CN103471910A - Intelligent breaking elongation test method of metal material based on random point tracking - Google Patents
Intelligent breaking elongation test method of metal material based on random point tracking Download PDFInfo
- Publication number
- CN103471910A CN103471910A CN2013103759635A CN201310375963A CN103471910A CN 103471910 A CN103471910 A CN 103471910A CN 2013103759635 A CN2013103759635 A CN 2013103759635A CN 201310375963 A CN201310375963 A CN 201310375963A CN 103471910 A CN103471910 A CN 103471910A
- Authority
- CN
- China
- Prior art keywords
- image
- metal material
- fracture
- point
- tracks
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The invention relates to an intelligent breaking elongation test method of a metal material based on random point tracking. Before test, random points in irregular sizes and shapes are sprayed on the surface of the detected metal material by paint spraying equipment as marks. The method comprises the steps of: during the test, acquiring an image sequence of surface deformation of the detected material with a machine vision system, automatically selecting a best original gauge length and a final gauge length for calculating breaking elongation of the metal material according to a breaking position and preset local elongation, and accurately working out the breaking elongation of the metal material. With the adoption of the method, the detection precision of the breaking elongation of the metal material can be improved, different local elongations and a deformation trend of the material during tension can be automatically analyzed and calculated, and the method provides scientific bases for material detection and product design.
Description
Technical field
The present invention relates to metal material performance parameter detection technique field, particularly relate to a kind of Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point.
Background technology
Tension test is that sample is applied to the test method that increases continuously uniaxial tension power and while test samples elongation, and it is widely used in detecting metal material and product mechanical property.Tension test is as the most extensively estimating the test method of metal material performance, and the reliability of its test findings is for user or all extremely important for quality management and the control of factory.
Measuring at present metal material elongation after fracture common method is first on tensile sample, to determine original gauge length, after sample is pulled off, no matter the fracture position of sample, in where, is directly measured the gauge length of having no progeny, then according to definition, calculates elongation after fracture.Such test result is inaccurate often, presses GB/T228-2002 regulation, only have in principle breaking part with approach most gauge length mark position point apart from being not less than, original gauge length three/for the moment, measured elongation after fracture is valid.When the fracture of tensile sample during at diverse location, obviously the elongation after fracture obtained of measuring is not have uniqueness, and while only having when fracture the mid point at original gauge length, the gauge length of having no progeny measured is only maximal value, could truly be represented the elongation strain characteristic of material by the corresponding elongation after fracture of the gauge length of having no progeny of maximum.Along with the extensive employing of computer control drawing machine and extensometer technology, realized artificial handling sample, then by computer automatic analysis, calculate stretch test result.These methods are measured and can be reached very high precision parts, but, because there are many defects in method of testing, cause the measurement reliability existing problems of elongation after fracture.Particularly metal material is in drawing process, and the error of the automatic measurement that randomness causes of its fracture position, be that the user is difficult to accept.The development of machine vision and digital image processing techniques, can obtain measurand appearance images information directly and accurately, and from the image of objective things, information extraction is processed and analyzed, and realizes the intellectuality of metal material elongation after fracture, high-precision detection.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point, while to solve, measuring elongation after fracture, metal material is detected to sample and preset original gauge length and cause the poor problem of metal material elongation after fracture accuracy of detection.
The technical solution adopted for the present invention to solve the technical problems is: a kind of Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point is provided, comprises the following steps:
(1) metal material with gauge point is stretched, when stretching, gathering stretches starts to the consecutive image sequence F of the metal material surface deformation till Fracture of Metal Material
s=f
1, f
2... f
n, f wherein
1for the image that stretching starts to obtain, f
nthe image obtained during for tension failure;
(2) according to image f
ntextural characteristics, determine its fracture position at draw direction, and successively image sequence carried out to reverse regional coherent detection, this fracture position of deriving is at other picture frame of image sequence f
sin the corresponding position of draw direction, and all frames of image sequence are set up to coordinate system, wherein, take the axis that is detected the material during tensile direction is X-axis, and fracture position is true origin O, by initial point O and the direction vertical with X-axis, is Y-axis;
(3) to the first two field picture f
1, the initial point O of X-axis of take is benchmark, in predetermined effective detection zone, it left and rightly is divided into to equal-sized consecutive image piece symmetrically, and stipulates the position coordinates of each image block at directions X, is that the X coordinate with the image block mid point means;
(4) to the first two field picture f
1each image block of dividing, X coordinate points with its position is made vertical line, image block is divided into to left and right two parts, select a random spot the most significant in every part, as meaning the representative point of this image block in the directions X position, and, obtained the accurate coordinates of these two representative points by the sub-pixel positioning algorithm after, calculate the scale-up factor k of this image block X coordinate to two representative point X coordinates;
(5) pass through other picture frame f
sframe f directly
s-1the selected representative point of all image blocks followed the tracks of, determine f
s-1all representative points corresponding its at f
sinterior position, recycling scale-up factor k, calculate picture frame f
sthe X coordinate of interior all consecutive image pieces;
(6) build two-dimentional form, in each frame of memory image sequence, all image blocks, at the position coordinates of directions X, calculate in drawing process the local elongation rate of time dependent adjacent image interblock and the displacement information of each image block to facilitate;
(7) according to the value that presets the local elongation rate of calculating the Fracture of Metal Material length growth rate, two-dimentional list data is processed, selected to calculate the image block of Fracture of Metal Material length growth rate the best, and determined the first two field picture f thus
1calculation level and image f during tension failure
ncalculation level;
(8) according to determined calculation level, obtain respectively original gauge length and the gauge length of having no progeny, calculate and calculate the Fracture of Metal Material length growth rate.
Described step (1) is front also comprise adopt air painting equipment to spray the irregular random labelling point of size and shape and dry on the surface uniform ground of detected metal material sample after the metal material of formation tape label point.
In described step (5) to all image blocks selected representative point to follow the tracks of be to utilize the relevant method in some zone first to carry out the tracking of Pixel-level precision, then adopt related function main peak distributed model fitting algorithm, realize that sub-pixel precision follows the tracks of.
In described step (7), two-dimentional list data being processed is by the search to two-dimentional list data and calculating, at the first two field picture f
1image f during with tension failure
nleft and right two groups of adjacent image pieces of interior search and fracture position symmetry, make it meet the local elongation rate and preset value is the most approaching, and at the first two field picture f
1image f during with tension failure
nin, using respectively determined two groups of adjacent image piece point midways respectively as the calculation level that calculates original gauge length and the gauge length of having no progeny, original gauge length and the gauge length of having no progeny are respectively the distance of two groups of calculation levels.
Tension failure picture frame f in described step (2)
nthe definite of fracture position coordinate be by the metal material sample surface is occurred to the slight crack grain is followed the tracks of, and after extracting the texture Main skeleton line, calculate respectively the texture Main skeleton line and stretch vector at X and Y-direction, to rupture the texture Main skeleton line when the vector of Y-direction is greater than a certain threshold value, the judgement metal material is pulled off, get fracture texture Main skeleton line and stretch the median location of vector at directions X, as its coordinate position of fracture at draw direction.
Beneficial effect
Owing to having adopted above-mentioned technical scheme, the present invention compared with prior art, has following advantage and good effect:
1) method that adopts image sequence afterwards to process, can be after obtaining the exact position of Fracture of Metal Material point, centered by fracture position, at its left and right symmetrically automatic searching local elongation rate and the immediate adjacent image piece of preset value, thereby optimize original gauge length and size and the position of the gauge length of having no progeny, reduce to greatest extent the error of breakaway poing testing result that Randomness of position causes.
2) there is isotropic random labelling point, can be subjected to displacement with metal material surface deformation, accurately mean the metal surface deformation behaviour.Simultaneously by random point track and localization technology, can be ingenious by breakaway poing and representative point position with the deformation map of material in each frame of whole image sequence, in test process, the efficacious prescriptions method of performance analysis metal material mechanics feature extraction.
3) utilize machine vision and image processing techniques to be detected the Fracture of Metal Material length growth rate, realized testing process intellectuality and robotization, there is easy to detect, quick and measuring accuracy high.
The accompanying drawing explanation
Fig. 1 is the system architecture schematic diagram that the present invention adopts;
Fig. 2 is that the present invention detects the division of sample image continuum and the optimized image piece is selected schematic diagram;
Fig. 3 is that piece movable information two-dimensional storage of the present invention means intention;
In figure: computing machine 1, display 2, ccd image controller 3, light source controller 4, LED light source 5, camera lens 6, signal adapter 7, metal material sample 8, drawing mechanism 9, the left adjacent image piece 10 of gauge length is calculated in selection, selects to calculate the right adjacent image piece 11 of gauge length.
Embodiment
Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiment only are not used in and limit the scope of the invention for the present invention is described.Should be understood that in addition those skilled in the art can make various changes or modifications the present invention after the content of having read the present invention's instruction, these equivalent form of values fall within the application's appended claims limited range equally.
The present invention relates to a kind of Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point.The method can be by system architecture realization as shown in Figure 1, and this system architecture installs Vision Builder for Automated Inspection and computer system additional on the basis of existing material during tensile equipment; Described computer system comprises computing machine 1 and display 2, described Vision Builder for Automated Inspection comprises ccd image sensor 3, LED light source 5 and camera lens 6, described display 2 links into an integrated entity with computing machine 1, also ccd image sensor 3, light source controller 4 are connected with signal adapter 7 described computing machine 1 respectively, described signal adapter 7 connects the control system of drawing mechanisms 9, and the camera lens 6 of described ccd image sensing 3 is over against the surface of the tested metal material sample 8 in its place ahead and be positioned at the lower end of LED light source 5.Wherein, described LED light source can be the strong White LED light source of controllable light.
Method of the present invention is: first adopt the template of standard to carry out distortion correction to digital CCD and optical system, then adopt the scaling board that precision is 10 μ m, imaging system is demarcated, to obtain the corresponding actual physics area of the single pixel of imaging system.The present invention adopts the bearing calibration of standard checkerboard image, and adopting the interior angle point of standard checkerboard image grid is unique point, according to it, at volume coordinate plane and the corresponding relation that gathers the coordinate of image own, obtains the distortion parameter of pick-up lens, realizes its rectification.Then, utilize high-precision calibrating template, system is demarcated.
In the project implementation process, its signal adapter is to utilize the microcontroller implementation that adopts FreeScale_HCS12 series.First set up the communication protocol of controller and computer system, and, by the analog quantity input of this controller, directly connect the controller of drawing machine, take out its enabling signal, and convert digital signal to and pass to the control computing machine.
In addition, aspect imaging, can also before the camera lens of imaging system, install the method that the light reflection suppresses film additional, eliminate by detected object reflection of light, the image quality of raising to detected object, obtain metal material surface surveyed area image clearly, to improve the accuracy of detection of system.
The present invention, in implementation process, can adopt following equipment:
(1) PC: its major parameter is: dual core processor, and dominant frequency is 3GHz, supports the JPEG hardware compression, maximum 8192 * 8192 resolution of supporting, inside save as 4GbitsDDR3.Support RGB24Bit interface and the output of TVOUT video.
(2) digital CCD: the CMOS model of employing is OV3460, the 2048*1536 pixel, and the imaging region size is 3626 μ mx2709 μ m, and pixel size is 1.75 μ mx1.75 μ m, and top speed can reach 30 frame per seconds.
(3) light source: LED bar shaped white light source, electric parameter 24v/8.6w, physical dimension (mm) is 212 * 44 * 22, light-emitting area is of a size of 199 * 36, the band diffusion disk, when environment temperature is 25 ℃, brightness and setting angle be adjustable, high brightness, low temperature, equilibrium, flicker free all.
(4) optical lens: M3Z1228C-MPFA industry 300 everything element camera lenses, specification Format, 2/3 "; Interface mode: C; Focal length (mm): 12-36(is variable); Aperture (F): 2.8-16C; Field angle (horizontal HOR) °: 41.0-13.6; Recently object image distance is from (M): 0.2; Effective aperture: front Front
: 27.2; Rear Rear
: 12.1; The pre-filter screw thread
physical dimension
(5) signal adapter: adopt the FreeScale_HCS12 microcontroller as signal converter.Wherein, in the HCS12 microcontroller, AD modular converter correlation parameter is: 8/10 precision, the 10-position single conversion time is 7 μ s, the sampling buffer amplifier, the programmable sample time, external trigger is controlled, convert interruption, continuous translative mode, analog input 8 channel multiplexings, and there is the hyperchannel scan mode.
A kind of Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point comprises the following steps:
Step 1: employing carries standard form the distortion of imaging system camera lens 6 is corrected, adjusting focal length and light intensity, and utilize high-precision scaling board to carry out Accurate Calibration to vision system;
Step 2: before test, adopt air painting equipment the surface uniform of detected metal material sample spray the irregular random labelling point of size and shape, and wait processing after slightly drying;
Step 3: the metal material be labeled is installed to the assigned address of stretching device, and testing requirement routinely, set extensograph parameter etc.;
Step 4: start tensile testing device, start metal material is stretched.Simultaneously, the stretching enabling signal is passed to computing machine after by signal adapter, controls the high precision imaging system, and gathering stretches starts to the consecutive image sequence F of metal material surface deformation till Fracture of Metal Material
s=f
1, f
2... f
n, f wherein
1for the image that stretching starts to obtain, f
nthe image obtained during for tension failure;
Step 5: according to image f
ntextural characteristics, determine its fracture position at draw direction, and successively image sequence carried out to reverse regional coherent detection, this fracture position of deriving is at other picture frame of image sequence f
s(1≤s≤N-1) is in the corresponding position of draw direction.All frames to image sequence are set up coordinate system, and wherein, take the axis that is detected the material during tensile direction is X-axis, and fracture position is true origin O, by initial point O and the direction vertical with X-axis, is Y-axis, and unit length is pixel.
Step 6: to the first two field picture f
1, the initial point O of X-axis of take is benchmark, in predetermined effective detection zone, it left and rightly is divided into to the strict equal consecutive image piece of size symmetrically, and stipulates the position coordinates of each image block at directions X, is that the X coordinate with the image block mid point means.
Step 7: to f
1each image block of dividing, X coordinate points with its position is made vertical line, image block is divided into to left and right two parts, select a random spot the most significant in every part, as meaning the representative point of this image block in the directions X position, and, obtained the accurate coordinates of these two representative points by the sub-pixel positioning algorithm after, calculate the scale-up factor k of this image block X coordinate to two representative point X coordinates.
Step 8: other picture frame f
s(2≤s≤N), the consecutive image piece in its image, in the calculating of directions X position coordinates, is first by frame f before direct to it
s-1the selected representative point of all image blocks followed the tracks of, determine f
s-1all representative points corresponding its at f
sinterior position, recycling scale-up factor k, calculate picture frame f
sthe X coordinate of interior all consecutive image pieces.
Step 9: build two-dimentional form, in each frame of memory image sequence, all image blocks, at the position coordinates of directions X, calculate in drawing process the local elongation rate of time dependent adjacent image interblock and the displacement information of each image block to facilitate.
Step 10: according to the value that presets the local elongation rate of calculating the Fracture of Metal Material length growth rate, two-dimentional list data is calculated, select to calculate the image block of Fracture of Metal Material length growth rate the best, and determine the first two field picture f thus
1calculation level and image f during tension failure
ncalculation level.
Step 11: according to determined calculation level, obtain respectively original gauge length and the gauge length of having no progeny, the then definition to elongation after fracture according to GB/T228-2002, calculate and calculate the Fracture of Metal Material length growth rate., can select according to the user simultaneously, from bivariate table, extract relevant information, the automatically deformation tendency of analysis and calculation metal material in drawing process.
Below with a specific embodiment, further illustrate the present invention.
Also need the air painting equipment that adopts the droplet size dimension controlled before detecting enforcement, the surface uniform of detected metal material sample spray the irregular random labelling point of size and shape, the size of point is relevant with the division of image block, meets it and in image block, exists its maximum radial dimension to should be 1/5 the most suitable at the directions X width of image block.And be arranged on detection position wait processing after slightly drying.As shown in Figure 2, at metal material, in drawing process, the O ' O ' that often might not plunk in the middle ruptures, and likely in the fracture of the OO place of random site.Determining of breakaway poing position, to process by crack image, comprise gray processing, binaryzation and adopt regional dilation erosion, opening and closing operations and thinning processing, extract the skeleton image of crackle, and, on the basis that obtains the crackle skeleton image, adopt data structure to carry out inventory analysis to the crackle framework information, remove exactly the limb of crackle with the method for recurrence, after extracting the texture Main skeleton line, calculate the length of crackle.Calculate respectively it and stretch vector at X and Y-direction, and, to rupture the texture Main skeleton line when the vector of Y-direction is greater than a certain threshold value, the judgement metal material is pulled off; And with this understanding, calculate the texture Main skeleton line that now ruptures and stretch the median location of vector at directions X, as its coordinate position of fracture at draw direction.After determining its fracture position at draw direction, and successively image sequence is carried out to reverse regional coherent detection, this fracture position of deriving is at other picture frame of image sequence f
s(1≤s≤N-1) is in the corresponding position of draw direction.All frames to image sequence are set up coordinate system, and wherein, take the axis that is detected the material during tensile direction is X-axis, and fracture position is true origin O, by initial point O and the direction vertical with X-axis, is Y-axis, and unit length is pixel.Then to the first two field picture f
1, the initial point O of X-axis of take is benchmark, in predetermined effective detection zone, by its left and right strict equal consecutive image piece of size that is divided into symmetrically, the size of piece, with how many, is determined according to resolution and the accuracy of detection of image.To cutting apart the consecutive image piece obtained, stipulate its position coordinates at directions X, by the X coordinate of image block mid point, meaned.To f
1each image block of dividing, X coordinate points with its position is made vertical line, image block is divided into to left and right two parts, select a random spot the most significant in every part, as meaning the representative point of this image block in the directions X position, and, obtained the accurate coordinates of these two representative points by the sub-pixel positioning algorithm after, calculate the scale-up factor k of this image block X coordinate to two representative point X coordinates.As in implementation process, the effective surveyed area that is 100mm for length, it can be distributed to 100 continuous image blocks (can be adjusted according to accuracy requirement), be that image block is 1mm in the shared length of directions X, we can be nearest from X-axis in the search of the left and right regions of image block center line, and the spot of the size circle that is regional 1/8～1/2 or oval similar features is as two representative points of this image block.And and f
1corresponding other picture frame f
s(2≤s≤N), the consecutive image piece in its image, in the calculating of directions X position coordinates, is first by frame f before direct to it
s-1all image block representative points followed the tracks of, determine f
s-1all representative points are at f
sinterior corresponding position, recycling scale-up factor k, calculate picture frame f
sthe X coordinate of interior all consecutive image pieces.
The described representative point to all image blocks is followed the tracks of, and is to utilize the relevant method in some zone, first carries out the tracking of Pixel-level precision, then adopts related function main peak distributed model fitting algorithm, realizes the sub-pixel precision tracking.In the present invention, consider the spot that metal material surface sprays, it is oval that its shape is approximately mostly, and with background, discrimination is preferably arranged, so select to adopt the quadratic fit algorithm of elliptical center, is conducive to realize spot sub-pixel precision location.This algorithm, based on criterion of least squares, carries out matching by the coordinate to target or gray scale, just can obtain the continuous function form of target, thereby can determine the parameters value of describing object.In the present invention, selected have and circle or the spot of oval similar features, first extracts one group of point on blob target border, then carry out oval least square fitting, thereby can determine center and the major axes orientation of target.Usually, quadratic curve equation is expressed as:
x
2+2Bxy+Cy
2+2Dx+2Ey+F=0 （1）
If (1) meet: B
2-C<0 and (1+C) (CF+2BDE-D
2c-B
2f-E
2)<0 condition, (1) formula just means elliptic equation.Its mean square deviation and being expressed as:
To the B in formula (2), C, D, E, F gets respectively local derviation, and to make each formula be zero, can obtain a system of equations that comprises 5 equations and 5 unknown parameters, can calculate the centre coordinate (x of spot by separating this system of equations
0, y
0), be expressed as:
In fact, the tile size size of dividing except the first two field picture strictly equates, along with the stretching of metal material, material deformation make the follow-up image institute obtained to by image block shape and size all in constantly changing.But the present invention is concerned about, in drawing process, the image block in image sequence is in the variation of the position of its draw direction, to obtain material deformation data at directions X in drawing process.Therefore need the method that adopts point to follow the tracks of to position respective image piece corresponding representative point position in other each frame of image sequence, in order to obtain in metal material detects the variation of image block position.In the present invention, the precision of the track algorithm of representative point, be to realize the pinpoint committed step of image block equally.Employing is followed the tracks of successively to the representative point in each image of image sequence, obtains the exact position of representative point in all corresponding blocks of image sequence.In general, the coupling based on point target in image, can reach 1/2 pixel (half-pixel) precision.Consider that the present invention reaches high-precision measurement to the Fracture of Metal Material length growth rate, need to reach sub-pixel precision to the tracking of the representative point of image block.In implementation process of the present invention, first representative point is carried out the tracking and matching of Pixel-level, and then, by the modeling matching, further improve tracking accuracy.The two continuous width images of supposing image sequence are respectively f
t-1and f
t.Wherein, we are by f
tregard as present image, and f
t-1it is the directly front frame of present image.The task of following the tracks of is exactly according to f
t-1the feature spot image d at known representative point p (x, the y) place on image, adopt the method for relevant matches at f
tthe enterprising line search of image, find the speckle patterns d ' mated most with it.After searching for successfully, by corresponding some p ' of speckle patterns d ' (x, y) point, be exactly the position of p (x, y) after the d distortion.Wherein, in the distance of directions X, meaned that this point is at image f between being put to p ' (x, y) by a p (x, y)
t-1and f
tcompartment is in the displacement of directions X.In order to realize a coupling, can define a related function, mean the matching degree of 2 place feature speckle patterns d of p (x, y) and p ' (x, y) and d '.If it is f that two width mate the image calculated
t-1and f
t, at f
t-1the area image at the feature speckle patterns d place of middle selection is Ω
d, its size is m * n, and at image f
tselected matching area figure is S, and its size is M * N, uses S
x,ymean to take (x, y) as upper left angle point and Ω in S
dthe sub-block that size is identical.Mean the correlation computations expression formula by formula (4), when the value of γ reaches maximum, its extreme point (maximal value) corresponding coordinate (x, y) means optimum matching.In formula
with
the gray average that means respectively its image-region.
In fact, the result that above-mentioned match search algorithm provides is only to reach the precision of Pixel-level, in implementation process of the present invention, in order to reach the precision of higher coupling, the method that the present invention utilizes sub-pix to solve, further bring up to subpixel accuracy by the coupling of representative point.The main peak of supposing related function distributes and to meet a certain model γ (x, y), x wherein, and y is volume coordinate.Related coefficient by some known whole pixels on main peak (the present invention rounds the neighborhood of 3 * 3 centered by the peak point of pixel) is determined the unknown parameter in model γ (x, y), finally by solving following system of equations:
Draw extreme point and the maximum correlation coefficient of sub-pix.According to the difference of γ (x, y) Model Selection, its method for solving is varied.In the present invention, γ (x, y) selects the computation model of 9 parameters, that is:
γ(x,y)=ax
2y
2+bxy
2+cx
2y+dx
2+ey
2+fxy+gx+hy+i （6）
The value of 9 points that 9 parameters of this model can be calculated by 3 * 3 coordinate points of its symmetry, that is: γ
1(x
1, y
1), γ
2(x
2, y
2) ..., γ
9(x
9, y
9) try to achieve.Then by definite curved surface form substitution formula (6) formula, just can try to achieve the maximum γ of sub-pixel precision
maxbe worth corresponding point (x
s, y
s).
In measuring process, the two-dimentional form of the block motion vector of memory image sequence chart picture frame, as shown in Figure 3, wherein, the piece number that laterally gauge outfit is image, the frame number that row are image sequence to gauge outfit, the tabular value number corresponding with frame number and piece, the position coordinates of piece in presentation video sequence different images frame.Adopt this form, can conveniently calculate the displacement of specify image piece between two picture frames, local elongation rate that particularly can the time dependent adjacent image interblock of fast search, realize automatic analysis and calculate the deformation tendency of metal material in drawing process.
By the search to two-dimentional list data and calculating, at f
1and f
nleft and right two groups of adjacent image pieces of interior search and fracture position symmetry, meet local elongation rate and preset value the most approaching.At f
1and f
nin, two groups of adjacent image piece point midways determining, as the calculation level that calculates original gauge length and the gauge length of having no progeny.Finally, according to determined calculation level, obtain respectively original gauge length and the gauge length of having no progeny, the then definition to elongation after fracture according to GB/T228-2002, calculate the Fracture of Metal Material length growth rate.
The present invention can overcome existing Fracture of Metal Material length growth rate intelligent test method shortcoming, and the requirement that realization is quick, high-accuracy intelligent detects, for the quality testing of metal material production is applied scientific experimental data is provided with design.
Claims (5)
1. a Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point, is characterized in that, comprises the following steps:
(1) metal material with gauge point is stretched, when stretching, gathering stretches starts to the consecutive image sequence F of the metal material surface deformation till Fracture of Metal Material
s=f
1, f
2... f
n, f wherein
1for the image that stretching starts to obtain, f
nthe image obtained during for tension failure;
(2) according to image f
ntextural characteristics, determine its fracture position at draw direction, and successively image sequence carried out to reverse regional coherent detection, this fracture position of deriving is at other picture frame of image sequence f
sin the corresponding position of draw direction, and all frames of image sequence are set up to coordinate system, wherein, take the axis that is detected the material during tensile direction is X-axis, and fracture position is true origin O, by initial point O and the direction vertical with X-axis, is Y-axis;
(3) to the first two field picture f
1, the initial point O of X-axis of take is benchmark, in predetermined effective detection zone, it left and rightly is divided into to equal-sized consecutive image piece symmetrically, and stipulates the position coordinates of each image block at directions X, is that the X coordinate with the image block mid point means;
(4) to the first two field picture f
1each image block of dividing, X coordinate points with its position is made vertical line, image block is divided into to left and right two parts, select a random spot the most significant in every part, as meaning the representative point of this image block in the directions X position, and, obtained the accurate coordinates of these two representative points by the sub-pixel positioning algorithm after, calculate the scale-up factor k of this image block X coordinate to two representative point X coordinates;
(5) pass through other picture frame f
sframe f directly
s-1the selected representative point of all image blocks followed the tracks of, determine f
s-1all representative points corresponding its at f
sinterior position, recycling scale-up factor k, calculate picture frame f
sthe X coordinate of interior all consecutive image pieces;
(6) build two-dimentional form, in each frame of memory image sequence, all image blocks, at the position coordinates of directions X, calculate in drawing process the local elongation rate of time dependent adjacent image interblock and the displacement information of each image block to facilitate;
(7) according to the value that presets the local elongation rate of calculating the Fracture of Metal Material length growth rate, two-dimentional list data is processed, selected to calculate the image block of Fracture of Metal Material length growth rate the best, and determined the first two field picture f thus
1calculation level and image f during tension failure
ncalculation level;
(8) according to determined calculation level, obtain respectively original gauge length and the gauge length of having no progeny, calculate and calculate the Fracture of Metal Material length growth rate.
2. the Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point according to claim 1, it is characterized in that, described step (1) is front also comprise adopt air painting equipment to spray the irregular random labelling point of size and shape and dry on the surface uniform ground of detected metal material sample after the metal material of formation tape label point.
3. the Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point according to claim 1, it is characterized in that, in described step (5) to all image blocks selected representative point to follow the tracks of be to utilize the relevant method in some zone first to carry out the tracking of Pixel-level precision, then adopt related function main peak distributed model fitting algorithm, realize the sub-pixel precision tracking.
4. the Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point according to claim 1, it is characterized in that, in described step (7), two-dimentional list data being processed is by the search to two-dimentional list data and calculating, at the first two field picture f
1image f during with tension failure
nleft and right two groups of adjacent image pieces of interior search and fracture position symmetry, make it meet the local elongation rate and preset value is the most approaching, and at the first two field picture f
1image f during with tension failure
nin, using respectively determined two groups of adjacent image piece point midways respectively as the calculation level that calculates original gauge length and the gauge length of having no progeny, original gauge length and the gauge length of having no progeny are respectively the distance of two groups of calculation levels.
5. the Fracture of Metal Material length growth rate intelligent test method of following the tracks of based on random point according to claim 1, is characterized in that, tension failure picture frame f in described step (2)
nthe definite of fracture position coordinate be by the metal material sample surface is occurred to the slight crack grain is followed the tracks of, and after extracting the texture Main skeleton line, calculate respectively the texture Main skeleton line and stretch vector at X and Y-direction, to rupture the texture Main skeleton line when the vector of Y-direction is greater than a certain threshold value, the judgement metal material is pulled off, get fracture texture Main skeleton line and stretch the median location of vector at directions X, as its coordinate position of fracture at draw direction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310375963.5A CN103471910B (en) | 2013-08-26 | 2013-08-26 | A kind of elongation at break of metal material intelligent test method followed the tracks of based on random point |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310375963.5A CN103471910B (en) | 2013-08-26 | 2013-08-26 | A kind of elongation at break of metal material intelligent test method followed the tracks of based on random point |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103471910A true CN103471910A (en) | 2013-12-25 |
| CN103471910B CN103471910B (en) | 2015-11-18 |
Family
ID=49796851
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310375963.5A Expired - Fee Related CN103471910B (en) | 2013-08-26 | 2013-08-26 | A kind of elongation at break of metal material intelligent test method followed the tracks of based on random point |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103471910B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105510124A (en) * | 2016-01-12 | 2016-04-20 | 济南大学 | System for automatic measurement of tensile test percentage elongation of test piece after fracture and method thereof |
| CN105588767A (en) * | 2014-10-24 | 2016-05-18 | 北京强度环境研究所 | Test piece tensile test system for optical strain field measurement under ultralow temperature environment |
| CN107179240A (en) * | 2016-03-09 | 2017-09-19 | 通用汽车环球科技运作有限责任公司 | Method for avoiding side from splitting |
| CN109470165A (en) * | 2018-12-07 | 2019-03-15 | 广州大学 | A kind of extensometer measurement method of the measurement material bending deformation based on machine vision |
| CN109540664A (en) * | 2018-10-31 | 2019-03-29 | 陕西航空电气有限责任公司 | Detection method for brittleness band elongation after fracture |
| CN109870354A (en) * | 2019-01-21 | 2019-06-11 | 吉林大学 | Round metal bars sample is uniaxially stretched elongation after fracture method for automatic measurement |
| CN110118691A (en) * | 2019-06-11 | 2019-08-13 | 成渝钒钛科技有限公司 | A kind of original gauge length mantissa is 5 elongation after fracture test methods |
| CN110658062A (en) * | 2019-09-06 | 2020-01-07 | 武汉大学 | Indoor model test system for researching damage process of anti-tilt rock slope |
| CN110658063A (en) * | 2019-09-06 | 2020-01-07 | 武汉大学 | Non-contact type indoor slope model displacement monitoring method |
| CN111238934A (en) * | 2020-01-19 | 2020-06-05 | 苏州新苏理化测试服务有限公司 | Metal test piece tensile mechanical property device and test method thereof |
| CN111829876A (en) * | 2020-06-30 | 2020-10-27 | 安钢自动化软件股份有限公司 | Be applied to visual equipment that deformed steel bar elongation after fracture measured |
| CN112611656A (en) * | 2020-12-14 | 2021-04-06 | 中南大学 | Accurate measurement method for low-temperature elongation of aluminum alloy for aerospace |
| CN112630025A (en) * | 2020-12-04 | 2021-04-09 | 安徽坤源铝业有限公司 | Aluminum plate performance detection device |
| CN113310437A (en) * | 2021-05-28 | 2021-08-27 | 广东韶钢松山股份有限公司 | Method and device for automatically measuring wire section shrinkage |
| CN113506327A (en) * | 2021-07-16 | 2021-10-15 | 成都产品质量检验研究院有限责任公司 | Elongation at break calculation method and device and electronic equipment |
| CN113569679A (en) * | 2021-07-19 | 2021-10-29 | 成都产品质量检验研究院有限责任公司 | Method, device and system for measuring elongation at break |
| CN113933157A (en) * | 2021-10-12 | 2022-01-14 | 江苏省电力试验研究院有限公司 | Tension test method and device for cable insulation dumbbell test piece |
| CN114187348A (en) * | 2022-02-16 | 2022-03-15 | 成都大公博创信息技术有限公司 | Method for measuring elongation at break of cable sheath based on computer vision |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000186990A (en) * | 1998-12-22 | 2000-07-04 | Shimadzu Corp | Material testing machine |
-
2013
- 2013-08-26 CN CN201310375963.5A patent/CN103471910B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000186990A (en) * | 1998-12-22 | 2000-07-04 | Shimadzu Corp | Material testing machine |
Non-Patent Citations (4)
| Title |
|---|
| 唐正宗等: "大变形测量数字图像的种子点匹配方法", 《西安交通大学学报》 * |
| 彭瑞东等: "数字散斑相关法在SEM观测岩石变形时的应用", 《中国矿业大学学报》 * |
| 梁园: "数字散斑相关亚像素位移测量方法研究与实验", 《中国优秀硕士学位论文全文数据库》 * |
| 潘兵等: "数字图像相关中基于可靠变形初值估计的大变形测量", 《光学学报》 * |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105588767A (en) * | 2014-10-24 | 2016-05-18 | 北京强度环境研究所 | Test piece tensile test system for optical strain field measurement under ultralow temperature environment |
| CN105510124A (en) * | 2016-01-12 | 2016-04-20 | 济南大学 | System for automatic measurement of tensile test percentage elongation of test piece after fracture and method thereof |
| CN107179240A (en) * | 2016-03-09 | 2017-09-19 | 通用汽车环球科技运作有限责任公司 | Method for avoiding side from splitting |
| CN109540664A (en) * | 2018-10-31 | 2019-03-29 | 陕西航空电气有限责任公司 | Detection method for brittleness band elongation after fracture |
| CN109470165A (en) * | 2018-12-07 | 2019-03-15 | 广州大学 | A kind of extensometer measurement method of the measurement material bending deformation based on machine vision |
| CN109870354B (en) * | 2019-01-21 | 2021-06-01 | 吉林大学 | Automatic measurement method for elongation after uniaxial tension fracture of metal round bar sample |
| CN109870354A (en) * | 2019-01-21 | 2019-06-11 | 吉林大学 | Round metal bars sample is uniaxially stretched elongation after fracture method for automatic measurement |
| CN110118691A (en) * | 2019-06-11 | 2019-08-13 | 成渝钒钛科技有限公司 | A kind of original gauge length mantissa is 5 elongation after fracture test methods |
| CN110658062A (en) * | 2019-09-06 | 2020-01-07 | 武汉大学 | Indoor model test system for researching damage process of anti-tilt rock slope |
| CN110658063A (en) * | 2019-09-06 | 2020-01-07 | 武汉大学 | Non-contact type indoor slope model displacement monitoring method |
| CN111238934A (en) * | 2020-01-19 | 2020-06-05 | 苏州新苏理化测试服务有限公司 | Metal test piece tensile mechanical property device and test method thereof |
| CN111238934B (en) * | 2020-01-19 | 2022-04-05 | 苏州新苏理化测试服务有限公司 | Metal test piece tensile mechanical property device and test method thereof |
| CN111829876A (en) * | 2020-06-30 | 2020-10-27 | 安钢自动化软件股份有限公司 | Be applied to visual equipment that deformed steel bar elongation after fracture measured |
| CN112630025A (en) * | 2020-12-04 | 2021-04-09 | 安徽坤源铝业有限公司 | Aluminum plate performance detection device |
| CN112611656A (en) * | 2020-12-14 | 2021-04-06 | 中南大学 | Accurate measurement method for low-temperature elongation of aluminum alloy for aerospace |
| CN112611656B (en) * | 2020-12-14 | 2024-03-08 | 中南大学 | Accurate measurement method for low-temperature elongation of aluminum alloy for aerospace |
| CN113310437A (en) * | 2021-05-28 | 2021-08-27 | 广东韶钢松山股份有限公司 | Method and device for automatically measuring wire section shrinkage |
| CN113506327A (en) * | 2021-07-16 | 2021-10-15 | 成都产品质量检验研究院有限责任公司 | Elongation at break calculation method and device and electronic equipment |
| CN113569679A (en) * | 2021-07-19 | 2021-10-29 | 成都产品质量检验研究院有限责任公司 | Method, device and system for measuring elongation at break |
| CN113569679B (en) * | 2021-07-19 | 2024-03-15 | 成都产品质量检验研究院有限责任公司 | Method, device and system for measuring elongation at break |
| CN113933157A (en) * | 2021-10-12 | 2022-01-14 | 江苏省电力试验研究院有限公司 | Tension test method and device for cable insulation dumbbell test piece |
| CN114187348A (en) * | 2022-02-16 | 2022-03-15 | 成都大公博创信息技术有限公司 | Method for measuring elongation at break of cable sheath based on computer vision |
| CN114187348B (en) * | 2022-02-16 | 2022-05-06 | 成都大公博创信息技术有限公司 | Method for measuring elongation at break of cable sheath based on computer vision |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103471910B (en) | 2015-11-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103471910B (en) | A kind of elongation at break of metal material intelligent test method followed the tracks of based on random point | |
| CN102944559B (en) | Vision measurement method for anisotropic performance parameters in sheet forming | |
| CN102589516B (en) | Dynamic distance measuring system based on binocular line scan cameras | |
| CN108986070B (en) | Rock crack propagation experiment monitoring method based on high-speed video measurement | |
| CN105258710B (en) | A kind of high-precision principal point for camera scaling method | |
| CN108535321A (en) | A kind of building thermal technique method for testing performance based on three-dimensional infrared thermal imaging technique | |
| CN106780483B (en) | More continuous casting billet end face visual identifying systems and centre coordinate acquiring method | |
| Liu et al. | An improved online dimensional measurement method of large hot cylindrical forging | |
| CN104634261B (en) | Line laser source based medium plate shape inspection system and method | |
| CN104111485A (en) | Stereo imaging based observation method for raindrop size distribution and other rainfall micro physical characteristics | |
| CN107044821A (en) | A kind of measuring method and system of contactless tubing object | |
| CN103782232A (en) | Projector and control method thereof | |
| CN108225798B (en) | Tire slippage testing system and method based on edge enhancement identification | |
| CN103542815A (en) | Large size speckle full-field strain measurement method | |
| CN104700395A (en) | Method and system for detecting appearance crack of structure | |
| CN107084671B (en) | A kind of recessed bulb diameter measuring system and measurement method based on three wire configuration light | |
| CN110108715B (en) | Defect detection method for transparent parallel flat plate | |
| CN102003945A (en) | Virtual optical extensometer and measurement method thereof | |
| CN101776437B (en) | Calibration technology for vision sub-pixel of embedded type machine with optical path adjustment | |
| CN104215179A (en) | Method for dynamically measuring width of steel billet by using laser displacement sensors | |
| CN115717867A (en) | Bridge deformation measurement method based on airborne double cameras and target tracking | |
| CN103093214B (en) | A kind of pedestrian detection method based on vehicle mounted infrared camera | |
| CN109785290A (en) | Normalized steel plate defect detection method is shone based on local light | |
| CN114140534A (en) | Combined calibration method for laser radar and camera | |
| CN204027528U (en) | A kind of vision inspection apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20151118 Termination date: 20180826 |