle often needs to build a complex experimental device, and a large amount of sensing data needs to be acquired. According to the main shaft internal discipline mechanism, a mathematical model reflecting the main shaft performance change rule is established, simulation solution of the model is carried out by setting boundary conditions and system parameters of the model, and performance evaluation is carried out by simulation results. However, the main shaft is used as a complex electromechanical product formed by coupling a plurality of systems such as a machine, electricity, heat, liquid and the like, the model is difficult to construct, the characteristics of the multi-system coupling of the main shaft cannot be accurately described, the built model is only constructed based on the assumed working condition and cannot be consistent with the actual working condition, and therefore the problem of inaccurate performance evaluation index data is caused.
Therefore, how to obtain more effective performance evaluation index data is a difficult problem to be solved in the present machine tool spindle performance evaluation.
Table 1 comparison of spindle Performance index acquisition methods
In addition, the traditional method for evaluating the performance of the machine tool spindle mainly aims at single factor indexes such as spindle vibration, thermal deformation and the like, the machine tool spindle is complex in structure and variable in working condition, the performance of the machine tool spindle is comprehensively influenced by a plurality of factors, and the integral performance of the machine tool spindle is difficult to reflect by the single index.
In order to consider the influence of various parameter indexes, researchers have proposed various comprehensive performance evaluation methods of machine tool spindles, and common comprehensive performance evaluation methods include Fuzzy Comprehensive Evaluation (FCE), analytic Hierarchy Process (AHP), rank Sum Ratio (RSR), approximate ideal solution (TOPSIS), comprehensive Index Method (CIM), and the like. While the above methods can evaluate the overall performance of the spindle, they all suffer from some drawbacks or limitations, as shown in Table 2.
Therefore, an effective evaluation method needs to be studied to obtain a more accurate and comprehensive evaluation result.
Table 2 comparison of comprehensive Performance assessment methods
Disclosure of Invention
Aiming at the problems that the difficulty of comprehensively evaluating a main shaft by means of experimental tests is increased in the current time-varying environment and complex working conditions, the invention provides the machine tool main shaft performance evaluation method based on digital twinning, which effectively improves the management and operation and maintenance level of the machine tool main shaft and ensures the safe and reliable operation of the machine tool main shaft.
The method comprises the following steps:
step 1: acquiring machine tool spindle parameters, wherein the spindle parameters comprise: real-time parameter information and working condition data in the running process of a machine tool spindle;
step 2: constructing a digital twin model of a machine tool spindle;
Step 3: establishing a digital twin model optimization method based on SA-BOA-MCMC, and optimizing the acquired machine tool spindle parameters;
step 4: packaging the constructed machine tool spindle digital twin model, reserving a data interface, and transmitting the acquired machine tool spindle information to the machine tool spindle digital twin model to realize real-time mapping based on multiple dimensions of geometry, physics, behavior and rules;
step 5: constructing a virtual-real fusion evaluation index system;
step 6: based on the evaluation index weight distribution mode, combining the advantages and disadvantages of the evaluation indexes, and determining the machine tool spindle performance evaluation index weight by using a subjective and objective fusion mode;
step 7: and (3) evaluating the comprehensive performance of the machine tool spindle, establishing a machine tool spindle performance evaluation model based on a comprehensive index method, normalizing each index data in an evaluation index system, combining a weight value obtained by a combined weighting method, and carrying out weighted summation on evaluation indexes of each index by the comprehensive index method to obtain a comprehensive index CI of the electric spindle running state evaluation to obtain a quantized evaluation result.
In the step 2, the machine tool spindle is divided into a mechanical subsystem, an electrical control subsystem and a heat transfer subsystem by analyzing the structure and the coupling relation of the machine tool spindle system;
Selecting preset physical parameters according to the mechanical structure and the function of a main shaft of a machine tool;
compiling and describing the operation mechanism of each functional element of each subsystem of the machine tool spindle by adopting Modelica multi-field unified modeling language to form each functional element model;
constructing a digital twin model of each subsystem based on the connection relation of the functional element models;
and according to the coupling relation and the coupling mechanism among all subsystems of the machine tool spindle, correlating the digital twin models of all subsystems to form the digital twin model of the machine tool spindle.
It should be further noted that, step 3 further includes:
(1) Determining parameters to be optimized;
determining parameters to be optimized through sensitivity analysis;
the sensitivity analysis is shown in formula (1):
in the method, in the process of the invention,
is the firstiSensitivity ratio of the individual parameters,i=1,2,3,…,n；nis the number of parameters; />
Is the firstiThe relative sensitivity of the individual parameters;
wherein the sensitivity ratio can be calculated from formula (2);
in the method, in the process of the invention,
as vector (I)>
，/>
Is the average value of the parameters to be selected;
(2) Determining a target optimization function;
taking the error between the calculated value and the actual test value of the digital twin model smaller than a preset test threshold as the basis for constructing a target optimization function and taking the main shaft temperature as the basisTEstablishing an objective function for an exampleFThen the formula (11) is available according to the formula (10)
Wherein, the liquid crystal display device comprises a liquid crystal display device,
for the parameters to be updated->
For temperature, < >>
For the experimental value of temperature, +.>
Is the variance of the experimental values;
the objective function is defined as
Converting the maximum of the posterior distribution into a solution
Is the minimum of (2);
(3) And optimizing the parameters based on the determined target optimization function.
It should be further noted that the sampling flow in the step (3) is as follows:
1) Selecting initial values of Markov chains
；
2) From conditional probability distribution
Obtaining a sample value->
；
3) From uniform divisionIn-fabric sampling
If->
Accept->
I.e. +.>
；
Otherwise not accept, i.e
；
4) Repeating the steps 2) to 3) until convergence is achieved;
5) From taking samples
Removing samples before smoothing, estimating the remaining sample characteristics as corrected +.>
。
It should be further noted that the procedure of step 6 includes the following steps:
(1) Determining weights based on an analytic hierarchy process;
(2) Determining weights based on an entropy weight method;
(3) Determining comprehensive weights;
Partial subjective weight obtained by combining AHP
And objective weight derived from EWM +.>
Calculate the first using equation (26)jComprehensive weight of item index>
；
It should be further noted that, step (1) further includes:
(1) constructing a hierarchical model;
dividing the hierarchical model into a target layer, a criterion layer and a scheme layer according to the actual decision index;
(2) constructing a judgment matrix;
by carrying out pairing comparison on the relative importance among indexes, a judgment matrix is constructedA；
Matrix arrayAIs provided with thereinmThe number of the indexes is equal to the number of the indexes,
represents the firstiAnd (b)jRelative importance of each index, judgment matrixAAs shown in formula (13)>
，/>
；
(3) Calculating weights;
according to the judgment matrixATo solve for the maximum feature root
Obtaining corresponding feature vectors according to the maximum feature root passing formula (14)WThe weight of each index is obtained through normalization>
；
(4) Performing consistency test;
calculating a consistency index using (15)CICalculating the consistency ratio of formula (16)CRSuch asCR<0.1, if the judgment matrix passes the consistency test, if not, the judgment matrix does not have consistency and needs to be judged again;
it should be further noted that, step (2) further includes:
the flow is as follows:
(1) normalizing the data;
assuming that there isnA number of samples of the sample were taken, mThe number of the indexes is equal to the number of the indexes,a ij represent the firstiSample numberjIndividual index [ (]
，/>
）；
For the forward index, the normalization formula is:
for negative indicators, the normalization formula is:
(2) calculating index weight;
evaluation indexjThe corresponding information entropy of (a) is:
then corresponding firstjThe weights of the individual indexes are:
it should be further noted that, in the step 7, the standard processing is performed by using a range method, and the original data is mapped to the interval of [0,1 ];
if it is
Calculated by equation (22) for positive index, if +.>
Is calculated by a formula (23) for a negative index;
wherein:
index values for various factors; />
And->
The maximum value and the minimum value of the factor index in the reasonable operation range are respectively; />
Is the index value after normalization;
after the normalized data is obtained, the weight value obtained by combining the weighting method is combined, the evaluation indexes of all indexes are weighted and summed by the comprehensive index method, and the comprehensive index of the running state evaluation of the electric spindle is obtainedCI；
The operation state evaluation results are classified into grades by integrating the evaluation standards,CIthe calculation formula is that
Wherein:
is the i index weight value; />
Normalized data for the factor indicator.
The invention also provides a machine tool spindle performance evaluation system based on digital twinning, which comprises: the system comprises a data perception module, a digital twin model construction module, a digital twin model parameter optimization module, a digital twin model real-time mapping module, an evaluation index system construction module, an evaluation index weight calculation module and a machine tool spindle comprehensive performance evaluation module;
The data perception module is used for acquiring machine tool spindle parameters, and the spindle parameters comprise: real-time parameter information and working condition data in the running process of a machine tool spindle;
the digital twin model construction module is used for analyzing a machine tool main shaft, dividing the machine tool main shaft to form a coupling relation among parts, components and systems, dividing the coupling relation into a plurality of subsystems, respectively modeling each subsystem by adopting a multi-field modeling method, analyzing the operation characteristics of the machine tool main shaft and the coupling relation among the subsystems, and coupling and connecting the subsystems to obtain a multi-system coupling digital twin model of the machine tool main shaft;
the digital twin model parameter optimization module is used for establishing a digital twin model based on MCMC and optimizing the acquired machine tool spindle parameters;
the digital twin model real-time mapping module is used for packaging the constructed machine tool spindle digital twin model, reserving a data interface, transmitting the acquired machine tool spindle information to the machine tool spindle digital twin model, and realizing real-time mapping based on multiple dimensions of geometry, physics, behavior and rules;
the evaluation index system construction module is used for constructing an evaluation index system with virtual-real fusion;
The evaluation index weight calculation module is used for determining the machine tool spindle performance evaluation index weight by combining the advantages and disadvantages of the evaluation indexes and using a subjective and objective fusion mode based on the evaluation index weight distribution mode;
and the machine tool spindle comprehensive performance evaluation module is used for evaluating the comprehensive performance of the machine tool spindle to obtain a quantification result of the multi-dimensional index fusion evaluation of the machine tool spindle, and combining the twin data with the test data to establish a machine tool spindle performance evaluation model based on a comprehensive index method, and obtaining the quantification score by carrying out index transformation and weighting on index values of different dimensions.
It should be further noted that the system further includes: a machine tool spindle performance evaluation system development module;
the machine tool spindle performance evaluation system development module builds a platform based on the front end framework and the rear end framework of the Vue and FastAPI webpage system, realizes man-machine interaction, parameter setting and instruction input functions at the front end of the system, and transmits set parameter content to the rear end; the functions of uploading and downloading related data, data processing and communication with a physical entity numerical control system are realized at the back end of the system, and the original data and the processing operation result are sent to the front end for visual display.
From the above technical scheme, the invention has the following advantages:
aiming at the problems of low fidelity and difficult real-time mapping of the existing digital twin model, the invention establishes a model parameter optimization method based on SA-BOA-MCMC and a model updating strategy based on real-time working conditions, optimizes model parameters by comparing model output data with test data, improves the fidelity of the model, enables the digital twin model of the numerical control machine spindle to accurately reflect the change of the spindle performance by inputting the real-time working conditions, and improves the capability of the twin model for representing a physical system.
The machine tool spindle performance evaluation system and method also aim at the problem that the traditional spindle performance evaluation index is incomplete, index data are difficult to acquire, and the result is inaccurate due to inaccurate data, twin data are used, and a virtual-real integrated machine tool spindle performance evaluation index system is constructed from multiple dimensions such as vibration, rigidity, temperature, current, noise and precision by combining machine tool spindle test data with the twin data, so that the feasibility and the comprehensiveness of the performance evaluation method are improved. Compared with the traditional method, the method can more effectively and comprehensively reflect the overall performance level of the machine tool spindle and provide guidance for the actual operation of the machine tool spindle.
Aiming at the problem that the evaluation result is incomplete and inaccurate due to the lack of system evaluation indexes in the comprehensive performance evaluation of the machine tool spindle, the invention establishes a performance index weight distribution method based on AHP-EWM comprehensive weighting and a machine tool spindle comprehensive performance evaluation model based on CIM, and establishes a machine tool spindle comprehensive performance quantitative evaluation grade, so that the final evaluation result can reflect subjective experience and accords with objective facts, thereby accurately reflecting the running state of the spindle. By the method, the performance of the machine tool spindle can be evaluated, the management and operation and maintenance levels of the machine tool spindle are effectively improved, and safe and reliable operation of the machine tool spindle is ensured.
The built machine tool spindle performance evaluation system realizes the functions of online evaluation, real-time state monitoring and the like of the machine tool spindle through the webpage system, and through the system, a worker can remotely check the state of the machine tool spindle in real time, perform real-time performance evaluation on the machine tool spindle, maintain and repair the physical system of the machine tool spindle according to the analysis result, and greatly improve the working efficiency of the machine tool spindle.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram of a machine tool spindle performance evaluation system architecture based on digital twinning;
fig. 2 is a flow chart of a machine tool spindle performance evaluation method based on digital twinning.
Detailed Description
The present invention relates to a machine tool spindle performance evaluation method, wherein the basic idea of the present invention is only illustrated by a schematic way, only the modules related to the present invention are shown in the drawings, not according to the number and functions of the modules in actual implementation, the functions, the number and the functions of the modules in actual implementation can be changed at will, and the functions and the purposes of the modules can be more complex.
The machine tool spindle performance evaluation method can acquire and process the associated data based on an artificial intelligence technology. The intelligent diagnosis method of the digital twin-driven numerical control machine tool utilizes a digital computer or a machine controlled by the digital computer to simulate, extend and expand the intelligence of people, sense the environment, acquire knowledge and acquire the theory, method, technology and application device of the best result by using the knowledge.
For the machine tool spindle performance evaluation method, the real-time interactive mapping of the machine tool spindle and the digital space twin model is realized by establishing the twin model of the machine tool spindle and utilizing the technologies of sensor monitoring, data transmission and the like, so that the full life cycle state of the machine tool spindle is reflected, and the intelligent design, operation and maintenance and performance evaluation of the full life cycle of the machine tool spindle are realized.
The machine tool spindle performance evaluation method has a hardware level technology and a software level technology. The basic technology of the intelligent diagnosis method of the numerical control machine tool generally comprises technologies such as a sensor, a special artificial intelligent chip, cloud computing, distributed storage, big data processing technology, an operation/interaction system, electromechanical integration and the like. The intelligent diagnosis method software technology of the numerical control machine mainly comprises a computer visual angle technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The method for evaluating the performance of the main shaft of the machine tool fuses a large amount of data generated by the digital twin model with real-time monitoring data of parameters of the main shaft of the machine tool, and establishes a virtual-real fused main shaft performance evaluation index system from multiple dimensions; and finally, carrying out weight distribution on each evaluation index, and realizing the performance evaluation of the machine tool spindle by combining with a comprehensive performance evaluation method.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 to 2, a flowchart of a method for evaluating performance of a spindle of a machine tool based on digital twin is shown, and the method includes:
s101: acquiring machine tool spindle parameters, wherein the spindle parameters comprise: real-time parameter information and working condition data in the running process of a machine tool spindle;
in this embodiment, the machine tool spindle entity related data is the fact base for constructing the digital twin model of the machine tool spindle. Static data information such as the geometric dimension, the system structure, the physical attribute, the working capacity, the model and the like of the machine tool spindle can be directly obtained through tools such as machine tool spindle design parameters, design drawings, a three-dimensional scanner and the like. And for dynamic data information such as working condition, vibration, temperature, rigidity, noise, loading force and the like of a machine tool spindle, a reasonable sensing monitoring scheme is formulated by analyzing the structure and working environment of the machine tool spindle and selecting a proper data acquisition point. For example, a vibration sensor, a temperature sensor, a rotating speed sensor and a displacement sensor are arranged on a machine tool, and information such as vibration, temperature, rotating speed, displacement and the like in the running process of a main shaft of the machine tool is collected, so that a data base is provided for the establishment of performance evaluation indexes and the establishment of a digital twin model.
S102: constructing a digital twin model of a machine tool spindle;
specifically, a digital twin model of a machine tool spindle is constructed, and when the digital twin model is constructed according to the characteristic of high coupling of the machine tool spindle structure, the digital twin model is modularized and modularized by analyzing the structure and the coupling relation of a machine tool spindle system and is divided into a plurality of subsystems such as mechanical, electrical control, heat transfer and the like.
Secondly, selecting proper physical parameters such as torque, rigidity, heat transfer coefficient and the like according to the mechanical structure and functional characteristics of a machine tool spindle, compiling and describing the operation mechanism of each functional element of each subsystem of the machine tool spindle by adopting Modelica multi-field unified modeling language to form each functional element model, and further constructing a digital twin model of each subsystem by connecting each element; and then, according to the complex coupling relation and the coupling mechanism among all subsystems of the machine tool spindle, all subsystem models are connected to form a multi-system coupling digital twin model of the machine tool spindle.
S103: establishing a digital twin model optimization method based on SA-BOA-MCMC, and optimizing the acquired machine tool spindle parameters;
in the digital twin modeling process, the invention relates to the selection of a plurality of physical parameters such as rigidity, a heat transfer system and the like, wherein the parameters are often obtained according to human experience or a mechanism formula, and have certain errors with the actual parameters of a machine tool spindle physical system, thereby influencing the precision of the model. Therefore, the parameters of the digital twin model need to be optimized to ensure high fidelity of the model.
The invention establishes a digital twin model parameter optimization method based on SA-BOA-MCMC, improves the precision of a model, and optimizes the parameters of a machine tool spindle, which comprises the following steps:
(1) Determining parameters to be optimized;
the digital twin model of the machine tool spindle relates to a plurality of parameters including mechanical system parameters, electrical control system parameters and heat transfer system parameters, so that parameters to be optimized are firstly determined when the model parameters are optimized, and main parameters affecting the performance of the model are determined through Sensitivity Analysis (SA). The sensitivity analysis is shown in formula (1):
in the method, in the process of the invention,
is the firstiSensitivity ratio of the individual parameters,i=1,2,3,…,n；nis the number of parameters; />
Is the firstiRelative sensitivity of the individual parameters.
Wherein the sensitivity ratio can be calculated from equation (2).
In the method, in the process of the invention,
as vector (I)>
，/>
Is the average value of the parameters to be selected; />
And->
Respectively outputting results of two different models; />
And->
An upper limit and a lower limit set for each parameter, respectively; />
And->
Is input parameter +.>
And->
Outputting the result. The critical value of the sensitivity is usually set to between 5% and 10% to determine the parameters of the model that need to be updated.
(2) Determining a target optimization function;
after the main updating parameters of the model are determined, the target optimization is determined by using a Bayesian inference theory (BOA) And (5) transforming the function. Bayesian inference is to update uncertainty parametersθField test dataxConsidered as random variables, assumingθIs a priori distributed as
The bayesian formula can be expressed as:
in the method, in the process of the invention,
is a parameterθA posterior probability density function of (2); />
Is a likelihood function; />
Is a parameterθThe prior probability density function is a normalization constant, so the Bayesian inference formula can also be expressed as
Wherein the method comprises the steps of
Is thatxIs a function of the edge probability density of (a).
Test data of hypothesis experiment
Simulation data of model->
Test dataXAnd model equationX(θ) The relationship can be expressed as
In the method, in the process of the invention,
for measuring errorsmDimension vectors, including errors caused by testing, environment, uncertainty parameters, etc.; />
For the observed valuex i Is a simulation of the values of (a).
The likelihood function is expressed as
Wherein the method comprises the steps of
Representation +.>
Simulation->
Probability density function at the time, usually assume +.>
Is subject to a positive too-distribution, so the likelihood function of the measurement error can be expressed as
In the middle of
And->
Mean and standard deviation of measurement errors.
Substitution of formula (7) into formula (4) to obtain posterior distribution
For uncertain parameters of machine tool spindle models, it is common toIn all cases positive values are taken
，/>
To reduce the influence of human assumptions on the final correction parameters, a priori distribution is taken >
Is uniformly distributed
Wherein, the liquid crystal display device comprises a liquid crystal display device,
、/>
is the firstiIndividual uncertainty parameters +.>
Upper and lower limit values of (2). Substituting formula (9) into formula (8), taking +.>
，
Can be obtained
Under normal conditions, the minimum error between the model calculated value and the actual test value is used as the basis for constructing the target optimization function, and the spindle temperature is used according to the spindle performance evaluation requirement of the numerical control machine toolTEstablishing an objective function for an exampleFThen it can be obtained according to formula (10)
Wherein, the liquid crystal display device comprises a liquid crystal display device,
for the parameters to be updated->
For temperature, < >>
For the experimental value of temperature, +.>
Is the variance of the experimental values.
The objective function can thus be defined as
Therefore, the maximum value of the posterior distribution can also be converted into the
Is a minimum of (2).
(3) Optimizing parameters;
as can be seen from equation (10), the posterior distribution of the uncertain parameters can be directly solved by integration, but from the above equation, the posterior distribution is difficult to calculate, and when the dimension of the corrected parameters is high, an analytical solution of the posterior distribution cannot be obtained generally, and then the posterior distribution needs to be obtained by a mathematical method. Thus, the posterior probability density function is solved by Markov Monte Carlo (MCMC). The basic idea is to construct a Markov chain and then sample the Markov chain to obtain posterior distribution samples. The flow is as follows:
The sampling flow is as follows:
1. selecting initial values of Markov chains
；
2. From conditional probability distribution
Obtaining a sample value->
；
3. Sampling from uniform distribution
If->
Accept->
I.e. +.>
The method comprises the steps of carrying out a first treatment on the surface of the Otherwise not accepted, i.e.)>
；
4. Repeating the steps for 2-3 until convergence is reached.
5. From taking samples
Removing samples before smoothing, estimating the remaining sample characteristics as corrected +.>
。/>
Step 4: packaging the constructed machine tool spindle digital twin model, reserving a data interface, and transmitting the acquired machine tool spindle information to the machine tool spindle digital twin model to realize real-time mapping based on multiple dimensions of geometry, physics, behavior and rules;
the traditional digital model is mostly based on an ideal working condition and an object health state, is a static and single model, and cannot reflect the performance change of the whole life cycle of a machine tool spindle. In addition, the digital twin modeling tool focuses on a specific aspect, so that compatibility and openness of a built digital twin model are poor, and therefore, the model can be packaged into an FMU (Functional Mock-up Units) model with a unified platform interface through a FMI (Functional Mockup Interface) standard model, and the model built by different tools can be conveniently used for coupling and interaction. Packaging the constructed machine tool spindle multisystem coupling digital twin model, reserving a related data interface, providing a model updating method based on working condition parameters, transmitting acquired machine tool spindle data information to the digital twin model, updating the model, realizing real-time mapping of the model from multiple dimensions of geometry, physics, behavior, rules and the like, and ensuring that the machine tool spindle physical system operation and model response have good consistency.
Step 5: constructing a virtual-real fusion evaluation index system;
the invention establishes a multi-dimensional performance evaluation index system of the machine tool spindle from multiple dimensions such as rigidity, vibration, temperature, noise, current, precision and the like by analyzing the fault mode of the machine tool spindle and performance degradation influence factors and considering the comprehensiveness and operability of performance indexes and combining expert experience with national relevant standards,
when the virtual-real fusion evaluation index system is constructed, the problem that part of index data is difficult to obtain through experiments due to the complexity of a machine tool spindle structure and the data obtained through part of the experiments are not accurate enough due to environmental influence is considered, and the part of data can be replaced by twin data of a digital twin model to provide sufficient data samples for performance evaluation. Meanwhile, in order to simplify the comprehensive performance evaluation method of the main shaft, performance indexes and running states of the main shaft are classified according to relevant national standards, and health states of the main shaft are classified into four types of excellent, good, medium and poor, wherein each threshold value can be determined according to the relevant national standards.
Step 6: based on the evaluation index weight distribution mode, combining the advantages and disadvantages of the evaluation indexes, and determining the machine tool spindle performance evaluation index weight by using a subjective and objective fusion mode;
The weight of the invention can be used for representing the influence degree of the evaluation index on the result in the evaluation system. The comprehensive performance evaluation system is an organic whole formed by a plurality of index evaluation methods for representing the characteristics of the main shaft of the machine tool, the evaluation indexes are numerous, and the influence of each index on the performance of the main shaft of the machine tool is different, namely the weight of each index is different, so that the weight of each index needs to be analyzed, and the weight of each index needs to be calculated.
The specific flow of the step 6 is as follows:
(1) Determining weights by an Analytic Hierarchy Process (AHP);
(1) constructing a hierarchical model
The method is divided into a target layer, a criterion layer and a scheme layer according to actual decision indexes.
(2) Constructing a judgment matrix
In the analytic hierarchy process, a judgment matrix is constructed by first performing pairing comparison on the relative importance among indexesAThe comparison of the importance levels of the factors is shown in Table 3. Assuming that there ismIndividual index [ (]
And->
Respectively represent the firstiAnd (b)jIndex of individuals),>
represents the firstiAnd (b)jRelative importance of each index, judgment matrixAAs shown in formula (13)>
，/>
。
TABLE 3 factor importance quantization table
| Ratio of index i to index j | Quantized value |
| Equally important | 1 |
| Slightly important | 3 |
| Is of great importance | 5 |
| Is of great importance | 7 |
| Extremely important | 9 |
| Intermediate value of two |
2，4，6，8 |
(3) Calculating weights
According to the judgment matrixATo solve for the maximum feature root
Obtaining corresponding feature vectors according to the maximum feature root passing formula (14)WThe weight of each index is obtained through normalization>
。
(4) Consistency check
Calculating a consistency index using (15)CICalculating the consistency ratio of formula (16)CRIn general, the number of the cells in the cell,CR<0.1, the judgment matrix passes the consistency check, otherwise, it does not haveConsistency, a new determination is required.
The RI values are shown in Table 4.
Table 4 RI reference table
(2) Determining weights by an Entropy Weight Method (EWM);
the entropy weight method is used for distributing entropy weight to each index according to the change degree of each evaluation index value, and is not influenced by subjective factors. In general, there are two types of indicators: positive values (e.g., stiffness) and negative values (e.g., vibration and temperature rise). Since the values of each index are very different, they are converted into the range of [0,1] by normalization. The flow is as follows:
(1) data normalization
Because the dimensions of each index are not uniform, in order to eliminate the influence, the values of each index are generally converted into values between [0,1] in a normalization mode, so that the influence of the non-uniform dimensions of the indexes on index evaluation is solved. In general, the index is classified into two types, one of which is positive index as the index value increases over time and the other is negative index as the index value decreases over time.
Assuming that there isnA number of samples of the sample were taken,mthe number of the indexes is equal to the number of the indexes,a ij represent the firstiSample numberjIndividual index [ (]
，/>
）。
For the forward index, the normalization formula is:
for negative indicators, the normalization formula is:
(2) calculating index weights
Evaluation indexjThe corresponding information entropy of (a) is:
then corresponding firstjThe weights of the individual indexes are:
(3) Comprehensive weight determination
Partial subjective weight obtained by combining AHP
And objective weight derived from EWM +.>
Calculate the first using equation (26)jComprehensive weight of item index>
And the evaluation result is more reliable.
Step 7: in order to realize the quantitative result of the multi-dimension index fusion evaluation of the machine tool spindle, a machine tool spindle performance evaluation model based on a Comprehensive Index Method (CIM) is established by combining twin data and test data, and quantitative scores are obtained by carrying out index transformation and weighting on index values of different dimensions.
The CIM of the invention firstly performs standardized processing on each index, and the data of each influencing factor of the running state of the main shaft of the machine tool is complex and various. To simplify the operation, normalization is performed using the pole difference method, mapping the raw data to [0,1 ]]Is defined in the range of (2). If it is
Calculated by equation (22) for positive index, if +.>
Is calculated by equation (23) for negative indicators.
Wherein:
index values for various factors; />
And->
The maximum value and the minimum value of the factor index in the reasonable operation range are respectively; />
Is the normalized index value.
After the normalized data is obtained, the weight value obtained by combining the weighting method is combined, the evaluation indexes of all indexes are weighted and summed by the comprehensive index method, and the comprehensive index of the running state evaluation of the electric spindle is obtainedCI. The operation state evaluation results were classified into grades by integrating the respective evaluation criteria, as shown in table 5.CIThe larger indicates the better running performance of the motorized spindle.CIThe calculation formula is that
Wherein:
is the firstiA personal index weight value; />
Normalized data for the factor indicator.
Table 5 performance evaluation rating of numerically controlled machine tool
| Grade | CI range | Description of the class |
| Excellent (excellent) | [0.8,1] | The main shaft has good running state and can run for a long time |
| Good grade (good) | [0.6,0.8) | The main shaft has good condition and acceptable processing precision |
| In (a) | [0.4,0.6) | The main shaft has moderate running state and lower processing precision, and is not suitable for long-term running |
| Difference of difference | [0,0.4) | The main shaft has poor running state and faults, and needs toShutdown inspection |
Thus, according to the weight of each evaluation index, the analysis result of each performance index evaluation level is synthesized, the comprehensive performance quantitative evaluation of the machine tool spindle is realized, and guidance is provided for the operation and maintenance of the machine tool spindle.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following is an embodiment of a digital twin-based machine tool spindle performance evaluation system provided by the embodiment of the present disclosure, where the digital twin-based machine tool spindle performance evaluation system and the digital twin-based machine tool spindle performance evaluation method of the foregoing embodiments belong to the same inventive concept, and details that are not described in detail in the digital twin-based machine tool spindle performance evaluation system embodiment may refer to the foregoing digital twin-based machine tool spindle performance evaluation method embodiment.
The system comprises: the system comprises a data perception module, a digital twin model construction module, a digital twin model parameter optimization module, a digital twin model real-time mapping module, an evaluation index system construction module, an evaluation index weight calculation module and a machine tool spindle comprehensive performance evaluation module;
the data perception module is used for acquiring machine tool spindle parameters, and the spindle parameters comprise: real-time parameter information and working condition data in the running process of a machine tool spindle;
The digital twin model construction module is used for analyzing a machine tool main shaft, dividing the machine tool main shaft to form a coupling relation among parts, components and systems, dividing the coupling relation into a plurality of subsystems, respectively modeling each subsystem by adopting a multi-field modeling method, analyzing the operation characteristics of the machine tool main shaft and the coupling relation among the subsystems, and coupling and connecting the subsystems to obtain a multi-system coupling digital twin model of the machine tool main shaft;
the digital twin model parameter optimization module is used for establishing a digital twin model based on MCMC and optimizing the acquired machine tool spindle parameters;
the digital twin model real-time mapping module is used for packaging the constructed machine tool spindle digital twin model, reserving a data interface, transmitting the acquired machine tool spindle information to the machine tool spindle digital twin model, and realizing real-time mapping based on multiple dimensions of geometry, physics, behavior and rules;
the evaluation index system construction module is used for constructing an evaluation index system with virtual-real fusion;
the evaluation index weight calculation module is used for determining the machine tool spindle performance evaluation index weight by combining the advantages and disadvantages of the evaluation indexes and using a subjective and objective fusion mode based on the evaluation index weight distribution mode;
And the machine tool spindle comprehensive performance evaluation module is used for evaluating the comprehensive performance of the machine tool spindle to obtain a quantification result of the multi-dimensional index fusion evaluation of the machine tool spindle, and combining the twin data with the test data to establish a machine tool spindle performance evaluation model based on a comprehensive index method, and obtaining the quantification score by carrying out index transformation and weighting on index values of different dimensions.
In an exemplary embodiment of the system, further comprising: a machine tool spindle performance evaluation system development module; the development module of the machine tool spindle performance evaluation system realizes data acquisition, state information monitoring, online performance evaluation and the like of the machine tool spindle.
The machine tool spindle performance evaluation system development module uses front and rear end frames of a Vue+FastAPI webpage system to build a platform, realizes man-machine interaction, parameter setting and instruction input functions at the front end of the system, and transmits set parameter content to the rear end; the functions of uploading and downloading related data, data processing and communication with a physical entity numerical control system are realized at the back end of the system, and the original data and the processing operation result are sent to the front end for visual display.
The staff can remotely check the state of the machine tool spindle in real time based on the development module of the machine tool spindle performance evaluation system, and perform performance evaluation, when the evaluation result is not ideal, the physical system of the machine tool spindle is timely maintained and repaired, the maintenance efficiency and the starting rate of the machine tool spindle are improved, the maintenance cost and the maintenance cost are reduced, and the manpower and material resources are saved.
The units and algorithm steps of each example described in the embodiments disclosed in the machine tool spindle performance evaluation system of the present invention can be implemented in electronic hardware, computer software, or a combination of both, and in order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functions in the above description. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Machine tool spindle performance evaluation systems and methods flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. Two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The machine tool spindle performance evaluation system and method are the units and algorithm steps of the examples described in connection with the embodiments disclosed herein, and can be implemented in electronic hardware, computer software, or a combination of both, and to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described generally in terms of functionality in the foregoing description. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The method for evaluating the performance of the machine tool spindle based on digital twinning is characterized by comprising the following steps of:
step 1: acquiring machine tool spindle parameters, wherein the spindle parameters comprise: real-time parameter information and working condition data in the running process of a machine tool spindle;
step 2: constructing a digital twin model of a machine tool spindle;
step 3: establishing a digital twin model optimization method based on SA-BOA-MCMC, and optimizing the acquired machine tool spindle parameters;
step 4: packaging the constructed machine tool spindle digital twin model, reserving a data interface, and transmitting the acquired machine tool spindle information to the machine tool spindle digital twin model to realize real-time mapping based on multiple dimensions of geometry, physics, behavior and rules;
step 5: constructing a virtual-real fusion evaluation index system;
step 6: based on the evaluation index weight distribution mode, combining the advantages and disadvantages of the evaluation indexes, and determining the machine tool spindle performance evaluation index weight by using a subjective and objective fusion mode;
step 7: and (3) evaluating the comprehensive performance of the machine tool spindle, establishing a machine tool spindle performance evaluation model based on a comprehensive index method, normalizing each index data in an evaluation index system, combining a weight value obtained by a combined weighting method, and carrying out weighted summation on evaluation indexes of each index by the comprehensive index method to obtain a comprehensive index CI of the electric spindle running state evaluation to obtain a quantized evaluation result.
2. The method for evaluating the performance of a machine tool spindle based on digital twinning according to claim 1, wherein in the step 2, the machine tool spindle is divided into a mechanical subsystem, an electrical control subsystem and a heat transfer subsystem by analyzing the structure and the coupling relation of the machine tool spindle system;
selecting preset physical parameters according to the mechanical structure and the function of a main shaft of a machine tool;
compiling and describing the operation mechanism of each functional element of each subsystem of the machine tool spindle by adopting Modelica multi-field unified modeling language to form each functional element model;
constructing a digital twin model of each subsystem based on the connection relation of the functional element models;
and according to the coupling relation and the coupling mechanism among all subsystems of the machine tool spindle, correlating the digital twin models of all subsystems to form the digital twin model of the machine tool spindle.
3. The method for evaluating the performance of a machine tool spindle based on digital twinning according to claim 1, wherein the step 3 further comprises:
(1) Determining parameters to be optimized;
determining parameters to be optimized through sensitivity analysis;
the sensitivity analysis is shown in formula (1):
wherein lambda is i Sensitivity ratio for the i-th parameter, i=1, 2,3, …, n; n is the number of parameters; w (x) i ) The relative sensitivity for the ith parameter;
wherein the sensitivity ratio can be calculated from formula (2);
wherein X is a vector,
is the average value of the parameters to be selected;
g 1 (X) and g 2 (X) two different output results of the model respectively;
(2) Determining a target optimization function;
taking the error between the calculated value and the actual test value of the digital twin model smaller than the preset test threshold as the basis for constructing the target optimization function, and taking the main shaft temperature T as an example to establish the target function F, the formula (11) can be obtained according to the formula (10)
Wherein θ is a parameter to be updated, T is a temperature,
as experimental values of temperature cov T Is the variance of the experimental values;
the objective function is defined as
F(θ)＝min[J(θ)] (12)
Converting the maximum value of the posterior distribution into the minimum value of J (theta);
(3) And optimizing the parameters based on the determined target optimization function.
4. The method for evaluating the performance of a machine tool spindle based on digital twinning according to claim 3, wherein the sampling flow of the step (3) is as follows:
1) Selecting an initial value theta of a Markov chain 0 ；
2) From the conditional probability distribution q (θθ) t ) Obtaining a sample value theta * ；
3) Sampling U from uniform distribution [0,1 ] ]If u<α(θ t ,θ * ) Accept θ t →θ * I.e. θ t+1 ＝θ * ；
Otherwise not accepted, i.e. theta t+1 ＝θ t ；
4) Repeating the steps 2) to 3) until convergence is achieved;
5) From taking sample θ 0 ,θ 1 ,...,θ T Removing samples before smoothing, and estimating the remainderThe sample feature is taken as the corrected θ.
5. The method for evaluating the performance of a machine tool spindle based on digital twinning according to claim 1, wherein the flow of the step 6 comprises the following steps:
(1) Determining weights based on an analytic hierarchy process;
(2) Determining weights based on an entropy weight method;
(3) Determining comprehensive weights;
partial subjective weight w obtained by combining AHP j And objective weights derived from EWM
Calculating the comprehensive weight mu of the jth index by using a formula (26) j ；
6. The method for evaluating the performance of a machine tool spindle based on digital twinning of claim 5, wherein the step (1) further comprises:
(1) constructing a hierarchical model;
dividing the hierarchical model into a target layer, a criterion layer and a scheme layer according to the actual decision index;
(2) constructing a judgment matrix;
by carrying out pairing comparison on the relative importance among indexes, a judgment matrix is constructedA；
Matrix arrayAIs provided with thereinmThe number of the indexes is equal to the number of the indexes,
represents the firstiAnd (b)jRelative importance of each index, judgment matrixAAs shown in formula (13)>
，/>
；
(3) Calculating weights;
according to the judgment matrix ATo solve for the maximum feature root
Obtaining corresponding feature vectors according to the maximum feature root passing formula (14)WThe weight of each index is obtained through normalization>
；
(4) Performing consistency test;
calculating a consistency index using (15)CICalculating the consistency ratio of formula (16)CRSuch asCR<0.1, if the judgment matrix passes the consistency test, if not, the judgment matrix does not have consistency and needs to be judged again;
7. the method for evaluating the performance of a machine tool spindle based on digital twinning of claim 5, wherein the step (2) further comprises:
the flow is as follows:
(1) normalizing the data;
assume that there are n samples, m indices, a ij Represent the firstThe j index of i samples (i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m);
for the forward index, the normalization formula is:
for negative indicators, the normalization formula is:
(2) calculating index weight;
the corresponding information entropy of the evaluation index j is:
the corresponding j-th index is weighted as follows:
8. the method for evaluating the performance of a machine tool spindle based on digital twinning according to claim 1, wherein in the step 7, a standard deviation method is adopted to perform standard processing, and raw data is mapped to an interval of [0,1 ];
if x ij Calculated from equation (22) for positive index, if x ij Is calculated by a formula (23) for a negative index;
wherein: x is x ij For each ofA factor index value; max (x) ij ) And min (x) ij ) The maximum value and the minimum value of the factor index in the reasonable operation range are respectively; phi (phi) ij Is the index value after normalization;
after the normalized data are obtained, the weight values obtained by combining a weighting method are combined, and the evaluation indexes of all indexes are weighted and summed by a comprehensive index method to obtain a comprehensive index CI for evaluating the running state of the electric spindle;
dividing the operation state evaluation result into grades by integrating each evaluation standard, wherein the CI calculation formula is as follows
Wherein: mu (mu) i Is the i index weight value; phi (phi) ij Normalized data for the factor indicator.
9. A machine tool spindle performance evaluation system based on digital twinning, which is characterized in that the system adopts the machine tool spindle performance evaluation method based on digital twinning according to any one of claims 1 to 8;
the system comprises: the system comprises a data perception module, a digital twin model construction module, a digital twin model parameter optimization module, a digital twin model real-time mapping module, an evaluation index system construction module, an evaluation index weight calculation module and a machine tool spindle comprehensive performance evaluation module;
the data perception module is used for acquiring machine tool spindle parameters, and the spindle parameters comprise: real-time parameter information and working condition data in the running process of a machine tool spindle;
The digital twin model construction module is used for analyzing a machine tool main shaft, dividing the machine tool main shaft to form a coupling relation among parts, components and systems, dividing the coupling relation into a plurality of subsystems, respectively modeling each subsystem by adopting a multi-field modeling method, analyzing the operation characteristics of the machine tool main shaft and the coupling relation among the subsystems, and coupling and connecting the subsystems to obtain a multi-system coupling digital twin model of the machine tool main shaft;
the digital twin model parameter optimization module is used for establishing a digital twin model based on MCMC and optimizing the acquired machine tool spindle parameters;
the digital twin model real-time mapping module is used for packaging the constructed machine tool spindle digital twin model, reserving a data interface, transmitting the acquired machine tool spindle information to the machine tool spindle digital twin model, and realizing real-time mapping based on multiple dimensions of geometry, physics, behavior and rules;
the evaluation index system construction module is used for constructing an evaluation index system with virtual-real fusion;
the evaluation index weight calculation module is used for determining the machine tool spindle performance evaluation index weight by combining the advantages and disadvantages of the evaluation indexes and using a subjective and objective fusion mode based on the evaluation index weight distribution mode;
And the machine tool spindle comprehensive performance evaluation module is used for evaluating the comprehensive performance of the machine tool spindle to obtain a quantification result of the multi-dimensional index fusion evaluation of the machine tool spindle, and combining the twin data with the test data to establish a machine tool spindle performance evaluation model based on a comprehensive index method, and obtaining the quantification score by carrying out index transformation and weighting on index values of different dimensions.
10. The digital twinning-based machine tool spindle performance evaluation system of claim 9, further comprising: a machine tool spindle performance evaluation system development module;
the machine tool spindle performance evaluation system development module builds a platform based on the front end framework and the rear end framework of the Vue and FastAPI webpage system, realizes man-machine interaction, parameter setting and instruction input functions at the front end of the system, and transmits set parameter content to the rear end; the functions of uploading and downloading related data, data processing and communication with a physical entity numerical control system are realized at the back end of the system, and the original data and the processing operation result are sent to the front end for visual display.
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