CN113199184B - Weld joint shape prediction method based on improved self-adaptive fuzzy neural network - Google Patents

Weld joint shape prediction method based on improved self-adaptive fuzzy neural network Download PDF

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CN113199184B
CN113199184B CN202110754330.XA CN202110754330A CN113199184B CN 113199184 B CN113199184 B CN 113199184B CN 202110754330 A CN202110754330 A CN 202110754330A CN 113199184 B CN113199184 B CN 113199184B
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陶永
兰江波
高赫
温宇方
任帆
江山
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Abstract

The embodiment of the invention provides a weld joint shape prediction method based on an improved self-adaptive fuzzy neural network, which is used for initializing the weld joint shape prediction fuzzy neural network and determining a welding fuzzy rule; determining input welding parameter variables of the fuzzy neural network and output quantity of the fuzzy neural network; calculating the membership degree of the welding parameters of each welding parameter variable; optimizing and calculating unknown parameters in a welding parameter membership function by using an intuitive fuzzy C mean value clustering (IFCM) and adaptive inertial weight particle swarm optimization (APSO) fusion algorithm to obtain the welding parameter membership of each welding parameter variable; performing fuzzy calculation according to the membership degree of the welding parameters of each input variable; and predicting output values of the weld width and the weld height. The prediction method provided by the invention reduces the labor cost, effectively improves the accuracy of welding result prediction, and solves the problem that the traditional fuzzy neural network training process is easy to fall into local minimum points.

Description

Weld joint shape prediction method based on improved self-adaptive fuzzy neural network
Technical Field
The invention relates to the field of robot welding, in particular to a weld joint shape prediction method based on an improved self-adaptive fuzzy neural network, and relates to a robot welding weld joint shape prediction method.
Background
The quality of welding quality directly influences the processing precision and the service life of a welding part. In the welding process, the welding quality influence factors are numerous, and the welding quality detection of the titanium alloy mainly comprises the compactness, the physical property, the mechanical property, the welding defect, the appearance size and the like of a welding joint. The final welding quality is affected by abnormal fluctuation of welding parameters, vibration of the mechanical arm, selection of welding parameters by a welder and operation specifications of welding equipment. In the existing processing factory, welding workers generally set welding parameters and then sample test pieces are utilized to judge whether the parameter setting is reasonable or not, if the quality of the test pieces does not meet the requirements, the parameters are continuously adjusted and the sample test is carried out, a large amount of materials can be wasted in the process, and long time and labor are consumed in the process.
The existing neural network algorithm can map any complex nonlinear function due to the strong nonlinear capability, and is primarily applied to a robot welding system. However, in the actual welding process, the correlation between the welding parameters and the appearance characteristics of the welding seam is difficult to quantitatively describe, the factors influencing the welding quality of the robot are more, the fuzzy algorithm can express fuzzy knowledge, fuzzy reasoning is realized, and the defect of poor generalization capability of a neural network can be effectively overcome.
In 1984, a fuzzy C-means clustering algorithm (FCM) is proposed by J.C.Bezdek, membership degree of a sample to a clustering center is introduced, membership degree of each sample point to the clustering center is obtained by optimizing a target function, and the category of the sample points is determined so as to achieve the purpose of automatically classifying sample data. In 1995, Kennedy and Eberhart proposed a particle swarm optimization (POS) algorithm, which has the advantages of swarm intelligence, simple iteration format, rapid convergence to obtain the region where the optimal solution is located, and so on, and so far, both the FCM algorithm and the PSO algorithm have been applied well. However, in the process of determining the width of the membership function of the welding parameter by using the PSO algorithm, a smaller weight factor is beneficial to performing accurate local search on the current search area, a smaller weight factor is selected to solve the problem that the current search area is prone to fall into a local minimum point in the fuzzy neural network training process, a larger weight factor is selected to facilitate jumping out of the local minimum point, so that global search is facilitated, but the prediction accuracy is insufficient, the membership function is difficult to determine, and the prediction accuracy and the global optimality of the prediction result are difficult to take into account.
Disclosure of Invention
The invention provides a robot welding weld joint shape prediction method based on an improved self-adaptive fuzzy neural network, aiming at solving the fitting problem between welding parameters and weld joint outer dimension. According to the method, the welding seam appearance is predicted by adopting the self-adaptive fuzzy neural network, and the welding parameters are adjusted according to the predicted data.
A weld seam appearance prediction method based on an improved adaptive fuzzy neural network comprises the following steps:
step 1: initializing a weld contour prediction fuzzy neural network, and determining a welding fuzzy rule;
step 2: determining input welding parameter variables of the fuzzy neural network and output quantity of the fuzzy neural network;
and step 3: calculating the welding parameter membership of each welding parameter variable according to the welding fuzzy rule determined in the step 1;
and 4, step 4: optimizing and calculating unknown parameters in the welding parameter membership by using an intuitive fuzzy C mean value clustering (IFCM) and adaptive inertial weight particle swarm optimization (APSO) fusion algorithm to obtain the welding parameter membership of each welding parameter variable;
and 5: performing fuzzy calculation according to the welding parameter membership of each welding parameter variable;
step 6: obtaining the predicted output values of the width and height of the welding seam of the robot according to the fuzzy calculation result obtained in the step 5;
the step 3 comprises the following steps:
for input welding parameter variables
Figure 924609DEST_PATH_IMAGE001
Calculating the welding parameter membership of each welding parameter input quantity according to the welding fuzzy rule determined in the step 1:
Figure 100002_DEST_PATH_IMAGE002
wherein,
Figure DEST_PATH_IMAGE003
and
Figure 100002_DEST_PATH_IMAGE004
respectively the center and the width of the membership function of the welding parameters,
Figure DEST_PATH_IMAGE005
is the degree of membership of the welding parameters,
Figure 100002_DEST_PATH_IMAGE006
is based on
Figure DEST_PATH_IMAGE007
Input quantity of welding parameters
Figure 100002_DEST_PATH_IMAGE008
The membership of the welding parameters calculated by the fuzzy subsets,
Figure DEST_PATH_IMAGE009
is the fuzzy subset number of the welding parameter variables,
Figure 100002_DEST_PATH_IMAGE010
is the number of welding parameter variables entered.
Preferably, the step 1 specifically includes:
describing the weld shape prediction fuzzy neural network by using an if-then definition rule, wherein the weld shape prediction fuzzy neural network can be defined by using the following if-then rule:
Figure DEST_PATH_IMAGE011
wherein,
Figure 100002_DEST_PATH_IMAGE012
is the first
Figure 751226DEST_PATH_IMAGE008
Strip welding dieThe rule is pasted,
Figure 452465DEST_PATH_IMAGE013
，
Figure 100002_DEST_PATH_IMAGE014
is the number of weld fuzzy rules;
Figure DEST_PATH_IMAGE015
is a variable of a welding parameter
Figure 100002_DEST_PATH_IMAGE016
To (1) a
Figure DEST_PATH_IMAGE017
A fuzzy subset;
Figure 100002_DEST_PATH_IMAGE018
is an input welding parameter variable, and
Figure 84433DEST_PATH_IMAGE001
；
Figure 222023DEST_PATH_IMAGE010
for the number of welding parameter variables that are input,
Figure DEST_PATH_IMAGE019
is the first
Figure 100002_DEST_PATH_IMAGE020
Output of the bar fuzzy rule, wherein
Figure DEST_PATH_IMAGE021
Are the weight coefficients.
Preferably, the step 2 specifically includes:
the flow of shielding gas, the welding speed, the wire feeding speed and the laser power are taken as four input welding parameter variables of the fuzzy neural network, and the welding width and the welding height are respectively taken as network output.
Preferably, the step 4 specifically includes:
step 4.1, giving an initial value to the membership matrix of the welding parameter by using a random number generator;
step 4.2, determining a welding direct fuzzy set, calculating the uncertainty of a welding parameter sample based on the welding direct fuzzy set, and changing a membership matrix of welding parameters into a fuzzy membership matrix;
4.3, calculating the distance from the welding parameter to be classified to the clustering center based on the fuzzy membership matrix, and dividing the sample into various classes;
step 4.4, recalculating the distance from the clustering center of each class and the welding parameter sample to the clustering center, replacing the original membership matrix with an intuitive fuzzy membership matrix, and reclassifying the samples into each class;
step 4.5, iteratively searching the optimal solution of the membership function;
and 4.6, repeating the steps 4.2-4.5 until the fitness function reaches a specified threshold value.
Preferably, the step 4.2 specifically includes:
determining a welding intuition fuzzy set WIFS, and increasing the non-membership degree of welding parameter variables in the WIFS
Figure 100002_DEST_PATH_IMAGE022
And uncertainty
Figure DEST_PATH_IMAGE023
Assuming intuitive fuzzy sets of welding
Figure 100002_DEST_PATH_IMAGE024
Representing welding parameter variables
Figure DEST_PATH_IMAGE025
And universe of discourse
Figure 100002_DEST_PATH_IMAGE026
The relationship of (1) is as follows:
Figure DEST_PATH_IMAGE027
wherein,
Figure 100002_DEST_PATH_IMAGE028
for welding intuitive fuzzy sets
Figure 930959DEST_PATH_IMAGE024
The degree of membership of (a) is,
Figure DEST_PATH_IMAGE029
for welding intuitive fuzzy sets
Figure 413936DEST_PATH_IMAGE024
The degree of non-membership of (a) is,
Figure 100002_DEST_PATH_IMAGE030
for welding intuitive fuzzy sets
Figure 215539DEST_PATH_IMAGE024
When the condition is satisfied
Figure DEST_PATH_IMAGE031
And
Figure 100002_DEST_PATH_IMAGE032
and is
Figure DEST_PATH_IMAGE033
The uncertainty of the welding parameter variable may be expressed as:
Figure 100002_DEST_PATH_IMAGE034
。
preferably, the step 4.3 specifically includes:
defining the welding intuitive fuzzy membership as:
Figure DEST_PATH_IMAGE035
wherein,
Figure 100002_DEST_PATH_IMAGE036
for welding intuitive fuzzy membership, express
Figure 558533DEST_PATH_IMAGE007
A welding parameter point pair
Figure 572626DEST_PATH_IMAGE008
The intuitive fuzzy membership of the clustering center of each welding parameter,
Figure DEST_PATH_IMAGE037
is shown as
Figure 169829DEST_PATH_IMAGE007
A welding parameter variable is in
Figure 470230DEST_PATH_IMAGE008
The uncertainty under each fuzzy subset,
Figure 100002_DEST_PATH_IMAGE038
is shown as
Figure 248655DEST_PATH_IMAGE007
A welding parameter variable is in
Figure 410646DEST_PATH_IMAGE008
The degree of membership under each fuzzy subset,
Figure DEST_PATH_IMAGE039
，
Figure 862356DEST_PATH_IMAGE010
for the number of welding parameter variables that are input,
Figure 68079DEST_PATH_IMAGE009
for the number of fuzzy subsets of corresponding weld parameter variables,
Figure 418289DEST_PATH_IMAGE038
and
Figure 243025DEST_PATH_IMAGE037
can be calculated from the following formula:
Figure 100002_DEST_PATH_IMAGE040
Figure DEST_PATH_IMAGE041
wherein,
Figure 100002_DEST_PATH_IMAGE042
is the number of initialized membership matrix,
Figure 329668DEST_PATH_IMAGE010
for the number of welding parameter variables that are input,
Figure DEST_PATH_IMAGE043
is a weight factor that is a function of,
Figure 100002_DEST_PATH_IMAGE044
is a positive constant.
Preferably, the step 4.4 specifically includes:
by using an intuitive membership matrix formed by welding intuitive fuzzy membership of welding parameter variables, a new welding parameter clustering center formula can be obtained:
Figure DEST_PATH_IMAGE045
。
preferably, the step 5 specifically includes:
substituting the unknown number in the optimized welding parameter membership obtained in the step 4 into a calculation formula of the welding parameter membership, and calculating the membership of each input welding parameter in a fuzzy manner, wherein the fuzzy operator is a continuous multiplication operator:
Figure 100002_DEST_PATH_IMAGE046
。
preferably, the step 6 specifically includes:
and (5) substituting the fuzzy result obtained in the step (5) into an equation to calculate the predicted output values of the width and the height of the welding seam of the robot:
Figure DEST_PATH_IMAGE047
wherein,
Figure 100002_DEST_PATH_IMAGE048
is a joint product of the degrees of membership of the input weld parameters, a weight
Figure 100002_DEST_PATH_IMAGE049
The following formula is adopted for updating:
Figure 100002_DEST_PATH_IMAGE050
Figure 100002_DEST_PATH_IMAGE051
wherein,
Figure DEST_PATH_IMAGE052
is the network learning rate;
Figure 100002_DEST_PATH_IMAGE053
welding parameters input for the neural network;
Figure DEST_PATH_IMAGE054
in the formula,
Figure 100002_DEST_PATH_IMAGE055
is a netThe welding seam welding width or the welding height output is expected;
Figure DEST_PATH_IMAGE056
the actual welding seam width or welding height output of the network is realized;
Figure 100002_DEST_PATH_IMAGE057
is the error between the desired output and the actual output.
The robot welding weld joint shape prediction method based on the improved adaptive fuzzy neural network realizes the prediction of the weld joint outer dimension based on the improved adaptive fuzzy neural network, takes the weld leg width and the weld height of a T-shaped weld joint as evaluation standards, selects four variables with the largest influence factors on welding quality as input parameters, optimizes the central value and the width of a membership function in the adaptive neural network fuzzy algorithm, and predicts the weld joint shape so as to adjust the welding parameters according to the prediction result. According to the robot welding seam appearance prediction method based on the improved self-adaptive fuzzy neural network, the fuzzy neural network is used for predicting the welding quality, so that the labor cost is reduced, and the material waste caused by manual trial welding is avoided; the input parameters and the output parameters of welding are subjected to nonlinear fitting, the accuracy of welding result prediction is effectively improved, two unknown parameters in a membership function are subjected to optimization calculation by using an IFCM-APSO algorithm, and the problem that a local minimum point is easy to fall into in the traditional fuzzy neural network training process is solved.
Drawings
FIG. 1 is a schematic view of a robotic welding platform;
FIG. 2 is a flowchart of a robot welding seam profile prediction method based on a fuzzy neural network in an embodiment;
FIG. 3 is a graph of the optimized membership function;
FIG. 4 is a shape parameter of a typical T-weld;
FIG. 5 is a comparison graph of weld profile prediction results before optimization in the examples;
FIG. 6 is a schematic diagram of a weld joint shape prediction result after optimization in the embodiment;
FIG. 7 is a welding result of a prior art trial welding method based on empirical search for welding parameters;
FIG. 8 is a diagram illustrating a welding result predicted by a robot welding seam profile prediction method based on a fuzzy neural network in an embodiment;
FIG. 9 is a flowchart of a training process of a neural network in an embodiment;
fig. 10 is a schematic diagram of a robotic welding station assembly.
Detailed Description
The technical solutions in the embodiments of the present application are described below clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. As can be known to those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solution provided in the embodiments of the present application is also applicable to similar technical problems.
The terms "comprises" and "comprising," and any variations thereof, in the description and claims of this application and the above-described drawings are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
This example was carried out in a welding center using a robotic welding platform (platform assembly shown in fig. 1). The platform includes: welding protective gas, silk laser sensor, robot control cabinet, send a machine and laser instrument, KUKA arm and robot control cabinet signal connection, PLC switch board connects above-mentioned subassembly, realizes predictive computation, parameter setting and mechanical arm control.
Example one
The first embodiment of the invention relates to a shape prediction method based on an improved adaptive fuzzy neural network, the flow of which is shown in fig. 2, and the specific steps are as follows:
step 1: initializing a weld contour prediction fuzzy neural network, and determining a welding fuzzy rule;
step 2: determining input welding parameter variables of the fuzzy neural network and output quantity of the fuzzy neural network;
and step 3: calculating the welding parameter membership of each welding parameter variable according to the welding fuzzy rule determined in the step 1;
and 4, step 4: optimizing and calculating unknown parameters in the welding parameter membership by using an intuitive fuzzy C mean value clustering (IFCM) and adaptive inertial weight particle swarm optimization (APSO) fusion algorithm to obtain the welding parameter membership of each welding parameter variable;
and 5: performing fuzzy calculation according to the welding parameter membership of each welding parameter variable;
step 6: and 5, obtaining the predicted output values of the width and height of the welding seam of the robot according to the fuzzy calculation result obtained in the step 5.
According to the robot welding seam shape prediction method based on the improved self-adaptive fuzzy neural network, the nonlinear relation between the welding parameter variable and the neural network output quantity is obtained based on fuzzy neural network fitting, the width and the height of the welding seam are predicted according to the fuzzy neural network, compared with a traditional trial welding method, the shape after welding can be predicted according to the input welding parameters, the labor cost is reduced, and material waste caused by manual trial welding is avoided. The IFCM-APSO algorithm is used for carrying out optimization calculation on two unknown parameters in the membership function, and the problem that a local minimum point is easy to fall into in the traditional fuzzy neural network training process is solved.
Example two
Further, a second embodiment of the present invention relates to a weld seam shape prediction method based on an improved adaptive fuzzy neural network, which specifically includes the following steps:
step 1: initializing a weld contour prediction fuzzy neural network, and determining a welding fuzzy rule;
describing the weld shape prediction fuzzy neural network by using an if-then definition rule, wherein the weld shape prediction fuzzy neural network in the step 1 can be defined by using the following if-then rule:
Figure 787849DEST_PATH_IMAGE011
wherein,
Figure 625355DEST_PATH_IMAGE012
is the first
Figure 378417DEST_PATH_IMAGE008
The rule of the welding of the bars is fuzzy,
Figure 883347DEST_PATH_IMAGE013
，
Figure DEST_PATH_IMAGE058
is the number of weld fuzzy rules;
Figure 368555DEST_PATH_IMAGE015
is a variable of a welding parameter
Figure 960203DEST_PATH_IMAGE016
To (1) a
Figure 2108DEST_PATH_IMAGE008
A fuzzy subset;
Figure 79655DEST_PATH_IMAGE018
is an input welding parameter variable, and
Figure 814392DEST_PATH_IMAGE001
；
Figure 344600DEST_PATH_IMAGE010
for the number of welding parameter variables that are input,
Figure 721354DEST_PATH_IMAGE019
is the first
Figure 653407DEST_PATH_IMAGE008
Output of the bar fuzzy rule, wherein
Figure 100002_DEST_PATH_IMAGE059
Are the weight coefficients.
Step 2: determining input welding parameter variables of the fuzzy neural network and output quantity of the fuzzy neural network;
in the welding process, factors affecting the quality of a welded product are numerous. When the flow of the protective gas is too large, the gas impacts a molten pool, so that the splashing of the molten pool is increased, and the surface of a welding seam is not smooth; when the flow of the protective gas is too small, the protective effect on a molten pool is reduced, and defects such as air holes and the like are easily generated. When the welding speed is too high, the gas protection effect is damaged, so that the welding seam is not formed well; when the welding speed is too slow, the fusion width is too large, the molten pool becomes large, and the material is easy to weld through. If the wire feeding speed is too high, the welding width and the welding height of a welding seam can be increased; if the speed is too low, cold joint is easily caused, and the mechanical property of a welding workpiece is influenced. The selection of laser power also has great influence on the formation of a welding seam, the melting depth is increased quickly when the power is too large, and the welding width and the welding height are correspondingly increased. Therefore, the flow of the shielding gas, the welding speed, the wire feeding speed and the laser power are taken as welding parameter variables of four inputs of the fuzzy neural network, and the welding width and the welding height are taken as network outputs respectively.
And step 3: calculating the welding parameter membership of each welding parameter variable according to the welding fuzzy rule determined in the step 1;
in step 3, for the input welding parameter variable
Figure 745997DEST_PATH_IMAGE001
Calculating the welding parameter membership of each welding parameter input quantity according to the welding fuzzy rule determined in the step 1:
Figure 779812DEST_PATH_IMAGE002
wherein,
Figure 412788DEST_PATH_IMAGE003
and
Figure 215659DEST_PATH_IMAGE004
respectively the center and the width of the membership function of the welding parameters,
Figure 151254DEST_PATH_IMAGE005
is the degree of membership of the welding parameters,
Figure 921632DEST_PATH_IMAGE006
is based on
Figure 499244DEST_PATH_IMAGE007
Input quantity of welding parameters
Figure 281255DEST_PATH_IMAGE008
The membership of the welding parameters calculated by the fuzzy subsets,
Figure 528697DEST_PATH_IMAGE009
is the fuzzy subset number of the welding parameter variables,
Figure 792231DEST_PATH_IMAGE010
is the number of welding parameter variables entered.
And 4, step 4: optimizing and calculating unknown parameters in the welding parameter membership by using an intuitive fuzzy C mean value clustering (IFCM) and adaptive inertial weight particle swarm optimization (APSO) fusion algorithm to obtain the welding parameter membership of each welding parameter variable;
step 4, calculating the unknown number in the welding parameter membership by using an IFCM-APSO algorithm, namely the center and the width of a welding parameter membership function, and the specific process is as follows:
step 4.1, giving an initial value to the membership matrix of the welding parameter by using a random number generator;
and the membership matrix of the welding parameters is formed by the membership of the welding parameters of all welding parameter variables.
Step 4.2, determining a welding direct fuzzy set, calculating the uncertainty of a welding parameter sample based on the welding direct fuzzy set, and changing a membership matrix of welding parameters into a fuzzy membership matrix;
firstly, determining a welding intuitive modelThe method is characterized in that a fuzzy set WIFS is an important expansion of a traditional fuzzy set, and the non-membership degree of welding parameter variables is increased in the WIFS
Figure 783321DEST_PATH_IMAGE022
And uncertainty
Figure DEST_PATH_IMAGE060
Assuming intuitive fuzzy sets of welding
Figure 482156DEST_PATH_IMAGE024
Representing welding parameter variables
Figure 884187DEST_PATH_IMAGE025
And universe of discourse
Figure 379891DEST_PATH_IMAGE026
The relationship of (1) is as follows:
Figure 158360DEST_PATH_IMAGE027
wherein,
Figure 524750DEST_PATH_IMAGE028
for welding intuitive fuzzy sets
Figure 363262DEST_PATH_IMAGE024
The degree of membership of (a) is,
Figure 346261DEST_PATH_IMAGE029
for welding intuitive fuzzy sets
Figure 538208DEST_PATH_IMAGE024
The degree of non-membership of (a) is,
Figure 273952DEST_PATH_IMAGE030
for welding intuitive fuzzy sets
Figure 299677DEST_PATH_IMAGE024
Uncertainty of (d). When satisfying the stripPiece
Figure 753661DEST_PATH_IMAGE031
And
Figure 890244DEST_PATH_IMAGE032
and is
Figure 480494DEST_PATH_IMAGE033
The uncertainty of the welding parameter variable may be expressed as:
Figure 677120DEST_PATH_IMAGE034
4.3, calculating the distance from the welding parameter to be classified to the clustering center based on the fuzzy membership matrix, and dividing the sample into various classes;
in order to combine the welding intuitive fuzzy characteristic with the traditional fuzzy clustering method, the welding intuitive fuzzy membership is defined as:
Figure 612541DEST_PATH_IMAGE035
wherein,
Figure 21657DEST_PATH_IMAGE036
for welding intuitive fuzzy membership, express
Figure 731993DEST_PATH_IMAGE007
A welding parameter point pair
Figure 365099DEST_PATH_IMAGE008
The intuitive fuzzy membership of the clustering center of each welding parameter,
Figure 262517DEST_PATH_IMAGE037
is shown as
Figure 740903DEST_PATH_IMAGE007
A welding parameter variable is in
Figure 305745DEST_PATH_IMAGE008
The uncertainty under each fuzzy subset,
Figure 578595DEST_PATH_IMAGE038
is shown as
Figure 494467DEST_PATH_IMAGE007
A welding parameter variable is in
Figure 510965DEST_PATH_IMAGE008
The degree of membership under each fuzzy subset,
Figure 195893DEST_PATH_IMAGE039
，
Figure 374064DEST_PATH_IMAGE010
for the number of welding parameter variables that are input,
Figure 777233DEST_PATH_IMAGE009
the number of fuzzy subsets of the corresponding welding parameter variables.
Figure 456476DEST_PATH_IMAGE038
And
Figure 746643DEST_PATH_IMAGE037
can be calculated from the following formula:
Figure 876142DEST_PATH_IMAGE040
Figure 251759DEST_PATH_IMAGE041
wherein,
Figure 599607DEST_PATH_IMAGE042
is the number of initialized membership matrix,
Figure 9860DEST_PATH_IMAGE010
for the number of welding parameter variables that are input,
Figure 451206DEST_PATH_IMAGE043
is a weight factor that is a function of,
Figure 173174DEST_PATH_IMAGE044
a positive constant to ensure that the sum of the degree of membership and the degree of non-membership is no greater than 1.
And 4.4, recalculating the distance from the cluster center of each class and the welding parameter sample to the cluster center. Each calculation uses an intuitive fuzzy membership matrix to replace the original membership matrix, and samples are divided into various classes again;
by using an intuitive membership matrix formed by welding intuitive fuzzy membership of welding parameter variables, a new welding parameter clustering center formula can be obtained:
Figure 318853DEST_PATH_IMAGE045
4.5, iterating by using the objective function, and searching an optimal solution of the membership function;
the IFCM-APSO objective function can be expressed as:
Figure 100002_DEST_PATH_IMAGE061
wherein,
Figure DEST_PATH_IMAGE062
，
Figure 442667DEST_PATH_IMAGE009
is shown as
Figure 179548DEST_PATH_IMAGE009
The data of the 4-dimensional vectors,
Figure 100002_DEST_PATH_IMAGE063
is shown as
Figure 451129DEST_PATH_IMAGE008
Common in each cluster
Figure 416811DEST_PATH_IMAGE063
The number of the elements is one,
Figure 519765DEST_PATH_IMAGE042
is the number of the centers of the clusters,
Figure DEST_PATH_IMAGE064
is as follows
Figure 99651DEST_PATH_IMAGE007
The number of data points is, for example,
Figure 100002_DEST_PATH_IMAGE065
is as follows
Figure 876107DEST_PATH_IMAGE008
The center of each cluster is determined by the center of each cluster,
Figure DEST_PATH_IMAGE066
is shown as
Figure 566851DEST_PATH_IMAGE007
Individual clustering point pair
Figure 540623DEST_PATH_IMAGE008
Intuitive fuzzy membership of individual cluster centers;
Figure 963514DEST_PATH_IMAGE043
as a weighting factor, in general
Figure 100002_DEST_PATH_IMAGE067
；
Figure DEST_PATH_IMAGE068
For distortion, it is generally expressed as Euclidean distance
Figure 100002_DEST_PATH_IMAGE069
；
Figure DEST_PATH_IMAGE070
In order to be the true value of the value,
Figure 100002_DEST_PATH_IMAGE071
predicting an output value for the neural network; FIG. 3 is the optimized membership function parameters.
And 4.6, repeating the steps 4.2-4.5 until the fitness function reaches a specified threshold value.
And updating the intuitive membership matrix of the welding parameters by using the updated value of the welding parameter clustering center. In the process of each iteration, the numerical value of the welding parameter intuitive membership matrix of the welding parameter clustering center is updated once until the difference value between the former welding parameter intuitive membership matrix and the updated welding parameter intuitive membership matrix is smaller than a set threshold value, at the moment, the iteration process is finished, and the clustering center reaches the optimum value.
The fitness function is as follows:
Figure DEST_PATH_IMAGE072
the above formula is an iterative objective function formula of IFCM, and the membership matrix after parameter updating
Figure 100002_DEST_PATH_IMAGE073
And membership matrix before update
Figure DEST_PATH_IMAGE074
Is less than
Figure 100002_DEST_PATH_IMAGE075
If so, ending the iteration process; finding the optimum width of the membership function using modified particle swarm optimization, i.e.
Figure 364015DEST_PATH_IMAGE042
A value of (d); particlesAre grouped into
Figure DEST_PATH_IMAGE076
Population of individual particles
Figure 100002_DEST_PATH_IMAGE077
；
First, the
Figure DEST_PATH_IMAGE078
Each particle represents one
Figure 100002_DEST_PATH_IMAGE079
Dimension vector
Figure DEST_PATH_IMAGE080
；
First, the
Figure 192206DEST_PATH_IMAGE078
The velocity of each particle is expressed as
Figure 100002_DEST_PATH_IMAGE081
；
Individual extreme value of
Figure DEST_PATH_IMAGE082
；
Global extreme value of
Figure 100002_DEST_PATH_IMAGE083
；
The speed is updated to
Figure DEST_PATH_IMAGE084
，
Figure 100002_DEST_PATH_IMAGE085
Is a local learning factor and is used as a local learning factor,
Figure DEST_PATH_IMAGE086
in order to be a global learning factor,
Figure 100002_DEST_PATH_IMAGE087
to represent
Figure DEST_PATH_IMAGE088
At the first moment
Figure 440391DEST_PATH_IMAGE078
The velocity of the individual particles is determined,
Figure 100002_DEST_PATH_IMAGE089
represents the historical best record of the individual particle,
Figure DEST_PATH_IMAGE090
indicating the optimal position for the global duration,
Figure 100002_DEST_PATH_IMAGE091
to represent
Figure 74330DEST_PATH_IMAGE088
At the first moment
Figure 620849DEST_PATH_IMAGE078
The position of the particles is determined by the position of the particles,
Figure DEST_PATH_IMAGE092
and
Figure 100002_DEST_PATH_IMAGE093
random number of 0 to 1, position update to
Figure DEST_PATH_IMAGE094
。
Figure 100002_DEST_PATH_IMAGE095
Represents the welding dynamic inertia weight coefficient:
Figure DEST_PATH_IMAGE096
wherein,
Figure 100002_DEST_PATH_IMAGE097
，
Figure DEST_PATH_IMAGE098
respectively represent
Figure 100002_DEST_PATH_IMAGE099
The maximum value and the minimum value of (c),
Figure DEST_PATH_IMAGE100
the current value of the objective function for the particle is indicated,
Figure 100002_DEST_PATH_IMAGE101
and
Figure DEST_PATH_IMAGE102
respectively representing the average target value and the minimum target value of all the particles at present.
And 5: performing fuzzy calculation according to the welding parameter membership of each welding parameter variable;
substituting the unknown number in the optimized welding parameter membership obtained in the step 4 into a calculation formula of the welding parameter membership, and calculating the membership of each input welding parameter in a fuzzy manner, wherein the fuzzy operator is a continuous multiplication operator:
Figure 338881DEST_PATH_IMAGE046
step 6: and 5, obtaining the predicted output values of the width and height of the welding seam of the robot according to the fuzzy calculation result obtained in the step 5.
And (5) substituting the fuzzy result obtained in the step (5) into an equation to calculate the predicted output values of the width and the height of the welding seam of the robot:
Figure 21667DEST_PATH_IMAGE047
wherein,
Figure 707732DEST_PATH_IMAGE048
is a joint product of the degrees of membership of the input weld parameters, a weight
Figure 403199DEST_PATH_IMAGE049
The following formula is adopted for updating:
Figure 52486DEST_PATH_IMAGE050
Figure 370203DEST_PATH_IMAGE051
wherein,
Figure 181165DEST_PATH_IMAGE052
is the network learning rate;
Figure 951543DEST_PATH_IMAGE053
welding parameters input for the neural network;
Figure 138942DEST_PATH_IMAGE054
in the formula,
Figure 311166DEST_PATH_IMAGE055
is the expected weld width or weld height output of the network;
Figure 558608DEST_PATH_IMAGE056
the actual welding seam width or welding height output of the network is realized;
Figure 426070DEST_PATH_IMAGE057
is the error between the desired output and the actual output.
According to the robot welding seam appearance prediction method based on the improved adaptive fuzzy neural network, four input welding parameters with large influence factors are selected to respectively predict the welding width and the welding height, in the prediction process, the prediction is carried out based on the improved adaptive fuzzy neural network, the input and output parameters of the welding are subjected to nonlinear fitting, and the accuracy of welding result prediction is effectively improved; the local learning factor and the global learning factor are simultaneously considered in the speed updating, and the problem that the traditional fuzzy neural network training process is easy to fall into a local minimum point is solved.
EXAMPLE III
The accuracy and applicability of the fuzzy neural network depend to a large extent on the available training data set, the larger the input range it covers, and the better the network generated. In order to compare the predicted performance of the algorithm, the invention carries out welding test on the titanium alloy steel plate with the thickness of 3mm, and generates 250 data sets by changing the process parameters. Randomly selecting 200 of the fuzzy neural networks for training, and testing the fitting degree of the networks for the rest 50; two independent fuzzy neural network models are established and are respectively used for predicting welding width and welding height data. The shape parameters of a typical T-weld are shown in fig. 4.
The fuzzy neural network determines that the number of input nodes is 4 and the number of output nodes is 1 according to the input and output dimensions of the training sample, and artificially determines that the number of membership function is 8 according to the number of input and output nodes of the network. In order to calculate the central value and the width of the membership function, an IFCM-APSO algorithm programming program is adopted in Matlab2019b to optimize the parameters of the predecessor, and data testing is performed on the optimized fuzzy neural network, wherein the error change before and after optimization is shown in fig. 5 and 6.
Compared with actual output, the predicted output data has larger fluctuation, after the front-part parameters are optimized by the IFCM-APSO algorithm, the fitting degree of the fuzzy neural network is greatly improved, the test data set is substituted into the optimized fuzzy neural network, and the output predicted data can reduce the error between the predicted output value and the actual output value.
FIG. 7 shows the welding results obtained after five parameter adjustments by using the manual trial welding method, and it can be seen that the weld surface is rough and uneven, and the expected welding effect is difficult to achieve within the range of the limited number of adjustments; and FIG. 8 is a weld forming diagram obtained by using the improved fuzzy neural network to adjust the welding parameters three times before welding according to the prediction result, and the welding effect diagram shows that the improved fuzzy neural network can predict the overall dimension of the weld within a certain error range, thereby effectively improving the adjustment efficiency of the welding parameters.
The steps of the above methods are divided for clarity, and the implementation may be combined into one step or split some steps, and the steps are divided into multiple steps, so long as the same logical relationship is included, which are all within the protection scope of the present patent; it is within the scope of the patent to add insignificant modifications to the algorithms or processes or to introduce insignificant design changes to the core design without changing the algorithms or processes.
The above examples are only specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (4)

1. A weld seam appearance prediction method based on an improved adaptive fuzzy neural network is characterized by comprising the following steps:
step 1: initializing a weld contour prediction fuzzy neural network, and determining a welding fuzzy rule;
step 2: determining input welding parameter variables of the fuzzy neural network and output quantity of the fuzzy neural network;
and step 3: calculating the welding parameter membership of each welding parameter variable according to the welding fuzzy rule determined in the step 1;
and 4, step 4: optimizing and calculating unknown parameters in the welding parameter membership by using a fusion algorithm based on intuitive fuzzy C-means clustering and self-adaptive inertial weight particle swarm optimization to obtain the welding parameter membership of each welding parameter variable;
and 5: performing fuzzy calculation according to the welding parameter membership of each welding parameter variable;
step 6: obtaining the predicted output values of the width and height of the welding seam of the robot according to the fuzzy calculation result obtained in the step 5;
the step 3 comprises the following steps:
for input welding parameter variables
Figure DEST_PATH_IMAGE002
Calculating the welding parameter membership of each welding parameter input quantity according to the welding fuzzy rule determined in the step 1:
Figure DEST_PATH_IMAGE004
wherein,
Figure DEST_PATH_IMAGE006
and
Figure DEST_PATH_IMAGE008
respectively the center and the width of the membership function of the welding parameters,
Figure DEST_PATH_IMAGE010
is the degree of membership of the welding parameters,
Figure DEST_PATH_IMAGE012
is based on
Figure DEST_PATH_IMAGE014
Input quantity of welding parameters
Figure DEST_PATH_IMAGE016
The membership of the welding parameters calculated by the fuzzy subsets,
Figure DEST_PATH_IMAGE018
is the fuzzy subset number of the welding parameter variables,
Figure DEST_PATH_IMAGE020
is the number of input welding parameter variables;
the step 4 specifically includes:
step 4.1, assigning an initial value to a membership matrix of the welding parameters by using a random number generator, wherein the membership matrix of the welding parameters is formed by the membership of the welding parameters of all welding parameter variables;
step 4.2, determining a welding intuitive fuzzy set, calculating the uncertainty of a welding parameter sample based on the welding intuitive fuzzy set, and changing a membership matrix of welding parameters into a fuzzy membership matrix;
determining a welding intuition fuzzy set WIFS, and increasing the non-membership degree of welding parameter variables in the WIFS
Figure DEST_PATH_IMAGE022
And uncertainty
Figure DEST_PATH_IMAGE024
Assuming intuitive fuzzy sets of welding
Figure DEST_PATH_IMAGE026
Representing welding parameter variables
Figure DEST_PATH_IMAGE028
And universe of discourse
Figure DEST_PATH_IMAGE030
The relationship of (1) is as follows:
Figure DEST_PATH_IMAGE032
wherein,
Figure DEST_PATH_IMAGE034
intuitive fuzzy set for welding
Figure 2606DEST_PATH_IMAGE026
The degree of membership of (a) is,
Figure DEST_PATH_IMAGE036
intuitive fuzzy set for welding
Figure 4951DEST_PATH_IMAGE026
The degree of non-membership of (a) is,
Figure DEST_PATH_IMAGE038
intuitive fuzzy set for welding
Figure 971639DEST_PATH_IMAGE026
When the condition is satisfied
Figure DEST_PATH_IMAGE040
And
Figure DEST_PATH_IMAGE042
and is
Figure DEST_PATH_IMAGE044
The uncertainty of the welding parameter variable may be expressed as:
Figure DEST_PATH_IMAGE046
；
4.3, calculating the distance from the welding parameter to be classified to the clustering center based on the fuzzy membership matrix, and dividing the sample into various classes;
wherein, the welding intuitive fuzzy membership is defined as:
Figure DEST_PATH_IMAGE048
wherein,
Figure DEST_PATH_IMAGE049
is shown as
Figure 908459DEST_PATH_IMAGE014
A welding parameter point pair
Figure 519569DEST_PATH_IMAGE016
The intuitive fuzzy membership of the clustering center of each welding parameter,
Figure DEST_PATH_IMAGE050
is shown as
Figure 997824DEST_PATH_IMAGE014
A welding parameter variable is in
Figure 327174DEST_PATH_IMAGE016
The uncertainty under each fuzzy subset,
Figure DEST_PATH_IMAGE051
is shown as
Figure 306500DEST_PATH_IMAGE014
A welding parameter variable is in
Figure 286963DEST_PATH_IMAGE016
The degree of membership under each fuzzy subset,
Figure DEST_PATH_IMAGE053
，
Figure 467278DEST_PATH_IMAGE020
for the number of welding parameter variables that are input,
Figure 80662DEST_PATH_IMAGE018
for corresponding welding parameter variablesThe number of the fuzzy subsets is determined,
wherein,
Figure DEST_PATH_IMAGE055
Figure DEST_PATH_IMAGE057
Figure DEST_PATH_IMAGE059
is the number of initialized membership matrix,
Figure 598099DEST_PATH_IMAGE020
for the number of welding parameter variables that are input,
Figure DEST_PATH_IMAGE061
is a weight factor that is a function of,
Figure DEST_PATH_IMAGE063
is a positive constant;
step 4.4, recalculating the distance from the clustering center of each class and the welding parameter sample to the clustering center, replacing the original membership matrix with an intuitive fuzzy membership matrix, and reclassifying the samples into each class;
obtaining a new welding parameter clustering center formula by using an intuitive membership matrix formed by welding intuitive fuzzy membership of welding parameter variables:
Figure DEST_PATH_IMAGE065
step 4.5, iteratively searching the optimal solution of the welding parameter membership;
performing iteration by using an objective function, wherein the objective function is as follows:
Figure DEST_PATH_IMAGE067
wherein,
Figure DEST_PATH_IMAGE069
，
Figure 495386DEST_PATH_IMAGE018
is shown as
Figure 10985DEST_PATH_IMAGE018
The data of the 4-dimensional vectors,
Figure DEST_PATH_IMAGE071
is shown as
Figure 377244DEST_PATH_IMAGE016
Common in each cluster
Figure 449105DEST_PATH_IMAGE071
The number of the elements is one,
Figure 154893DEST_PATH_IMAGE059
is the number of the centers of the clusters,
Figure DEST_PATH_IMAGE073
is as follows
Figure 411431DEST_PATH_IMAGE014
The number of data points is, for example,
Figure DEST_PATH_IMAGE075
is as follows
Figure 264986DEST_PATH_IMAGE016
The center of each cluster is determined by the center of each cluster,
Figure DEST_PATH_IMAGE077
is shown as
Figure 999593DEST_PATH_IMAGE014
Individual clustering point pair
Figure 559887DEST_PATH_IMAGE016
Intuitive fuzzy membership of individual cluster centers;
Figure 393851DEST_PATH_IMAGE061
is a weight factor;
Figure DEST_PATH_IMAGE079
expressed by Euclidean distance for distortion degree
Figure DEST_PATH_IMAGE081
；
Figure DEST_PATH_IMAGE083
In order to be the true value of the value,
Figure DEST_PATH_IMAGE085
predicting an output value for the neural network;
step 4.6, repeating the steps 4.2-4.5 until the fitness function reaches a specified threshold value;
updating the welding parameter intuitive membership matrix by using the updated value of the welding parameter clustering center, and if the difference value of the former welding parameter intuitive membership matrix and the updated welding parameter intuitive membership matrix is less than a set threshold value
Figure DEST_PATH_IMAGE087
The iterative process is ended;
wherein the fitness function is as follows:
Figure DEST_PATH_IMAGE089
；
the step 5 specifically includes:
substituting the unknown number in the optimized welding parameter membership obtained in the step 4 into a calculation formula of the welding parameter membership, and calculating the membership of each input welding parameter in a fuzzy manner, wherein the fuzzy operator is a continuous multiplication operator:
Figure DEST_PATH_IMAGE091
。
2. the weld joint shape prediction method based on the improved adaptive fuzzy neural network according to claim 1, wherein the step 1 specifically comprises:
describing the weld shape prediction fuzzy neural network by using an if-then definition rule, wherein the weld shape prediction fuzzy neural network can be defined by using the following if-then rule:
Figure DEST_PATH_IMAGE093
wherein,
Figure DEST_PATH_IMAGE095
is the first
Figure 794090DEST_PATH_IMAGE016
The rule of the welding of the bars is fuzzy,
Figure DEST_PATH_IMAGE097
，
Figure DEST_PATH_IMAGE099
is the number of weld fuzzy rules;
Figure DEST_PATH_IMAGE101
is a variable of a welding parameter
Figure DEST_PATH_IMAGE103
To (1) a
Figure DEST_PATH_IMAGE104
A fuzzy subset;
Figure DEST_PATH_IMAGE106
is an input welding parameter variable, and
Figure 299764DEST_PATH_IMAGE002
；
Figure 42461DEST_PATH_IMAGE020
for the number of welding parameter variables that are input,
Figure DEST_PATH_IMAGE108
is the first
Figure DEST_PATH_IMAGE109
Output of the bar fuzzy rule, wherein
Figure DEST_PATH_IMAGE111
Are the weight coefficients.
3. The weld seam shape prediction method based on the improved adaptive fuzzy neural network according to claim 1, wherein the step 2 specifically comprises:
the flow of shielding gas, the welding speed, the wire feeding speed and the laser power are taken as four input welding parameter variables of the fuzzy neural network, and the welding width and the welding height are respectively taken as network output.
4. The weld seam shape prediction method based on the improved adaptive fuzzy neural network according to claim 1, wherein the step 6 specifically comprises:
and (5) substituting the fuzzy result obtained in the step (5) into an equation to calculate the predicted output values of the width and the height of the welding seam of the robot:
Figure DEST_PATH_IMAGE113
wherein,
Figure DEST_PATH_IMAGE115
is a joint product of the degrees of membership of the input weld parameters, a weight
Figure DEST_PATH_IMAGE117
The following formula is adopted for updating:
Figure DEST_PATH_IMAGE119
Figure DEST_PATH_IMAGE121
wherein,
Figure DEST_PATH_IMAGE123
is the network learning rate;
Figure DEST_PATH_IMAGE125
welding parameters input for the neural network;
Figure DEST_PATH_IMAGE127
in the formula,
Figure DEST_PATH_IMAGE129
is the expected weld width or weld height output of the network;
Figure DEST_PATH_IMAGE131
the actual welding seam width or welding height output of the network is realized;
Figure DEST_PATH_IMAGE133
is the error between the desired output and the actual output.
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