CN111951908A - Strain-displacement construction method of flexible material under action of external load - Google Patents
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- 238000006073 displacement reaction Methods 0.000 title claims abstract description 63
- 239000000463 material Substances 0.000 title claims abstract description 56
- 238000010276 construction Methods 0.000 title description 8
- 238000013528 artificial neural network Methods 0.000 claims abstract description 62
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000005259 measurement Methods 0.000 claims abstract description 25
- 238000012937 correction Methods 0.000 claims description 12
- 230000001537 neural effect Effects 0.000 claims 1
- 238000005457 optimization Methods 0.000 abstract description 8
- 210000002569 neuron Anatomy 0.000 description 18
- 238000012549 training Methods 0.000 description 10
- 230000004913 activation Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 239000000835 fiber Substances 0.000 description 2
- 239000000758 substrate Substances 0.000 description 2
- 230000002194 synthesizing effect Effects 0.000 description 2
- 241001391944 Commicarpus scandens Species 0.000 description 1
- 229910021417 amorphous silicon Inorganic materials 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000010408 film Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000010008 shearing Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000010409 thin film Substances 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/0058—Kind of property studied
- G01N2203/0069—Fatigue, creep, strain-stress relations or elastic constants
- G01N2203/0075—Strain-stress relations or elastic constants
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/0202—Control of the test
- G01N2203/0212—Theories, calculations
- G01N2203/0218—Calculations based on experimental data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/06—Indicating or recording means; Sensing means
- G01N2203/067—Parameter measured for estimating the property
- G01N2203/0682—Spatial dimension, e.g. length, area, angle
Abstract
The invention discloses a method for constructing a strain-displacement relation of a flexible material under the action of an external load, which is used for correcting a system error when a product manufactured by using the flexible material is subjected to strain-displacement measurement. The method is based on basic measurement data, a neural network fitting method is selected, single fitting is carried out on measurement strain and measurement displacement, then cross fitting is carried out on the fitted data, and finally a corrected strain-corrected displacement relation is obtained. The method is used for correcting the uncertain system errors, and solves the problem that the measurement errors caused by the system error characteristics of the measurement tool affect the analysis and optimization of subsequent products.
Description
Technical Field
The invention relates to a method for constructing a strain-displacement relation, in particular to a method for constructing a strain-displacement relation of a flexible material under the action of an external load.
Background
Compared with the traditional rigid material, the flexible material has excellent structural performance, and also has good performances in the aspects of electric conductivity, heat conductivity, expansion performance and the like, and is gradually applied to more product designs. Such as PI-substrate-based magnetic acoustic surface wave resonators, amorphous silicon thin films prepared on PMDS substrates, ITO transparent conductive films, etc., flexible materials play an important role therein. With the appearance of more and more flexible material products, the optimization of the structure of the electromechanical product containing the flexible material is also a concern. However, compared with a rigid material, the flexible material has the problem of high nonlinearity of a strain-displacement relation during a material structure test, and due to the system error of a measuring instrument used in the current measuring method, the strain and displacement detection has certain errors, and only after the errors are corrected, the product can be designed and optimized at a later stage to obtain a better result. The inverse finite element method is used as an auxiliary tool for constructing the displacement field, and the whole structural displacement field required in the analysis process can be constructed through the conversion of the shape function only by a small amount of measurement data. The existing strain-displacement relation structure is directed at rigid structure analysis of airplane wings subjected to aerodynamic load, or strain-displacement field reconstruction of rigid structures like patent CN201910000649.6, and the like, and CN110470236A provides a flexible structure deformation reconstruction method embedded with fiber bragg gratings, and is also directed at large structures like wings, and due to the force transmission effect between the gratings and the structural materials, the optical fibers are easy to break under the shearing force effect, and in patent CN109766617A, the large deformation and large displacement measurement effect of a component based on the fiber bragg grating sensor is poor. Due to the influence of the scale effect, when the flexible structure is small, the gratings and the connections arranged on the surface of the structure influence the overall mass distribution and other properties of the structure, and the strain-displacement field reconstruction of the flexible material cannot be performed. In addition, because different circuits, micro-sensing devices, micro-execution devices and other structures are distributed on the flexible material, and the deformation of different parts does not show the same regularity, the strain-displacement relation cannot be mathematically expressed by a conventional fitting mode, and no specific solution is provided for the construction of the strain-displacement relation concerned in the application of the existing flexible material.
The invention content is as follows:
the invention aims to provide a strain-displacement relation construction method related to a flexible material aiming at the error existing in the existing strain-displacement construction method.
In order to achieve the purpose, the technical scheme of the invention is as follows: a method for constructing a strain-displacement relation of a flexible material comprises the following steps:
step 1: and installing strain gauges and displacement sensors on joint points on the flexible material structure to obtain a measured strain value and a measured displacement value of each joint point.
Step 2: and fitting a neural network to the measured strain value to obtain the overall measured strain data of the flexible material structure, and specifically comprises the following steps:
step 21: the activation function of the neural network is set to ReLU.
Step 22: and setting the maximum and minimum neuron numbers of the neural network and the number of layers of the neural network according to the final required result precision and the hardware operation condition.
Step 23: and setting the stop condition of the neural network according to the optimization requirement of the product.
Step 24: after the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measurement strain value of the relevant node is input.
Step 25: training the neural network, fixing the weight and the threshold value when a stopping condition is reached or the number of the neurons reaches the maximum value, and outputting the overall measurement strain data of the flexible material structure; otherwise, updating the weight value and the threshold value and increasing the number of the neurons until the output condition is met.
And step 3: and fitting a neural network on the measured displacement value to obtain the whole displacement data of the flexible material structure, and specifically comprises the following steps:
step 31: the activation function of the neural network is set to ReLU.
Step 32: and setting the maximum and minimum neuron numbers of the neural network and the number of layers of the neural network according to the final required result precision and the hardware operation condition.
Step 33: and setting the stop condition of the neural network according to the optimization requirement of the product.
Step 34: after the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measurement displacement value of the relevant node is input.
Step 35: training the neural network, fixing the weight and the threshold value when a stopping condition is reached or the number of the neurons reaches the maximum value, and outputting the integral displacement data of the flexible material structure; otherwise, updating the weight value and the threshold value and increasing the number of the neurons until the output condition is met.
And 4, step 4: and (3) inverse solving the flexible material structure integral displacement data output by the neural network by using an inverse finite element method to obtain the flexible material structure integral inverse solution strain data.
And 5: synthesizing the overall measurement strain data of the flexible material structure and the overall inverse solution strain data of the flexible material structure, and obtaining the corrected strain by applying neural network fitting training, wherein the steps are as follows:
step 51: the activation function of the neural network is set to ReLU.
Step 52: and setting the maximum and minimum neuron numbers of the neural network and the number of layers of the neural network according to the final required result precision and the hardware operation condition.
Step 53: and setting the stop condition of the neural network according to the optimization requirement of the product.
Step 54: after the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measurement displacement value of the relevant node is input.
Step 55: training a neural network, fixing a weight and a threshold value when a stopping condition is reached or the number of neurons reaches a maximum value, and outputting a relation between correction strain, measurement strain and inverse solution strain; otherwise, updating the weight value and the threshold value and increasing the number of the neurons until the output condition is met.
Step 6: and (4) solving the correction strain by applying the relation of correction strain-measurement strain-inverse solution strain completed by the training of the neural network.
And 7: thereby obtaining the relation of the correction strain and the correction displacement.
Description of the drawings:
FIG. 1 is a flow chart of a method for constructing a strain-displacement relationship of a flexible material under an external load.
The specific implementation mode is as follows:
the invention will now be further described with reference to the accompanying drawings and specific embodiments.
The invention aims to provide a strain-displacement relation construction method related to a flexible material, aiming at the problem that the existing strain-displacement relation construction method has errors.
In order to achieve the purpose, the technical scheme of the invention is as follows: a strain-displacement construction method of a flexible material comprises the following steps:
step 1: and installing strain gauges and displacement sensors on joint points on the flexible material structure to obtain a measured strain value and a measured displacement value of each joint point.
Step 2: and fitting a neural network to the measured strain value to obtain the overall measured strain data of the flexible material structure, and specifically comprises the following steps:
step 21: the activation function of the neural network is set to ReLU.
Step 22: and setting the maximum and minimum neuron numbers of the neural network and the number of layers of the neural network according to the final required result precision and the hardware operation condition.
Step 23: and setting the stop condition of the neural network according to the optimization requirement of the product.
Step 24: after the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measurement strain value of the relevant node is input.
Step 25: training the neural network, fixing the weight and the threshold value when a stopping condition is reached or the number of the neurons reaches the maximum value, and outputting the overall measurement strain data of the flexible material structure; otherwise, updating the weight value and the threshold value and increasing the number of the neurons until the output condition is met.
And step 3: and fitting a neural network on the measured displacement value to obtain the whole displacement data of the flexible material structure, and specifically comprises the following steps:
step 31: the activation function of the neural network is set to ReLU.
Step 32: and setting the maximum and minimum neuron numbers of the neural network and the number of layers of the neural network according to the final required result precision and the hardware operation condition.
Step 33: and setting the stop condition of the neural network according to the optimization requirement of the product.
Step 34: after the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measurement displacement value of the relevant node is input.
Step 35: training the neural network, fixing the weight and the threshold value when a stopping condition is reached or the number of the neurons reaches the maximum value, and outputting the integral displacement data of the flexible material structure; otherwise, updating the weight value and the threshold value and increasing the number of the neurons until the output condition is met.
And 4, step 4: and (3) inverse solving the flexible material structure integral displacement data output by the neural network by using an inverse finite element method to obtain the flexible material structure integral inverse solution strain data.
And 5: synthesizing the overall measurement strain data of the flexible material structure and the overall inverse solution strain data of the flexible material structure, and obtaining the corrected strain by applying neural network fitting training, wherein the steps are as follows:
step 51: the activation function of the neural network is set to ReLU.
Step 52: and setting the maximum and minimum neuron numbers of the neural network and the number of layers of the neural network according to the final required result precision and the hardware operation condition.
Step 53: and setting the stop condition of the neural network according to the optimization requirement of the product.
Step 54: after the initialization of the neural network is completed, the forward propagation of the neural network is activated, and the measurement displacement value of the relevant node is input.
Step 55: training a neural network, fixing a weight and a threshold value when a stopping condition is reached or the number of neurons reaches a maximum value, and outputting a relation between correction strain, measurement strain and inverse solution strain; otherwise, updating the weight value and the threshold value and increasing the number of the neurons until the output condition is met.
Step 6: and (4) solving the correction strain by applying the relation of correction strain-measurement strain-inverse solution strain completed by the training of the neural network.
And 7: thereby obtaining the relation of the correction strain and the correction displacement.
Claims (7)
1. A method for constructing a strain-displacement relationship of a flexible material under an external load, comprising the following steps:
step 1, acquiring basic measurement data of nodes, acquiring strain data of each node by adopting a strain gauge, and acquiring displacement data of each node by adopting a displacement sensor.
Step 2: performing single fitting on the obtained basic measurement data by using a neural network fitting method; and fitting the measured strain data of the nodes through a neural network to obtain overall measured strain data, and fitting the measured displacement data of the nodes through the neural network to obtain overall measured displacement data.
And step 3: and solving the overall measured displacement data obtained by fitting by using an inverse finite element method to obtain overall inverse solution strain data.
And 4, step 4: and performing cross fitting on the overall measured strain data obtained by fitting and the overall inverse solution strain data inversely solved by using an inverse finite element method by using a neural network fitting method to obtain the relation between the measured strain, the corrected strain and the inverse solution strain.
And 5: and after the correction strain is obtained, solving the correction displacement.
2. The method for constructing the strain-displacement relationship of the flexible material under the action of the external load as claimed in claim 1, wherein each node is arranged on the whole structure of the flexible material, and the strain gauge and the displacement sensor are used for obtaining the measured strain data and the measured displacement data of each node.
3. The method of claim 1, wherein a neural network fitting method, such as an artificial neural network, a BP neural network, etc., is used to perform a single fitting of the measured strain data and the measured displacement data to obtain the overall measured strain data and the overall measured displacement data of the flexible material structure.
4. The method for constructing the strain-displacement relationship of the flexible material under the action of the external load according to claim 1, wherein the overall inverse solution strain data of the flexible material structure is obtained by inverse solution of the overall measurement displacement data of the flexible material structure fitted by using a neural network based on an inverse finite element method.
5. The method for constructing the strain-displacement relationship of the flexible material under the action of the external load as claimed in claim 1, wherein a neural fitting method is used to cross-fit the overall measured strain data of the flexible material structure fitted by using the neural network and the inverse solved strain data of the overall inverse flexible material structure, so as to obtain the relationship between the measured strain-corrected strain-inverse solved strain.
6. The method for constructing the strain-displacement relationship of the flexible material under the action of the external load as claimed in claim 1, wherein after the data of the overall corrected strain of the flexible material structure is obtained, the overall corrected displacement of the flexible material structure can be obtained.
7. The method for constructing the strain-displacement relationship of the flexible material under the action of the external load as claimed in claim 1, wherein the overall "corrected strain-corrected displacement" relationship of the flexible material structure is obtained after the overall corrected displacement of the flexible material structure is obtained.
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