CN108563831A - Optimization method for transmission precision of RV reducer - Google Patents
Optimization method for transmission precision of RV reducer Download PDFInfo
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Abstract
A method for optimizing the transmission precision of an RV reducer comprises the following steps: determining influence factors of the transmission precision of the RV reducer, wherein the influence factors comprise part errors, part fit clearances and working loads; constructing an orthogonal test scheme by combining the influence factors; constructing a virtual prototype experimental group by adopting three-dimensional modeling software CREO and multi-body dynamics simulation software ADAMS according to an orthogonal experimental scheme; measuring the transmission error of each virtual prototype experimental group in simulation software Adams; analyzing the orthogonal test result; and (4) taking the orthogonal test result data as the input end data of the BP neural network to predict the optimal combination.
Description
Technical field
The present invention relates to high precision machines people's RV retarder transmission accuracy optimization methods.
Background technology
Due to temperature deformation in RV retarder parts foozle, rigging error and transmission process and flexible deformation
In the presence of input and output angular errors are unavoidable.Angular errors refer to the deviation between output shaft actual rotational angle and theoretical corner
Value is the important indicator for evaluating RV retarder transmission accuracies.The application field of RV retarders is mostly that transmission accuracy is more demanding
Accurate transmission mechanism, such as robot, radar, precision machine tool etc., in order to ensure that transmission device repeatedly completes same period
Accuracy when movement between its position, RV retarders must have higher transmission accuracy.
With the extensive use of robot, the correlative study of RV retarders is more and more concerned, especially transmission accuracy
Optimization problem.RV retarder parts are numerous and requirement on machining accuracy is higher, are limited to fabricate the requirement of cost, passed
When the optimization of dynamic precision, each parts foozle, mismatch error are difficult to optimize one by one, slow down to RV so distinguishing each error term
The influence susceptibility of device transmission accuracy is the reasonable premise for improving RV retarder transmission accuracies.According to RV retarder transmission accuracy shadows
The influence susceptibility of the factor of sound could be realized in conjunction with production and processing cost and more be improved to lean to RV retarders transmission accuracy.
When the problem of removing research RV retarder transmission accuracies using manual measurement and by the way of being tested, inevitably
Measurement error is will produce, with the continuous accumulation of error, the accuracy of result of study is difficult to ensure, and experimental cost is high, the period
It is long.The utilization of Virtual Prototype Technique solves the problems, such as this just, and three-dimensional is carried out according to part error using 3 d modeling software
The structure of model, and model is imported into simulation software and carries out motion simulation, the influence of other noise factors is eliminated, reality is improved
The accuracy tested saves experimental cost, shortens experimental period.
Invention content
It is difficult to differentiate between in the present invention RV of being solved retarder transmission accuracy optimization process between part error, part cooperation
The influence factors such as gap, service load are to the influence degrees of RV retarder transmission accuracies, and the numerous processing essences of RV retarder parts
The shortcomings that degree is higher, and man-made chamber is difficult to find out transmission accuracy optimum combination provides a kind of based on Virtual Prototype Technique and BP god
The optimization method of RV retarder transmission accuracies through network.
In order to solve the above technical problem, the present invention provides a kind of RV based on Virtual Prototype Technique and BP neural network
The optimization method of retarder transmission accuracy, includes the following steps：
S1, the influence factor for determining RV retarder transmission accuracies include mainly part error, part fit clearance, work
Three aspects of load；
S11, selection RV40-E type speed reducers are research object, and choosing several part errors according to parts drive connection is
Experimental factor；
S12, the influence relationship to part gap in RV speed reducers to transmission accuracy are analyzed, and are chosen more main several
A part fit clearance is experimental factor；
S2, orthogonal test scheme is built in conjunction with influence factor；
S3, using 3 d modeling software CREO and Dynamics Simulation software ADAMS according to orthogonal test scheme constructs
Virtual prototype experimental group；
S31, the virtual sample of RV retarders is established according to part error in orthogonal test scheme using 3 d modeling software CREO
Machine model subtracts due to being difficult to build complicated threedimensional model in ADAMS so establishing RV according to orthogonal test scheme using CREO
Fast device virtual prototype experimental group simplifies parts thereof in modeling process, cycloid equation is established in CREO and is put
Line wheel tooth curve, cycloid equation are as follows：
Wherein rz is centre circle of gear pins radius, and zb is the needle tooth number of teeth, and rzz is needle tooth radius, and za is the Cycloidal Wheel number of teeth, and e is
Eccentricity, for drz to move away from the amount of practicing Buddhism or Taoism, drzz is the equidistant amount of practicing Buddhism or Taoism；
Half of flank profil of complete Cycloidal Wheel is drawn by equation, and pendulum then can be obtained by mirror image, array, stretching order
Line wheel threedimensional model, the method for building up of other physical models is similarly.All material object parts models are assembled, it is then right
Assembly progress determines that assembly is correctly literary among CREO and ADAMS without assembly is saved as after part interference without interference detection
Part format parasolid (* .x_t).
S32, virtual prototype is imported into the definition that Dynamics Simulation software carries out material property；Pass through
The model file of intermediate form is imported ADAMS softwares by File/Import orders, and carries out the definition of parts material, is based on
ADAMS contact-impacts are theoretical, and the flexible deformation that feature contacts generate when colliding can also influence RV retarder turn errors,
So the material properties such as elasticity modulus, density, Poisson's ratio of addition each part of model.
S33, restriction relation is added to virtual prototype in ADAMS；To ensure the correctness of each parts relative motion, structure
It builds virtual prototype to be also required to provide constraint or contact relation according to component movement track, passes through the movement of RV retarder parts
And contact analysis, determine each parts constraint, contact relation.
S34, virtual prototype is emulated, whether correct verifies constructed model；Measure input shaft, planet carrier rotating speed
The two ratio is compared with RV retarder theory transmission ratios, and whether verification model is accurate.
S4, the driving error that each virtual prototype experimental group is measured in simulation software Adams；
The evaluation index that turn error is transmission accuracy is chosen, selected RV retarders test model transmission ratio is 121, according to
Turn error calculation formula
In formula,For angular errors；Corner is inputted for input shaft (i.e. sun gear)；For output shaft (i.e. planet
Frame) actual rotational angle；I is retarder transmission ratio.
The corner of input shaft, output shelf is measured in real time in ADAMS, both output rotation curve such as Fig. 5 (a),
(b) shown in.Establish measurement functions
In FUNCTION=.JOINT_1_MEA_1/121-.JOINT_16_MEA_1 formulas, FUNCTION is reality output
The difference of corner and theoretical output corner, i.e. turn error；JOINT_1_MEA_1 is input shaft revolute pair corner；.JOINT_
16_MEA_1 is planet carrier revolute pair corner.
It establishes virtual prototype respectively according to orthogonal design table and imports ADAMS emulation, measure each group turn error, it is complete
Kind orthogonal experiments table.
S5, orthogonal experiments are analyzed；
To orthogonal experiments progressive range analysis, range analysis i.e. using data it is very poor come problem analysis, by right
Than the mean range of each experimental result, the Main Factors for influencing test index are found out.Its principle is to consider single-factor A to result
Influence when, it is believed that influence of the other factors to result is balanced, and the difference of each level of the A factors is since A factors itself are drawn
It rises.It is very poor bigger, illustrate that the influence of the factor pair experimental index is bigger, the very poor R following equation of each factor is calculated.
R=max { ki}-min{ki}
T=∑s ki
In formula, R is very poor；I is the number of levels of factor；kiFor i levels when the sum of corresponding turn error mean value；Ki
For i levels when corresponding the sum of turn error；N is each horizontal number occurred on either rank；T is the sum of turn error；
According to the very poor size of each influence factor obtain each factor of RV retarder dynamic rotation errors influence sensitivity degree from
Small sequence and factor level optimal combination are arrived greatly.
S6, using orthogonal experiments data as the fan-in evidence of BP neural network, carry out the prediction of optimum combination.
The foundation and training of S61, BP artificial neural network
The principal element of RV retarder turn errors is influenced using in Orthogonal Experiment and Design as the defeated of built BP neural network
Enter layer, the output layer of network corresponds to evaluation index turn error comprising an output node.It is programmed using MATLAB softwares,
Select hidden layer transmission function, output layer transmission function, training function；The parameters such as hidden layer, training precision, learning rate are set,
Comparison is trained to the network containing different neuron numbers.
S62, the prediction that optimum combination is carried out using BP artificial neural networks；
In previous step research, using MATLAB software programmings, pass through training to experimental data sample and BP nerve nets
The optimization of network parameter, functional relation between RV retarders turn error and its evaluation index can accurately be described by being successfully established
BP neural network model, analogue simulation is carried out using the network model established, with several influence factor values of orthogonal test
As independent variable, then it is respectively its assignment, a suitable step-length is set, using the correlation function in MATLAB to each factor
Definition thresholding be programmed, in the hope of the minimum combination value of its output valve turn error.
S63, optimum results are examined
Virtual prototype is established according to the optimum results in upper step, and imports ADAMS and carries out Dynamics Simulation, is measured
Virtual prototype turn error obtains transmission accuracy effect of optimization with comparison is carried out in orthogonal experiments.
A kind of RV retarder transmission accuracy optimization methods based on Virtual Prototype Technique and BP neural network of the application, tool
It has the advantages that：
1, solved using the method for the present invention be limited to processing cost in RV retarder transmission accuracy optimization process can not be to every
A factor optimizes, and improves RV influence importance further lean of each influence factor to transmission accuracy cannot be distinguished
The transmission accuracy of retarder, formulation part fit tolerance provide foundation.
2, it is avoided by modeling and simulating and generates measurement error and other noise factors during artificial measurement experiment
It influences to make the more accurate of experimental result.
3, Virtual Simulation and BP neural network algorithm is combined to carry out RV retarder transmission accuracies in the method for the present invention
Research realizes the factor combination accurate prediction to optimal transmission accuracy, without carrying out the experiment of entity model machine, saves research
Cost and time, greatly improve Efficiency.
Description of the drawings
Fig. 1 is flow chart of the method for the present invention.
Fig. 2 is RV retarder entire assembly model figures
Fig. 3 is virtual prototype (hiding planet carrier)
Fig. 4 a are input shaft rotating speed curves, and Fig. 4 b are output mechanism planet carrier speed curves
Fig. 5 a are input rotation curves, and Fig. 5 b are output corner curves, and Fig. 5 c are 1 angular errors curves of virtual prototype
Fig. 6 is initial data and the comparison diagram through neural network prediction value
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes.
The present invention provides a kind of RV retarder transmission accuracies optimization side based on Virtual Prototype Technique and BP neural network
Method, flow such as Fig. 1, includes the following steps：
S1, the influence factor for determining RV retarder transmission accuracies include mainly part error, part fit clearance, work
Three aspects of load；
S11, selection RV40-E type speed reducers are research object, are with the transmission of two level Cycloidal Wheel according to parts drive connection
Main study portion, in conjunction with《Practical design of gears reckoner》Crank axle eccentricity is chosen in part error part, Cycloidal Wheel is moved
Away from the amount of practicing Buddhism or Taoism, Cycloidal Wheel equidistantly 5 amount of practicing Buddhism or Taoism, needle tooth radius error, centre circle of gear pins radius error influence factors；
S12, the influence relationship to part gap in RV speed reducers to transmission accuracy are analyzed, and part gap is directed to
Mainly have sun gear planetary gear back lash, the bearing clearance of Cycloidal Wheel crank axis hole and crank between centers, planet carrier crank axle
Bearing clearance, Cycloidal Wheel needle tooth engagement gap, needle tooth between hole and the bearing clearance and planet carrier and rack of crank between centers with
Wheel spider fit clearance.Virtual prototype middle (center) bearing is replaced with axle sleeve, and needle tooth and wheel spider are consolidated, so part coordinates
Gap portion is chosen between Cycloidal Wheel endoporus and pivoted arm shaft room gap, crank axle and pivoted arm shaft room gap, crank axle and support sleeve
5 gap, support sleeve and planet carrier gap, support sleeve and flange dish gap impact factors are experimental factor, in addition work carries
Lotus chooses 11 influence factors altogether；
S2, orthogonal test scheme is built in conjunction with influence factor；
S21, factor level table is formulated.RV is subtracted in order to analyze the factors such as part's machining errors, part fit clearance, load
The influence degree of fast device dynamic rotation error, it is experimental factor to choose 11 factors altogether, selects orthogonal arrage L (313), factor level
Table is as shown in table 1；
Table 1
S22, orthogonal test scheme such as table 2 is formulated in conjunction with factor level table；
Table 2
S3, in conjunction with 3 d modeling software CREO and Dynamics Simulation software ADAMS according to orthogonal test scheme constructs
Virtual prototype；
S31,27 groups of RV retarders void are established according to part error in orthogonal test scheme using 3 d modeling software CREO
Quasi- PM prototype model；Due to being difficult to build complicated threedimensional model in ADAMS, so being established according to orthogonal test scheme using CREO
RV retarder virtual prototype experimental groups simplify parts thereof in modeling process, including：Bearing is with axle sleeve generation
It replaces, ignores the fine structures such as cap bolt, replace pin with consolidation, key, be bolted；Sealing ring, gasket etc. are removed to studying nothing
The part of influence.Cycloid equation is established in CREO and obtains Cycloid tooth profile curve, and cycloid equation is as follows：
X=(rz+drz) * (sin (360*t)-(k1/zb) * sin (zb*360*t))+(rzz
+drzz)*(-sin(360*t)+k1*sin(zb*360*t))/sqrt(1
+k1*k1-2*k1*cos(za*360*t))
Y=(rz+drz) * (cos (360*t)-(k1/zb) * cos (zb*360*t))-(rzz
+drzz)*(cos(360*t)-k1*cos(zb*360*t))/sqrt(1
+k1*k1-2*k1*cos(za*360*t))
K1=e*zb/ (rz+drz)
Wherein rz is centre circle of gear pins radius, and zb is the needle tooth number of teeth, and rzz is needle tooth radius, and za is the Cycloidal Wheel number of teeth, and e is
Eccentricity, for drz to move away from the amount of practicing Buddhism or Taoism, drzz is the equidistant amount of practicing Buddhism or Taoism；
Half of flank profil of complete Cycloidal Wheel is drawn by cycloid equation, then can be obtained by mirror image, array, stretching order
Go out Cycloidal Wheel threedimensional model, the method for building up of other physical models is similarly.All material object parts models are assembled, are obtained
To RV retarders entire assembly model such as Fig. 2.Then assembly is carried out without interference detection, after determining that assembly is correctly interfered without part
Assembly is saved as into CREO and ADAMS intermediate file formats parasolid (* .x_t).
S32, virtual prototype is imported into the definition that Dynamics Simulation software carries out material property；Pass through
The model file of intermediate form is imported ADAMS softwares by File/Import orders, and carries out the definition of parts material, is based on
ADAMS contact-impacts are theoretical, and the flexible deformation that feature contacts generate when colliding can also influence RV retarder turn errors,
So the material properties such as table 3 such as elasticity modulus, density, Poisson's ratio of addition each part of model.Table 3
S33, restriction relation is added to virtual prototype in ADAMS；To ensure the correctness of each parts relative motion, structure
It builds virtual prototype to be also required to provide constraint or contact relation according to component movement track, passes through the movement of RV retarder parts
And contact analysis, determine that each parts constraint, contact relation are as shown in table 4.
Table 4
S34, virtual prototype is emulated, whether correct verifies constructed model；It will be established in assembly by CREO
Between format model file import ADAMS systems, add parts material property and restriction relation and obtain such as Fig. 3 (hiding planet carrier)
Shown in virtual prototype.Virtual prototype includes part 58, constrains 18, contacts 94.Setting rotation driving input speed letter
Load torque is arranged according to testing program in number F (time)=7000d*time*step (time, 0,1,0,1), and definition load is turned round
Moment function F (time)=step (time, 1,1.5,0, X), X is organized according to the experiment depending on data.Self-defining 4S simulation times, 100
Step emulation step number.Input shaft, planet carrier speed curves are measured as shown in Fig. 4 a, Fig. 4 b, model motion stabilization after 1.5s, input shaft
Rotating speed is 7000 °/s, and planet carrier rotating speed mean value is 57.7445 °/s, and the two ratio is 121.2236, with 121 phase of theoretical transmission ratio
It coincide, it was demonstrated that model is accurate and reliable.
S4, the driving error that each virtual prototype experimental group is measured in simulation software Adams；
The evaluation index that turn error is transmission accuracy is chosen, selected RV retarders test model transmission ratio is 121, according to
Turn error calculation formula
In formula,For angular errors；Corner is inputted for input shaft (i.e. sun gear)；For output shaft (i.e. planet
Frame) actual rotational angle；I is retarder transmission ratio.
The corner of input shaft, output shelf is measured in real time in ADAMS, both output rotation curve such as Fig. 5 a, figure
Shown in 5b.Establish measurement functions
In FUNCTION=.JOINT_1_MEA_1/121-.JOINT_16_MEA_1 formulas, FUNCTION is reality output
The difference of corner and theoretical output corner, i.e. turn error；JOINT_1_MEA_1 is input shaft revolute pair corner；.JOINT_
16_MEA_1 is planet carrier revolute pair corner.
It establishes virtual prototype respectively according to orthogonal design table and imports ADAMS emulation, measure each group turn error, scheme
5c is the turn error curve of virtual prototype 1, and since experimental group is more, other group of empirical curve will not enumerate.
Since 0~1.5s internal loads, driving at the uniform velocity load, model sport state labile, so choosing 1.5~4s times
Section error curve mean value is evaluation index, and it is as shown in table 2 to improve orthogonal experiments.
S5, orthogonal experiments are analyzed；
To orthogonal experiments progressive range analysis, range analysis i.e. using data it is very poor come problem analysis, by right
Than the mean range of each experimental result, the Main Factors for influencing test index are found out.Its principle is to consider single-factor A to result
Influence when, it is believed that influence of the other factors to result is balanced, and the difference of each level of the A factors is since A factors itself are drawn
It rises.It is very poor bigger, illustrate that the influence of the factor pair experimental index is bigger, the very poor R following equation of each factor is calculated.
R=max { ki}-min{ki}
T=∑s ki
In formula, R is very poor；I is the number of levels of factor；kiFor i levels when the sum of corresponding turn error mean value；Ki
For i levels when corresponding the sum of turn error；N is each horizontal number occurred on either rank；T is the sum of turn error；
Carrying out range analysis to test result, the results are shown in Table 2.Show that RV slows down according to the very poor size of each influence factor
It is centre circle of gear pins radius error, needle tooth radius mistake that each factor of device dynamic rotation error, which influences sensitivity degree descending order,
Difference, Cycloidal Wheel are moved away from the amount of practicing Buddhism or Taoism, magnitude of load, crank axle and pivoted arm shaft room gap, the Cycloidal Wheel equidistantly amount of practicing Buddhism or Taoism, upper support sleeve
With planet carrier gap, crank axle eccentric error, crank axle and load-bearing shaft room gap, Cycloidal Wheel endoporus and pivoted arm shaft room gap, under
Support sleeve and flange dish gap；Factor level optimal combination is A1B1C3D3E1F3G2H2I3J1K1。
S6, using orthogonal experiments data as the fan-in evidence of BP neural network, carry out the prediction of optimum combination.
The foundation and training of S61, BP artificial neural network
The principal element of RV retarder turn errors is influenced using in Orthogonal Experiment and Design as the defeated of built BP neural network
Enter layer, including 11 input nodes correspond to respectively needle tooth radius error, centre circle of gear pins radius error, crank axle eccentric error,
Cycloidal Wheel modification of moved distance amount, Cycloidal Wheel modification of equidistance amount, Cycloidal Wheel endoporus and pivoted arm shaft room gap, crank axle and pivoted arm shaft room
Gap, crank axle and support sleeve gap, upper support sleeve and planet carrier gap, lower support sleeve and flange dish gap, work carry
The output layer of lotus, network corresponds to evaluation index turn error comprising an output node.Using MATLAB (R2016a versions, U.S.
State) software is programmed, and select tanh transmission function (tansig) as hidden layer transmission function, linear transfer function
(purelin) it is used as output layer transmission function, hidden layer 1 is set, by being trained to the network containing different neuron numbers
Comparison, determination include neuron 23, and creating BP networks using traingdm function pairs is trained, setting network training parameter
Value, maximum frequency of training are 100 times, training precision 0.0001, learning rate 0.1, other parameters are default value.Fig. 6
For initial data and the comparison diagram through neural network prediction value, the two registration is higher as seen from the figure, it was demonstrated that the BP neural network
It can be used for predicting the transmission accuracy prediction of RV retarders, and result is more accurate.
S62, BP artificial neural network combination optimization of orthogonal test factor parameter
In previous step research, using MATLAB software programmings, pass through training to experimental data sample and BP nerve nets
The optimization of network parameter, functional relation between RV retarders turn error and its evaluation index can accurately be described by being successfully established
BP neural network model, carry out analogue simulation using the network model established, with A, B of orthogonal test, C, D, E, F, G,
H, I, J, K11 influence factor values are as independent variable, then respectively this 11 factor assignment, and a suitable step-length is arranged, makes
The definition thresholding of each factor is programmed with the correlation function in MATLAB, in the hope of the most group of its output valve turn error
Conjunction value.Factor is combined as when obtaining RV retarder turn error minimums through BP artificial nerve network model simulation optimizations:Needle tooth half
Diameter error 0.01mm, centre circle of gear pins radius error 0.01mm, crank axle eccentric error 0.1mm, Cycloidal Wheel modification of moved distance amount
0.03mm, Cycloidal Wheel modification of equidistance amount 0.01mm, Cycloidal Wheel endoporus and pivoted arm shaft room gap 0.01mm, crank axle and pivoted arm axle sleeve
Gap 0.0042mm, crank axle and support sleeve gap 0.0058, upper support sleeve and planet carrier gap 0.01, lower support sleeve
With flange dish gap 0.0012, service load 300Nmm.
S63, optimum results are examined
Virtual prototype is established according to the optimum results in upper step, and imports ADAMS and carries out Dynamics Simulation, is obtained
Virtual prototype turn error is 0.0512 °, and compared in orthogonal experiments optimal 0.0611 °, transmission accuracy improves
16.2%, prove that the inventive method is apparent to RV retarder transmission accuracy effect of optimization.
A kind of RV retarder transmission accuracy optimization methods based on Virtual Prototype Technique and BP neural network of the application, tool
It has the advantages that：
1, solved using the method for the present invention be limited to processing cost in RV retarder transmission accuracy optimization process can not be to every
A factor optimizes, it is difficult to distinguish influence importance this problem of each influence factor to transmission accuracy, be further
The transmission accuracy for improving RV retarders provides a kind of new method.
2, it is avoided by modeling and simulating and generates measurement error and other noise factors during artificial measurement experiment
It influences to make the more accurate of experimental result.
3, Virtual Simulation and BP neural network algorithm is combined to carry out RV retarder transmission accuracies in the method for the present invention
Research, realizes the accurate prediction combined to optimal transmission accuracy factor and saves research without carrying out the experiment of entity model machine
Cost and time, greatly improve Efficiency.
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, protection of the invention
Range is not construed as being only limitted to the concrete form that embodiment is stated, protection scope of the present invention is also and in art technology
Personnel according to present inventive concept it is conceivable that equivalent technologies mean.
Claims (1)
1. a kind of optimization method of RV retarders transmission accuracy, includes the following steps：
S1, the influence factor for determining RV retarder transmission accuracies, including part error, part fit clearance, service load；
S11, selection RV40-E type speed reducers are research object, and it is experiment to choose several part errors according to parts drive connection
Factor；
S12, the influence relationship to part gap in RV speed reducers to transmission accuracy are analyzed, and choose more main several zero
Part fit clearance is experimental factor；
S2, orthogonal test scheme is built in conjunction with influence factor；
It is S3, virtual according to orthogonal test scheme constructs using 3 d modeling software CREO and Dynamics Simulation software ADAMS
Prototype experiment group；
S31, RV retarder virtual prototype moulds are established according to part error in orthogonal test scheme using 3 d modeling software CREO
Type, due to being difficult to build complicated threedimensional model in ADAMS, so establishing RV retarders according to orthogonal test scheme using CREO
Virtual prototype experimental group simplifies parts thereof in modeling process, and cycloid equation is established in CREO and obtains Cycloidal Wheel
Tooth curve, cycloid equation are as follows：
Wherein rz is centre circle of gear pins radius, and zb is the needle tooth number of teeth, and rzz is needle tooth radius, and za is the Cycloidal Wheel number of teeth, and e is bias
Away from for drz to move away from the amount of practicing Buddhism or Taoism, drzz is the equidistant amount of practicing Buddhism or Taoism；
Half of flank profil of complete Cycloidal Wheel is drawn by equation, and Cycloidal Wheel three-dimensional is then obtained by mirror image, array, stretching order
Model, the method for building up of other physical models is similarly；All material object parts models are assembled, then to assembly into
Row is determined and assembly is saved as CREO and ADAMS intermediate file formats after assembly is correctly interfered without part without interference detection
parasolid(*.x_t)；
S32, virtual prototype is imported into the definition that Dynamics Simulation software carries out material property；Pass through File/
The model file of intermediate form is imported ADAMS softwares by Import orders, and carries out the definition of parts material, is based on ADAMS
Contact-impact is theoretical, and the flexible deformation that feature contacts generate when colliding can also influence RV retarder turn errors, so
Add the material properties such as elasticity modulus, density, the Poisson's ratio of each part of model；
S33, restriction relation is added to virtual prototype in ADAMS；To ensure that the correctness of each parts relative motion, structure are empty
Quasi- model machine is also required to provide constraint or contact relation according to component movement track, by the movements of RV retarder parts and connects
Analysis is touched, determines each parts constraint, contact relation；
S34, virtual prototype is emulated, whether correct verifies constructed model；Measure both input shaft, planet carrier rotating speed
Ratio is compared with RV retarder theory transmission ratios, and whether verification model is accurate；
S4, the driving error that each virtual prototype experimental group is measured in simulation software Adams；
The evaluation index that turn error is transmission accuracy is chosen, selected RV retarders test model transmission ratio is 121, according to revolution
Error calculation formula
In formula,For angular errors；Corner is inputted for input shaft (i.e. sun gear)；For output shaft (i.e. planet carrier)
Actual rotational angle；I is retarder transmission ratio；
The corner of input shaft, output shelf is measured in real time in ADAMS, both output rotation curve establishes measurement functions
FUNCTION=.JOINT_1_MEA_1/121-.JOINT_16_MEA_1
In formula, FUNCTION is the difference of reality output corner and theoretical output corner, i.e. turn error；JOINT_1_MEA_1
For input shaft revolute pair corner；.JOINT_16_MEA_1 it is planet carrier revolute pair corner；
It establishes virtual prototype respectively according to orthogonal design table and imports ADAMS emulation, measure each group turn error, improve just
Hand over test result table；
S5, orthogonal experiments are analyzed；
To orthogonal experiments progressive range analysis, range analysis i.e. using data it is very poor come problem analysis, it is each by comparing
The mean range of experimental result finds out the Main Factors for influencing test index；Its principle is in the shadow for considering single-factor A to result
When ringing, it is believed that influence of the other factors to result is balanced, and the difference of each level of the A factors is caused by A factors itself；
It is very poor bigger, illustrate that the influence of the factor pair experimental index is bigger, the very poor R following equation of each factor is calculated；
R=max { ki}-min{ki}
T=∑s ki
In formula, R is very poor；I is the number of levels of factor；kiFor i levels when the sum of corresponding turn error mean value；KiFor i water
Usually corresponding the sum of turn error；N is each horizontal number occurred on either rank；T is the sum of turn error；
According to the very poor size of each influence factor obtain each factor of RV retarder dynamic rotation errors influence sensitivity degree from greatly to
Small sequence and factor level optimal combination；
S6, using orthogonal experiments data as the fan-in evidence of BP neural network, carry out the prediction of optimum combination；
The foundation and training of S61, BP artificial neural network；
Using in Orthogonal Experiment and Design influence RV retarder turn errors principal element as built BP neural network input layer,
The output layer of network corresponds to evaluation index turn error comprising an output node；It is programmed, is selected using MATLAB softwares
Hidden layer transmission function, output layer transmission function, training function；Hidden layer, training precision, Study rate parameter are set, to containing not
Network with neuron number is trained comparison；
S62, the prediction that optimum combination is carried out using BP artificial neural networks；
It is successfully built by the optimization of training and BP neural network parameter to experimental data sample using MATLAB software programmings
The BP neural network model of functional relation between RV retarders turn error and its evaluation index can accurately be described by having stood, and be answered
Analogue simulation is carried out with the network model established, using several influence factor values of orthogonal test as independent variable, then is respectively
Its assignment is arranged a suitable step-length, is programmed to the definition thresholding of each factor using the correlation function in MATLAB,
In the hope of the minimum combination value of its output valve turn error；
S63, optimum results are examined；
Virtual prototype is established according to the optimum results in upper step, and imports ADAMS and carries out Dynamics Simulation, is measured virtual
Model machine turn error obtains transmission accuracy effect of optimization with comparison is carried out in orthogonal experiments.
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