CN115130237A - Method for determining structural size parameters of magnetic suspension workbench - Google Patents
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Abstract
The invention provides a method for determining a structural size parameter of a magnetic suspension workbench. Determining variables to be optimized, wherein the optimization variables comprise: structural parameters to be optimized of the magnetic suspension workbench and optimization constraint conditions of the magnetic suspension workbench; constructing a magnetic force numerical model of the magnetic suspension workbench based on the magnetic field model; and normalizing a plurality of optimization targets such as mass reduction, power consumption, efficiency improvement, rigidity reduction and the like, and then combining the normalized optimization targets to form a composite optimization target function, wherein a related magnetic force calculation part in the composite optimization target function is solved based on the established integral magnetic force numerical model of the magnetic suspension workbench. The method has the advantages that the multi-objective intelligent optimization method is combined with the parallelizable numerical electromagnetic model of the magnetic suspension workbench, so that the method for selecting the structure size of the magnetic suspension workbench with good universality is obtained, and the optimization efficiency of the structure parameters of the magnetic suspension workbench and the accuracy of the optimization result are improved.
Description
Technical Field
The invention belongs to the field of magnetic suspension workbenches, and particularly relates to a method for determining structural size parameters of a magnetic suspension workbench.
Background
Magnetic levitation tables have been extensively studied and developed over the past few decades as a new type of drive element. The magnetic suspension workbench is not required to be supported by a mechanical guide rail, can directly realize two-dimensional plane driving with large stroke, greatly simplifies a mechanical motion structure, and has small volume, light weight and capability of realizing high-speed motion. In addition, because no mechanical or air-floating support is needed, precise movement can be realized under the vacuum condition. These advantages make it have wide application prospect in semiconductor lithography system and other high precision industrial fields.
High precision manufacturing fields such as semiconductor lithography have stringent requirements for power consumption and heat dissipation. And proper electromagnetic structure parameters are selected, so that the power consumption can be effectively reduced, and the motor efficiency is improved. In addition, during the machining process, especially during contact operation, in order to ensure the stability of the worktable, the magnetic suspension system should have high structural static rigidity characteristics, and at the same time, in order to improve the dynamic rigidity characteristics of the system, the motion actuator is required to be capable of outputting enough magnetic force to compensate the change of the load. The change of the structural parameters of the actuator can affect the structural static rigidity and the upper limit of the output magnetic force in the motion range of the system. The selection of proper structural parameters is a basic condition for obtaining high performance, and meanwhile, the burden of a control system can be reduced, and the manufacturing cost of the magnetic suspension workbench is reduced. Therefore, how to optimize the structural parameters of the magnetic suspension workbench to obtain the magnetic suspension workbench with optimal motion performance and cost is an urgent task in the research of magnetic suspension motors.
However, due to the structural particularity of the magnetic suspension workbench, the optimization method aiming at the traditional motor is not suitable for the magnetic suspension workbench. At present, the domestic magnetic suspension motor structure optimization method still stays in the optimization aiming at a single optimization target or a plurality of optimization targets, and each optimization target is separated and optimized respectively. The optimization method mostly adopts a finite element optimization method which takes longer time and has rough optimization results. The method for optimizing the structural parameters of the magnetic suspension motor is not effective and universal, so that the research and development period of the magnetic suspension workbench is long, and the cost is high.
Disclosure of Invention
In view of the above situation, it is necessary to provide a general method for optimizing the structural parameters of the magnetic suspension table, which is efficient and not universal in the optimization process of the structural parameters of the magnetic suspension table in the prior art.
A method for optimizing structural parameters of a general magnetic suspension workbench comprises the following steps:
step 1: determining variables to be optimized, wherein the optimization variables comprise: structural parameters to be optimized of the magnetic suspension workbench and optimization constraint conditions of the magnetic suspension workbench;
the step 1 specifically comprises the following steps:
according to the structural parameters to be optimized and the residual fixed structural parameter values preset by the motor, performing numerical modeling on the magnetic field generated by each permanent magnet in the magnetic suspension motor, and superposing the numerical modeling results of all the magnets to obtain an integral magnetic field model of the magnetic suspension workbench;
step 2: constructing a magnetic force numerical model of the magnetic suspension workbench based on the magnetic field model;
the step 2 specifically comprises the following steps:
the magnetic force model of the magnetic suspension motor is a superposition result of the magnetic force borne by each coil of the magnetic suspension motor, the Lorenz force borne by each coil in the magnetic suspension workbench is calculated, a magnetic force numerical model is established for each coil, and the magnetic force models of all the coils are superposed to obtain an integral magnetic force numerical model of the magnetic suspension workbench;
and step 3: normalizing a plurality of optimization targets such as mass reduction, power consumption, efficiency improvement, rigidity reduction and the like, and then combining the normalized optimization targets to form a composite optimization target function, wherein a related magnetic force calculation part in the composite optimization target function is solved based on the established integral magnetic force numerical model of the magnetic suspension workbench;
and 4, step 4: and performing parallelization processing on the whole magnetic force numerical model of the magnetic suspension workbench in the composite optimization objective function according to the superposition form of the magnetic suspension workbench to form a parallelization structure, then combining the composite optimization objective function with an intelligent optimization algorithm, and searching and obtaining the optimal magnetic suspension workbench structure parameters in the optimization constraint conditions of the preset minimum suspension force, the structure range and the like.
The magnetic field model and the magnetic force model of the multi-freedom magnetic suspension workbench are established based on a magnetic charge node method and a Gaussian quadrature method according to the determined structural parameters of the motor to be optimized, and the magnetic suspension workbench has universality on different types of magnetic suspension motors. And then, optimizing the motor by combining an intelligent optimization algorithm particle swarm optimization method and the established numerical model of the magnetic suspension planar motor according to the multi-objective optimization function subjected to normalization and combination, thereby achieving the purpose of optimizing the structural parameters of the magnetic suspension workbench.
The method has the advantages that the multi-objective intelligent optimization method is combined with the parallelizable numerical electromagnetic model of the magnetic suspension workbench, so that the method for selecting the structure size of the magnetic suspension workbench with good universality is obtained, and the optimization efficiency of the structure parameters of the magnetic suspension workbench and the accuracy of the optimization result are improved.
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FIG. 1: the invention is a flow chart of a magnetic suspension workbench multi-objective optimization method in an implementation example of the invention.
FIG. 2: the invention provides a flow chart for modeling the magnetic field of a magnetic suspension workbench in the optimization method.
FIG. 3: the optimization process based on the intelligent optimization algorithm PSO in the embodiment of the invention is disclosed.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely in the following with reference to the accompanying 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, the overall process of optimizing the structural parameters of the magnetic suspension workbench of the present invention includes steps S1-S4.
Step S1, determining structural parameters (such as thickness of coil, length and thickness of permanent magnet) of the magnetic suspension table to be optimized, and constraint conditions (such as minimum suspension force, range of parameters to be optimized, etc.) in the optimization process.
One or more structural parameters to be optimized in the above steps can be selected according to requirements, and the constraint optimization conditions can also be set according to actual equality constraints and inequality constraints.
And step S2, establishing a magnetic field numerical model and a magnetic force numerical model of the magnetic suspension workbench according to the parameters to be optimized and the rest fixed parameters.
The magnetic field model is constructed by adopting a magnetic charge node method, the overall magnetic field effect of the permanent magnets is equivalent to the effect superposition of the magnetic fields generated by a plurality of fixed points, the magnetic field of each magnet in the magnetic suspension workbench is respectively constructed, and the magnetic field effects of all the permanent magnets are superposed to form the magnetic field model of the magnetic suspension workbench. Referring to fig. 2, because the permanent magnets are not affected by each other, the magnetic field models of all the permanent magnets are constructed simultaneously in a multi-path parallel manner, whether the model construction of each permanent magnet is completed or not is judged, and after the magnetic field models of all the permanent magnets are constructed, the construction results are superposed, so that the total magnetic field intensity of the magnetic suspension workbench is obtained.
For the magnetic force model construction in step S2, the volume fractions of the coils in the magnetic force model are converted into a weighted sum form by using a gaussian product method for each coil of the magnetic levitation motor, so as to obtain a numerical model, and the magnetic force models of all the coils are superimposed to obtain the numerical magnetic force model of the magnetic levitation workbench.
Step S3, the method is universal for multi-objective optimization problems and single-objective optimization problems, when multiple optimization targets are faced, normalization processing needs to be carried out on the optimization targets, and therefore when an intelligent optimization algorithm is adopted conveniently, the evaluation function can truly reflect actual optimization conditions. Step S3 is to perform multi-objective optimization, based on the maximum or minimum of each objective, and merge and convert the objective into single-objective optimization by means of division-objective multiplication-division.
And step S4, based on the composite optimization objective function obtained in the step S3, an intelligent optimization algorithm is introduced to search global optimal parameters, an addition form of the magnetic suspension workbench magnetic force numerical model constructed in the step S2 is utilized to conduct parallelization processing on evaluation functions in the intelligent optimization algorithm, the global search speed of the intelligent optimization algorithm is increased, and therefore the optimal magnetic suspension workbench structure size parameters are obtained quickly.
Please refer to fig. 2, which is a specific process for constructing the magnetic field model of the magnetic suspension table in step S2.
According to the principle of the magnetic charge method, the magnetic field generated by the permanent magnet can be equivalent to a plurality of independent source points D k The magnetic flux density B at point G can be regarded as the sum effect of all the magnetic charge nodes on the magnet, and is calculated by the expression:
in the formula, T corresponds to the number of end points of the permanent magnet geometry.
According to the expression, for the magnetic field at a certain point in space, the magnitude of the electromagnetic force generated by each permanent magnet i of the magnetic suspension workbench at the position can be calculated firstly, then the magnetic fields generated by each permanent magnet are superposed in a memorial manner, whether the calculation of the magnetic fields of all the permanent magnets of the whole magnetic suspension workbench at the point is completed or not is judged, and if the calculation is completed, the magnetic fields obtained by calculation are superposed, so that a magnetic field numerical model is obtained.
Due to the superposition property of the numerical model, the evaluation function can be conveniently subjected to parallelization processing in the subsequent optimization, and meanwhile, the numerical model has higher magnetic force calculation precision. Therefore, based on the obtained magnetic field model, a magnetic force numerical model of the magnetic suspension workbench is constructed through Lorenz force integration.
Since the Lorenz force integral is continuous, the volume division for the coil can be simplified by Gaussian integration into the form of a sum of the three-fold numerical integrals:
in the formula (I), the compound is shown in the specification,
is a Gaussian node acting on the coil i, w gi For the weight of Gaussian product, the whole area of the coil is divided into 8 parts, and the whole coil integral is correspondingly converted into eight segmented integrals, so that the Gaussian node
In fact, the method can correspond to actual Gaussian nodes on different coil segments
In this way, there is no problem of area not being able to integrate, because each derivation between the inner and outer integration is independent. Considering the difference between the coil coordinate system and the permanent magnet coordinate system, it is necessary to construct a correlation mechanism of magnetic flux density, current density and electromagnetic force vector by converting the matrix R and the translation vector t, so as to realize arbitrary conversion between the coordinate systems. The force between a single coil and a magnet can be expressed as:
in the formula, q c Is an index number of the coil segment, and
calculations can be made based on the design parameters of the coils and the permanent magnet array. As can be seen from the expression result, the solution of each layer is independent by the numerical integration mode, and the subsequent evaluation function calculation can be performed in a parallel mode, so that the optimization efficiency is improved.
Please refer to fig. three, which is an embodiment of a Particle Swarm Optimization algorithm (PSO) based on the intelligent Optimization algorithm. The particle swarm optimization algorithm belongs to a global search random algorithm, and the design principle is derived from social behaviors of organisms in nature. In each particle swarm optimization iteration, the speed and the position of the particle are updated according to the following two elements: one is the optimal position of the individual (particle); the other is the optimal location of the population. Therefore, the optimization method can realize the optimization of the structure size of the magnetic suspension workbench based on the particle swarm intelligent optimization algorithm.
In the whole optimization process, k is firstly matched d When optimizing in each dimension, the number N of the population is determined g And size N p . The parameters to be optimized mainly comprise the thickness h of the permanent magnet of the magnetic suspension workbench c Thickness h of coil m And a width R out The parameter range can be set according to actual requirements, and three degrees of freedom of each particle in the particle swarm optimization algorithm respectively correspond to the optimization variable h c 、h m And R out ：
x 1 →h c ,x 2 →h m ,x 3 →R out (4)
Thus, each particle is a three-dimensional vector, and the jth particle in the ith population can be represented as:
the velocity of each particle can also be represented by a three-dimensional vector:
first, at h c 、h m And R out Randomly generate the initial position and velocity of all particles within a particular range or the entire searchable space. Then, the fitness of each particle, i.e., the ith particle of the jth group, is calculated based on the numerical magnetic model and the objective functionIndividual historical optimal location of children
For its current bit:
by comparing each
Fitness f of obj As a result, the optimal group position is selected from among them
If the position of the particle belongs to the feasible domain range, the evaluation function result f can be calculated obj . If f is obj Is superior to
Iterating over the history of (1) to optimize the result, then
The spatial position of the representation is replaced by the position of the preceding particle. Accordingly, the method can be used for solving the problems that,
the updating is also performed by a similar comparative replacement. The updating amount of the moving speed of the particles is determined by the optimal position of the seed group every time the iterative updating is performed
And optimal historical location of individuals
And (6) determining. First, theBetween s iterations and the (s + 1) th iteration, the updating method of the particle speed comprises the following steps:
wherein w is the coefficient of inertia, C 1 And C 2 Is that
And
acceleration coefficients of relative attraction for each particle, which are the cognitive coefficient and sociality parameters of the particle. r is 1 And r 2 Are all random numbers and are in the interval [0, 1 ]]And (4) the following steps. For the s +1 th iteration, the position of the particle should be:
where χ represents the time step, actually as a puncturing coefficient. If the new particle position updated by equation (3.28) is outside the feasible region given by the constraint, some algorithm is needed to select a boundary position closest to the current position. In another mode, the constraint problem can be converted into an unconstrained problem to be optimized by adding a penalty function item. The algorithm will repeat the above iterative update process continuously until a global optimal solution is finally obtained.
In each iterative search process, the corresponding fitness f of each particle obj The accuracy of the calculation of (2) can directly influence the effectiveness of the final optimization result, and the calculation of the fitness also determines the whole multi-objective optimization efficiency.
Therefore, as can be seen from fig. 3, by introducing a numerical electromagnetic force model in the multi-objective optimization and combining the addition expression form of the numerical model with the parallel computation, the electromagnetic force solution in the fitness function of each particle is converted into the superposition form of the acting force acting on each permanent magnet in the Halbach permanent magnet array, when the fitness computation is performed, the electromagnetic force solution part in the fitness function of each particle is computed by the corresponding permanent magnet computation module, and when the electromagnetic force computation corresponding to all the permanent magnets is judged to be finished, the computation results of each module are added, so that the corresponding total magnetic force is rapidly obtained and is brought into the computation of the fitness function.
On the other hand, the combination of the numerical electromagnetic model of the magnetic suspension workbench and the multi-objective optimization can also help the numerical model to have higher calculation precision, thereby further ensuring the effectiveness of the optimization result.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. The method for determining the structural size parameters of the magnetic suspension workbench is characterized by comprising the following steps:
step 1: determining variables to be optimized, wherein the optimization variables comprise: structural parameters to be optimized of the magnetic suspension workbench and optimization constraint conditions of the magnetic suspension workbench;
the step 1 specifically comprises the following steps:
according to the structural parameters to be optimized and the residual fixed structural parameter values preset by the motor, performing numerical modeling on the magnetic field generated by each permanent magnet in the magnetic suspension motor, and superposing the numerical modeling results of all the magnets to obtain an integral magnetic field model of the magnetic suspension workbench;
and 2, step: constructing a magnetic force numerical model of the magnetic suspension workbench based on the magnetic field model;
the step 2 specifically comprises the following steps:
calculating Lorenz force borne by each coil in the magnetic suspension workbench, establishing a magnetic force numerical model for each coil, and superposing the magnetic force models of all the coils to obtain an integral magnetic force numerical model of the magnetic suspension workbench;
and 3, step 3: normalizing a plurality of optimization targets such as mass reduction, power consumption, efficiency improvement, rigidity reduction and the like, and then combining the normalized optimization targets to form a composite optimization target function, wherein a related magnetic force calculation part in the composite optimization target function is solved based on the established integral magnetic force numerical model of the magnetic suspension workbench;
and 4, step 4: and performing parallelization processing on the whole magnetic force numerical model of the magnetic suspension workbench in the composite optimization objective function according to the superposition form of the magnetic suspension workbench to form a parallelization structure, then combining the composite optimization objective function with an intelligent optimization algorithm, and searching and obtaining the optimal magnetic suspension workbench structure parameters in the optimization constraint conditions of the preset minimum suspension force, the structure range and the like.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102681364A (en) * | 2012-05-16 | 2012-09-19 | 华中科技大学 | Six-degree-of-freedom magnetic suspension micro-positioner |
| CN109245449A (en) * | 2018-11-12 | 2019-01-18 | 南京工程学院 | A kind of optimum design method of axial phase magnetic levitation switch magnetic resistance fly-wheel motor |
| CN111931406A (en) * | 2020-08-07 | 2020-11-13 | 南京工程学院 | Method for establishing dynamic equivalent magnetic network model of axial permanent magnetic suspension flywheel motor |
| CN113434958A (en) * | 2021-06-25 | 2021-09-24 | 南京工程学院 | Magnetic suspension flywheel motor multi-target optimization method based on heterogeneous integrated proxy model |
| US20220043950A1 (en) * | 2020-05-27 | 2022-02-10 | Jiangsu University | Parametric equivalent magnetic network modeling method for multi objective optimization of permanent magnet motor |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102681364A (en) * | 2012-05-16 | 2012-09-19 | 华中科技大学 | Six-degree-of-freedom magnetic suspension micro-positioner |
| CN109245449A (en) * | 2018-11-12 | 2019-01-18 | 南京工程学院 | A kind of optimum design method of axial phase magnetic levitation switch magnetic resistance fly-wheel motor |
| US20220043950A1 (en) * | 2020-05-27 | 2022-02-10 | Jiangsu University | Parametric equivalent magnetic network modeling method for multi objective optimization of permanent magnet motor |
| CN111931406A (en) * | 2020-08-07 | 2020-11-13 | 南京工程学院 | Method for establishing dynamic equivalent magnetic network model of axial permanent magnetic suspension flywheel motor |
| CN113434958A (en) * | 2021-06-25 | 2021-09-24 | 南京工程学院 | Magnetic suspension flywheel motor multi-target optimization method based on heterogeneous integrated proxy model |
Non-Patent Citations (1)
| Title |
|---|
| 包广清;江建中;: "一种新型横向磁通永磁电机的建模与参数优化", 大电机技术, no. 04, 15 July 2008 (2008-07-15) * |
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