CN113340732A - Heterogeneous material multi-parameter inversion method and device based on automatic image partitioning - Google Patents

Heterogeneous material multi-parameter inversion method and device based on automatic image partitioning Download PDF

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CN113340732A
CN113340732A CN202110598649.8A CN202110598649A CN113340732A CN 113340732 A CN113340732 A CN 113340732A CN 202110598649 A CN202110598649 A CN 202110598649A CN 113340732 A CN113340732 A CN 113340732A
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CN113340732B (en
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刘战伟
李伊炀
赵家业
周江帆
吴东亮
刘胜
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Abstract

The invention discloses a heterogeneous material multi-parameter inversion method and a device based on automatic image partitioning, wherein the method comprises the following steps: manufacturing a speckle test piece, fixing the speckle test piece on a testing machine, collecting an original speckle image, applying a first load to the speckle test piece through the testing machine, and collecting M first speckle images; calculating a displacement field, and eliminating rigid translation and rigid rotation to obtain M corrected second speckle images; selecting a range to be measured on the original speckle image; and calculating a strain gradient according to the displacement field, automatically dividing the range to be measured into a first area to an Nth area, and respectively calculating the elastic-plastic constitutive parameters of the first area to the Nth area. The range to be measured is automatically divided into a plurality of areas by calculating the strain gradient, the area division of the range to be measured can be automatically completed in one loading experiment, manual partition is not needed to be performed by methods such as hardness test before the loading experiment, and the process of measuring the elastoplasticity constitutive parameters of the heterogeneous material is simplified.

Description

Heterogeneous material multi-parameter inversion method and device based on automatic image partitioning
Technical Field
The invention relates to the technical field of optical measurement in experimental mechanics, in particular to a heterogeneous material multi-parameter inversion method and device based on automatic image partitioning.
Background
In classical parametric inversion methods, the test piece is usually a homogeneous material, i.e. the material parameters are consistent over the entire area of the test piece to be tested. In practical applications, however, there may be differences in material properties of different areas of the test piece, such as welding materials, etc. At present, the patent with application publication number CN102778403A proposes a weld material parameter identification method, which includes the steps of firstly partitioning a material according to a hardness test result, then performing a tensile test, and acquiring real-time full-field main and secondary strain values of each region of a tensile test piece by adopting digital image related technical equipment; and calculating the material parameters of each area of the welding seam through a plastic mechanics formula according to the obtained hardness value and the primary and secondary strain values, and verifying the accuracy by combining finite element simulation. The application publication number is CN102288499A, and provides a detection method for identifying static mechanical property parameters of materials in different areas of a weld joint, and according to the simulation result and the corresponding experiment result of a finite element numerical model of an indentation test, a target response function optimization mathematical model in different areas of the weld joint is formed; and (4) combining an optimized genetic algorithm, and iterating the selected target response function so as to obtain the static mechanical property parameters of different areas of the welding seam. The application publication No. CN108281193A proposes a method for processing a femoral head mechanical simulation model, in which a femoral head heterogeneous material simulation unit is equally divided into a plurality of parts by density values, and a compressive linear reinforced elastoplasticity constitutive structure and a tensile linear reinforced elastoplasticity constitutive structure are adopted for the femoral head simulation unit. And extracting and analyzing the simulation result to obtain the strain of the compressive yield point, the strain of the tensile yield point and the strain of the tensile fracture point. However, the existing method has the problems that the experimental process is complex, manual partition is needed for measuring the parameters of the heterogeneous material, the obtained parameters of the material are not comprehensive enough, and the precision is low due to the fact that only displacement field deformation information at one moment is utilized.
Therefore, it is an urgent need to solve the problem of providing a heterogeneous material multi-parameter inversion method and device based on image automatic partition, which can quickly and accurately partition the heterogeneous material.
Disclosure of Invention
In view of the above, the present invention provides a heterogeneous material multi-parameter inversion method based on image automatic partitioning, which includes:
manufacturing a speckle test piece, wherein the speckle test piece comprises speckle mark points;
fixing the speckle test piece on a testing machine, collecting an original speckle image of the speckle test piece before deformation, applying a first load to the speckle test piece through the testing machine, and collecting M first speckle images of the speckle test piece in a deformation process in an elastoplasticity section, wherein the elastoplasticity section comprises an elasticity stage and a plasticity stage, and M is a positive integer;
calculating a displacement field of the speckle test piece in a deformation process according to the original speckle image and the M first speckle images, and eliminating rigid translation and rigid rotation to obtain M corrected second speckle images;
selecting a range to be measured on the original speckle image;
calculating a strain gradient in the range to be measured according to the displacement field, and automatically dividing the range to be measured into a plurality of regions according to the strain gradient, wherein the plurality of regions comprise a first region to an Nth region;
sequentially selecting the first area to the Nth area, and respectively obtaining the elastic-plastic constitutive parameters of the first area to the Nth area according to the following method:
selecting an area to be measured, taking the elastoplasticity constitutive parameters to be measured of the speckle test piece as the quantity to be optimized, and defining the iteration initial value p of the area to be measured in the following wayi,0：
pi,0＝[E,v,A,B,n]i,0，
Wherein p isi,0Taking the iteration initial value as E is an elastic modulus, v is a Poisson's ratio, A is a yield strength, B is a hardening coefficient, and n is a hardening index;
respectively carrying out time sequence affine transformation on the M second speckle images in the region to be detected according to the iteration initial value and the experiment boundary condition to obtain corresponding M third speckle images before structural deformation;
constructing an objective function, setting an iteration termination condition, substituting M third speckle images and the original speckle images into the objective function to carry out iterative optimization on the elastoplasticity constitutive parameters to be detected, and updating the elastoplasticity constitutive parameter correction value of the region to be detected according to correction modifiers obtained by iteration;
and if the target function or the correction modifier meets the iteration termination condition, outputting the elastic-plastic constitutive parameter correction value as the elastic-plastic constitutive parameter of the area to be detected.
Preferably, the performing time-series affine transformation on the M second speckle images in the region to be measured according to the iteration initial value and the experimental boundary condition to obtain corresponding M third speckle images before structural deformation includes,
in the elasticity stage, performing time-series affine transformation on the second speckle image according to the following method:
x2(t)＝x1(t)+UFe(E,v,F(t),x1(t),y1(t))+Uθ+U,
y2(t)＝y1(t)+VFe(E,v,F(t),x1(t),y1(t))+Vθ+V,
wherein x is1(t) is the abscissa of the speckle marking point in the second speckle image, y1(t) is the ordinate, U, of the speckle marking point in the second speckle imageFeFor elastic deformation in the direction of the transverse axis by load, VFeFor elastic deformation in the direction of the longitudinal axis by a load, E is the modulus of elasticity and v is the modulusPoisson's ratio, F (t) is the load at time t, UθIs a displacement component of rigid body rotation along the direction of the transverse axis, VθIs the displacement component of rigid body rotation along the direction of longitudinal axis, U is rigid body translation along the direction of transverse axis, V is rigid body translation along the direction of longitudinal axis, and x2(t) is the abscissa of the speckle marking point in the third speckle image, y2(t) is the ordinate of the speckle marking point in the third speckle image.
Preferably, the performing time-series affine transformation on the M second speckle images in the region to be measured according to the iteration initial value and the experimental boundary condition to obtain corresponding M third speckle images before structural deformation includes,
in the plasticity stage, performing time-series affine transformation on the second speckle image according to the following method:
x2(t)＝x1(t)+UFe(E,v,F(t),x1(t),y1(t))+UFp(A,B,n,F(t),x1(t),y1(t))+Uθ+U,
y2(t)＝y1(t)+VFe(E,v,F(t),x1(t),y1(t))+VFp(A,B,n,F(t),x1(t),y1(t))+Vθ+V,
wherein x is1(t) is the abscissa of the speckle marking point in the second speckle image, y1(t) is the ordinate, U, of the speckle marking point in the second speckle imageFeFor elastic deformation in the direction of the transverse axis by load, VFeFor elastic deformation in the direction of the longitudinal axis by the load, E is the modulus of elasticity, v is the Poisson's ratio, F (t) is the load at time t, UFpFor plastic deformation in the direction of the transverse axis by load, VFpFor the plastic deformation in the direction of the longitudinal axis resulting from the load, A is the yield strength, B is the hardening coefficient, n is the hardening index, UθIs a displacement component of rigid body rotation along the direction of the transverse axis, VθIs the displacement component of rigid body rotation along the direction of longitudinal axis, and U is the rigid body rotation along the direction of transverse axisBody translation, V being rigid body translation in the direction of the longitudinal axis, x2(t) is the abscissa of the speckle marking point in the third speckle image, y2(t) is the ordinate of the speckle marking point in the third speckle image.
Preferably, the objective function is constructed and calculated according to the following method:
Figure BDA0003092113770000041
wherein (x)2,y2)∈Ωi，(x0,y0)∈Ωi，(x0,y0) As coordinates of points in the original speckle image, (x)2,y2) Is the sum (x) in the third speckle image0,y0) Coordinates of the corresponding point, 0<t≤S，pi,kFor the k-th elastic-plastic constitutive parameter correction value, C (p)i,k) The difference value f ((x) of M third speckle images and the original speckle image at the k-th iteration0,y0) 0) is the gray value distribution of the original speckle image, g ((x)2,y2) And t) is the gray value distribution of the third speckle image at the t-th moment, omegaiI-th region, S is a time period.
Preferably, the M third speckle images and the original speckle images are substituted into the objective function to perform iterative optimization on the elastoplasticity constitutive parameters to be detected, the elastoplasticity constitutive parameter correction value of the region to be detected is updated according to the correction modifier obtained through iteration, and the calculation is performed according to the following method:
pi,k+1＝pi,k+Δpi,k,
wherein p isi,k+1For the (k + 1) th elasto-plastic constitutive parameter correction value, p, of the region to be measuredi,kFor the kth elastic-plastic constitutive parameter correction value, delta p, of the region to be measuredi,kCorrecting the modifier for the k time;
Δpi,k＝-H(pi,k)-1J(pi,k)，
wherein, J (p)i,k) Is C (p)i,k) First order partial derivative of (1), H (p)i,k) Is C (p)i,k) Second order partial derivatives of (1);
H(pi,k)＝J(pi,k)TJ(pi,k)，
wherein, J (p)i,k)TIs J (p)i,k) The transposed matrix of (2);
Figure BDA0003092113770000042
where ξ is a small quantity, C (p)i,k) And obtaining difference values of the M third speckle images and the original speckle images at the k-th iteration.
Preferably, the setting of the termination iteration condition is performed according to the following method:
‖C(pi,k+1)-C(pi,k)‖≤10-5,
wherein, C (p)i,k+1) The difference value of the M third speckle images and the original speckle image at the k +1 th iteration, C (p)i,k) Obtaining difference values of the M third speckle images and the original speckle images in the k iteration;
alternatively, the setting is performed as follows:
‖Δpi,k‖≤10-3，
wherein, Δ pi,kIs the k correction modifier.
Preferably, the calculating the strain gradient in the range to be measured according to the displacement field, and automatically dividing the range to be measured into a plurality of regions according to the strain gradient includes,
and adopting the first speckle images of the to-be-measured ranges in the plastic stage, wherein the strain gradient in the to-be-measured range comprises strain, a first derivative of strain and a second derivative of strain in the to-be-measured range, and automatically dividing the to-be-measured range into a plurality of regions by taking positive and negative changes of the second derivative of strain as region boundaries.
The invention provides a heterogeneous material multi-parameter inversion device based on automatic image partitioning, which comprises,
the image acquisition module is coupled with the displacement field calculation module and is used for acquiring an original speckle image of the speckle test piece before deformation and acquiring M first speckle images of the speckle test piece in the deformation process in the elastoplasticity section and transmitting the M first speckle images to the displacement field calculation module, wherein M is a positive integer;
the displacement field calculation module is respectively coupled with the image acquisition module and the range selection module to be detected and is used for calculating a displacement field of the speckle test piece in the deformation process according to the original speckle image and the M first speckle images, eliminating rigid translation and rigid rotation, obtaining M corrected second speckle images and transmitting the M corrected second speckle images to the range selection module to be detected;
the range to be measured selecting module is respectively coupled with the displacement field calculating module and the area dividing module and is used for selecting a range to be measured on the original speckle image and transmitting the range to be measured to the area dividing module;
the region division module is respectively coupled with the range-to-be-measured selection module and the region elastic-plastic constitutive parameter calculation module, and is used for calculating a strain gradient in the range-to-be-measured according to the displacement field, automatically dividing the range-to-be-measured into a plurality of regions according to the strain gradient and transmitting the regions to the region elastic-plastic constitutive parameter calculation module, wherein the plurality of regions comprise a first region to an Nth region;
the region elastic-plastic constitutive parameter calculation module is coupled with the region division module and is used for sequentially selecting the first region to the Nth region and respectively obtaining the elastic-plastic constitutive parameters of the first region to the Nth region according to the following method:
selecting an area to be measured, taking the elastoplasticity constitutive parameters to be measured of the speckle test piece as the quantity to be optimized, and defining the iteration initial value p of the area to be measured in the following wayi,0：
pi,0＝[E,v,A,B,n]i,0，
Wherein p isi,0For the initial value of the iterationE is elastic modulus, v is Poisson's ratio, A is yield strength, B is hardening coefficient, and n is hardening index;
respectively carrying out time sequence affine transformation on the M second speckle images in the region to be detected according to the iteration initial value and the experiment boundary condition to obtain corresponding M third speckle images before structural deformation;
constructing an objective function, setting an iteration termination condition, substituting M third speckle images and the original speckle images into the objective function to carry out iterative optimization on the elastoplasticity constitutive parameters to be detected, and updating the elastoplasticity constitutive parameter correction value of the region to be detected according to correction modifiers obtained by iteration;
and if the target function or the correction modifier meets the iteration termination condition, outputting the elastic-plastic constitutive parameter correction value as the elastic-plastic constitutive parameter of the area to be detected.
Compared with the prior art, the heterogeneous material multi-parameter inversion method and device based on automatic image partitioning at least realize the following beneficial effects:
1. according to the heterogeneous material multi-parameter inversion method and device based on automatic image partitioning, the strain gradient in the range to be measured is calculated according to the displacement field, the range to be measured is automatically partitioned into a plurality of areas according to the strain gradient, the area partitioning of the range to be measured can be automatically completed in one loading experiment, manual partitioning through methods such as hardness testing and the like before the loading experiment is not needed, and the process of measuring the elastoplasticity constitutive parameters of the heterogeneous material is simplified.
2. According to the heterogeneous material multi-parameter inversion method and device based on automatic image partitioning, the gray distribution of each region of M third speckle images and the gray distribution of the corresponding region of the original speckle image are substituted into the target function to perform iterative optimization on the elastoplasticity constitutive parameters to be detected, compared with strain field calculation material parameters obtained from digital image correlation technology, the influence of random noise on the inversion of the elastoplasticity constitutive parameters of the heterogeneous material can be effectively reduced, and the accuracy of the inversion result of the elastoplasticity constitutive parameters of the heterogeneous material is improved.
3. The heterogeneous material multi-parameter inversion method and device based on automatic image partitioning can simultaneously obtain a plurality of elastoplasticity constitutive parameters of the region to be detected, including elastic modulus, Poisson's ratio, yield strength, hardening coefficient and hardening index, through one experiment.
Of course, it is not necessary for any product in which the present invention is practiced to achieve all of the above-described technical effects simultaneously.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart of one embodiment of a heterogeneous material multi-parameter inversion method based on automatic image partitioning provided by the present invention;
FIG. 2 is a flow chart of another embodiment of a heterogeneous material multi-parameter inversion method based on automatic image partitioning provided by the present invention;
FIG. 3 is a block diagram of one embodiment of an apparatus for multi-parameter inversion of heterogeneous materials based on automatic image partitioning according to the present invention;
FIG. 4 is a schematic structural view of an aluminum alloy friction stir welding test piece;
FIG. 5 is a comparison graph of inversion stress-strain curves versus localized digital image correlation DIC method calculations;
301-an image acquisition module, 302-a displacement field calculation module, 303-a range to be measured selection module, 304-an area division module and 305-an area elastic-plastic constitutive parameter calculation module.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Example 1
A specific embodiment of the heterogeneous material multi-parameter inversion method based on image automatic partitioning according to the present invention is described below with reference to fig. 1, which includes:
s11: manufacturing a speckle test piece, wherein the speckle test piece comprises speckle mark points;
s12: fixing a speckle test piece on a testing machine, collecting an original speckle image of the speckle test piece before deformation, applying a first load to the speckle test piece through the testing machine, and collecting M first speckle images of the speckle test piece in a deformation process in an elastoplasticity section, wherein the elastoplasticity section comprises an elasticity stage and a plasticity stage, and M is a positive integer;
when the external force is smaller than the elastic limit load, the test piece can completely recover the original shape after the external force causing deformation is removed, the recoverable deformation is called elastic deformation, and the stage that the test piece only generates elastic deformation is called an elastic stage; when the external force exceeds the elastic limit load, and the load is removed, the test piece can not restore, a part of deformation which can not disappear is reserved, the reserved permanent deformation is called plastic deformation, and the stage is called a plastic stage.
S13: calculating a displacement field of the speckle test piece in the deformation process according to the original speckle image and the M first speckle images, and removing rigid translation and rigid rotation to obtain M corrected second speckle images;
in step S13, a displacement field of the speckle test piece during deformation is calculated by using two-dimensional digital image correction software (Ncorr), which is an MATLAB program related to an open-source 2D digital image.
S14: selecting a range to be measured on the original speckle image;
in step S14, the selected range to be measured may be randomly selected, or an area with research value in the original speckle image may be designated as the range to be measured.
S15: the method comprises the steps of calculating the strain gradient in the range to be measured according to the displacement field, automatically dividing the range to be measured into a plurality of regions according to the strain gradient, wherein the plurality of regions comprise a first region to an Nth region, automatically completing region division of the range to be measured in one loading experiment, avoiding manual partition by methods such as hardness test before the loading experiment, and simplifying the measurement process of the elastoplasticity constitutive parameters of the heterogeneous material.
In step S15, a first speckle image in which the range to be measured is in the plastic phase is used, the strain gradient in the range to be measured includes strain, a first derivative of strain, and a second derivative of strain in the range to be measured, and the range to be measured is divided into a plurality of regions by using positive and negative changes of the second derivative of strain as region boundaries.
S16: sequentially selecting a first region to an Nth region, and respectively obtaining the elastic-plastic constitutive parameters of the first region to the Nth region according to the following method:
s161: selecting an area to be measured, taking the elastoplasticity constitutive parameters to be measured of the speckle test piece as the quantity to be optimized, and defining the iteration initial value p of the area to be measured according to the following modei,0：
pi,0＝[E,v,A,B,n]i,0，
Wherein p isi,0The method comprises the following steps of (1) taking an iteration initial value, wherein E is an elastic modulus, v is a Poisson's ratio, A is a yield strength, B is a hardening coefficient, and n is a hardening index; can pass one experiment at the same timeAnd obtaining a plurality of elastoplasticity constitutive parameters of the region to be measured, wherein the parameters comprise elastic modulus, Poisson's ratio, yield strength, hardening coefficient and hardening index.
S162: respectively carrying out time sequence affine transformation on the M second speckle images in the region to be tested according to the iteration initial value and the experiment boundary condition to obtain corresponding M third speckle images before structural deformation;
s163: constructing an objective function, setting a termination iteration condition, substituting M third speckle images and original speckle images into the objective function to carry out iteration optimization on the elastoplasticity constitutive parameters to be detected, and updating the elastoplasticity constitutive parameter correction value of the region to be detected according to the correction modifier obtained by iteration; and substituting the gray distribution of each region of the M third speckle images and the gray distribution of the corresponding region of the original speckle image into an objective function to carry out iterative optimization on the elastoplasticity constitutive parameters to be detected, and compared with strain field calculation material parameters obtained from a digital image correlation technology, the method can effectively reduce the influence of random noise on the inversion of the elastoplasticity constitutive parameters of the heterogeneous material and improve the accuracy of the inversion result of the elastoplasticity constitutive parameters of the heterogeneous material.
In step S163, the iteration value is updated by Newton-Raphson in the iteration process.
S164: and if the target function or the correction modifier meets the condition of terminating iteration, outputting the elastic-plastic constitutive parameter correction value as the elastic-plastic constitutive parameter of the region to be detected.
Example 2
Another specific embodiment of the heterogeneous material multi-parameter inversion method based on image automatic partitioning according to the present invention is described below with reference to fig. 2, which includes:
s21: manufacturing a speckle test piece, wherein the speckle test piece comprises speckle mark points;
in step S21, a speckle test piece is produced as follows:
white paint is uniformly sprayed on a piece to be tested to serve as a white substrate, the white paint is dried to form a film, and black speckles are uniformly sprayed on the white substrate by using black paint.
S22: fixing a speckle test piece on a testing machine, collecting an original speckle image of the speckle test piece before deformation, applying a first load to the speckle test piece through the testing machine, and collecting M first speckle images of the speckle test piece in a deformation process in an elastoplasticity section, wherein the elastoplasticity section comprises an elasticity stage and a plasticity stage, and M is a positive integer;
when the external force is smaller than the elastic limit load, the test piece can completely recover the original shape after the external force causing deformation is removed, the recoverable deformation is called elastic deformation, and the stage that the test piece only generates elastic deformation is called an elastic stage; when the external force exceeds the elastic limit load, and the load is removed, the test piece can not restore, a part of deformation which can not disappear is reserved, the reserved permanent deformation is called plastic deformation, and the stage is called a plastic stage.
S23: calculating a displacement field of the speckle test piece in the deformation process according to the original speckle image and the M first speckle images, and removing rigid translation and rigid rotation to obtain M corrected second speckle images;
in step S23, a displacement field of the speckle test piece during deformation is calculated by using two-dimensional digital image correction software (Ncorr), which is an MATLAB program related to an open-source 2D digital image.
S24: determining the pixel length according to the speckle test piece, and completing unit pixel calibration on the original speckle image;
in step S24, the pixel length, i.e., the scale, is determined according to the speckle test piece, the characteristics of the speckle test piece can be reflected in an equal proportion, and the calibration result is used in the construction of the experimental boundary condition of the affine transformation in step S272.
S25: selecting a range to be measured on the original speckle image;
in step S25, the selected range to be measured may be randomly selected, or an area with research value in the original speckle image may be designated as the range to be measured.
S26: the method comprises the steps of calculating the strain gradient in the range to be measured according to the displacement field, automatically dividing the range to be measured into a plurality of regions according to the strain gradient, wherein the plurality of regions comprise a first region to an Nth region, automatically completing region division of the range to be measured in one loading experiment, avoiding manual partition by methods such as hardness test before the loading experiment, and simplifying the measurement process of the elastoplasticity constitutive parameters of the heterogeneous material.
In step S26, at the boundary between two series materials, the displacement is a piecewise inflection point, the strain is a gradual transition midpoint, the first derivative of the strain is an extreme point, and the second derivative of the strain is equal to 0, the basis of the divided region can be selected as required, and in step S26 of this embodiment, the second derivative of the strain is used as the basis of the divided region.
Specifically, a first speckle image with a range to be measured in a plastic stage is adopted, a strain gradient in the range to be measured comprises strain, a first derivative of the strain and a second derivative of the strain in the range to be measured, and the range to be measured is divided into a plurality of regions by taking positive and negative change positions of the second derivative of the strain as region boundaries.
S27: sequentially selecting a first region to an Nth region, and respectively obtaining the elastic-plastic constitutive parameters of the first region to the Nth region according to the following method:
s271: selecting an area to be measured, taking the elastoplasticity constitutive parameters to be measured of the speckle test piece as the quantity to be optimized, and defining the iteration initial value p of the area to be measured according to the following modei,0：
pi,0＝[E,v,A,B,n]i,0，
Wherein p isi,0The method comprises the following steps of (1) taking an iteration initial value, wherein E is an elastic modulus, v is a Poisson's ratio, A is a yield strength, B is a hardening coefficient, and n is a hardening index; a plurality of elastic-plastic constitutive parameters of the region to be measured including the elastic modulus, the Poisson ratio, the yield strength, the hardening coefficient and the hardening index can be obtained simultaneously through one experiment.
S272: respectively carrying out time sequence affine transformation on the M second speckle images in the region to be tested according to the iteration initial value and the experiment boundary condition to obtain corresponding M third speckle images before structural deformation;
because the elastic-plastic section comprises an elastic stage and a plastic stage, when the M second speckle images are subjected to sequential affine transformation, the discussion is respectively carried out according to whether the second speckle images are located in the elastic stage or the plastic stage;
if in the elastic stage, performing time-series affine transformation on the second speckle image according to the following method:
x2(t)＝x1(t)+UFe(E,v,F(t),x1(t),y1(t))+Uθ+U,
y2(t)＝y1(t)+VFe(E,v,F(t),x1(t),y1(t))+Vθ+V,
wherein x is1(t) is the abscissa of the speckle marking point in the second speckle image, y1(t) is the ordinate, U, of the speckle marking point in the second speckle imageFeFor elastic deformation in the direction of the transverse axis by load, VFeFor elastic deformation in the longitudinal axis direction by the load, E is the modulus of elasticity, v is the Poisson's ratio, F (t) is the load at time t, UθIs a displacement component of rigid body rotation along the direction of the transverse axis, VθIs the displacement component of rigid body rotation along the direction of longitudinal axis, U is rigid body translation along the direction of transverse axis, V is rigid body translation along the direction of longitudinal axis, and x2(t) is the abscissa of the speckle marking point in the third speckle image, y2And (t) is the ordinate of the speckle marking point in the third speckle image.
If in the plasticity stage, performing time-series affine transformation on the second speckle image according to the following method:
x2(t)＝x1(t)+UFe(E,v,F(t),x1(t),y1(t))+UFp(A,B,n,F(t),x1(t),y1(t))+Uθ+U,
y2(t)＝y1(t)+VFe(E,v,F(t),x1(t),y1(t))+VFp(A,B,n,F(t),x1(t),y1(t))+Vθ+V,
wherein x is1(t) is the abscissa of the speckle marking point in the second speckle image, y1(t) is the ordinate, U, of the speckle marking point in the second speckle imageFeFor elastic deformation in the direction of the transverse axis by load, VFeFor elasticity in the direction of the longitudinal axis due to loadingDeformation, E is the modulus of elasticity, v is the Poisson's ratio, F (t) is the load at time t, UFpFor plastic deformation in the direction of the transverse axis by load, VFpFor plastic deformation in the direction of the longitudinal axis by load, A is the yield strength, B is the hardening coefficient, n is the hardening index, UθIs a displacement component of rigid body rotation along the direction of the transverse axis, VθIs the displacement component of rigid body rotation along the direction of longitudinal axis, U is rigid body translation along the direction of transverse axis, V is rigid body translation along the direction of longitudinal axis, and x2(t) is the abscissa of the speckle marking point in the third speckle image, y2And (t) is the ordinate of the speckle marking point in the third speckle image.
In step S272, in the process of performing time-series affine transformation on the M second speckle images to obtain corresponding M third speckle images before structural deformation, the method further includes excluding displacement U of rigid body rotation along the horizontal axis directionθDisplacement V of rigid body rotation in the direction of longitudinal axisθThe rigid translation U along the direction of the transverse axis and the rigid translation V along the direction of the longitudinal axis are calculated according to the following method:
in the experimental process, according to the assumption that the gray scale is unchanged, the speckle mark points of the speckle test piece passively deform along with the speckle test piece, and the deformation field of the speckle mark points has continuity in space and time, so that the gray scale of the speckle images before and after deformation meets the following requirements:
Figure BDA0003092113770000121
Figure BDA0003092113770000122
Figure BDA0003092113770000123
wherein,
Figure BDA0003092113770000124
the gray scale distribution of the original speckle image in the horizontal axis direction,
Figure BDA0003092113770000125
is the image coordinate, x is the image abscissa, y is the image ordinate,
Figure BDA0003092113770000126
for the displacement field of the speckle test piece at time t,
Figure BDA0003092113770000127
is the coordinate of displacement, u 'is the abscissa of the displacement field of the speckle test piece, v' is the ordinate of the displacement field of the speckle test piece,
Figure BDA0003092113770000131
for the residual error caused by the random noise,
Figure BDA0003092113770000132
to construct the gray scale distribution of the third speckle image before deformation.
The displacement field of the speckle test piece is controlled by loading boundary conditions and material parameters. The boundary condition is a loading mode of uniaxial stretching, and the material parameters are elastic-plastic constitutive parameters of different areas.
In the (i) th area(s),
Figure BDA0003092113770000133
wherein,
Figure BDA0003092113770000134
also coordinates of the displacement,. epsilonxxIs the strain in the direction of the transverse axis,. epsilonyyIs the strain in the direction of the longitudinal axis, which is the direction of stretching, u0Is a rigid body displacement in the direction of the transverse axis, v0The rigid body displacement in the loading process can be obtained by a digital image correlation method for the rigid body displacement in the direction of the longitudinal axis.
According to hooke's law, in the elastic phase, the strain satisfies:
Figure BDA0003092113770000135
Figure BDA0003092113770000136
wherein E is the elastic modulus, v is the Poisson's ratio, and σ is the stress,
Figure BDA0003092113770000137
the elastic strain in the direction of the longitudinal axis,
Figure BDA0003092113770000138
is the elastic strain along the transverse axis.
According to the Johnson-Cook constitutive model, this should be satisfied during the plasticity phase:
Figure BDA0003092113770000139
Figure BDA00030921137700001310
wherein σ is stress, A is yield strength, B is hardening coefficient, n is hardening index,
Figure BDA00030921137700001311
for plastic strain in the direction of the longitudinal axis,
Figure BDA00030921137700001312
is the plastic strain in the direction of the transverse axis.
In the uniaxial stretching process, the following conditions are satisfied:
Figure BDA00030921137700001313
S(t)＝S0exp(-Sεyy),
wherein σ(t)Stress at time t, F(t)Is the load at time t, S(t)The cross-sectional area of the specimen at time t, S0The cross-sectional area of the original test piece.
According to the above formula, the elastic deformation U in the horizontal axis direction caused by the load can be respectively obtainedFeElastic deformation V in the direction of the longitudinal axis by loadFePlastic deformation U in the direction of the transverse axis by loadFpAnd plastic deformation V in the direction of the longitudinal axis resulting from the loadFpEliminating the influence of rigid translation and rigid rotation, and completing the solution of the coordinates of the speckle mark points in the third speckle image before structural deformation0Affine transformation of the second speckle image at each of the time instants to the tth time instant to a third speckle image before the formation deformation.
S273: constructing an objective function, setting a termination iteration condition, substituting M third speckle images and original speckle images into the objective function to carry out iteration optimization on the elastoplasticity constitutive parameters to be detected, and updating the elastoplasticity constitutive parameter correction value of the region to be detected according to the correction modifier obtained by iteration;
in step S273, an objective function is constructed and calculated as follows:
Figure BDA0003092113770000141
wherein (x)2,y2)∈Ωi，(x0,y0)∈Ωi，(x0,y0) As coordinates of points in the original speckle image, (x)2,y2) Is the sum (x) in the third speckle image0,y0) Coordinates of the corresponding point, 0<t≤S，pi,kFor the correction of the elasto-plastic constitutive parameters, C (p), of the kth region to be examinedi,k) The difference value f ((x) of the M third speckle images and the k iteration of the original speckle image0,y0) 0) the gray value of the original speckle imageDistribution, g ((x)2,y2) And t) is the gray value distribution of the third speckle image at the t-th time, omegaiI-th region, S is a time period.
When p isi,kIn different time, the displacement fields applied to the M second speckle images in the affine transformation are different, so that the gray scale distribution of the constructed third speckle image is different.
In step S273, an iteration termination condition is set in the following manner:
‖C(pi,k+1)-C(pi,k)‖≤10-5,
wherein, C (p)i,k) The difference value C (p) of the M third speckle images and the original speckle image at the k +1 th iterationi,k) And the difference value of the M third speckle images and the original speckle image at the k-th iteration is obtained.
Alternatively, the setting is performed as follows:
‖Δpi,k‖≤10-3，
wherein, Δ pi,kIs the k correction modifier.
The iteration termination condition may be selected according to requirements, and is not limited herein.
In step S273, substituting M third speckle images and the original speckle image into the objective function to perform iterative optimization on the elastoplasticity constitutive parameters to be measured, updating the elastoplasticity constitutive parameter correction value of the region to be measured according to the correction modifier obtained by the iteration, and calculating according to the following method:
pi,k+1＝pi,k+Δpi,k,
wherein p isi,k+1As a correction value of the elasto-plastic constitutive parameter of the (k + 1) th region to be measured, pi,kFor the correction value of the elasto-plastic constitutive parameter, Δ p, of the kth region to be measuredi,kCorrecting the modifier for the k time;
Δpi,k＝-H(pi,k)-1J(pi,k)，
wherein, J (p)i,k) Is C (p)i,k) First order partial derivative of (1), H (p)i,k) Is C (p)i,k) Second order partial derivatives of (1);
H(pi,k)＝J(pi,k)TJ(pi,k)，
wherein, J (p)i,k)TIs J (p)i,k) The transposed matrix of (2);
Figure BDA0003092113770000151
where ξ is a small quantity, C (p)i,k) And the difference value of the M third speckle images and the original speckle image at the k-th iteration is obtained.
Optionally, after calculating C (p)i,k) The first order partial derivatives of (2) can also be calculated by the following method:
Figure BDA0003092113770000152
wherein, J (p)i,k) Is Jacobian matrix, corresponding to C (p)i,k) First order partial derivative of (1), C (p)i,k) Difference value p of the M third speckle images and the k-th iteration of the original speckle imagei,kThe correction value of the elastic-plastic constitutive parameter of the kth region to be measured is shown, E is the elastic modulus, v is the Poisson's ratio, A is the yield strength, B is the hardening coefficient, n is the hardening index, and T is the transposition symbol.
And substituting the M third speckle images and the original speckle images into a target function to perform iterative optimization on the elastoplasticity constitutive parameters to be detected, so that the influence of random noise on the inversion of the elastoplasticity constitutive parameters of the heterogeneous material can be effectively reduced, and the accuracy of the inversion result of the elastoplasticity constitutive parameters of the heterogeneous material is improved.
In step S273, the iteration value is updated by Newton-Raphson in the iteration process.
S274: and if the target function or the correction modifier meets the condition of terminating iteration, outputting the elastic-plastic constitutive parameter correction value as the elastic-plastic constitutive parameter of the region to be detected.
A specific embodiment of the heterogeneous material multi-parameter inversion apparatus based on image automatic partitioning according to the present invention is described below with reference to FIG. 3.
Example 3
This is a specific embodiment of the heterogeneous material multi-parameter inversion apparatus based on image automatic partitioning according to the present invention, which comprises,
the image acquisition module 301 is coupled with the displacement field calculation module 302 and is used for acquiring an original speckle image of the speckle test piece before deformation and acquiring M first speckle images of the speckle test piece in a deformation process in an elastoplasticity section and transmitting the M first speckle images to the displacement field calculation module 302, wherein M is a positive integer;
the displacement field calculation module 302 is respectively coupled with the image acquisition module 301 and the range-to-be-detected selection module 303, and is used for calculating a displacement field of the speckle test piece in the deformation process according to the original speckle image and the M first speckle images, eliminating rigid translation and rigid rotation, obtaining M corrected second speckle images, and transmitting the M corrected second speckle images to the range-to-be-detected selection module 303;
the range to be measured selecting module 303 is coupled to the displacement field calculating module 302 and the region dividing module 304, and is configured to select a range to be measured on the original speckle image and transmit the range to be measured to the region dividing module 304;
the region division module 304 is coupled to the to-be-measured range selection module 303 and the region elastoplasticity constitutive parameter calculation module 305, and is configured to calculate a strain gradient in a to-be-measured range according to the displacement field, automatically divide the to-be-measured range into a plurality of regions according to the strain gradient, and transmit the plurality of regions to the region elastoplasticity constitutive parameter calculation module 305, where the plurality of regions include a first region to an nth region, and can automatically complete region division of the to-be-measured range in a single loading experiment without manual partition by methods such as a hardness test before the loading experiment, and simplify a process of measuring the elastoplasticity constitutive parameters of the heterogeneous material.
A region elastic-plastic constitutive parameter calculating module 305, coupled to the region dividing module 304, configured to sequentially select the first region to the nth region, and obtain elastic-plastic constitutive parameters of the first region to the nth region according to the following methods:
selecting an area to be tested, and taking the elastoplasticity constitutive parameters to be tested of the speckle test piece as the optimizationQuantifying, defining an iteration initial value p of the region to be measured in the following wayi,0：
pi,0＝[E,v,A,B,n]i,0，
Wherein p isi,0The method comprises the following steps of (1) taking an iteration initial value, wherein E is an elastic modulus, v is a Poisson's ratio, A is a yield strength, B is a hardening coefficient, and n is a hardening index; a plurality of elastic-plastic constitutive parameters of the region to be measured including the elastic modulus, the Poisson ratio, the yield strength, the hardening coefficient and the hardening index can be obtained simultaneously through one experiment.
Respectively carrying out time sequence affine transformation on the M second speckle images in the region to be tested according to the iteration initial value and the experiment boundary condition to obtain corresponding M third speckle images before structural deformation;
constructing an objective function, setting a termination iteration condition, substituting M third speckle images and original speckle images into the objective function to carry out iteration optimization on the elastoplasticity constitutive parameters to be detected, and updating the elastoplasticity constitutive parameter correction value of the region to be detected according to the correction modifier obtained by iteration; and substituting the gray distribution of each region of the M third speckle images and the gray distribution of the corresponding region of the original speckle image into an objective function to carry out iterative optimization on the elastoplasticity constitutive parameters to be detected, and compared with strain field calculation material parameters obtained from a digital image correlation technology, the method can effectively reduce the influence of random noise on the inversion of the elastoplasticity constitutive parameters of the heterogeneous material and improve the accuracy of the inversion result of the elastoplasticity constitutive parameters of the heterogeneous material.
And if the target function or the correction modifier meets the condition of terminating iteration, outputting the elastic-plastic constitutive parameter correction value as the elastic-plastic constitutive parameter of the region to be detected.
When the k iteration is carried out, the objective function or the correction modifier meets the condition of terminating the iteration and is output according to the following method:
P＝pi,k＝[E,v,A,B,n]i,k,
p is the elasto-plastic constitutive parameter of the region to be measured, Pi,kAnd E is the elastic-plastic constitutive parameter correction value of the kth region to be measured, v is the elastic modulus, v is the Poisson's ratio, A is the yield strength, B is the hardening coefficient, and n is the hardening index.
Example 4
Another specific embodiment of the heterogeneous material multi-parameter inversion method based on image automatic partitioning according to the present invention is described with reference to fig. 4 and 5, which includes:
in the present embodiment, friction stir welding is taken as an example, and the vicinity of the friction stir welding joint can be generally divided into a weld nucleus region (weld nugget), a thermo-mechanical effect region (thermo-mechanical effect zone), and a heat-effect region (heat-effect zone) according to microscopic results through microscopic observation. The mechanical properties of the parent metal, the weld nucleus area, the heat engine affected area and the heat affected area are also different. The base metal is a material to be welded in the welding process, and the base metal is not affected by welding. The mechanical properties of three zones of the welding seam are obviously different from those of the base metal under the influence of welding process and the like. In the embodiment, a displacement field in a tensile experiment is used as a basis for material partitioning, and the distribution identification of elastoplastic constitutive parameters is completed through affine optimal matching by combining a time sequence integrated image correlation algorithm.
The friction stir welding material adopted in the embodiment is aluminum alloy 6061T6, the plate thickness is 3mm, the modulus is 60MPa, the yield strength is 250MPa, and the process parameters of the friction stir welding are shown in Table 1:
TABLE 1
Speed of welding Rotational speed Shaft shoulder Length of needle Amount of press-in Inclination angle
300mm/min 1200rpm 10mm 2.8mm 2.8mm 2.5°
And (3) friction stir welding to prepare a to-be-tested piece, uniformly spraying white paint on the to-be-tested piece to serve as a white substrate, drying the white paint to form a film, and uniformly spraying black speckles on the white substrate by using black paint to prepare the speckle test piece.
The speckle test piece is fixed on a uniaxial tensile testing machine, the experimental setting parameters are that the loading speed is 1mm/min, the image acquisition is 1fps, the image resolution is 2448pixel multiplied by 1942pixel, and the original speckle image of the speckle test piece before deformation is acquired. Applying a tensile load, and collecting M first speckle images of the speckle test piece in the deformation process in the elastoplasticity section, wherein M is a positive integer;
calculating a displacement field of the speckle test piece in the deformation process according to the original speckle image and the M first speckle images, and removing rigid translation and rigid rotation to obtain M corrected second speckle images;
determining the pixel length according to the speckle test piece, and completing unit pixel calibration on the original speckle image; selecting a range to be measured on the original speckle image;
and selecting a second speckle image at a certain moment after the speckle test piece begins to yield, calculating the strain gradient at the moment by using digital image related software in the selected range to be measured, and taking the second derivative of the strain as the basis for dividing the welding seam area. Because different areas in the area to be detected have obvious extreme values, the strain second derivative is subjected to binarization processing by taking 0 as a boundary, so that rapid area division is realized, and a plurality of areas are obtained, wherein the plurality of areas comprise a first area to an Nth area;
sequentially selecting a first region to an Nth region, and respectively obtaining the elastic-plastic constitutive parameters of the first region to the Nth region according to the following method: selectingDetermining the area to be measured, using the elastoplasticity constitutive parameters to be measured of the speckle test piece as the quantity to be optimized, and defining the iteration initial value p of the area to be measured according to the following modei,0：
pi,0＝[E,v,A,B,n]i,0，
Wherein p isi,0The method comprises the following steps of (1) taking an iteration initial value, wherein E is an elastic modulus, v is a Poisson's ratio, A is a yield strength, B is a hardening coefficient, and n is a hardening index;
respectively carrying out time sequence affine transformation on the M second speckle images in the region to be tested according to the iteration initial value and the experiment boundary condition to obtain corresponding M third speckle images before structural deformation;
constructing an objective function, setting a termination iteration condition, substituting M third speckle images and original speckle images into the objective function to carry out iteration optimization on the elastoplasticity constitutive parameters to be detected, and updating the elastoplasticity constitutive parameter correction value of the region to be detected according to the correction modifier obtained by iteration;
and if the target function or the correction modifier meets the condition of terminating iteration, outputting the elastic-plastic constitutive parameter correction value as the elastic-plastic constitutive parameter of the region to be detected.
When the elastic-plastic constitutive parameters of the first region to the Nth region are identified, the automatic region division of the material welding seam is completed in one experiment, and the elastic-plastic constitutive parameters comprising the elastic modulus, the Poisson ratio, the yield strength, the hardening coefficient and the hardening index are identified.
In order to further verify the effectiveness, stress-strain curves of 14 points in total on different areas are drawn according to the material elastic-plastic constitutive parameters identified by the algorithm, and compared with the calculation result of the localized digital image correlation technique DIC method, the comparison result is shown in FIG. 5. The calculated results of the DIC method have good consistency with the stress and strain curves obtained by the image automatic partition-based heterogeneous material multi-parameter inversion method and the localized digital image correlation technique, and effectiveness and accuracy of the method are proved.
According to the embodiment, the heterogeneous material multi-parameter inversion method and device based on automatic image partitioning at least achieve the following beneficial effects:
1. according to the heterogeneous material multi-parameter inversion method and device based on automatic image partitioning, the strain gradient in the range to be measured is calculated according to the displacement field, the range to be measured is automatically partitioned into a plurality of areas according to the strain gradient, the area partitioning of the range to be measured can be automatically completed in one loading experiment, manual partitioning through methods such as hardness testing and the like before the loading experiment is not needed, and the process of measuring the elastoplasticity constitutive parameters of the heterogeneous material is simplified.
2. According to the heterogeneous material multi-parameter inversion method and device based on automatic image partitioning, the gray distribution of each region of M third speckle images and the gray distribution of the corresponding region of the original speckle image are substituted into the target function to perform iterative optimization on the elastoplasticity constitutive parameters to be detected, compared with strain field calculation material parameters obtained from digital image correlation technology, the influence of random noise on the inversion of the elastoplasticity constitutive parameters of the heterogeneous material can be effectively reduced, and the accuracy of the inversion result of the elastoplasticity constitutive parameters of the heterogeneous material is improved.
3. The heterogeneous material multi-parameter inversion method and device based on automatic image partitioning can simultaneously obtain a plurality of elastoplasticity constitutive parameters of the region to be detected, including elastic modulus, Poisson's ratio, yield strength, hardening coefficient and hardening index, through one experiment.
Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustrative purposes only and are not intended to limit the scope of the present invention. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims (8)

1. A heterogeneous material multi-parameter inversion method based on automatic image partitioning is characterized by comprising the following steps:
manufacturing a speckle test piece, wherein the speckle test piece comprises speckle mark points;
fixing the speckle test piece on a testing machine, collecting an original speckle image of the speckle test piece before deformation, applying a first load to the speckle test piece through the testing machine, and collecting M first speckle images of the speckle test piece in a deformation process in an elastoplasticity section, wherein the elastoplasticity section comprises an elasticity stage and a plasticity stage, and M is a positive integer;
calculating a displacement field of the speckle test piece in a deformation process according to the original speckle image and the M first speckle images, and eliminating rigid translation and rigid rotation to obtain M corrected second speckle images;
selecting a range to be measured on the original speckle image;
calculating a strain gradient in the range to be measured according to the displacement field, and automatically dividing the range to be measured into a plurality of regions according to the strain gradient, wherein the plurality of regions comprise a first region to an Nth region;
sequentially selecting the first area to the Nth area, and respectively obtaining the elastic-plastic constitutive parameters of the first area to the Nth area according to the following method:
selecting an area to be measured, taking the elastoplasticity constitutive parameters to be measured of the speckle test piece as the quantity to be optimized, and defining the iteration initial value p of the area to be measured in the following wayi,0：
pi,0＝[E,v,A,B,n]i,0，
Wherein p isi,0Taking the iteration initial value as E is an elastic modulus, v is a Poisson's ratio, A is a yield strength, B is a hardening coefficient, and n is a hardening index;
respectively carrying out time sequence affine transformation on the M second speckle images in the region to be detected according to the iteration initial value and the experiment boundary condition to obtain corresponding M third speckle images before structural deformation;
constructing an objective function, setting an iteration termination condition, substituting M third speckle images and the original speckle images into the objective function to carry out iterative optimization on the elastoplasticity constitutive parameters to be detected, and updating the elastoplasticity constitutive parameter correction value of the region to be detected according to correction modifiers obtained by iteration;
and if the target function or the correction modifier meets the iteration termination condition, outputting the elastic-plastic constitutive parameter correction value as the elastic-plastic constitutive parameter of the area to be detected.
2. The heterogeneous material multi-parameter inversion method based on image automatic partitioning according to claim 1, wherein the time-series affine transformation is respectively performed on the M second speckle images in the region to be measured according to the iteration initial values and experimental boundary conditions to obtain corresponding M third speckle images before structural deformation, including,
in the elasticity stage, performing time-series affine transformation on the second speckle image according to the following method:
x2(t)＝x1(t)+UFe(E,v,F(t),x1(t),y1(t))+Uθ+U,
y2(t)＝y1(t)+VFe(E,v,F(t),x1(t),y1(t))+Vθ+V,
wherein x is1(t) is the abscissa of the speckle marking point in the second speckle image, y1(t) is the ordinate, U, of the speckle marking point in the second speckle imageFeFor elastic deformation in the direction of the transverse axis by load, VFeFor elastic deformation in the direction of the longitudinal axis by the load, E is the modulus of elasticity, v is the Poisson's ratio, F (t) is the load at time t, UθIs a displacement component of rigid body rotation along the direction of the transverse axis, VθIs the displacement component of rigid body rotation along the direction of longitudinal axis, U is rigid body translation along the direction of transverse axis, V is rigid body translation along the direction of longitudinal axis, and x2(t) is the abscissa of the speckle marking point in the third speckle image, y2(t) is the ordinate of the speckle marking point in the third speckle image.
3. The heterogeneous material multi-parameter inversion method based on image automatic partitioning according to claim 1, wherein the time-series affine transformation is respectively performed on the M second speckle images in the region to be measured according to the iteration initial values and experimental boundary conditions to obtain corresponding M third speckle images before structural deformation, including,
in the plasticity stage, performing time-series affine transformation on the second speckle image according to the following method:
x2(t)＝x1(t)+UFe(E,v,F(t),x1(t),y1(t))+UFp(A,B,n,F(t),x1(t),y1(t))+Uθ+U,
y2(t)＝y1(t)+VFe(E,v,F(t),x1(t),y1(t))+VFp(A,B,n,F(t),x1(t),y1(t))+Vθ+V,
wherein x is1(t) is the abscissa of the speckle marking point in the second speckle image, y1(t) is the ordinate, U, of the speckle marking point in the second speckle imageFeFor elastic deformation in the direction of the transverse axis by load, VFeFor elastic deformation in the direction of the longitudinal axis by the load, E is the modulus of elasticity, v is the Poisson's ratio, F (t) is the load at time t, UFpFor plastic deformation in the direction of the transverse axis by load, VFpFor the plastic deformation in the direction of the longitudinal axis resulting from the load, A is the yield strength, B is the hardening coefficient, n is the hardening index, UθIs a displacement component of rigid body rotation along the direction of the transverse axis, VθIs the displacement component of rigid body rotation along the direction of longitudinal axis, U is rigid body translation along the direction of transverse axis, V is rigid body translation along the direction of longitudinal axis, and x2(t) is the abscissa of the speckle marking point in the third speckle image, y2(t) is the ordinate of the speckle marking point in the third speckle image.
4. The heterogeneous material multi-parameter inversion method based on automatic image partitioning as claimed in claim 1, wherein the objective function is constructed and calculated according to the following method:
Figure FDA0003092113760000031
wherein (x)2,y2)∈Ωi，(x0,y0)∈Ωi，(x0,y0) As coordinates of points in the original speckle image, (x)2,y2) Is the sum (x) in the third speckle image0,y0) Coordinates of the corresponding point, 0<t≤S，pi,kFor the k-th elastic-plastic constitutive parameter correction value, C (p)i,k) The difference value f ((x) of M third speckle images and the original speckle image at the k-th iteration0,y0) 0) is the gray value distribution of the original speckle image, g ((x)2,y2) And t) is the gray value distribution of the third speckle image at the t-th moment, omegaiI-th region, S is a time period.
5. The heterogeneous material multi-parameter inversion method based on image automatic partitioning according to claim 4, wherein the M third speckle images and the original speckle images are substituted into the objective function to perform iterative optimization on the elastoplasticity constitutive parameters to be measured, the elastoplasticity constitutive parameter correction value of the region to be measured is updated according to correction modifiers obtained through iteration, and the calculation is performed according to the following method:
pi,k+1＝pi,k+Δpi,k,
wherein p isi,k+1For the (k + 1) th elasto-plastic constitutive parameter correction value, p, of the region to be measuredi,kFor the kth elastic-plastic constitutive parameter correction value, delta p, of the region to be measuredi,kCorrecting the modifier for the k time;
Δpi,k＝-H(pi,k)-1J(pi,k)，
wherein, J (p)i,k) Is C (p)i,k) First order partial derivative of (1), H (p)i,k) Is C (p)i,k) Second order partial derivatives of (1);
H(pi,k)＝J(pi,k)TJ(pi,k)，
wherein, J (p)i,k)TIs J (p)i,k) The transposed matrix of (2);
Figure FDA0003092113760000041
where ξ is a small quantity, C (p)i,k) And obtaining difference values of the M third speckle images and the original speckle images at the k-th iteration.
6. The heterogeneous material multi-parameter inversion method based on automatic image partitioning as claimed in claim 5, wherein the termination iteration condition is set according to the following method:
‖C(pi,k+1)-C(pi,k)‖≤10-5,
wherein, C (p)i,k+1) The difference value of the M third speckle images and the original speckle image at the k +1 th iteration, C (p)i,k) Obtaining difference values of the M third speckle images and the original speckle images in the k iteration;
alternatively, the setting is performed as follows:
‖Δpi,k‖≤10-3，
wherein, Δ pi,kIs the k correction modifier.
7. The heterogeneous material multi-parameter inversion method based on automatic image partitioning according to claim 1, wherein the strain gradient in the range to be measured is calculated according to the displacement field, the range to be measured is automatically divided into a plurality of regions according to the strain gradient, comprising,
and adopting the first speckle images of the to-be-measured ranges in the plastic stage, wherein the strain gradient in the to-be-measured range comprises strain, a first derivative of strain and a second derivative of strain in the to-be-measured range, and automatically dividing the to-be-measured range into a plurality of regions by taking positive and negative changes of the second derivative of strain as region boundaries.
8. A heterogeneous material multi-parameter inversion device based on image automatic partition is characterized by comprising,
the image acquisition module is coupled with the displacement field calculation module and is used for acquiring an original speckle image of the speckle test piece before deformation and acquiring M first speckle images of the speckle test piece in the deformation process in the elastoplasticity section and transmitting the M first speckle images to the displacement field calculation module, wherein M is a positive integer;
the displacement field calculation module is respectively coupled with the image acquisition module and the range selection module to be detected and is used for calculating a displacement field of the speckle test piece in the deformation process according to the original speckle image and the M first speckle images, eliminating rigid translation and rigid rotation, obtaining M corrected second speckle images and transmitting the M corrected second speckle images to the range selection module to be detected;
the range to be measured selecting module is respectively coupled with the displacement field calculating module and the area dividing module and is used for selecting a range to be measured on the original speckle image and transmitting the range to be measured to the area dividing module;
the region division module is respectively coupled with the range-to-be-measured selection module and the region elastic-plastic constitutive parameter calculation module, and is used for calculating a strain gradient in the range-to-be-measured according to the displacement field, automatically dividing the range-to-be-measured into a plurality of regions according to the strain gradient and transmitting the regions to the region elastic-plastic constitutive parameter calculation module, wherein the plurality of regions comprise a first region to an Nth region;
the region elastic-plastic constitutive parameter calculation module is coupled with the region division module and is used for sequentially selecting the first region to the Nth region and respectively obtaining the elastic-plastic constitutive parameters of the first region to the Nth region according to the following method:
selecting an area to be measured, defining the iteration initial of the area to be measured by taking the elastoplasticity constitutive parameters to be measured of the speckle test piece as the quantity to be optimized according to the following modeValue pi,0：
pi,0＝[E,v,A,B,n]i,0，
Wherein p isi,0Taking the iteration initial value as E is an elastic modulus, v is a Poisson's ratio, A is a yield strength, B is a hardening coefficient, and n is a hardening index;
respectively carrying out time sequence affine transformation on the M second speckle images in the region to be detected according to the iteration initial value and the experiment boundary condition to obtain corresponding M third speckle images before structural deformation;
constructing an objective function, setting an iteration termination condition, substituting M third speckle images and the original speckle images into the objective function to carry out iterative optimization on the elastoplasticity constitutive parameters to be detected, and updating the elastoplasticity constitutive parameter correction value of the region to be detected according to correction modifiers obtained by iteration;
and if the target function or the correction modifier meets the iteration termination condition, outputting the elastic-plastic constitutive parameter correction value as the elastic-plastic constitutive parameter of the area to be detected.
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