CN109902436B - Forward design method for RV reducer - Google Patents
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Abstract
The invention relates to a forward design method of an RV reducer, and belongs to the field of forward design of reducers. The method comprises the following steps: s1: establishing an RV reducer optimization module, initially calculating structural parameters according to basic performance parameters of the RV reducer, establishing a corresponding optimal structural model through a target function and constraint conditions, solving a group of pareto optimal solutions by using a differential evolution algorithm, and selecting the optimal solution from a pareto solution set by using a multi-objective evaluation method; s2: establishing a parametric modeling system of the RV reducer to generate a three-dimensional model of each component of the RV reducer; s3: establishing a finite element analysis module of the RV reducer, performing contact strength analysis and torsional rigidity analysis by using a three-dimensional model of the RV reducer, and determining a design scheme of the RV reducer. The method simplifies the design process of the RV reducer, improves the modeling efficiency and reduces the experiment cost.
Description
Technical Field
The invention belongs to the field of forward design of a speed reducer, and relates to a digital forward design method of an RV speed reducer.
Background
The RV reducer is one of core technologies of a robot, and along with the development of manufacturing technologies, the RV reducer is gradually applied to the fields of machine tools, medical instruments, aerospace and the like due to the advantages of light weight, small size, large transmission ratio, high precision, high efficiency, high rigidity and the like. The design parameters of the RV reducer are complex, the constraint conditions are numerous, the accuracy of design by adopting the traditional method is difficult to guarantee, the experimental period of the RV reducer is long, the difficulty is high, and the rapid and steady design is difficult to realize. The evolutionary algorithm is an optimization method with strong robustness, good convergence and high parallelism, and is suitable for solving the multi-objective optimization problems of multiple solving parameters and large variable space in the design of RV reducer key parameters. Based on the problem that the optimal solution set obtained by the evolutionary algorithm solution needs further screening, the solution set is weighted by adopting the multi-target evaluation of the entropy weight method.
The digital forward design technology is a novel design technology which finishes various works (such as structural member parameterization design, key parameter optimization design, part design modeling, strength checking calculation, finite element analysis and the like) in the product design process by taking digitalization as a core, and is a popular research content in the field of mechanical product design and manufacturing. With the gradual maturity of design theory and the high-speed development of computer technology, digital design methods are more and more emphasized. The existing mainstream design mode is manual drawing based on two-dimensional or three-dimensional drawing software, and an automatic model generation means is lacked, so that the market demand cannot be quickly responded.
The contact stress, the torsional rigidity and other performance indexes of the RV reducer determine the quality of the performance of the reducer, and the traditional experimental method is high in cost and long in period and cannot adapt to the market with rapid change. Therefore, a new design method of the RV reducer is needed, a three-dimensional modeling system of the RV reducer is constructed, parametric design of series products is realized, and the requirement of rapid design is met. And the finite element simulation analysis module is compiled, the three-dimensional model is imported into the module to be solved, the three-dimensional digital prototype model of the reducer is used for carrying out performance simulation verification, and the design scheme of the RV reducer is selected according to the simulation result, so that the product development efficiency is improved, and the experiment cost is reduced.
Disclosure of Invention
In view of this, the invention aims to provide a forward design method for an RV reducer, which solves the multi-objective optimization problems of multiple solution parameters and large variable space in the design of key parameters of the RV reducer. The parameterized design of series products is realized, and the requirement of rapid design is met. The modeling efficiency is improved, the experiment cost is reduced, and the maximized enterprise benefit is obtained finally
In order to achieve the purpose, the invention provides the following technical scheme:
a forward design method of an RV reducer specifically comprises the following steps:
s1: establishing an RV reducer optimization module, initially calculating structural parameters according to basic performance parameters of the RV reducer, establishing a corresponding structure optimization model through a target function and constraint conditions, solving a group of pareto optimal solutions by using a differential evolution algorithm, and selecting the optimal solution from a pareto solution set by using a multi-objective evaluation method;
s2: establishing a parametric modeling system of the RV reducer to generate a three-dimensional model of each component of the RV reducer;
s3: and establishing a finite element analysis module of the RV reducer, carrying out contact strength analysis and torsional rigidity analysis by using a three-dimensional model of the RV reducer, and determining a design scheme of the RV reducer.
Further, in step S1, initially calculating structural parameters according to the basic performance parameters of the RV reducer, and establishing a corresponding structural optimization model through an objective function and a constraint condition, specifically including: according to the basic performance parameters of the RV reducer, namely input power P, input rotating speed n and number n of planet wheels p And output torque T 2 Primarily selecting basic parameters such as central gear tooth number, primary transmission ratio, pin gear tooth number and center distance range; and establishing a functional relation between the structural parameters and performance parameters such as efficiency, volume, mass, torsional rigidity, transmission error and service life of the whole machine, and obtaining an RV reducer structural optimization model according to constraint conditions of the involute planetary transmission mechanism, the rotary arm bearing and the cycloid pinwheel transmission mechanism.
Further, in step S1, the differential evolution algorithm uses a real number encoding population, first generates a real number population R according to an independent variable range matrix, and generates two offspring chromosomes using SBX; the offspring chromosomes adopt a differential mutation operator, and the expression is as follows:
wherein F is a scale factor for controlling the difference vector, t is an evolution algebra,
vector for an individual>
The ith dimension variable of (2); x is the number of j (t) is an individual vector @>
In which a random number j ∈ { r ∈ { c } 1 ,r 2 ,r 3 }，r 1 、r 2 、r 3 Belongs to N and is random;
and calculating objective function values corresponding to the feasible solution column vectors under the constraint condition by using a Kriging agent model, and selecting a group of pareto optimal solutions according to the crowded distance.
Further, in step S1, the expression of the Kriging agent model is as follows:
y(x)＝F(β,x)+z(x)＝f T (x)β+z(x)
where F (β, x) is a global model of the argument space, z (x) is a randomly distributed local bias, F T (x) Is a vector synthesized by polynomial basis functions, and beta is a regression parameter vector; and designing a Latin hypercube sampling table to obtain a group of structural parameter sample points, and obtaining corresponding performance parameters through finite element calculation for fitting a Kriging proxy model.
Further, in step S1, the multi-objective evaluation method adopts an entropy weight method, which is an objective weighting method, the entropy weight is calculated by using the entropy of the decision index, and the performance index X is obtained at i 1 ,X 2 ,···,X i Of the j pareto optimal solutions, the m-th performance index X m ＝{x 1 ,x 2 ,···,x j }，X m Entropy H of m Is defined as follows:
wherein r is mn In order to be an index after the standardization,
the larger the entropy of the index is, the smaller the entropy weight is, and the entropy weight w of the mth individual performance index is m Is defined as:
weighting each performance index by using the entropy weight value to obtain a more objective pareto optimal solution.
Further, the step S2 specifically includes: establishing a RV reducer parametric modeling system, wherein the system comprises a user interface, a database and a size-driven RV reducer model; inputting pareto optimal solution obtained by a multi-target evaluation method into a user interface, and generating a three-dimensional model of the cycloidal gear, the pin gear shell and other parts by combining other structural parameters in a database;
the user interface is realized by a Block UI (user interface) builder of three-dimensional modeling software UG (user generated content), the user interface editing and the size parameter definition are completed through a C + + programming language, and finally, a Visual Studio is adopted to compile and link a program;
the database adopts an ODBC database, and realizes the intercommunication of internal and external data and the parameter transmission between a user interface and a model through ADO.
The size-driven RV reducer model decomposes the process of creating a three-dimensional model, the operation aiming at the characteristics, the objects and the entities is respectively subjected to functional processing, the size of the three-dimensional model is driven by adopting the size of a two-dimensional engineering drawing, and a parametric modeling system of the RV reducer can be driven to generate a three-dimensional part drawing by inputting key parameters through a user interface.
Further, the step S3 specifically includes: establishing a RV reducer finite element analysis module, wherein the system comprises a graphical interface, a python pretreatment program and a post-treatment module; importing a three-dimensional model generated by a parametric modeling system into a finite element analysis module, calling a pre-processing program through an operation graphical interface to calculate, returning a result through a post-processing module, and determining an RV reducer design scheme according to a finite element simulation result;
the graphical interface is created by an Abaqus GUI, editing of the graphical interface and definition of a control are completed through a Python language, a menu plug-in is generated by an RSG Builder, and communication between the graphical interface and a preprocessing program is achieved through a script interface; the python pretreatment program establishes a finite element analysis process comprising RV reducer entity introduction, stress loading and grid division, replaces parameters in modeling with abstracted characteristic parameters by Fortran, and constructs a finite element analysis module with adjustable parameters; assigning values for characteristic parameters according to actual working conditions, and carrying out finite element analysis and solving on indexes such as contact stress, torsional rigidity and the like;
and the post-processing module and the Fortran subprogram return the solving result to a post-processing interface, and related calculation results such as stress, strain cloud charts and the like of the pin gear shell and the cycloid wheel are obtained through the interface.
The invention has the beneficial effects that:
(1) The evolutionary algorithm adopted by the invention is used as an optimization method with strong robustness, good convergence and high parallelism, and is suitable for solving the multi-objective optimization problems of multiple solving parameters and large variable space in the design of RV reducer key parameters. Based on the problem that the optimal solution set obtained by the evolutionary algorithm solution needs further screening, the solution set is weighted and screened by adopting the multi-target evaluation of the entropy weight method.
(2) The invention constructs a three-dimensional modeling system of the RV reducer, realizes the parametric design of series products and meets the requirement of rapid design.
(3) According to the invention, the finite element simulation analysis module is compiled, the three-dimensional model is imported into the module for solving, and the design scheme of the RV reducer is selected according to the simulation result, so that the modeling efficiency is improved, and the experiment cost is reduced.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
fig. 1 is a functional module structure diagram of the RV reducer forward design method of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustration only and not for the purpose of limiting the invention, shown in the drawings are schematic representations and not in the form of actual drawings; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
Referring to fig. 1, the RV reducer forward design method is divided into an RV reducer optimization module, an RV reducer parametric modeling system and an RV reducer finite element analysis module, wherein the RV reducer optimization module calculates structural parameters according to basic performance parameters of the RV reducer, establishes a corresponding structural optimization model through a target function and constraint conditions, obtains a set of pareto optimal solutions by using a differential evolution algorithm, and selects an optimal solution from a pareto solution set by using a multi-objective evaluation method. And the RV reducer parametric modeling system generates a three-dimensional model of each component of the RV reducer. And the RV reducer finite element analysis module is used for analyzing the contact strength and the torsional rigidity by utilizing the three-dimensional model of the RV reducer and determining the design scheme of the RV reducer.
The method comprises the following specific steps:
the method comprises the following steps: compiling an RV reducer optimization module based on pyside2 according to basic performance parameters of the RV reducer, namely input power P, input rotating speed n and number n of planet wheels p Output torque T 2 And primarily selecting basic parameters such as the number of teeth of the central gear, the primary transmission ratio, the number of teeth of the pin gear, the range of the center distance and the like. Establishing structural parameters and performance parameters such as efficiency, volume, mass, torsional rigidity, transmission error, overall machine life, etcAnd obtaining a key parameter optimization model of the RV reducer according to constraint conditions of the involute planetary transmission mechanism, the rotary arm bearing and the cycloid pinwheel transmission mechanism.
The optimization model adopts a Kriging agent model, and the expression is as follows:
y(x)＝F(β,x)+z(x)＝f T (x)β+z(x)
where F (β, x) is a global model of the argument space, z (x) is a randomly distributed local bias, F T (x) Is a vector synthesized by polynomial basis functions, and beta is a regression parameter vector. And designing a Latin hypercube sampling table to obtain a group of structural parameter sample points, and obtaining corresponding performance parameters through finite element calculation for fitting a Kriging proxy model.
The differential evolution algorithm adopts a real number encoding population, firstly generates a real number population R according to an independent variable range matrix, and generates two offspring chromosomes by adopting SBX (simulated binary single-point crossing). The offspring chromosomes adopt a differential mutation operator, and the expression is as follows:
wherein F is a scale factor for controlling the difference vector, t is an evolution algebra,
vector for an individual>
The ith dimension variable of (1); x is a radical of a fluorine atom j (t) is an individual vector @>
In which a random number j ∈ { r ∈ { c } 1 ,r 2 ,r 3 }，r 1 、r 2 、r 3 e.N and random. And calculating objective function values corresponding to the feasible solution column vectors under the constraint condition by using a Kriging agent model, and selecting a group of pareto optimal solutions according to the crowded distance.
Multi-purposeThe evaluation method adopts an entropy weight method, which is an objective weighting method, the entropy weight is calculated by using the entropy of the decision index, and the performance index X is measured in the i individual 1 ,X 2 ,···,X i Of the j pareto optimal solutions, the m-th performance index X m ＝{x 1 ,x 2 ,···,x j Its entropy is defined as:
wherein r is mn In order to be an index after the standardization,
the larger the entropy of the index is, the smaller the entropy weight is, and the entropy weight of the mth individual performance index is defined as:
weighting each performance index by using the entropy weight value to obtain a more objective pareto optimal solution.
Step two: and establishing a parametric modeling system of the RV reducer, wherein the system comprises a user interface, a database and a size-driven RV reducer model. And inputting pareto optimal solution obtained by adopting a multi-target evaluation method into a user interface, and generating a three-dimensional model of the cycloidal gear, the pin gear shell and other parts by combining other structural parameters in the database.
The user interface is realized by a Block UI builder of three-dimensional modeling software UG, the user interface editing and the size parameter definition are completed through a C + + programming language, and finally, the program is compiled and linked by Visual Studio.
The database adopts an ODBC database, and the intercommunication of internal and external data and the parameter transmission between the user interface and the model are realized through ADO.
The size-driven RV reducer model decomposes the process of creating a three-dimensional model, the operation aiming at the characteristics, the objects and the entities is subjected to functional processing respectively, the size of the three-dimensional model is driven by adopting the size of a two-dimensional engineering drawing, and a parametric modeling system of the RV reducer can be driven to generate a three-dimensional part drawing by inputting key parameters through a user interface.
Step three: and establishing a RV reducer finite element analysis module, wherein the system comprises a graphical interface, a python pretreatment program and a post-treatment module. And importing the three-dimensional model generated by the parametric modeling system into a finite element analysis module, calling a pre-processing program through an operation graphical interface to calculate, returning a result through a post-processing module, and determining the design scheme of the RV reducer according to the finite element simulation result.
The graphical interface is created by an Abaqus GUI, editing of the graphical interface and definition of a control are completed through a Python language, a menu plug-in is generated by an RSG Builder, and communication between the graphical interface and a preprocessing program is achieved through a script interface.
The python pretreatment program establishes a finite element analysis process comprising RV reducer entity introduction, stress loading and grid division, replaces parameters in modeling with abstracted characteristic parameters by Fortran, and constructs a finite element analysis module with adjustable parameters. And (4) assigning values for the characteristic parameters according to the actual working conditions, and carrying out finite element analysis and solving on indexes such as contact stress, torsional rigidity and the like.
And the post-processing module and the Fortran subprogram return the solved result to a post-processing interface, and related calculation results such as stress of a pin gear shell and a cycloidal gear, a strain cloud chart and the like can be obtained through the interface.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. A forward design method of an RV reducer is characterized by comprising the following steps:
s1: establishing an RV reducer optimization module, initially calculating structural parameters according to basic performance parameters of the RV reducer, establishing a corresponding structure optimization model through a target function and constraint conditions, solving a group of pareto optimal solutions by using a differential evolution algorithm, and selecting the optimal solution from a pareto solution set by using a multi-objective evaluation method;
according to the basic performance parameters of the RV reducer, initially calculating structural parameters, and establishing a corresponding structural optimization model through an objective function and constraint conditions, wherein the method specifically comprises the following steps: according to the basic performance parameters of the RV reducer, namely input power P, input rotating speed n and number n of planet wheels p And output torque T 2 Initially selecting the number of teeth of a central gear, a primary transmission ratio, the number of teeth of a pin gear and a central distance range; establishing a functional relation between structural parameters and efficiency, volume, mass, torsional rigidity, transmission error and the service life of the whole machine, and obtaining an RV reducer structure optimization model according to constraint conditions of an involute planetary transmission mechanism, a rotary arm bearing and a cycloid pinwheel transmission mechanism;
the differential evolution algorithm adopts a real number encoding population, firstly generates a real number population R according to an independent variable range matrix, and generates two offspring chromosomes by adopting simulated binary single-point cross (SBX); the offspring chromosomes adopt a difference mutation operator, and the expression is as follows:
wherein F is a scale factor for controlling the difference vector, t is an evolution algebra,
is an individual vector->
The ith dimension variable of (1);
is an individual vector->
In which a random number j ∈ { r ∈ { c } 1 ,r 2 ,r 3 }，r 1 、r 2 、r 3 Belongs to N and is random;
calculating objective function values corresponding to feasible solution column vectors under constraint conditions by using a Kriging agent model, and selecting a group of pareto optimal solutions according to the crowded distance;
the expression of the Kriging agent model is as follows:
wherein,
is a global model of the argument space, z (x) is a randomly distributed local variance, f T (x) Is a vector synthesized by polynomial basis functions, and beta is a regression parameter vector; designing a Latin hypercube sampling table to obtain a group of structural parameter sample points, and obtaining corresponding performance parameters through finite element calculation for fitting a Kriging proxy model;
the multi-target evaluation method adopts an entropy weight method, calculates an entropy weight by using the entropy of a decision index, and calculates the entropy weight at the i individual performance index X 1 ,X 2 ,···,X i In the evaluation problem of j pareto optimal solutions, the mth individual performance index X m ＝{x 1 ,x 2 ,···,x j }，X m Entropy H of m Is defined as:
wherein r is mn The standard index is the standard index; the larger the entropy of the index is, the smaller the entropy weight is, and the entropy weight w of the mth individual performance index is m Is defined as:
weighting each performance index by using the entropy weight value to obtain a more objective pareto optimal solution;
s2: establishing a parametric modeling system of the RV reducer to generate a three-dimensional model of each component of the RV reducer; the method specifically comprises the following steps: establishing a RV reducer parametric modeling system, wherein the system comprises a user interface, a database and a size-driven RV reducer model; inputting pareto optimal solution obtained by adopting a multi-target evaluation method into a user interface, and generating a three-dimensional model of a cycloidal gear and a pin gear shell by combining other structural parameters in a database;
the size-driven RV reducer model decomposes the process of creating a three-dimensional model, respectively performs functional processing on operations aiming at features, objects and entities, adopts the size of a two-dimensional engineering drawing to drive the size of the three-dimensional model, and can drive the RV reducer parametric modeling system to generate a three-dimensional part drawing by inputting key parameters through a user interface;
s3: establishing a RV reducer finite element analysis module, performing contact strength analysis and torsional rigidity analysis by using a three-dimensional model of the RV reducer, and determining a design scheme of the RV reducer; the method specifically comprises the following steps: establishing a RV reducer finite element analysis module, wherein the system comprises a graphical interface, a python pretreatment program and a post-treatment module; importing a three-dimensional model generated by a parametric modeling system into a finite element analysis module, calling a pre-processing program through an operation graphical interface to calculate, returning a result through a post-processing module, and determining an RV reducer design scheme according to a finite element simulation result;
the graphical interface realizes the communication between the graphical interface and the preprocessing program through a script interface;
the python pretreatment program establishes a finite element analysis process comprising RV reducer entity introduction, stress loading and grid division, replaces parameters in modeling with abstracted characteristic parameters by Fortran, and constructs a finite element analysis module with adjustable parameters; assigning values for characteristic parameters according to actual working conditions, and carrying out finite element analysis and solving on indexes such as contact stress, torsional rigidity and the like;
and the post-processing module and the Fortran subprogram return the solving result to a post-processing interface, and the related calculation result is obtained through the interface.
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