CN117824487A - High-precision intelligent detection method for differential mechanism tool of pipeline robot - Google Patents
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Abstract
The invention discloses a high-precision intelligent detection method for a differential mechanism tool of a pipeline robot, which comprises the following steps: cylindricity error evaluation, error optimization calculation technology and differential detection evaluation. Firstly, determining the distance from a measuring point of a high-precision inductive sensor to the axis of a working machine of a differential case, and determining cylindricity error assessment indexes of a piece to be measured and a standard piece; then, optimizing the cylindricity error by using the provided neighborhood particle intelligent optimization method, so as to determine the section radius of the piece to be measured; finally, differential detection assessment is performed. The method provided by the invention overcomes the defects of complicated operation, low efficiency, low measurement precision, incapability of being applied to the field and the like in the prior detection technology, can realize online rapid detection, reduces errors introduced by human factors, and can finish rapid high-precision detection of the differential mechanism tool.
Description
Technical Field
The invention relates to the field of robot differential mechanism tools, in particular to a high-precision intelligent detection method for a pipeline robot differential mechanism tool.
Background
The differential is an important component of the pipe robot drive system. When the pipeline robot passes through the bent pipe, the differential mechanism can enable the left driving wheel and the right driving wheel (or the front driving wheel and the rear driving wheel) to realize a mechanism rotating at different rotating speeds, the rotating speed difference of the left driving wheel and the right driving wheel is adjusted, the left driving wheel and the right driving wheel roll at different rotating speeds, and therefore pure rolling motion of the driving wheels at two sides is guaranteed. The differential housing acts as an important base component of the differential, directly affecting the accuracy of the differential internal gear assembly and the differential and drive axle assembly. For special pipeline robots, the quality of the differential mechanism can be effectively improved by controlling the quality in two links of differential mechanism manufacturing and detection.
The inspection of the differential case is a very important step, and requires a large number of measurement items and high accuracy. The current commonly used form and position tolerance measurement method mainly comprises manual measurement and three-coordinate measurement. Manual approximate measurement method form and position tolerance measurement mainly measures roundness approximately through a dial indicator. This measurement method has the following problems: 1) Complicated operation and high labor intensity of workers; 2) Approximately judging the qualification condition of the part, and possibly judging the qualification by mistake; 3) The measuring precision is low, and the measuring precision has great relation with the operation proficiency and working state of workers; 4) The differential case needs to be manually transported from the machine tool to the measurement rotary table, and then the differential case is sorted and stored after the measurement is finished, which is labor-consuming and labor-consuming. The three-coordinate measuring method solves the problems to a certain extent, can quantitatively detect the shape, the position and the size of the part, can also carry out visual graphic description on the shape of the part, converts abstract numbers into visual images, and brings greater convenience to quality control. However, the three-coordinate measuring machine has low measuring efficiency, has strict requirements on measuring environment and poor measuring repeatability, and needs a special indoor constant-temperature measuring place.
In the prior art, a form and position tolerance measuring method has been studied more, and analysis is mainly performed on the measuring method, the evaluation principle and the measurement uncertainty, so that the development of the form and position tolerance measuring technology is greatly promoted. The geometric characteristics of the cylinder geometry are evaluated, a mathematical model of the cylindricity and the axis straightness is provided, and an evaluation method is provided, wherein the cylindricity can be evaluated by a least square cylindricity method, a maximum external cylindricity method, a minimum internal cylindricity method and a minimum area cylindricity method. However, such manual measurement is relatively complex and the measurement error is variable. The measurement process of the low-speed roundness measuring instrument is improved based on the neural network intelligent optimization algorithm, the capability of rapidly obtaining a result and judging an abnormal mode is realized, but the practical application difficulty of the method is high.
Disclosure of Invention
The invention aims to solve the technical problems that: the high-precision intelligent detection method for the differential mechanism tool of the pipeline robot aims at solving the problems of complex operation, low efficiency, low measurement precision and the like in manual measurement, three-coordinate measurement and intelligent optimization algorithm detection of a differential mechanism shell, and improves the efficiency and precision of the high-precision intelligent detection of the differential mechanism tool of the pipeline robot on the basis of determining an error assessment index.
The invention adopts the following technical scheme: a high-precision intelligent detection method for a differential mechanism tool of a pipeline robot comprises the following steps:
s1, determining cylindricity error evaluation indexes: according to the relative measurement principle, firstly, a plurality of high-precision inductive sensors are placed on the cylindrical surface of a differential housing tooling machine, then a standard part is placed, the readings of the sensors are zeroed, then a piece to be measured is placed, and the cylindricity error evaluation index of the piece to be measured and the standard part is determined by utilizing the distance from a measuring point to an axis;
s2, error optimization calculation: providing a neighborhood particle intelligent optimization method to determine parameters;
s3, differential detection and assessment: and calculating the error between the section radius of the to-be-measured differential part and the section radius of the standard differential part, wherein the smaller the value is, the closer the to-be-measured part is to the standard differential part.
Further, the determination of the cylindricity error assessment index in step S1 operates as follows:
s1.1, useQThe measurement sampling is carried out by a plurality of sensors, each sensor corresponds to x in three-dimensional space, y, z coordinates, the intersection point coordinates of the axis of the marked cylindrical surface and the xoy plane are%a,b0), the direction vector of the axis is%p,q,1），
Wherein,a、b、p、qis a parameter to be determined;
s1.2, theiThe z-coordinates of each sensor are determined by the sensor mounting height, the x, y-coordinates and the sensor readingsδ i The relation of (2) is:
；
wherein,i=1,2,...,Q，R 0 radius of standard contour (unit: mm),θthe angle (unit: degree) of the workpiece to be measured relative to the initial position;
s1.3, calculating any sampling point on the cylindrical surfaceDistance to axisd i ：
；
Wherein,；
s1.4, according to the least square principle, the sum of squares of the distances from each measuring point to the cylindrical surface is minimum, and then the error evaluation index function of the cylindricity is as follows:
；
wherein,a、b、p、qfor the parameter variables to be determined,Rthe radius of the section of the differential to be measured is the radius of the section of the differential to be measured.
Further, in step S2, the error optimization calculation is performed as follows:
s2.1, initializing: determining particle population sizeMIteration parametersk=1, the maximum number of iterations isK max The initial position vector of the particle is:
；
the initial velocity vector of the particle is:
；
maximum speed ofV max Each neighborhood particle number isN；
Respectively represent the 1 st iterationrThe position parameter variables corresponding to the individual particles,respectively indicate and->Corresponding speed parameter variables;
s2.2, calculating an error evaluation index: will beSubstituting the values into the formula in the step S1.4 to obtain an error evaluation index function value;
s2.3, determining a neighborhood scheme: if it isk<K max The method adopts the following steps:
structure of the deviceN m The number of neighbors that are in the neighborhood,；
otherwise, adopt:
structure of the deviceN m The first particle of each neighborhood can receive global information, and other particles only receive neighborhood information;
s2.4, updating particles: first, thek+1 iterations, the particles update speed and position according to the following formula:
；
wherein,wis an inertial weight;c 1 ,c 2 ,c 3 in order for the learning factor to be a function of,r 1 ,r 2 take [0,1 ]]Random numbers uniformly distributed among the two;
is the firstiThe best position (individual extremum) the individual particle experiences,>the best position (global extremum) experienced for all particle populations,>is the firstiThe individual particles correspond to the best locations (neighborhood extrema) that all particles in the neighborhood experience;
Y r for neighborhood learning ability function, take [0,1]Constant or random number between them to distinguish the ability of the particle to acquire global or neighborhood knowledge;
s2.5, judging termination conditions:
if the maximum iteration number is reachedk=K max ) Then the optimization is finished, and the obtained global optimal valueNamely, isa、b、p、q、RIs determined by the parameter values of the parameter values;
otherwise the first set of parameters is selected,step S2.2 is performed.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
according to the high-precision intelligent detection method for the differential mechanism tool of the pipeline robot, disclosed by the invention, the built-in high-precision inductive sensor is used for collecting coordinates in real time, so that the defects that the three-coordinate measurement method needs to collect coordinates point by point, is low in measurement speed and cannot be applied to the field are overcome; the neighborhood information sharing thought is integrated into the optimization algorithm in the parameter optimization algorithm, the evolution information among particles in the searching process is fully utilized, the interaction information among particles in the searching process is promoted, the diversity of the particles is increased, the premature convergence of the algorithm is avoided, the collaborative and intelligent exploration of the space region can be realized, the optimal searching efficiency and performance are greatly improved, and the global searching capability of the algorithm is enhanced.
Drawings
FIG. 1 is a flow chart of the steps of a high-precision intelligent detection method for a differential mechanism tool of a pipeline robot;
FIG. 2 is a schematic diagram of a high-accuracy inductive sensor distribution according to the present invention;
FIG. 3 is a flowchart of the cylindricity error assessment index calculation of the present invention;
FIG. 4 is a graph showing the relationship between sensor readings and measured point coordinates according to an embodiment of the present invention;
FIG. 5 is a flow chart of the error optimization calculation of the present invention.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings:
it will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The invention relates to a high-precision intelligent detection method for a differential mechanism tool of a pipeline robot, which is shown in fig. 1 and comprises the following steps:
s1, evaluating cylindricity errors: according to a relative measurement principle, a plurality of high-precision inductive sensors are placed on a cylindrical surface of a differential housing tooling machine, differential standard components are placed, readings of the sensors are zeroed, then the differential to-be-measured components are placed, and cylindricity error assessment indexes of the differential to-be-measured components and the standard components are determined by utilizing the distance from a measurement point to an axis;
s2, error optimization calculation: determining parameters in cylindricity error assessment by using a neighborhood particle intelligent optimization method;
s3, differential detection and assessment: calculating the section radius of to-be-measured part of differential mechanismRRadius of section of standard part of differential mechanismR 0 Error of (2)。
In one embodiment of the present invention, taking 10 sensors for measurement sampling as an example, to determine the cylindricity error assessment index, a plurality of high-precision inductive sensors are first placed on the cylindrical surface of the differential housing tooling machine, as shown in fig. 2.
The calculation flow of the cylindricity error evaluation index is shown in fig. 3, and the specific steps are as follows:
step 1: each sensor corresponds to x, y and z coordinates in a three-dimensional space, and the intersection point coordinates of the axis of the marked cylindrical surface and the xoy plane are @ coordinatesa,b0), the direction vector of the axis is%p,q,1）；
Wherein,a、b、p、qfor the parameter to be determinedA number;
step 2: first, theiThe z-coordinate of each sensor is determined by the mounting height of the sensor, the relation between the sensor reading and the measured point coordinate is shown in figure 4, the dotted line in figure 4 is the standard outline of the differential standard part, the solid line is the measured outline of the differential to-be-measured part, and the x-y coordinates and the sensor reading are determinedδ i The relation of (2) is:
；
wherein,i=1,2,...,10，R 0 radius of standard contour (unit: mm),θthe angle (unit: degree) of the workpiece to be measured relative to the initial position;
step 3: calculating any sampling point on cylindrical surfaceDistance to axisd i ：
；
Wherein,；
step 4: according to the least square principle, the square sum of the distances from each measuring point to the cylindrical surface is minimum, and the error evaluation index function of the cylindricity is as follows:
；
wherein,a、b、p、q、Ris the parameter variable to be determined.
Then, performing error optimization calculation, and determining the parameter variable determined in the calculation of the cylindricity error evaluation index by adopting a neighborhood particle intelligent optimization method, wherein the specific implementation flow is shown in fig. 5, and the neighborhood particle intelligent optimization method comprises the following steps:
(1) Initializing:
in this example, particle population size is determinedM=40, iteration parametersk=1, maximum number of iterationsK max =500；
Generating an initial position vector of the particle with a pseudo-random number generator:
；
and an initial velocity vector of the particle:
；
learning factorc 1 =c 2 =c 3 =1.5, maximum speedV max =10, per neighborhood particle countN=5；
(2) Calculating an error assessment index:
will beSubstituting the values into the formula in the step 4 to obtain the function value of the error evaluation index;
(3) Determining a neighborhood scheme:
if it isk<K max The method adopts the following steps:
structure of the deviceN m The number of neighbors that are in the neighborhood,；
otherwise, adopt:
structure of the deviceN m A number of neighbors;
the first particle of each neighborhood can receive global information, and other particles only receive neighborhood information;
(4) Updating particles:
first, thek+1 iterations, particle rootThe speed and position are updated according to the following formula:
；
wherein,wfor the inertial weight, in particular, linearly decreasing weights are employed in the present embodiment;r 1 ,r 2 take [0,1 ]]Random numbers uniformly distributed among the two;
is the firstiThe best position (individual extremum) the individual particle experiences,>the best position (global extremum) experienced for all particle populations,>is the firstiThe individual particles correspond to the best locations (neighborhood extrema) that all particles in the neighborhood experience;
Y r for neighborhood learning ability function, take [0,1]Constant or random number between them to distinguish the ability of the particle to acquire global or neighborhood knowledge;
(5) Judging termination conditions:
if the maximum iteration number is reachedk=K max ) Then the optimization is finished, and the obtained global optimal valueNamely, isa、b、p、q、RIs determined by the parameter values of the parameter values; otherwise, go (L)>Turning to the step (2).
Finally, differential mechanism detection evaluation in the high-precision intelligent detection method of the differential mechanism tool of the pipeline robot is carried out, namely the section radius of the differential mechanism to-be-detected part is calculatedRRadius of section of standard part of differential mechanismR 0 Error of (2)e：
；
Error ofeThe smaller the value of (c) indicates that the test piece is closer to the standard piece.
Through experiments, compared with the intelligent detection method based on the conventional particle swarm, the intelligent detection method for the differential mechanism tool of the pipeline robot has the advantage of high precision and erroreCan be reduced by 10 percent.
In an embodiment of the present invention, there is also provided an electronic device including: one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors implement the high-precision intelligent detection method for the differential gear tool of the pipeline robot described in any embodiment.
In the embodiment of the invention, a computer readable storage medium is also provided, and a computer program is stored on the computer readable storage medium, and when the program is executed by a processor, the steps in the high-precision intelligent detection method for the differential mechanism tool of the pipeline robot in any one of the above embodiments are realized.
The present embodiment is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the present invention.
Claims (7)
1. A high-precision intelligent detection method for a differential mechanism tool of a pipeline robot is characterized by comprising the following steps:
s1, evaluating cylindricity errors: according to a relative measurement principle, a plurality of high-precision inductive sensors are placed on a cylindrical surface of a differential housing tooling machine, differential standard components are placed, readings of the sensors are zeroed, then the differential to-be-measured components are placed, and cylindricity error assessment indexes of the differential to-be-measured components and the standard components are determined by utilizing the distance from a measurement point to an axis;
s2, error optimization calculation: determining parameters in cylindricity error assessment and the section radius of a differential to-be-measured part by using a neighborhood particle intelligent optimization method;
s3, differential detection and assessment: and calculating the error between the section radius of the differential to-be-measured piece and the section radius of the differential standard piece.
2. The intelligent high-precision detection method for the differential tool of the pipeline robot according to claim 1, wherein the cylindricity error assessment in the step S1 is carried out by adopting the following steps:
s1.1, useQThe inductive sensors perform measurement sampling, and each sensor corresponds to x, y, the z coordinate marks the intersection point coordinate of the axis of the cylindrical surface of the differential housing tooling machine and the xoy plane as%a,b0), the direction vector of the axis is%p,q1), wherein, the mixture is prepared from the components of the mixture,a、b、p、qis a parameter to be determined;
s1.2, theiZ-coordinates of individual sensorsz i Determined by the sensor mounting height, x, y coordinatesx i、 y i And sensor readingsδ i The relation of (2) is:
；
wherein,i=1,2,...,Q，R 0 is the radius of the cross section of the standard part of the differential mechanism,θthe angle of the differential mechanism to-be-measured piece relative to the initial position is changed;
s1.3, calculating any measuring point on the cylindrical surfaceDistance to axisd i ：
；
Wherein,；
s1.4, according to the least square principle, the sum of squares of the distances from each measuring point to the cylindrical surface is minimum, and the error evaluation index function of the cylindricity is as follows:
；
wherein,Rthe radius of the section of the differential to be measured is the radius of the section of the differential to be measured.
3. The high-precision intelligent detection method for the differential tool of the pipeline robot according to claim 2, wherein in the error optimization calculation of the step S2, the neighborhood particle intelligent optimization method comprises the following steps:
s2.1, initializing: determining particle population sizeMIteration parametersk=1, the maximum number of iterations isK max Maximum speed ofV max Each neighborhood particle number isN；
The initial position vector of the particle is:
；
the initial velocity vector of the particle is:
；
wherein the parameter variablea、b、p、q、RThe upper 1 of (1) represents the iteration number, the lowerrIndicating the number of particles;
respectively represent the 1 st iterationrThe position parameter variables corresponding to the individual particles,respectively indicate and->Corresponding speed parameter variables;
s2.2, calculating an error evaluation index: first, thekIn the second iterationrPosition of individual particlesThe expression is as follows:
；
will beSubstituting the values into the formula in the step S1.4 to obtain error evaluation index function values;
s2.3, determining a neighborhood scheme: for the firstkConstructing particles by multiple iterationsN m The first particle of each neighborhood receives global information, and other particles only receive neighborhood information;
s2.4, updating particles: first, thek +1 iterations, the particles update speed and position according to the following formula:
；
wherein,was the weight of the inertia is given,c 1 , c 2 , c 3 in order for the learning factor to be a function of,r 1 , r 2 take [0,1 ]]Random numbers uniformly distributed among the two;
represents the extreme value of the individual, namelyiThe best location for individual particles to experience; />Representing a global extremum, which is the best location experienced by all particle populations; />Representing the neighborhood extremum as the firstiThe individual particles correspond to the best locations experienced by all particles in the neighborhood;
Y r for neighborhood learning ability function, take [0,1]Constant or random number between them to distinguish the ability of the particle to acquire global or neighborhood knowledge;indicating particle numberkThe velocity of the secondary iteration particles;
s2.5, judging termination conditions: if the maximum number of iterations is reachedk=K max Then the optimization is finished, and the obtained global optimal valueNamely, isa、b、p、q、RIs determined by the parameter values of the parameter values;
otherwise the first set of parameters is selected,the step S2.2 is followed by an iterative search for an optimal value.
4. The high-precision intelligent detection method for the differential tool of the pipeline robot according to claim 3, wherein the neighborhood scheme is determined in the step S2.3, and the particles are constructed by adopting the following formulaN m The following neighbors:
if it isk<K max ：
；
Conversely:
；
wherein,。
5. the high-precision intelligent detection method for the differential tool of the pipeline robot according to claim 1, wherein in step S3, the radius of the section of the differential part to be detected is calculatedRRadius of section of standard part of differential mechanismR 0 Error of (2)e：
；
Error ofeThe smaller the value, the closer the part to be measured is to the standard.
6. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1 to 5.
7. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of the method for high-precision intelligent detection of a pipe robot differential tooling according to any one of claims 1 to 5.
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