CN108563831B - Optimization method for transmission precision of RV reducer - Google Patents
Optimization method for transmission precision of RV reducer Download PDFInfo
- Publication number
- CN108563831B CN108563831B CN201810218411.6A CN201810218411A CN108563831B CN 108563831 B CN108563831 B CN 108563831B CN 201810218411 A CN201810218411 A CN 201810218411A CN 108563831 B CN108563831 B CN 108563831B
- Authority
- CN
- China
- Prior art keywords
- reducer
- factor
- rotation
- error
- adams
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000003638 chemical reducing agent Substances 0.000 title claims abstract description 71
- 230000005540 biological transmission Effects 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000005457 optimization Methods 0.000 title claims description 27
- 238000012360 testing method Methods 0.000 claims abstract description 56
- 238000013528 artificial neural network Methods 0.000 claims abstract description 28
- 230000006870 function Effects 0.000 claims description 20
- 238000012549 training Methods 0.000 claims description 17
- 238000011160 research Methods 0.000 claims description 16
- 238000004088 simulation Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 10
- 239000000463 material Substances 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 8
- 238000012546 transfer Methods 0.000 claims description 8
- 238000006073 displacement reaction Methods 0.000 claims description 6
- 230000008439 repair process Effects 0.000 claims description 6
- 230000035945 sensitivity Effects 0.000 claims description 6
- 239000007787 solid Substances 0.000 claims description 6
- 238000013461 design Methods 0.000 claims description 5
- 230000005489 elastic deformation Effects 0.000 claims description 4
- 210000002569 neuron Anatomy 0.000 claims description 4
- 150000001875 compounds Chemical class 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 230000008676 import Effects 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 claims description 3
- 238000002474 experimental method Methods 0.000 description 12
- 238000005516 engineering process Methods 0.000 description 8
- 238000003754 machining Methods 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 230000009467 reduction Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000009966 trimming Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000007596 consolidation process Methods 0.000 description 1
- 238000005314 correlation function Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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 invention relates to a high-precision robot RV reducer transmission precision optimization method.
Background
Due to manufacturing errors and assembly errors of parts of the RV reducer and the existence of temperature deformation and elastic deformation in the transmission process, the error of an input/output rotation angle is inevitable. The rotation angle error is a deviation value between an actual rotation angle and a theoretical rotation angle of the output shaft, and is an important index for evaluating the transmission precision of the RV reducer. The application field of RV reduction gear is mostly the higher precision transmission of transmission precision requirement, for example robot, radar, precision machine tool etc. in order to guarantee the transmission when accomplishing the motion of same cycle many times the accuracy between its position, RV reduction gear must have higher transmission precision.
With the wide application of robots, research on RV reducers is more and more concerned, especially the optimization problem of transmission precision. The RV reducer has numerous parts and high machining precision requirement, is limited to the requirement of machining and manufacturing cost, and is difficult to optimize one by one according to the manufacturing error and the matching error of each part when the transmission precision is optimized, so that the condition that the sensitivity of the influence of each error item on the transmission precision of the RV reducer is reasonably improved is met. According to the influence sensitivity of the RV reducer transmission precision influence factors, the RV reducer transmission precision can be improved more subtly by combining the production and processing cost.
When the mode of adopting artifical measurement and carrying out the experiment goes to research RV reduction gear transmission precision's problem, inevitably can produce measuring error, along with the continuous accumulation of error, the accuracy of research result is difficult to guarantee, and experiment cost height, cycle length. The application of the virtual prototype technology just solves the problem, three-dimensional modeling software is adopted to construct a three-dimensional model according to part errors, the model is led into simulation software to carry out motion simulation, the influence of other noise factors is eliminated, the accuracy of the experiment is improved, the experiment cost is saved, and the experiment period is shortened.
Disclosure of Invention
The invention aims to solve the defects that influence factors such as part errors, part fit clearances and working loads are difficult to distinguish in the RV reducer transmission precision optimization process, the RV reducer parts have high machining precision, and the optimal combination of the transmission precision is difficult to find out through manual tests, and provides an optimization method of the RV reducer transmission precision based on a virtual prototype technology and a BP neural network.
In order to solve the technical problem, the invention provides an optimization method of the transmission precision of an RV reducer based on a virtual prototype technology and a BP neural network, which comprises the following steps:
s1, determining influence factors of the transmission precision of the RV reducer, wherein the influence factors mainly comprise three aspects of part errors, part fit clearances and working loads;
s11, selecting an RV40-E type speed reducer as a research object, and selecting a plurality of part errors as test factors according to the transmission relation of parts;
s12, analyzing the influence relation of the part clearance in the RV reducer on the transmission precision, and selecting a plurality of main part fit clearances as test factors;
s2, constructing an orthogonal test scheme by combining the influence factors;
s3, 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;
s31, establishing a virtual prototype model of the RV reducer according to part errors in an orthogonal test scheme by adopting three-dimensional modeling software CREO, and establishing a virtual prototype experimental group of the RV reducer according to the orthogonal test scheme by adopting the CREO because a complex three-dimensional model is difficult to construct in ADAMS, simplifying partial parts in the modeling process, and establishing a cycloid equation in the CREO to obtain a cycloid wheel tooth profile curve, wherein the cycloid equation is as follows:
wherein rz is the radius of the central circle of the pin gear, zb is the number of the pin gear teeth, rzz is the radius of the pin gear teeth, za is the number of the cycloid gear teeth, e is the eccentric distance, drz is the displacement repair quantity, and drzz is the equidistant repair quantity;
the half tooth profile of the complete cycloid wheel is drawn through an equation, then a three-dimensional model of the cycloid wheel can be obtained through mirroring, array and stretching commands, and the establishment methods of other solid models are similar to the above. And assembling all the part solid models, then carrying out interference-free inspection on the assembly body, and storing the assembly body as a CREO and ADAMS intermediate file format parasolid (. x _ t) after determining that the assembly is correct and no part interference exists.
S32, importing the virtual prototype model into multi-body dynamic simulation software to define material characteristics; the model File of the intermediate format is imported into ADAMS software through a File/Import command, the material of the part is defined, and based on the ADAMS contact collision theory, the elastic deformation generated during the contact collision of the part also influences the rotation error of the RV reducer, so the material characteristics such as the elastic modulus, the density, the Poisson ratio and the like of each part of the model are added.
S33, adding a constraint relation to the virtual prototype in the ADAMS; in order to ensure the correctness of the relative motion of each part, the construction of a virtual prototype also needs to provide a constraint or contact relation according to the motion track of the part, and the constraint and contact relation of each part is determined through the motion and contact analysis of the parts of the RV reducer.
S34, simulating the virtual prototype, and verifying whether the constructed model is correct; and (4) measuring the ratio of the rotating speed of the input shaft to the rotating speed of the planet carrier, and comparing the ratio with the theoretical transmission ratio of the RV reducer to verify whether the model is accurate or not.
S4, measuring the transmission error of each virtual prototype experimental group in simulation software Adams;
selecting the rotation error as an evaluation index of the transmission precision, selecting the RV reducer test model with the transmission ratio of 121, and calculating a formula according to the rotation error
In the formula (I), the compound is shown in the specification,is a corner error;inputting a rotation angle for an input shaft (namely a sun gear);is the actual rotation angle of the output shaft (namely the planet carrier); and i is the transmission ratio of the speed reducer.
The ADAMS measures the rotation angles of the input shaft and the output frame in real time, and outputs the rotation angle curves of both as shown in fig. 5(a) and (b). Establishing a measurement function
Funtion — 1_ MEA _1/121 —, in relation to JOINT — 16_ MEA _1, funtion is the difference between the actual output rotation angle and the theoretical output rotation angle, i.e., the rotation error; JOINT _1_ MEA _1 is the rotation angle of the input shaft revolute pair; JOINT _16_ MEA _1 is the carrier rotation pair angle.
And respectively establishing virtual prototype models according to the orthogonal test table, introducing ADAMS simulation, measuring the rotation errors of each group, and perfecting the orthogonal test result table.
S5, analyzing the results of the orthogonal test;
and (3) carrying out linear range analysis on orthogonal test results, wherein the linear range analysis is to analyze problems by using data linear range, and finding out main factors influencing test indexes by comparing average linear ranges of the test results. The principle is that when the influence of a single factor A on the result is considered, the influence of other factors on the result is considered to be balanced, and the difference of the levels of the factor A is caused by the factor A itself. The larger the range is, the larger the influence of the factor on the experimental index is, and the range R of each factor is calculated by the following formula.
R＝max{ki}-min{ki}
T＝∑ki
Wherein R is extremely poor; i is the number of levels of the factor; k is a radical ofiIs the average value of the sum of the corresponding rotation errors at the i level; kiIs the sum of the corresponding rotation errors at the i level; n is the number of occurrences of each level on any column; t is the sum of the rotation errors;
and obtaining the optimal combination of the sequence and factor level of the influence sensitivity degree of each factor of the RV reducer dynamic rotation error from large to small according to the extreme difference of each influence factor.
And S6, taking the orthogonal test result data as input end data of the BP neural network, and predicting the optimal combination.
S61, establishment and training of BP artificial neural network
The method is characterized in that main factors influencing the rotation error of the RV reducer in the orthogonal test design are used as an input layer of the established BP neural network, and an output layer of the network comprises an output node corresponding to the evaluation index rotation error. Programming by adopting MATLAB software, and selecting a hidden layer transfer function, an output layer transfer function and a training function; parameters such as a hidden layer, training precision and learning rate are set, and training comparison is carried out on networks containing different neuron numbers.
S62, predicting the optimal combination by adopting a BP artificial neural network;
in the previous step of research, MATLAB software programming is applied, a BP neural network model capable of accurately describing the functional relation between the RV reducer rotation error and the evaluation index thereof is successfully established through training of experimental data samples and optimization of BP neural network parameters, simulation is carried out by applying the established network model, a plurality of influence factor values of an orthogonal test are taken as independent variables and then assigned respectively, a proper step length is set, and a related function in MATLAB is used for programming a definition domain value of each factor so as to obtain the minimum combination value of the output value rotation error.
S63, checking optimization result
And establishing a virtual prototype according to the optimization result in the step, introducing ADAMS for multi-body dynamics simulation, measuring the rotation error of the virtual prototype, and comparing the rotation error with the orthogonal test result to obtain the transmission precision optimization effect.
The RV reducer transmission precision optimization method based on the virtual prototype technology and the BP neural network has the following beneficial effects:
1. the method solves the problems that in the optimization process of the transmission precision of the RV reducer, the machining cost cannot be limited to carry out optimization design on each factor, the influence importance of each influence factor on the transmission precision cannot be distinguished, the transmission precision of the RV reducer is further improved in a lean mode, and a basis is provided for formulating the matching tolerance of parts.
2. The influence of measurement errors and other noise factors generated in the artificial measurement experiment process is avoided through modeling simulation, so that the experiment result is more accurate.
3. According to the method, the virtual simulation technology and the BP neural network algorithm are combined to research the transmission precision of the RV reducer, so that the factor combination accurate prediction of the optimal transmission precision is realized, the test of an entity prototype is not needed, the research cost and time are saved, and the research efficiency is greatly improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a model diagram of an RV reducer assembly
FIG. 3 is a virtual prototype (hidden planet carrier)
FIG. 4a is an input shaft speed curve and FIG. 4b is an output mechanism planet carrier speed curve
FIG. 5a is an input rotation angle curve, FIG. 5b is an output rotation angle curve, and FIG. 5c is a virtual prototype 1 rotation angle error curve
FIG. 6 is a graph comparing raw data with predicted values via neural networks
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides a virtual prototype technology and BP neural network-based RV reducer transmission precision optimization method, the flow is shown as figure 1, and the method comprises the following steps:
s1, determining influence factors of the transmission precision of the RV reducer, wherein the influence factors mainly comprise three aspects of part errors, part fit clearances and working loads;
s11, selecting an RV40-E type speed reducer as a research object, taking secondary cycloidal gear transmission as a main research part according to the transmission relation of parts, and selecting 5 influence factors including the eccentric distance of a crankshaft, the shift distance trimming amount of the cycloidal gear, the equidistant trimming amount of the cycloidal gear, the radius error of a pin tooth and the central circle radius error of the pin tooth by combining a part error part of a practical gear design and calculation manual;
s12, analyzing the influence relation of the part clearance in the RV reducer on the transmission precision, wherein the part clearance mainly comprises a sun gear planet gear meshing clearance, a bearing clearance between a cycloid gear crank shaft hole and a crank shaft, a bearing clearance between a planet carrier crank shaft hole and the crank shaft, a bearing clearance between a planet carrier and a rack, a cycloid gear needle tooth meshing clearance, and a needle tooth and needle tooth disc matching clearance. The bearing in the virtual prototype model is replaced by a shaft sleeve, and the pin teeth are fixedly connected with the pin gear disc, so that 5 influence factors of a gap between an inner hole of a cycloidal gear and a shaft sleeve of a rotary arm, a gap between a crankshaft and the shaft sleeve of the rotary arm, a gap between the crankshaft and a supporting shaft sleeve, a gap between the supporting shaft sleeve and a planet carrier and a gap between the supporting shaft sleeve and a flange plate are selected as test factors in the part of the fit gap of the parts, and 11 influence factors are selected in;
s2, constructing an orthogonal test scheme by combining the influence factors;
and S21, establishing a factor level table. In order to analyze the influence degree of factors such as part machining errors, part fit clearance and loads on the dynamic rotation errors of the RV reducer, 11 factors are selected as test factors, and an orthogonal table L (3) is selected13) The factor levels are shown in table 1;
TABLE 1
S22, establishing an orthogonal test scheme by combining a factor level table as shown in a table 2; TABLE 2
S3, combining three-dimensional modeling software CREO and multi-body dynamics simulation software ADAMS to construct a virtual prototype according to an orthogonal test scheme;
s31, establishing a virtual prototype model of 27 groups of RV reducers by adopting three-dimensional modeling software CREO according to part errors in the orthogonal test scheme; because a complex three-dimensional model is difficult to construct in ADAMS, a virtual prototype experimental group of the RV reducer is established by adopting CREO according to an orthogonal experimental scheme, and part of parts are simplified in the modeling process, wherein the method comprises the following steps: the bearing is replaced by a shaft sleeve, fine structures such as chamfer bolts and the like are omitted, and pins, keys and bolts are connected by consolidation; and removing parts such as sealing rings, gaskets and the like which have no influence on the research. A cycloidal equation is established in CREO to obtain a cycloidal gear tooth profile curve, wherein the cycloidal 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 the radius of the central circle of the pin gear, zb is the number of the pin gear teeth, rzz is the radius of the pin gear teeth, za is the number of the cycloid gear teeth, e is the eccentric distance, drz is the displacement repair quantity, and drzz is the equidistant repair quantity;
the method is characterized in that a half tooth profile of the complete cycloid wheel is drawn through a cycloid equation, then a three-dimensional model of the cycloid wheel can be obtained through mirroring, array and stretching commands, and the establishment methods of other solid models are similar to the method. And assembling all the part solid models to obtain an RV reducer assembly model as shown in figure 2. And then performing interference-free inspection on the assembly, and storing the assembly as CREO and ADAMS intermediate file format parasolid (x _ t) after confirming that the assembly is correct and no part is interfered.
S32, importing the virtual prototype model into multi-body dynamic simulation software to define material characteristics; the intermediate format model File is imported into ADAMS software by File/Import command, and component materials are defined, and based on the ADAMS contact collision theory, elastic deformation generated during contact collision of parts also affects the RV reducer rotation error, so material characteristics such as elastic modulus, density, poisson ratio, and the like of each part of the additive model are shown in table 3. TABLE 3
S33, adding a constraint relation to the virtual prototype in the ADAMS; in order to ensure the correctness of the relative motion of each part, the construction of the virtual prototype also needs to provide a constraint or contact relation according to the motion track of the part, and the constraint and contact relation of each part is determined by the motion and contact analysis of the parts of the RV reducer as shown in the table 4.
TABLE 4
S34, simulating the virtual prototype, and verifying whether the constructed model is correct; and (3) importing the assembly intermediate format model file established by the CREO into an ADAMS system, and adding the material characteristics and the constraint relation of the parts to obtain a virtual prototype shown in FIG. 3 (a hidden planet carrier). The virtual prototype contained 58 parts, 18 constraints, and 94 contacts. Setting a rotation driving input rotation speed function F (time) ═ 7000d × time step (time,0,1,0,1), setting a load torque according to a test scheme, and defining a load torque function F (time) ═ step (time,1,1.5,0, X), wherein X is determined according to test group data. 4S simulation time and 100 simulation steps are defined by self. The measured curves of the rotating speeds of the input shaft and the planet carrier are shown in fig. 4a and fig. 4b, the model moves stably after 1.5s, the rotating speed of the input shaft is 7000 degrees/s, the mean rotating speed of the planet carrier is 57.7445 degrees/s, the ratio of the rotating speed of the input shaft to the rotating speed of the planet carrier is 121.2236, and the model is matched with the theoretical transmission ratio 121, so that the model is accurate and reliable.
S4, measuring the transmission error of each virtual prototype experimental group in simulation software Adams;
selecting the rotation error as an evaluation index of the transmission precision, selecting the RV reducer test model with the transmission ratio of 121, and calculating a formula according to the rotation error
In the formula (I), the compound is shown in the specification,is a corner error;inputting a rotation angle for an input shaft (namely a sun gear);is the actual rotation angle of the output shaft (namely the planet carrier); and i is the transmission ratio of the speed reducer.
The ADAMS measures the rotation angles of the input shaft and the output frame in real time, and outputs the rotation angle curves of both as shown in fig. 5a and 5 b. Establishing a measurement function
Funtion — 1_ MEA _1/121 —, in relation to JOINT — 16_ MEA _1, funtion is the difference between the actual output rotation angle and the theoretical output rotation angle, i.e., the rotation error; JOINT _1_ MEA _1 is the rotation angle of the input shaft revolute pair; JOINT _16_ MEA _1 is the carrier rotation pair angle.
And (3) respectively establishing virtual prototype models according to the orthogonal experiment table, introducing ADAMS simulation, and measuring the rotation errors of each group, wherein FIG. 5c is a rotation error curve of the virtual prototype 1, and because the number of experiment groups is large, the experiment curves of other groups are not listed one by one.
The load and the drive are loaded at a constant speed within 0-1.5 s, and the motion state of the model is unstable, so that the mean value of error curves in a 1.5-4 s time period is selected as an evaluation index, and the perfect orthogonal test result is shown in table 2.
S5, analyzing the results of the orthogonal test;
and (3) carrying out linear range analysis on orthogonal test results, wherein the linear range analysis is to analyze problems by using data linear range, and finding out main factors influencing test indexes by comparing average linear ranges of the test results. The principle is that when the influence of a single factor A on the result is considered, the influence of other factors on the result is considered to be balanced, and the difference of the levels of the factor A is caused by the factor A itself. The larger the range is, the larger the influence of the factor on the experimental index is, and the range R of each factor is calculated by the following formula.
R＝max{ki}-min{ki}
T＝∑ki
Wherein R is extremely poor; i is the number of levels of the factor; k is a radical ofiIs the average value of the sum of the corresponding rotation errors at the i level; kiIs the sum of the corresponding rotation errors at the i level; n is the number of occurrences of each level on any column; t is the sum of the rotation errors;
the results of the worst analysis of the test results are shown in Table 2. Obtaining the influence sensitivity degrees of various factors of the dynamic rotation error of the RV reducer from big to small according to the extreme difference of various influencing factors, wherein the influence sensitivity degrees are sequentially the radius error of a central circle of a pin tooth, the radius error of the pin tooth, the displacement repairing quantity of the cycloidal gear, the load size, the gap between a crankshaft and a sleeve of a rotating arm, the equidistant repairing quantity of the cycloidal gear, the gap between an upper support sleeve and a planet carrier, the eccentric error of the crankshaft, the gap between the crankshaft and a bearing sleeve, the gap between an inner hole of the cycloidal gear and the sleeve of the rotating arm and; the optimal combination of factor levels is A1B1C3D3E1F3G2H2I3J1K1。
And S6, taking the orthogonal test result data as input end data of the BP neural network, and predicting the optimal combination.
S61, establishment and training of BP artificial neural network
The main factors influencing the rotation error of the RV reducer in the orthogonal test design are used as an input layer of the established BP neural network, the BP neural network comprises 11 input nodes which respectively correspond to a pin tooth radius error, a pin tooth center circle radius error, a crank shaft eccentric error, a cycloidal gear displacement modification quantity, a cycloidal gear equidistant modification quantity, a gap between an inner hole of a cycloidal gear and a sleeve of a rotating arm, a gap between the crank shaft and the sleeve of the rotating arm, a gap between the crank shaft and a supporting sleeve, a gap between an upper supporting sleeve and a planet carrier, a gap between a lower supporting sleeve and a flange plate and a working load, and an output layer of the network comprises an output. The method comprises the steps of programming by adopting MATLAB (R2016a version, USA) software, selecting a hyperbolic tangent transfer function (tansig) as a hidden layer transfer function, using a linear transfer function (purelin) as an output layer transfer function, setting 1 hidden layer, determining 23 contained neurons by training and comparing networks containing different neuron numbers, training a newly-built BP network by adopting a tracing dm function, setting a network training parameter value, setting the maximum training frequency to be 100 times, training precision to be 0.0001, learning rate to be 0.1, and taking other parameters as default values. FIG. 6 is a comparison graph of the original data and the predicted value through the neural network, and the graph shows that the coincidence degree of the original data and the predicted value is high, so that the BP neural network can be used for predicting the transmission precision of the RV reducer, and the result is accurate.
S62 BP artificial neural network combined orthogonal test optimization factor parameter
In the previous step of research, MATLAB software programming is applied, a BP neural network model capable of accurately describing the functional relationship between the RV reducer revolution error and the evaluation index of the RV reducer is successfully established through training of experimental data samples and optimization of BP neural network parameters, simulation is carried out by applying the established network model, A, B, C, D, E, F, G, H, I, J, K11 influence factor values of an orthogonal test are used as independent variables, then the 11 factors are respectively assigned, a proper step size is set, and a correlation function in MATLAB is used for programming a definition domain value of each factor so as to obtain the minimum combination value of the revolution error of the output value. The factors when the RV reducer rotation error is minimum are obtained through BP artificial neural network model simulation optimization and are 0.01mm of pin tooth radius error, 0.01mm of pin tooth center circle radius error, 0.1mm of crank shaft eccentric error, 0.03mm of cycloidal gear displacement modification quantity, 0.01mm of cycloidal gear equidistant modification quantity, 0.01mm of gap between an inner hole of a cycloidal gear and a sleeve of a rotating arm shaft, 0.0042mm of gap between the crank shaft and the sleeve of the rotating arm shaft, 0.0058 of gap between the crank shaft and a supporting sleeve, 0.01 of gap between an upper supporting sleeve and a planet carrier, 0.0012 of gap between a lower supporting sleeve and a flange plate and 300 N.mm of working load.
S63, checking optimization result
And establishing a virtual prototype according to the optimization result in the step, and introducing ADAMS for multi-body dynamics simulation to obtain that the rotation error of the virtual prototype is 0.0512 degrees, and compared with the optimal 0.0611 degrees in the orthogonal test result, the transmission precision is improved by 16.2 percent, and the optimization effect of the method on the transmission precision of the RV reducer is proved to be obvious.
The RV reducer transmission precision optimization method based on the virtual prototype technology and the BP neural network has the following beneficial effects:
1. the method solves the problems that in the optimization process of the transmission precision of the RV reducer, the machining cost is limited, each factor cannot be optimally designed, and the influence importance of each influence factor on the transmission precision is difficult to distinguish, and provides a new method for further improving the transmission precision of the RV reducer.
2. The influence of measurement errors and other noise factors generated in the artificial measurement experiment process is avoided through modeling simulation, so that the experiment result is more accurate.
3. According to the method, the virtual simulation technology and the BP neural network algorithm are combined to research the transmission precision of the RV reducer, so that the precise prediction of the optimal transmission precision factor combination is realized, the test of an entity prototype is not needed, the research cost and time are saved, and the research efficiency is greatly improved.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.
Claims (1)
1. A method for optimizing the transmission precision of an RV reducer comprises the following steps:
s1, determining influence factors of the transmission precision of the RV reducer, wherein the influence factors comprise part errors, part fit clearances and working loads;
s11, selecting an RV40-E type speed reducer as a research object, and selecting a plurality of part errors as influence factors according to the transmission relation of parts;
s12, analyzing the influence relation of the part clearance in the RV reducer on the transmission precision, and selecting a plurality of main part fit clearances as influence factors;
s2, constructing an orthogonal test scheme by combining the influence factors;
s3, 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;
s31, establishing a virtual prototype model of the RV reducer according to part errors in an orthogonal test scheme by adopting three-dimensional modeling software CREO, and establishing a virtual prototype experimental group of the RV reducer according to the orthogonal test scheme by adopting the CREO because a complex three-dimensional model is difficult to construct in ADAMS, simplifying partial parts in the modeling process, and establishing a cycloid equation in the CREO to obtain a cycloid wheel tooth profile curve, wherein the cycloid equation is as follows:
wherein rz is the radius of the central circle of the pin gear, zb is the number of the pin gear teeth, rzz is the radius of the pin gear teeth, za is the number of the cycloid gear teeth, e is the eccentric distance, drz is the displacement repair quantity, and drzz is the equidistant repair quantity;
drawing a half tooth profile of the complete cycloid wheel through an equation, then obtaining a three-dimensional model of the cycloid wheel through mirror image, array and stretching commands, and establishing methods of other solid models are similar to the three-dimensional model; assembling all part solid models, then carrying out interference-free inspection on the assembly body, and storing the assembly body as a CREO and ADAMS intermediate file format parasolid (. x _ t) after determining that the assembly is correct and no part interference exists;
s32, importing the virtual prototype model into multi-body dynamic simulation software to define material characteristics; importing the model File in the intermediate format into ADAMS software through a File/Import command, defining the material of the part, and based on the ADAMS contact collision theory, the elastic deformation generated during the contact collision of the part also influences the rotation error of the RV reducer, so that the elastic modulus, the density and the Poisson ratio material characteristics of each part of the model are added;
s33, adding a constraint relation to the virtual prototype in the ADAMS; in order to ensure the correctness of the relative motion of each part, a virtual prototype is constructed by providing a constraint or contact relation according to the motion track of the part, and the constraint and contact relation of each part is determined through the motion and contact analysis of the parts of the RV reducer;
s34, simulating the virtual prototype, and verifying whether the constructed model is correct; measuring the ratio of the rotating speed of the input shaft to the rotating speed of the planet carrier, comparing the ratio with the theoretical transmission ratio of the RV reducer, and verifying whether the model is accurate or not;
s4, measuring the transmission error of each virtual prototype experimental group in simulation software Adams;
selecting the rotation error as an evaluation index of the transmission precision, selecting the RV reducer test model with the transmission ratio of 121, and calculating a formula according to the rotation error
In the formula (I), the compound is shown in the specification,is a corner error;inputting a rotation angle for the input shaft;is the actual rotation angle of the output shaft; i is the transmission ratio of the speed reducer;
in ADAMS, the rotation angles of an input shaft and an output frame are measured in real time, the rotation angle curves of the input shaft and the output frame are output, and a measurement function is established
FUNCTION＝.JOINT_1_MEA_1/121—.JOINT_16_MEA_1
In the formula, FUNCTION is a difference value between an actual output rotation angle and a theoretical output rotation angle, namely a rotation error; JOINT _1_ MEA _1 is the rotation angle of the input shaft revolute pair; JOINT _16_ MEA _1 is the rotating auxiliary angle of the planet carrier;
respectively establishing virtual prototype models according to the orthogonal test table, introducing ADAMS simulation, measuring each group of rotation errors, and perfecting the orthogonal test result table;
s5, analyzing the results of the orthogonal test;
performing linear range analysis on orthogonal test results, wherein the linear range analysis is to analyze problems by using data range, and finding out main factors influencing test indexes by comparing average range of each test result; the principle is that when the influence of a single factor A on the result is considered, the influence of other factors on the result is considered to be balanced, and the difference of the levels of the factor A is caused by the factor A; the larger the range is, the larger the influence of the factor on the experimental index is, and the range R of each factor is calculated by the following formula;
R＝max{ki}-min{ki}
T＝∑ki
wherein R is extremely poor; i is the number of levels of the factor; k is a radical ofiIs the average value of the sum of the corresponding rotation errors at the i level; kiIs the sum of the corresponding rotation errors at the i level; n is the number of occurrences of each level on any column; t is the sum of the rotation errors;
obtaining the optimal combination of the influence sensitivity degree of each factor of the RV reducer dynamic rotation error from a large sequence to a small sequence and the factor level according to the extreme difference of each influence factor;
s6, taking the orthogonal test result data as the input end data of the BP neural network, and predicting the optimal combination;
s61, establishing and training a BP artificial neural network;
the method comprises the following steps of taking main factors influencing the rotation error of the RV reducer in orthogonal test design as an input layer of the established BP neural network, wherein the output layer of the network comprises an output node corresponding to the evaluation index rotation error; programming by adopting MATLAB software, and selecting a hidden layer transfer function, an output layer transfer function and a training function; setting hidden layers, training precision and learning rate parameters, and training and comparing networks containing different neuron numbers;
s62, predicting the optimal combination by adopting a BP artificial neural network;
applying MATLAB software programming, successfully establishing a BP neural network model capable of accurately describing the functional relation between the RV reducer revolution error and the evaluation index thereof through training of an experimental data sample and optimization of BP neural network parameters, applying the established network model to carry out simulation, taking a plurality of influence factor values of an orthogonal test as independent variables, respectively assigning the independent variables, setting a proper step length, and programming a definition domain value of each factor by using a related function in the MATLAB to obtain a minimum combination value of the revolution error of the output value;
s63, checking an optimization result;
and establishing a virtual prototype according to the optimization result in the step, introducing ADAMS for multi-body dynamics simulation, measuring the rotation error of the virtual prototype, and comparing the rotation error with the orthogonal test result to obtain the transmission precision optimization effect.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810218411.6A CN108563831B (en) | 2018-03-16 | 2018-03-16 | Optimization method for transmission precision of RV reducer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810218411.6A CN108563831B (en) | 2018-03-16 | 2018-03-16 | Optimization method for transmission precision of RV reducer |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108563831A CN108563831A (en) | 2018-09-21 |
| CN108563831B true CN108563831B (en) | 2019-12-24 |
Family
ID=63531837
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810218411.6A Active CN108563831B (en) | 2018-03-16 | 2018-03-16 | Optimization method for transmission precision of RV reducer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108563831B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109376439B (en) * | 2018-10-30 | 2023-06-27 | 中车戚墅堰机车车辆工艺研究所有限公司 | RV reducer acting force analysis method and device |
| CN109407535A (en) * | 2018-12-07 | 2019-03-01 | 珠海格力智能装备有限公司 | Control method and simulation control method for motion of cycloidal gear and terminal equipment |
| CN111859720A (en) * | 2019-04-19 | 2020-10-30 | 中国科学院沈阳自动化研究所 | Virtual test method for reliability of multistage gear reducer |
| CN110674558B (en) * | 2019-09-22 | 2022-05-20 | 华东交通大学 | Noise reduction and shape modification optimization method for high-speed motor train unit traction gear |
| CN113946914B (en) * | 2020-07-16 | 2024-04-12 | 中国科学院沈阳自动化研究所 | Torque dynamics simulation method of rotary asteroid sampling device |
| CN112464486B (en) * | 2020-12-02 | 2024-03-19 | 华北水利水电大学 | Transmission error numerical simulation method of cycloidal pin gear speed reducer for robot |
| CN113111454B (en) * | 2021-04-01 | 2024-08-23 | 浙江工业大学 | RV reducer dynamic transmission error optimization method based on Kriging model |
| CN113326563B (en) * | 2021-05-24 | 2024-07-12 | 扬州大学 | Optimal working parameter determining method of intelligent ear plucking machine based on feedback screening |
| CN113673054B (en) * | 2021-08-19 | 2023-03-24 | 北京慧智神光科技有限公司 | Method and device for determining motion state data, electronic equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012009817A1 (en) * | 2010-07-22 | 2012-01-26 | Cogmation Robotics Inc. | A non-programmer method for creating simulation-enabled 3d robotic models for immediate robotic simulation, without programming intervention |
| CN102567576A (en) * | 2011-12-13 | 2012-07-11 | 北京交通大学 | Method for predicting rate of wheel load reduction |
| JP5330837B2 (en) * | 2009-01-15 | 2013-10-30 | 富士重工業株式会社 | Gear pair design device and gear pair |
| CN104597842A (en) * | 2015-02-02 | 2015-05-06 | 武汉理工大学 | BP neutral network heavy machine tool thermal error modeling method optimized through genetic algorithm |
| CN105184005A (en) * | 2015-09-21 | 2015-12-23 | 中国运载火箭技术研究院 | Method for optimizing general parameters of control surface transmission mechanism |
| CN106202731A (en) * | 2016-07-12 | 2016-12-07 | 南京理工大学 | Bridge crane multi-flexibl e dynamics structural optimization method |
| CN106934180A (en) * | 2017-04-12 | 2017-07-07 | 济南大学 | A kind of Optimization Design of high power density 2K H type planet circular systems |
-
2018
- 2018-03-16 CN CN201810218411.6A patent/CN108563831B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5330837B2 (en) * | 2009-01-15 | 2013-10-30 | 富士重工業株式会社 | Gear pair design device and gear pair |
| WO2012009817A1 (en) * | 2010-07-22 | 2012-01-26 | Cogmation Robotics Inc. | A non-programmer method for creating simulation-enabled 3d robotic models for immediate robotic simulation, without programming intervention |
| CN102567576A (en) * | 2011-12-13 | 2012-07-11 | 北京交通大学 | Method for predicting rate of wheel load reduction |
| CN104597842A (en) * | 2015-02-02 | 2015-05-06 | 武汉理工大学 | BP neutral network heavy machine tool thermal error modeling method optimized through genetic algorithm |
| CN105184005A (en) * | 2015-09-21 | 2015-12-23 | 中国运载火箭技术研究院 | Method for optimizing general parameters of control surface transmission mechanism |
| CN106202731A (en) * | 2016-07-12 | 2016-12-07 | 南京理工大学 | Bridge crane multi-flexibl e dynamics structural optimization method |
| CN106934180A (en) * | 2017-04-12 | 2017-07-07 | 济南大学 | A kind of Optimization Design of high power density 2K H type planet circular systems |
Non-Patent Citations (2)
| Title |
|---|
| 一种基于人工神经网络和优化算法库的复杂虚拟样机优化通用方法;杜中华 等;《机械工程师》;20091031;第39卷(第10期);第75-79页 * |
| 基于UG的平行轴系传动精度优化设计系统;汤辉 等;《工具技术》;20050930(第9期);第42-45页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108563831A (en) | 2018-09-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108563831B (en) | Optimization method for transmission precision of RV reducer | |
| CN112163325B (en) | RV reducer service life prediction method based on digital twinning | |
| US11656614B2 (en) | In-process digital twinning | |
| CN110007645B (en) | Feeding system hybrid modeling method based on dynamics and deep neural network | |
| CN106020147A (en) | Systematic analysis method for assembling precision of complex precise mechanical product | |
| CN108873809B (en) | High-order tooth surface error correction method for spiral bevel gear | |
| CN109240204B (en) | Numerical control machine tool thermal error modeling method based on two-step method | |
| CN109408857B (en) | Intelligent parameter driving modular design method for shape cooperative manufacturing of spiral bevel gear | |
| CN114237154B (en) | Thermal error prediction model based on ONT-GCN space-time model, modeling method and haze-edge-fog-cloud error compensation system | |
| CN112433507B (en) | LSO-LSSVM (least squares support vector machine) -based five-axis numerical control machine tool thermal error comprehensive modeling method | |
| CN111539156B (en) | Tooth profile deviation measuring method based on DP-PSO-SVR | |
| Liu et al. | Data-driven thermal error compensation of linear x-axis of worm gear machines with error mechanism modeling | |
| CN112720480A (en) | Robot track correction method and system based on grading errors | |
| Verma et al. | Track-based analysis for profile generation on globoidal cam in automatic tool changer of CNC machining center | |
| CN109202539B (en) | Online detection method for composite material weak-rigidity special-shaped structure | |
| CN113609672B (en) | Digital twin system co-evolution method based on incremental model | |
| CN113111454B (en) | RV reducer dynamic transmission error optimization method based on Kriging model | |
| CN108920794B (en) | Method for calculating inspection tolerance band of tested element following tolerance related requirement | |
| Jiang et al. | Review on logarithmic spiral bevel gear | |
| Wang et al. | Positioning accuracy prediction and tolerance allocation of cycloid reducers based on a BDTCA model | |
| Cheng et al. | Nonlinear dynamics of harmonic drive considering pitch deviation | |
| JP2000171238A (en) | Method for estimating performance of planetary gears | |
| Ge et al. | Simulation Analysis of Error Transfer in Assembly Process of Mechanical Meta-Action Unit | |
| CN116956497A (en) | Speed reducer transmission error analysis method considering main bearing flexibility factor | |
| CN110781564B (en) | Preparation method and system of knuckle bearing extrusion groove |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |