CN111353245B - Mechanical elastic wheel wear degree measuring method - Google Patents
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Abstract
The invention discloses a method for measuring the wear degree of a mechanical elastic wheel, which comprises the following steps: establishing a finite element model of the mechanical elastic wheel; verifying the correctness of the finite element model of the mechanical elastic wheel by using a mechanical elastic wheel modal test analysis system; calculating the natural frequency of each step of the mechanical elastic wheel under different wear degrees by using the verified mechanical elastic wheel finite element model; training a BP neural network model by taking the inherent frequency values of each order obtained by calculation as the input quantity of the BP neural network and the abrasion quantity of the mechanical elastic wheel as the output quantity of the BP neural network; and measuring the inherent frequency of each order of the mechanical elastic wheel, inputting the measured inherent frequency into the trained BP neural network, and solving to obtain the abrasion loss of the mechanical elastic wheel. The invention can accurately measure the mechanical elastic wheel pattern thickness under different wear degrees, ensure the tire pattern thickness within a safe range and reduce the safety problem caused by excessive wear of the tire.
Description
Technical Field
The invention belongs to the technical field of wear detection, and particularly relates to a wheel wear degree measuring method.
Background
In the driving process of a vehicle, the mechanical elastic wheel and the ground can be always rubbed, the tire can be worn after long-time friction of the pattern of the tire, the pattern of the tire can be gradually thinned, if the pattern of the tire is excessively worn, the thickness of the pattern of the tire is smaller than the safe thickness, the braking performance of the wheel in the driving process can be reduced, the driving smoothness is reduced, and even traffic safety accidents occur, so that how to accurately measure the thickness of the pattern of the mechanical elastic wheel under different wear degrees is realized, and the fact that the thickness of the pattern of the tire is in the safe range is ensured to be particularly important.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the invention provides a method for measuring the wear degree of a mechanical elastic wheel, which can accurately measure the pattern thickness of the mechanical elastic wheel under different wear degrees.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a method for measuring the wear degree of a mechanical elastic wheel comprises the following steps:
(1) establishing a three-dimensional model of the mechanical elastic wheel by using three-dimensional modeling software, importing the established three-dimensional model into finite element analysis software, and establishing a finite element model of the mechanical elastic wheel;
(2) performing modal test on the mechanical elastic wheel by using a mechanical elastic wheel modal test analysis system, and measuring to obtain the front m-order natural frequency and the natural vibration mode of the mechanical elastic wheel under the free suspension working condition, wherein m is more than or equal to 6 and less than or equal to 20; calculating to obtain the inherent frequency and the inherent vibration mode of the front m orders of the mechanical elastic wheel under the free suspension working condition by using the finite element model of the mechanical elastic wheel established in the step (1); comparing the natural frequency and the natural vibration mode obtained under the two conditions, and if the error between the two conditions is less than or equal to a set error threshold value, determining that the finite element model of the mechanical elastic wheel meets the requirement of accuracy; if the error between the two is larger than the set error threshold, the mechanical elastic wheel needs to be modeled again until the precision requirement is met;
(3) changing the pattern thickness of a finite element model of the mechanical elastic wheel, simulating the mechanical elastic wheel under different wear degrees, and calculating the first m-order natural frequency of the mechanical elastic wheel under the condition of different pattern thicknesses under the free suspension working condition by using finite element analysis software;
(4) taking the front m-order natural frequency of the mechanical elastic wheel under the free suspension working condition under different pattern thicknesses obtained by the step (3) as the input quantity of the BP neural network, taking the pattern thickness as the output quantity of the BP neural network, and training the BP neural network to obtain a trained BP neural network model;
(5) and (3) for the mechanical elastic wheel with any wear degree, measuring by using a mechanical elastic wheel modal test analysis system to obtain the first m-order natural frequency of the mechanical elastic wheel under the free suspension working condition, and inputting the natural frequency value into the BP neural network model trained in the step (4) to obtain the pattern thickness of the mechanical elastic wheel, namely the wear degree of the wheel.
Further, the specific process of step (1) is as follows:
(101) establishing a three-dimensional model of parts of the mechanical elastic wheel by using three-dimensional modeling software, wherein the three-dimensional model comprises a hub, a gasket, a hub connecting piece, an intermediate connecting piece, a hinge connecting piece, a clamping ring, an elastic ring and a rubber tire body, and assembling the parts to obtain a mechanical elastic wheel three-dimensional assembly body;
(102) importing the assembled mechanical elastic wheel three-dimensional model into finite element analysis software;
(103) endowing each part of the mechanical elastic wheel with corresponding material properties including density, Young modulus and Poisson ratio; and then setting corresponding contact attributes, and carrying out meshing on the mechanical elastic wheel to obtain a meshed mechanical elastic wheel finite element model.
Further, in step (103), for the rubber material, a Mooney-Rivlin model is selected as a superelasticity constitutive model thereof, and a strain energy density function W of the Mooney-Rivlin model of the rubber material is expressed as follows:
W＝C10(I1-3)+C01(I2-3)
in the above formula, C10、C01Is the material coefficient; i is1Is a first strain invariant; i is2Is a second strain invariant.
Further, in the step (2), the specific process of performing the modal test on the mechanical elastic wheel by using the mechanical elastic wheel modal test analysis system is as follows:
(201) installing and fixing a mechanical elastic wheel modal test analysis system on a horizontal ground, and installing a mechanical elastic wheel on the modal test analysis system in a hanging manner by using a soft rope to simulate the free suspension state of the mechanical elastic wheel;
(202) the method comprises the steps of knocking a wheel by using a force hammer for excitation input, guiding the wheel into a data acquisition and processing system by using a charge amplifier, and carrying out modal identification and verification on data by using a time domain analysis method and a frequency domain analysis method to obtain the first m-order natural frequency and the vibration mode of the wheel to be tested.
Further, in the step (3), the specific process of calculating the first m-order natural frequency of the mechanical elastic wheel under the free suspension working condition under the conditions of different pattern thicknesses by using finite element analysis software is as follows:
(301) calculating the front m-order natural frequency of the mechanical elastic wheel under the initial thickness under the free suspension working condition by using finite element analysis software;
(302) setting the maximum thickness of the mechanical elastic wheel pattern as dmaxThe minimum thickness of the mechanical elastic wheel for safe running of the vehicle is dmin(ii) a Let the pattern thickness deviation of the mechanically elastic wheel be deThe pattern thickness of the mechanical elastic wheel is formed by dmax+deGradually reducing the pattern thickness of the elastic wheel according to a preset thickness threshold delta d until the pattern thickness of the mechanical elastic wheel is smaller than dmin-de；
(303) And (3) respectively calculating the front m-order natural frequency of the mechanical elastic wheel under the free suspension working condition under each step of pattern thickness in the step (302) by using finite element analysis software to obtain the front m-order natural frequency of the mechanical elastic wheel under different pattern thicknesses under the free suspension working condition.
Further, in step (4), the BP neural network model includes an input layer, a hidden layer and an output layer, the number of neurons in the input layer is m, the number of neurons in the hidden layer is p, the number of neurons in the output layer is 1, the hidden layer adopts an S-type transfer function, and the transfer function in the output layer is purelin;
a sample pair (X, Y) of the BP neural network is X ═ X1,x2,...,xm]T，Y＝[y1]TThe hidden layer neuron is O ═ O1,O2,...,Op](ii) a Wherein x isiRepresenting the ith input layer neuron of the BP neural network, wherein i is more than or equal to 1 and less than or equal to m; y is1Representing a BP neural network output layer neuron; o isjRepresenting the jth hidden layer neuron of the BP neural network, wherein j is more than or equal to 1 and less than or equal to p, and superscript T represents transposition;
the output of the hidden layer neurons is as follows:
Oj＝tan sing(netj) j＝1,2,...,p
wherein the content of the first and second substances,
is the network weight between the neuron of the input layer and the hidden layer;
a threshold value for hidden layer neurons;
the output of the output layer neurons is as follows:
zk＝purelin(netk) k＝1
wherein the content of the first and second substances,
network weights between the hidden layer neurons and the output layer neurons;
is the threshold of the output layer neurons;
the error between the network output and the desired output is
Further, in order to reduce the error E between the network output and the expected output, the weight and the threshold of the BP neural network are adjusted along the negative gradient direction, and the weight is adjusted as follows:
the threshold adjustment is as follows:
wherein eta is1A learning step size for the hidden layer; eta2A learning step size for the output layer;
t represents the current time, and t +1 represents the next time.
Adopt the beneficial effect that above-mentioned technical scheme brought:
(1) when the abrasion loss of the mechanical elastic wheel is detected, the abrasion loss does not need to be directly measured, so that the measurement error caused by irregular abrasion of the tire can be reduced, and the accuracy of measuring the abrasion degree of the mechanical elastic wheel is improved;
(2) the invention does not need precise and complicated instrument equipment, and has low measurement cost;
(3) the invention has wide application range and simple operation.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is an assembly view of the mechanically resilient wheel;
FIG. 3 is a finite element model diagram of a mechanically resilient wheel;
FIG. 4 is a schematic view of a mechanical elastic wheel modal test analysis system;
FIG. 5 is a graph of the natural frequency and the natural mode shape of the first 6 th order obtained from a mechanical elastic wheel test;
FIG. 6 is a graph of the natural frequency and natural mode shape of the first 6 th order of a finite element analysis of a mechanically resilient wheel;
FIG. 7 is a schematic illustration of a mechanically resilient wheel pattern thickness;
FIG. 8 is a schematic diagram of a BP neural network model;
FIG. 9 is a graph of the results of a mechanical elastic wheel pattern thickness measurement.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
The invention designs a method for measuring the wear degree of a mechanical elastic wheel, which comprises the following steps as shown in figure 1:
step 1: establishing a three-dimensional model of the mechanical elastic wheel by using three-dimensional modeling software, importing the established three-dimensional model into finite element analysis software, and establishing a finite element model of the mechanical elastic wheel;
step 2: performing modal test on the mechanical elastic wheel by using a mechanical elastic wheel modal test analysis system, and measuring to obtain the front m-order natural frequency and the natural vibration mode of the mechanical elastic wheel under the free suspension working condition, wherein m is more than or equal to 6 and less than or equal to 20; calculating to obtain the inherent frequency and the inherent vibration mode of the front m orders of the mechanical elastic wheel under the free suspension working condition by using the finite element model of the mechanical elastic wheel established in the step 1; comparing the natural frequency and the natural vibration mode obtained under the two conditions, and if the error between the two conditions is less than or equal to a set error threshold value, determining that the finite element model of the mechanical elastic wheel meets the requirement of accuracy; if the error between the two is larger than the set error threshold, the mechanical elastic wheel needs to be modeled again until the precision requirement is met;
and 3, step 3: changing the pattern thickness of a finite element model of the mechanical elastic wheel, simulating the mechanical elastic wheel under different wear degrees, and calculating the front m-order natural frequency of the mechanical elastic wheel under the condition of different pattern thicknesses under the free suspension working condition by using finite element analysis software;
and 4, step 4: taking the front m-order natural frequency of the mechanical elastic wheel under the free suspension working condition under different pattern thicknesses obtained by the step (3) as the input quantity of the BP neural network, taking the pattern thickness as the output quantity of the BP neural network, and training the BP neural network to obtain a trained BP neural network model;
and 5: and (4) for the mechanical elastic wheel with any wear degree, measuring by using a mechanical elastic wheel modal test analysis system to obtain the first m-order natural frequency of the mechanical elastic wheel under the free suspension working condition, and inputting the natural frequency value into the BP neural network model trained in the step (4) to obtain the pattern thickness of the mechanical elastic wheel, namely the wear degree of the wheel.
In this embodiment, preferably, the step 1 is implemented by the following preferred scheme:
step 1.1, as an embodiment, as shown in fig. 2, a three-dimensional modeling software CREO is used to establish a three-dimensional model of a part of a mechanical elastic wheel, which includes a hub, a gasket, a hub connecting piece, an intermediate connecting piece, a hinge connecting piece, a snap ring, an elastic ring, a rubber tire body and the like, and the part is assembled to obtain a mechanical elastic wheel three-dimensional assembly body;
step 1.2, importing the assembled mechanical elastic wheel three-dimensional model into finite element analysis software ABAQUS;
step 1.3, endowing corresponding material attributes including parameters such as density, Young modulus, Poisson ratio and the like to each part of the mechanical elastic wheel, wherein a Mooney-Rivlin model is selected as a rubber superelasticity constitutive model for the rubber material, and a strain energy density function W of the Mooney-Rivlin model for the rubber material is as follows:
W＝C10(I1-3)+C01(I2-3)
in the above formula, C10、C01Is the material coefficient; I.C. A1Is a first strain invariant; i is2Is a second strain invariant;
then setting corresponding contact attributes, wherein the normal contact attribute is set as hard contact, and the tangential contact attribute is set as penalty;
step 1.4, as an embodiment, as shown in fig. 3, performing mesh division on the mechanical elastic wheel, wherein the rubber material adopts C3D8RH units, and the remaining materials are C3D8R units, so as to obtain a finite element model of the mechanical elastic wheel with divided meshes.
In this embodiment, preferably, the step 2 is implemented by the following preferred scheme:
step 2.1, installing and fixing a mechanical elastic wheel modal test analysis system on a horizontal ground, and installing the mechanical elastic wheel on the modal test analysis system in a hanging manner by using a soft rope to simulate the free suspension state of the mechanical elastic wheel, as shown in fig. 4;
step 2.2, knocking the wheel by using a force hammer for excitation input, guiding the wheel into a data acquisition and processing system by using a charge amplifier, and performing modal identification and verification on the data by using a time domain analysis and frequency domain analysis method to obtain the first 6-order natural frequency and the mode shape of the measured wheel shown in the figure 5;
step 2.3, calculating to obtain the first 6-order natural frequency and the natural vibration mode of the mechanical elastic wheel under the free suspension working condition shown in the figure 6 by using the precise finite element model of the mechanical elastic wheel in the step 1;
step 2.4, comparing the natural frequency and the natural vibration mode of the mechanical elastic wheel obtained in the step 2.2 with the natural frequency and the natural vibration mode of the mechanical elastic wheel obtained in the step 2.3, and if the error between the natural frequency and the natural vibration mode is smaller than an error threshold value, determining that the finite element model meets the precision requirement; and if the error between the two is larger than the error threshold value, repeating the step 1, and modeling the mechanical elastic wheel again until the finite element model meets the precision requirement.
In this embodiment, preferably, the step 3 is implemented by the following preferred scheme:
step 3.1, calculating the first 6-order natural frequency of the mechanical elastic wheel under the initial thickness under the free suspension working condition by using ABAQUS;
step 3.2, as an example, as shown in fig. 7, the maximum thickness of the mechanically elastic wheel pattern is dmaxThe minimum thickness of the mechanical elastic wheel for safe running of the vehicle is dmin1.6 mm; to ensure the accuracy of the measurements made by the method, the deviation of the pattern thickness of the mechanically elastic wheel is set to deD is 0.5mm, and the pattern thickness of the mechanical elastic wheel is measuredmax+deGradually reducing the pattern thickness of the elastic wheel according to a preset thickness threshold value delta d of 0.5mm until the pattern thickness of the mechanical elastic wheel is smaller than dmin-de＝1.1mm；
And 3.3, respectively calculating the first 6-order natural frequencies of the mechanical elastic wheel under the free suspension working condition under the pattern thickness in the step 3.2 by using ABAQUS to obtain the first 6-order natural frequencies of the mechanical elastic wheel under the free suspension working condition with different pattern thicknesses.
In this embodiment, preferably, the step 4 is implemented by the following preferred scheme:
as shown in fig. 8, the BP neural network model includes an input layer, a hidden layer, and an output layer, where the input layer has a neuron number m, the hidden layer has a neuron number p, the output layer has a neuron number 1, the hidden layer adopts an S-type transfer function, and the output layer has a purelin transfer function;
a sample pair (X, Y) of the BP neural network is X ═ X1,x2,...,xm]T，Y＝[y1]TThe hidden layer neuron is O ═ O1,O2,...,Op](ii) a Wherein x isiRepresenting the ith input layer neuron of the BP neural network, wherein i is more than or equal to 1 and less than or equal to m; y is1Representing a BP neural network output layer neuron; o isjRepresenting the jth hidden layer neuron of the BP neural network, wherein j is more than or equal to 1 and less than or equal to p, and superscript T represents transposition;
the output of the hidden layer neurons is as follows:
Oj＝tansing(netj) j＝1,2,...,p
wherein the content of the first and second substances,
is the network weight between the neuron of the input layer and the hidden layer;
a threshold value for hidden layer neurons;
the output of the output layer neurons is as follows:
zk＝purelin(netk) k＝1
wherein, the first and the second end of the pipe are connected with each other,
network weights between the hidden layer neurons and the output layer neurons;
is the threshold of the output layer neurons;
the error between the network output and the desired output is
In order to reduce the error E between the network output and the expected output, the weight and the threshold of the BP neural network are adjusted along the direction of the negative gradient, and the weight is adjusted as follows:
the threshold adjustment is as follows:
wherein eta is1A learning step size for the hidden layer; eta2A learning step size for the output layer;
t represents the current time, and t +1 represents the next time.
In step 5, as an embodiment, as shown in fig. 9, for a mechanical elastic wheel under a certain degree of wear, a mechanical elastic wheel modal test analysis system is used to measure and obtain the first 6-order natural frequency of the mechanical elastic wheel under the free suspension condition, and then the natural frequency value is input into a pre-trained BP neural network model, so that the pattern thickness of the mechanical elastic wheel can be obtained, and the degree of wear of the wheel can be obtained.
Claims (6)
1. A method for measuring the wear degree of a mechanical elastic wheel is characterized by comprising the following steps:
(1) establishing a three-dimensional model of the mechanical elastic wheel by using three-dimensional modeling software, importing the established three-dimensional model into finite element analysis software, and establishing a finite element model of the mechanical elastic wheel;
(2) performing modal test on the mechanical elastic wheel by using a mechanical elastic wheel modal test analysis system, and measuring to obtain the front m-order natural frequency and the natural vibration mode of the mechanical elastic wheel under the free suspension working condition, wherein m is more than or equal to 6 and less than or equal to 20; calculating to obtain the inherent frequency and the inherent vibration mode of the front m orders of the mechanical elastic wheel under the free suspension working condition by using the finite element model of the mechanical elastic wheel established in the step (1); comparing the natural frequency and the natural vibration mode obtained under the two conditions, and if the error between the two conditions is less than or equal to a set error threshold value, determining that the finite element model of the mechanical elastic wheel meets the requirement of accuracy; if the error between the two is larger than the set error threshold, the mechanical elastic wheel needs to be modeled again until the precision requirement is met;
(3) changing the pattern thickness of a finite element model of the mechanical elastic wheel, simulating the mechanical elastic wheel under different wear degrees, and calculating the first m-order natural frequency of the mechanical elastic wheel under the condition of different pattern thicknesses under the free suspension working condition by using finite element analysis software;
the specific process of calculating the front m-order natural frequency of the mechanical elastic wheel under the free suspension working condition under the condition of different pattern thicknesses by using finite element analysis software is as follows:
(301) calculating the front m-order natural frequency of the mechanical elastic wheel under the initial thickness under the free suspension working condition by using finite element analysis software;
(302) setting the maximum thickness of the mechanical elastic wheel pattern as dmaxThe minimum thickness of the mechanical elastic wheel for safe running of the vehicle is dmin(ii) a Let the pattern thickness deviation of the mechanically elastic wheel be deThe pattern thickness of the mechanical elastic wheel is formed by dmax+deGradually reducing the pattern thickness of the elastic wheel according to a preset thickness threshold delta d until the pattern thickness of the mechanical elastic wheel is smaller than dmin-de；
(303) Respectively calculating the front m-order natural frequency of the mechanical elastic wheel under the free suspension working condition under each step of pattern thickness in the step (302) by using finite element analysis software to obtain the front m-order natural frequency of the mechanical elastic wheel under different pattern thicknesses under the free suspension working condition;
(4) taking the first m-order natural frequency of the mechanical elastic wheel under the free suspension working condition under different pattern thicknesses obtained by the step (3) as the input quantity of a BP (back propagation) neural network, taking the pattern thickness as the output quantity of the BP neural network, and training the BP neural network to obtain a trained BP neural network model;
(5) and (3) for the mechanical elastic wheel with any wear degree, measuring by using a mechanical elastic wheel modal test analysis system to obtain the first m-order natural frequency of the mechanical elastic wheel under the free suspension working condition, and inputting the natural frequency value into the BP neural network model trained in the step (4) to obtain the pattern thickness of the mechanical elastic wheel, namely the wear degree of the wheel.
2. The method for measuring the wear degree of the mechanical elastic wheel according to the claim 1, characterized in that the specific process of the step (1) is as follows:
(101) establishing a three-dimensional model of parts of the mechanical elastic wheel by using three-dimensional modeling software, wherein the three-dimensional model comprises a hub, a gasket, a hub connecting piece, an intermediate connecting piece, a hinge connecting piece, a clamping ring, an elastic ring and a rubber tire body, and assembling the parts to obtain a mechanical elastic wheel three-dimensional assembly body;
(102) importing the assembled mechanical elastic wheel three-dimensional model into finite element analysis software;
(103) endowing each part of the mechanical elastic wheel with corresponding material properties including density, Young modulus and Poisson ratio; and then setting corresponding contact attributes, and carrying out meshing on the mechanical elastic wheel to obtain a meshed mechanical elastic wheel finite element model.
3. The method for measuring the degree of wear of a mechanically elastic wheel according to claim 2, wherein in step (103), a Mooney-Rivlin model is selected as the superelastic constitutive model for the rubber material, and the strain energy density function W of the Mooney-Rivlin model for the rubber material is expressed as follows:
W＝C10(I1-3)+C01(I2-3)
in the above formula, C10、C01Is the material coefficient; i is1Is a first strain invariant; i is2Is a second strain invariant.
4. The method for measuring the wear degree of the mechanical elastic wheel according to claim 1, wherein in the step (2), the modal test of the mechanical elastic wheel by using the mechanical elastic wheel modal test analysis system comprises the following specific processes:
(201) the method comprises the following steps of (1) installing and fixing a mechanical elastic wheel modal test analysis system on a horizontal ground, and installing a mechanical elastic wheel on the modal test analysis system in a hanging manner by using a soft rope to simulate the free suspension state of the mechanical elastic wheel;
(202) the method comprises the steps of knocking a wheel by using a force hammer for excitation input, guiding the wheel into a data acquisition and processing system by using a charge amplifier, and carrying out modal identification and verification on data by using a time domain analysis method and a frequency domain analysis method to obtain the first m-order natural frequency and the vibration mode of the wheel to be tested.
5. The method for measuring the degree of wear of a mechanically elastic wheel according to claim 1, wherein in step (4), the BP neural network model comprises an input layer, a hidden layer and an output layer, the number of neurons in the input layer is m, the number of neurons in the hidden layer is p, the number of neurons in the output layer is 1, the hidden layer adopts an S-type transfer function, and the output layer transfer function is purelin;
a sample pair (X, Y) of the BP neural network is X ═ X1,x2,...,xm]T，Y＝[y1]TThe hidden layer neuron is O ═ O1,O2,...,Op](ii) a Wherein x isiRepresenting the ith input layer neuron of the BP neural network, wherein i is more than or equal to 1 and less than or equal to m; y is1Representing the neural network output layer neuron of BP; o isjRepresenting the jth hidden layer neuron of the BP neural network, wherein j is more than or equal to 1 and less than or equal to p, and superscript T represents transposition;
the output of the hidden layer neurons is as follows:
Oj＝tansing(netj) j＝1,2,...,p
wherein the content of the first and second substances,
is the network weight between the neuron of the input layer and the hidden layer;
a threshold value for hidden layer neurons;
the output of the output layer neurons is as follows:
zk＝purelin(netk) k＝1
wherein the content of the first and second substances,
network weights between the hidden layer neurons and the output layer neurons;
is the threshold of the output layer neurons;
the error between the network output and the desired output is
6. The method of measuring the degree of wear of a mechanically resilient wheel according to claim 5, wherein the weight and threshold of the BP neural network are adjusted in the direction of negative gradient in order to reduce the error E between the output of the network and the desired output, the weight being adjusted as follows:
the threshold adjustment is as follows:
wherein eta is1A learning step size for the hidden layer; eta2A learning step size for the output layer;
k is 1; t represents the current time, and t +1 represents the next time.
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