CN115906570A - Composite material open-hole laminated plate progressive damage prediction method based on data driving - Google Patents
Composite material open-hole laminated plate progressive damage prediction method based on data driving Download PDFInfo
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Abstract
A composite material perforated laminated plate progressive damage prediction method based on data driving belongs to the technical field of composite material calculation. First, a rapid parameterization of a numerical model incorporating the characteristics of an open-cell laminate is constructed. Secondly, a damage analysis database is constructed based on the Hashin criterion. In the numerical calculation process, the Hashin criterion is used for progressive damage analysis of the composite material to form a numerical result database. And finally, writing a BP neural network to carry out the prediction of the progressive damage failure behavior of the composite material structure based on data driving based on the data extracted from the model database and the result database. The method can realize accurate prediction of the mechanical property of the composite laminated plate based on the BP neural network, and the established theoretical model can accurately, quickly and effectively predict the initial damage position and the end displacement of the laminated plate with holes under the action of tensile load, thereby avoiding the problems of high cost and difficult operation caused by repeated modeling and calculation and providing a new strategy for improving the design and application of the composite material.
Description
Technical Field
The invention belongs to the technical field of computing composite materials, and relates to a method for predicting progressive damage of a composite material open-cell laminated plate by a data driving method.
Technical Field
The composite material has the characteristics of high specific strength, fatigue resistance, strong fracture resistance, corrosion resistance and the like, and has important application in the key fields of aerospace, medicine, vehicles and the like. In an extreme service environment, the composite material can be damaged, and the damage forms mainly comprise matrix cracking, fiber fracture and interface fracture. These internal damages severely degrade the mechanical properties of the composite structure, thereby reducing the strength and service life of the structure and bringing about potential safety hazards. From the perspective of composite material equipment design and efficient reliability evaluation, the rapid prediction technology for the damage behavior of the composite material has high application value.
At present, progressive damage prediction of composite laminates is based primarily on the results of damage-related parameters of stress, energy to break, damage variables, etc. to enable characterization of the laminate damage status. For example, modeling a stiffness degradation process of the composite laminated plate based on an ANN neural network, solving an accumulated damage variable D of the laminated plate, and predicting the stiffness of the whole cycle domain; predicting a microstructure stress-strain curve of the composite material by adopting a convolutional neural network so as to obtain the modulus, strength and toughness related to the damage; constructing a neural network to characterize a lesion parameter G based on geometric parameters on a nonlinear load-displacement curve f 、σ 1 、σ peak Progressive damage within a layer is quantified under limited test data. However, there have been related studies less focused on methods for directly predicting the damage start state of a laminate, such as the damage start position, the start end displacement, and the like.
Based on the method, the progressive damage mechanical behavior of the composite material open-cell laminated plate is predicted by adopting a data driving method, the initial damage position and the end displacement of the composite material laminated plate are directly predicted based on the load distribution form and the elliptical open-cell position, and a new way is provided for the rapid performance evaluation and the structural design of the composite material.
Disclosure of Invention
The invention provides a data-driven composite material open-cell laminated plate progressive damage prediction method. The method takes a composite material perforated laminated plate containing bolt holes, weight-reducing holes and the like as a research object, and inspects the damage failure behavior of the laminated plate containing elliptical holes under the stretching working condition. And carrying out parametric modeling and automatic post-processing based on finite element software, and establishing a database required by data driving. And compiling a BP neural network by using the preprocessed simulation result database to predict the progressive damage failure behavior of the composite material structure based on data driving.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a composite material open-hole laminated plate progressive damage prediction method based on data driving is characterized in that firstly, a numerical model of characteristics of an open-hole laminated plate is combined to be constructed in a rapid parameterization mode. The numerical model database is generated in batches quickly by mainly writing a Python modeling script to define the value range of related variables. Secondly, a damage analysis database is constructed based on the Hashin criterion. In the numerical calculation process, the Hashin criterion is used for progressive damage analysis of the composite material to form a numerical result database. And finally, writing a BP neural network to carry out the prediction of the progressive damage failure behavior of the composite material structure based on data driving based on the data extracted from the model database and the result database. The method comprises the following steps:
firstly, a rapid parametric modeling method combining the characteristics of the open-cell laminated plate is constructed.
Adopting a parametric modeling method to construct a numerical model, and compiling a Python modeling script according to a data file format specified by Abaqus, wherein the method comprises the following steps: establishing a geometric model of the open-cell laminate and the reinforcing sheet, wherein the reinforcing sheet is used to protect the open-cell laminate sample to provide an effective boundary load; giving material properties and dividing a structured grid on the basis of the geometric model; and establishing a binding relationship between the perforated laminated plate and the reinforcing sheet, and applying corresponding boundary constraint conditions and load working conditions. The parametric modeling process is realized based on Python language, and parameters related to the position of the opening, the layering angle, the load information and the grid size are set as variables, so that a numerical model database can be generated rapidly in batches by defining the value range of the variables.
And secondly, constructing a damage analysis result database based on Hashin failure criteria.
And submitting the numerical model constructed in the first step to an Abaqus solver for operation based on parametric modeling and by combining a Hashin failure criterion. In the process of solving the numerical model, a two-dimensional Hashin failure criterion is selected as a theoretical basis for judging the damage of the open-pore laminated plate of the composite material, and the failure mode is predicted through a macroscopic stress state, so that the method is one of common criteria for the progressive damage analysis of the composite material at present. The two-dimensional stress-strain relational expression of the composite material perforated laminated plate is as follows:
in the formula (1), σ 11 Denotes the stress, σ, in the direction of the composite material 1 22 Denotes the stress in the direction of the composite material 2,. Tau. 12 Denotes the in-plane shear stress of the composite material, E 1 Denotes the modulus of elasticity in the 1-direction, E 2 Denotes the modulus of elasticity in the 2-direction, G 12 Denotes the in-plane shear modulus, v, of the composite 12 Poisson's ratio, v, representing the deformation of composite material 1 caused by a directional load in the 2 direction 21 Poisson's ratio, ε, representing the deformation of a composite material 2 in the direction 1 under a load 11 Denotes the strain in the direction of the composite material 1,. Epsilon 22 Represents the strain in the direction of the composite material 2, gamma 12 Showing the in-plane shear strain of the composite.
The two-dimensional Hashin failure criterion is divided into four failure modes of longitudinal stretching, longitudinal compression, transverse stretching and transverse compression, and the corresponding damage initiation criterion expression is as follows:
fiber direction stretch failure:
fiber direction compression failure:
and (3) elongation failure in the matrix direction:
compressive failure in the direction of the matrix:
in formulae (2) to (5), F ft Denotes failure of fiber direction by stretching, F fc Indicating compressive failure in the fiber direction, F mt Indicates tensile failure in the matrix direction, F mc Indicating a compressive failure in the direction of the substrate,
represents a component in the direction of the effective stress 1, is present>
Represents a shear component of the effective stress>
Denotes the component in the direction of the effective stress 2, alpha denotes the factor of the effect of shear on the initial failure of the fiber in tension, X T Denotes the longitudinal tensile strength, X C Denotes the longitudinal compressive strength, Y T Denotes transverse tensile Strength, Y C Denotes the transverse compressive strength, S T Denotes transverse shear strength, S L The longitudinal shear strength is indicated.
After the conditions of initial criterion formulas (2) to (5) are met, damage begins to occur, the rigidity of the material gradually degrades, and the material enters a damage evolution stage until the material completely fails. In the stage from damage to complete failure of the composite material, the stiffness matrix of the material needs to be updated according to the stress-strain result of the composite material in each iteration step, so as to realize the progressive damage analysis and calculation of the composite material based on the Hashin failure criterion.
Classifying and grading the simulation result obtained by calculating the Abaqus, compiling an automatic post-processing Python script according to requirements, and extracting corresponding data from the numerical model database and the simulation result to realize the construction of a damage analysis result database.
And thirdly, performing predictive analysis on the damage behaviors based on data driving.
And based on the damage analysis result database constructed in the second step, constructing a BP (back propagation) neural network by using a Matlab tool kit to train the damage analysis result database, wherein the topological structure of the BP neural network comprises 3 parts which are an input layer, an output layer and a hidden layer respectively. The input layer has 6 parameters which are respectively the central coordinates (x, y) of the opening of the laminated plate, a major semi-axis a, a minor semi-axis b, the slope k and the intercept c of the linear load; the output layer has 3 parameters, namely the coordinates (X, Y) of the damage starting position needing to be predicted and the end displacement D when the damage starts to occur; the number of layers and nodes of the hidden layer and the transfer function in the hidden layer are adjusted according to the complexity of the problem, including the layer angle, the elliptical hole characteristics and the like, so that the prediction error is minimum. And (3) completing the training of the BP neural network through the constructed result database, and further predicting the damage behavior of the perforated laminated plate, such as predicting the damage initial position and the initial end displacement of the laminated plate, so as to achieve the purpose of data-driven damage behavior prediction analysis.
The invention has the beneficial effects that:
according to the method, the damage analysis result database of the composite material laminated plate is constructed by performing simulation analysis on the open-pore laminated plate by using the parametric modeling and automatic post-processing technology, so that the intelligent prediction of the damage behavior of the laminated plate is realized. The method can avoid the problems of cost and operation caused by repeated modeling, realize batch construction, calculation and processing of simulation models and results, accurately predict the damage starting position and the damage starting end displacement, and provide a new way for the design and application of composite materials.
Drawings
FIG. 1 is a schematic view of a composite laminate geometry containing elliptical holes.
Fig. 2 is related finite element model information for a reinforcement sheet and a laminate. (a) is a dimension and grid division diagram of the reinforcing sheet; (b) is a ply form diagram of the laminate; (c) grid partitioning of the laminate finite element model.
FIG. 3 is a flow chart of the lesion evolution for Hashin failure criteria.
Fig. 4 is a topology structure diagram of the BP neural network.
Fig. 5 is a prediction error scatter diagram of the damage start position and the damage start end displacement of the laminate. (a) predicting a relative error scatter diagram of a set X; (b) predicting a Y relative error scatter diagram of the set; and (c) obtaining a prediction set D relative error scatter diagram.
Detailed Description
The following further describes a specific embodiment of the present invention with reference to the drawings and technical solutions.
The invention takes a data-driven method for predicting the progressive damage behavior of the composite material laminated plate with randomly distributed open hole positions as a specific embodiment, and further verifies the effectiveness and the correctness of the invention. The method comprises the following specific steps:
the hole-opened laminated plate has the advantages that the hole-opened shape is an elliptical hole, and the mode that the laminated plate with the elliptical hole and the reinforcing sheet are bound with each other is selected as a research model. The geometric configuration of the composite material laminated plate containing the elliptical holes is shown in figure 1, the length L of the laminated plate is 250mm, the width W of the laminated plate is 25mm, the major semi-axis a of each elliptical hole is 4mm, the minor semi-axis b of each elliptical hole is 2mm, and the included angle theta between the minor semi-axis and the x-axis is 30 degrees. The geometry of the reinforcing sheet is shown in FIG. 2 (a), where L1 is 47mm, L2 is 56mm, L3 is 25mm, and L4 is 2.4mm. A numerical model is constructed on a laminated plate and a reinforcing sheet with elliptical holes by adopting a parametric modeling method, a Python modeling script is compiled according to a data file format specified by Abaqus, parameters related to geometric dimension, hole opening position, layering angle, load information and grid dimension are set as variables, and a numerical model database containing 420 groups of different models is generated in batches quickly by defining the value range of the variables.
In the numerical model database, the finite element meshing strategy of the laminate with elliptical holes and the reinforcing sheet is shown in fig. 2. The origin of coordinates is selected at the lower left corner of the laminated plate; the x axis is the external load direction, and the y axis is the direction orthogonal to the x axis in the laminated plate surface; the z-axis is the thickness direction of the laminate layup. Adopt traditional shell unit S4R to discretize to the combined material plywood, strengthen the piece and adopt C3D8R unit to discretize. The reinforcing sheets and the composite material are connected in a binding mode, the upper reinforcing sheet layer and the lower reinforcing sheet layer on the left side are fully constrained, and the upper reinforcing sheet layer and the lower reinforcing sheet layer on the right side apply displacement loads along the x direction. Fig. 2 (a) shows the geometry and meshing of the stiffener. Fig. 2 (c) shows meshing of the laminate finite element model. Since the stress concentration phenomenon exists around the elliptical hole and is the place where the damage initially occurs, the structured grid is adopted to perform local grid refinement on the circumference of the elliptical hole. FIG. 2 (b) shows 16 plies laid in the thickness direction, each ply having a thickness of 0.15mm.
After parametric modeling is realized, 420 groups of numerical models constructed in the previous step are submitted to an Abaqus solver for operation by combining with a Hashin failure criterion, and the damage evolution flow of the Hashin failure criterion in the Abaqus is shown in FIG. 3. Classifying and grading the simulation result obtained by Abaqus calculation, compiling a batch Python script according to requirements, and extracting corresponding data from the numerical model database and the simulation result to realize the construction of 420 groups of damage analysis result databases.
And constructing a BP neural network by using a Matlab tool box based on the constructed database containing 420 groups of damage analysis results to predict the initial damage position and the end displacement of the composite material based on data driving. The topological structure of the BP neural network is shown in fig. 4, and the topological structure of the BP neural network is composed of 3 parts, namely an input layer, an output layer and a hidden layer. For the input layer, 6 parameters are provided, namely the center coordinates (x, y) of the elliptical hole, a major semi-axis a and a minor semi-axis b, and the slope k and intercept c of linear load; the output layer has 3 parameters, namely the coordinates (X, Y) of the damage starting position needing to be predicted and the end displacement D when the damage starts to occur; the number of layers of the hidden layer, the number of nodes and the transfer function in the hidden layer are adjusted according to the complexity of the problem.
In the training prediction process of the actual neural network, the data in the established 420 groups of damage analysis result databases are aimed at, wherein the first 400 groups are used as the training set of the neural network, and the last 20 groups are used as the prediction set. The BP neural network trained with 400 sets of data was used to predict the lesion initiation locations XY and tip displacement D at which lesion initiation occurred for 20 sets of prediction sets. Making a scatter diagram shown in FIG. 5 by using the predicted relative errors of 20 sets of prediction sets, wherein FIG. 5 (a) is a scatter diagram of a predicted set X relative error; FIG. 5 (b) is a relative error scatter plot of prediction set Y; fig. 5 (c) is a relative error scatter diagram of the prediction set D. In fig. 5, the average error of the coordinates (X, Y) of the damage position and the average error of the tip displacement D are both less than 1.98%. From the prediction results, the data-driven technology adopted by the invention can accurately predict the progressive damage behavior of the laminated plate containing the elliptical holes.
In summary, the present invention is only a specific embodiment, but the scope of the invention is not limited thereto, and any engineering person skilled in the art can make some changes within the technical scope of the invention, such as adjusting the sequence of the composite laminate layer, changing the geometrical parameters, etc., should be regarded as infringing the scope of the invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (4)
1. A data-driven progressive damage prediction method for a composite open-cell laminated plate is characterized by comprising the following steps:
firstly, constructing a rapid parametric modeling method combined with the characteristics of the open-cell laminated plate;
a numerical model is constructed by adopting a parametric modeling method, a Python modeling script is written according to a data file format specified by Abaqus, and the method comprises the following steps: establishing a geometric model of the open-cell laminate and the reinforcing sheet, wherein the reinforcing sheet is used to protect the open-cell laminate sample and provide an effective boundary load; giving material properties and dividing a structured grid on the basis of the geometric model; establishing a binding relationship between the perforated laminated plate and the reinforcing sheet, and applying corresponding boundary constraint conditions and load working conditions; the parametric modeling process is realized based on Python language, parameters related to the position of an opening, the layering angle, the load information and the grid size are set as variables, and a numerical model database is rapidly generated in batches by defining the value range of the variables;
secondly, constructing a damage analysis result database based on Hashin failure criterion;
based on parametric modeling, submitting the numerical model constructed in the first step to an Abaqus solver for operation by combining a Hashin failure criterion; in the process of solving the numerical model, a two-dimensional Hashin failure criterion is selected as a theoretical basis for judging damage of the composite material open-pore laminated plate, and the failure mode is predicted through a macroscopic stress state;
after the initial criterion formula condition of the two-dimensional Hashin failure criterion is met, damage begins to occur, the rigidity of the material is gradually degraded, and therefore the material enters a damage evolution stage until the material is completely failed; updating the rigidity matrix of the material according to the stress-strain result of the composite material in each iteration step at the stage from damage to complete failure of the composite material at the beginning of damage, so as to realize the progressive damage analysis and calculation of the composite material based on the Hashin failure criterion;
classifying and grading the simulation result obtained by Abaqus calculation, compiling an automatic post-processing Python script according to requirements, and extracting corresponding data from a numerical model database and the simulation result to realize the construction of a damage analysis result database;
thirdly, based on data-driven damage behavior prediction analysis;
and constructing a BP (back propagation) neural network by using a Matlab tool kit based on the damage analysis result database constructed in the second step to train the damage analysis result database, finishing the training of the BP neural network through the constructed result database, and predicting the damage behavior of the perforated laminated plate, wherein the damage behavior comprises the damage starting position and the starting end displacement of the laminated plate, so that the purpose of data-driven damage behavior prediction analysis is achieved.
2. The data-driven progressive damage prediction method for a composite open-hole laminate according to claim 1, wherein in the second step, the two-dimensional stress-strain relationship expression of the composite open-hole laminate is as follows:
in the formula (1), σ 11 Denotes the stress, σ, in the direction of the composite material 1 22 Denotes the stress in the direction of the composite material 2,. Tau 12 Denotes the in-plane shear stress of the composite material, E 1 Denotes the modulus of elasticity in the 1 direction, E 2 Denotes the modulus of elasticity in the 2-direction, G 12 Denotes the in-plane shear modulus, v, of the composite 12 Poisson's ratio, v, representing the deformation of a composite material caused by a load in the 1-direction in the 2-direction 21 Poisson's ratio, ε, representing the deformation of a composite material 2 in the direction 1 under a load 11 Denotes the strain in the direction of the composite material 1,. Epsilon 22 Represents the strain, γ, in the direction of the composite material 2 12 Showing the in-plane shear strain of the composite.
3. The progressive damage prediction method of a composite open-cell laminate based on data driving of claim 1, wherein in the second step, the two-dimensional Hashin failure criteria are divided into four failure modes of longitudinal stretching, longitudinal compression, transverse stretching and transverse compression, and the corresponding damage initiation criterion expression is as follows:
fiber direction stretch failure:
fiber direction compression failure:
and (3) elongation failure in the matrix direction:
compressive failure in the direction of the matrix:
in formulae (2) to (5), F ft Indicates a fiber direction tensile failure, F fc Indicating compressive failure in the fiber direction, F mt Indicates tensile failure in the direction of the substrate, F mc Indicating a compressive failure in the direction of the substrate,
represents a component in the direction of the effective stress 1, is present>
Represents a shear component of the effective stress>
Represents the component in the direction of the effective stress 2, alpha represents the influence factor of the shearing on the initial damage of the fiber drawing, X T Denotes the longitudinal tensile strength, X C Denotes the longitudinal compressive strength, Y T Denotes transverse tensile Strength, Y C Denotes the transverse compressive strength, S T Denotes transverse shear strength, S L The longitudinal shear strength is indicated.
4. The method for predicting progressive damage of the composite open-cell laminated plate based on data driving according to claim 1, wherein the topological structure of the BP neural network is composed of 3 parts, namely an input layer, an output layer and a hidden layer; the input layer has 6 parameters which are respectively the central coordinates (x, y) of the opening of the laminated plate, a major semi-axis a, a minor semi-axis b, the slope k and the intercept c of the linear load; the output layer has 3 parameters, namely the coordinates (X, Y) of the damage starting position needing to be predicted and the end displacement D when the damage starts to occur; the number of layers and nodes of the hidden layer and the transfer function in the hidden layer are adjusted according to the complexity of the problem, including the layer spreading angle, the elliptical hole characteristics and the like, so that the prediction error is minimum.
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