CN111624115B - Method for detecting peeling performance of oxide skin of hot-rolled steel bar - Google Patents
Method for detecting peeling performance of oxide skin of hot-rolled steel bar Download PDFInfo
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Abstract
The invention provides a method for detecting the peeling performance of a hot-rolled steel bar oxide skin, which comprises the following steps: taking a section of hot-rolled steel bar sample, and straightening the sample; after the clamp of the wire torsion testing machine is aligned, clamping two ends of a sample, wherein the clamp clamps the length of the sample; acquiring a surface image of a hot-rolled steel bar sample by image acquisition equipment; performing unilateral torsion on the sample by using a wire torsion testing machine to ensure that an oxide skin falls off in the sample torsion process; acquiring the surface image of the hot-rolled steel bar sample after torsion by image acquisition equipment, and calculating the falling area; collecting the scale skin which falls off, and weighing the scale skin; screening the fallen oxide skin by using a sieve, and weighing the screened large-size oxide skin; the peel performance index was calculated. According to the invention, the stripping performance index is introduced, and the stripping amount and the size of the oxide skin are considered, so that the stripping performance of the steel bar oxide skin can be accurately evaluated.
Description
Technical Field
The invention relates to the field of metal material analysis, in particular to a method for detecting the peeling performance of a hot-rolled steel bar oxide skin.
Background
In the subsequent deep processing process of the hot rolled steel bar, the metal product enterprises generally remove oxide skins by acid washing or mechanical shelling. The pickling is to remove oxide skin on the surface of steel by using an acid solution, and the method can generate a large amount of waste acid, acid-containing wastewater, acid slag residues and the like, and once the acid is not properly treated, the soil, water, air and the like can be polluted. The mechanical shelling is to continuously bend the reinforcing steel bars in different directions, oxide skin falls off from the surface of the reinforcing steel bars due to poor deformation capacity, and the environmental protection problem is basically avoided in the whole process. Therefore, with the increasing of the environmental protection requirement, the metal product enterprises gradually increase the specific gravity of mechanical husking or adopt mechanical husking to replace acid washing. However, the quality of the steel mill and the metal product enterprise is more and more incongruous due to the poor steel scale peeling performance, and it is imperative for the steel mill to find a method which is simple in operation and can accurately detect the peeling performance of the hot-rolled steel scale.
At present, in the related art disclosed in the literature, the method for detecting the peeling performance of the steel bar scale is mainly measured by a stretching method. The Chinese patent discloses a method for measuring the weight of the steel bar before and after uniform deformation in the stretching process and the weight of the steel bar after the oxide skin is completely removed from the surface of the steel bar, so as to obtain the stripping rate of the oxide skin on the surface of the material. The method has the following disadvantages: 1. the method has complicated detection steps, and has the steps of cleaning, drying and the like, and the steps have higher risk of human errors, thus seriously influencing the accuracy of the detection result. 2. The method has the limitation in the aspect of evaluating the stripping rate of the steel bar oxide skin; the stripping performance of the steel bar oxide scale is not only related to the stripping amount of the oxide scale, but also closely related to the size of the scale to be stripped. If the method is used for testing the stripping performance of the steel bar oxide skin, the stripping amount of the oxide skin is large, but the oxide skin is mainly or completely powdery, the steel bar testing result evaluates that the stripping effect of the oxide skin is good, and the actual mechanical stripping effect is very poor. 3. The method detects that the scale is peeled off at the axial uniform deformation stage in the stretching process, but the steel bar is not deformed but continuously bent in different directions in the actual mechanical peeling process, and the method has a large difference with the mechanical peeling principle, so that the mechanical peeling and scale peeling performance of the steel bar cannot be accurately represented.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for detecting the peeling performance of a hot-rolled steel bar oxide skin, which comprises the steps of performing single-side torsion on a sample to be detected by using a wire torsion testing machine, calculating the peeling area by using an image acquisition method, effectively simulating continuous bending of steel bars in different directions in mechanical peeling, considering the influence factor of the peeling performance evaluation of the steel bar oxide skin, introducing a peeling performance index, considering the peeling amount of the oxide skin and the size of the oxide skin, and accurately evaluating the peeling performance of the steel bar oxide skin.
The present invention achieves the above-described object by the following means.
A method for detecting the peeling performance of a hot-rolled steel bar oxide skin comprises the following steps:
taking a section of hot-rolled steel bar sample, and straightening the sample;
after the clamp of the wire torsion testing machine is aligned, clamping two ends of the sample;
collecting the surface image of the hot-rolled steel bar sample by image collection equipment;
performing unilateral torsion on the sample by using a wire torsion testing machine to ensure that an oxide skin falls off in the sample torsion process;
acquiring the surface image of the hot rolled steel bar sample after torsion through image acquisition equipment, and calculating the falling area;
collecting the scale skin which falls off, and weighing the scale skin; screening the scale cinder by using a sieve, and weighing the large-size scale cinder screened out; the peel performance index was calculated.
Further, the method for acquiring the surface image of the hot-rolled steel bar sample or the surface image of the twisted hot-rolled steel bar sample by using the image acquisition equipment comprises the following steps:
preprocessing a surface image;
segmenting the preprocessed image by utilizing a marked watershed segmentation algorithm;
and (5) carrying out edge extraction on the segmented image by using a canny operator to obtain an interface of the oxide skin on the surface of the sample.
Further, the image preprocessing comprises the following steps:
gray level conversion, converting the steel crystal grain image into a gray level image by using a gray level conversion formula;
and (4) bilateral filtering denoising is performed, and noise points in the steel grain image are reduced.
Further, the preprocessed image is segmented by using a marker watershed segmentation algorithm, and the method comprises the following steps:
obtaining a gradient amplitude image, filtering the preprocessed image in the horizontal and vertical directions by using a Sobel operator, and solving a module value to obtain the gradient amplitude image;
marking a foreground object, namely performing morphological reconstruction on the image obtained by preprocessing, and obtaining a foreground marking image by obtaining a local maximum value of the processed gray level image;
marking a background object, namely further processing the reconstructed image to obtain a background marked image;
modifying the gradient amplitude image, namely modifying the segmentation function, so that the gradient amplitude image only has local minimum values at the positions of the foreground mark and the background mark;
and (4) segmenting the modified gradient amplitude image by using a traditional watershed algorithm.
Further, the stripping performance index D = ml/m, m being a weight of the scale to be stripped, ml being a maximum sized scale.
The invention has the beneficial effects that:
according to the invention, the wire torsion testing machine is adopted to carry out single-side torsion on the sample to be tested, the continuous bending in different directions of the steel bar in mechanical shelling is effectively simulated, the influence factor of the stripping performance evaluation of the steel bar oxide skin is considered, the stripping performance index is introduced, the scale stripping amount and the scale size are considered, the stripping performance of the steel bar oxide skin can be accurately evaluated, the testing steps are simple, the operation is easy, and the result is accurate. The bilateral filtering denoising method is adopted to carry out bilateral filtering denoising processing on the original image of the steel crystal grains to be detected, in the filtering processing process, noise can be well removed, and boundary information of the image can be effectively protected.
Drawings
Fig. 1 is a flow chart of a method for detecting the peeling performance of a hot-rolled steel bar scale according to the invention.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, without limiting the scope of the invention.
As shown in fig. 1, the method for detecting the peeling performance of the hot-rolled steel bar scale according to the present invention includes the following steps:
straightening a sample: taking a section of hot-rolled wire rod sample, recording the length as L, and straightening the sample;
sample clamping: after the clamp of the wire torsion testing machine is aligned, the two ends of the sample are clamped, the length of the sample clamped by the clamp is recorded as L1, the length L of the sample is = (300 +/-50) +2Ll, and the length L of the sample is 15 +/-5 mm.
Acquiring a surface image of a hot-rolled steel bar sample by image acquisition equipment;
twisting: and (3) performing unilateral torsion on the sample by using a wire torsion testing machine to ensure that the oxide skin falls off in the sample torsion process. The single-side torsion can effectively simulate the continuous bending of the wire rod in different directions in mechanical shelling, and oxide skin falls off from the surface of the wire rod due to poor deformability. The torsion angle is 90-180 degrees, and the torsion speed is 360-720 degrees/min; the torsion angle mainly influences the scale stripping amount, the reasonable torsion angle is the key for accurately evaluating the scale stripping performance, and the torsion speed also influences the scale stripping amount, the scale size and the inspection efficiency.
Acquiring the surface image of the hot rolled steel bar sample after torsion through image acquisition equipment, and calculating the falling area;
weighing and screening: collecting the fallen oxide skin, weighing the oxide skin and recording as m; screening the scale with a 6-8 mesh screen, and weighing the large-size scale, and marking as nil. The setting of the aperture of the sieve is the key of the screening process, the deviation between the result of the method and the actual scale stripping performance of the wire rod is large when the aperture of the sieve is too large or too small, the screening is carried out by adopting a 6-8-mesh sieve, and the actual scale stripping performance of the wire rod can be accurately evaluated by the result of the method.
Calculating a stripping performance index D, wherein D = ml/m; when D is larger, the hot wire rod scale peeling property is better. Considering that the evaluation result of the scale peeling property of the wire rod is not only related to the scale peeling amount but also related to the scale size during the inspection, the introduction of the peeling property index into the self-tapping hole can achieve both the scale peeling amount and the scale size, and can accurately evaluate the scale peeling property of the wire rod.
The method for acquiring the surface image of the hot-rolled steel bar sample or the surface image of the hot-rolled steel bar sample after torsion by using the image acquisition equipment comprises the following steps:
s1: reading a surface image of a hot-rolled steel bar sample;
s2: pre-processing a surface image, the image pre-processing comprising:
s2.1: gray level conversion, namely converting the steel crystal grain image into a gray level image by using a gray level conversion formula;
s2.2: bilateral filtering and denoising, namely reducing noise points in the steel grain image;
s3: the method for segmenting the preprocessed grain image by using a marked watershed segmentation algorithm specifically comprises the following steps:
s3.1: obtaining a gradient amplitude image, namely filtering the preprocessed image in the horizontal and vertical directions by using a Sobel operator, and solving a module value to obtain the gradient amplitude image;
s3.2: marking a foreground object, namely performing morphological reconstruction on the image obtained by preprocessing, and obtaining a foreground marking image by obtaining a local maximum value of the processed gray image;
s3.3: marking a background object, namely further processing the reconstructed image to obtain a background marked image;
s3.4: modifying the gradient amplitude image, namely modifying the segmentation function, so that the gradient amplitude image only has local minimum values at the positions of the foreground mark and the background mark;
s3.5: segmenting the modified gradient amplitude image by using a traditional watershed algorithm;
s4: and (5) carrying out edge extraction on the image after segmentation in the S3 by using a canny operator to obtain an interface of the oxide skin on the surface of the sample.
The method for gray scale conversion in S2.1 includes: for a color image F with size M × N, it is converted into a Gray image F using the Gray conversion formula Gray = (R × 30+ G × 59+ B × 11+ 50)/10, where: m and N are positive integers, and R, G and B are values of three color components corresponding to the color image respectively.
In the image, the bilateral filtering denoising method in S2.2 is: for the gray image f, the pixel point (i, j) is processed by twoThe output pixel value after edge filtering is g (i, j), i.e.
Wherein:
Ω D (i, j) is a neighborhood window with the size of (2D + 1) x (2D + 1) at (i, j), D is a neighborhood radius, and i, j, k and l are positive integers; f (k, l) is the gray value of the gray image f at the pixel point (i, j);
w (i, j, k, l) is a weight coefficient which depends on the spatial kernel function
And defining domain kernel functions
Wherein the parameter σ d 、σ r The attenuation degree of the weight factors in the space domain and the value domain is controlled respectively.
The specific steps of obtaining the gradient amplitude image in the S3.1 are as follows:
s3.1.1: respectively solving gray value G of image detected at transverse and longitudinal edges by Sobel operator i 、G j ， G i ＝[g(i-1,j-1)+2g(i,j-1)+g(i+1,j-1)]-[g(i-1,j+1)+2g(i,j+1)+g(i+1,j+1)]， G j ＝[g(i-1，j-1)+2g(i-1,j)+g(i-1，j+1)]-[g(i+1，j-1)+2g(i+1，j)+g(i+1,j+1)]Wherein g (i, j) represents the gray scale value of the preprocessed image g at (i, j);
s3.1.2: after being filtered by Sobel operator, the filter is processed by formula
And solving the amplitude to obtain a gradient amplitude image h.
The specific steps of marking foreground objects in S3.2 are:
s3.2.1: performing open operation reconstruction on the image obtained by preprocessing, wherein the open operation reconstruction method specifically comprises the following steps:
s3.2.1.1: corroding the image g obtained by preprocessing to obtain a marked image g required by open operation reconstruction 1 ， g 1 ＝(gΘb)(s,t)＝min{g(s+i,t+j)-b(s,t)|(s+i),(t+j)∈D g ；(s,t)∈D b Wherein b is a structural element, D g 、D b Definition domains of the gray level image g and the structural element b respectively;
s3.2.1.2: repeating iterative operations
Up to m a+1 ＝m a To obtain an image g after the reconstruction of the opening operation 2 And g is 2 ＝m a+1 ＝m a Wherein m is 1 Initialized to marker image g 1 A is a positive integer;
s3.2.2: performing closed operation reconstruction on the image obtained by the open operation reconstruction, specifically as follows:
s3.2.2.1: image g reconstructed by bisection operation 2 Swelling is carried out to obtain an image g 3 ， 
S3.2.2.2: respectively to the image g 2 、g 3 Performing a negation operation to obtain an image g 2 ’、g 3 ', in g 2 ' is a mask image, g 3 ' for marking the image, performing the open operation reconstruction again, and performing the negation operation on the reconstructed result to obtain the image g reconstructed by the closed operation 4 ；
And S3.2.3, obtaining a local maximum value of the image reconstructed based on the opening and closing operation by using an eight-neighborhood method to obtain a local maximum image u.
The specific steps of marking the background object in step S3.3 are:
s3.3.1, performing distance transformation on the image reconstructed by the opening and closing operation to generate a distance map;
and S3.3.2, performing traditional watershed segmentation on the distance map to obtain a background mark image w.
The method for correcting the gradient amplitude image in the step S3.4 is as follows: by means of a mathematical morphology minimum value calibration technology, the obtained foreground object u and the background object w are used as local minimum values of the gradient amplitude image forcibly, and all original local minimum values are shielded to inhibit the phenomenon of excessive segmentation.
And (5) carrying out edge extraction on the segmented image by using a canny operator, and obtaining the interface of the oxide skin on the surface of the sample. The area of the interface can be calculated by a computer. The interfaces before and after twisting are subtracted to obtain the interface of the residual oxide scale.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.
Claims (3)
1. The method for detecting the peeling performance of the hot-rolled steel bar oxide skin is characterized by comprising the following steps of:
taking a section of hot-rolled steel bar sample, and straightening the sample;
after the clamp of the wire torsion testing machine is aligned, clamping two ends of the sample;
acquiring a surface image of a hot-rolled steel bar sample by image acquisition equipment;
performing unilateral torsion on the sample by using a wire torsion testing machine to ensure that an oxide skin falls off in the sample torsion process;
acquiring the surface image of the hot rolled steel bar sample after torsion through image acquisition equipment, and calculating the falling area;
collecting the fallen oxide skin, and weighing the oxide skin; screening the scale cinder by using a sieve, and weighing the screened large-size scale cinder; calculating a stripping performance index;
the method comprises the following steps of acquiring a surface image of a hot-rolled steel bar sample or a surface image of a twisted hot-rolled steel bar sample by image acquisition equipment, wherein the method comprises the following steps:
preprocessing a surface image;
the method for segmenting the preprocessed image by using the marked watershed segmentation algorithm specifically comprises the following steps:
obtaining a gradient amplitude image, filtering the preprocessed image in the horizontal and vertical directions by using a Sobel operator, and solving a module value to obtain the gradient amplitude image, wherein the method specifically comprises the following steps:
separately solving gray value G of image detected at transverse and longitudinal edges by Sobel operator i 、G j ，G i ＝[g(i-1,j-1)+2g(i,j-1)+g(i+1,j-1)]-[g(i-1,j+1)+2g(i,j+1)+g(i+1,j+1)]，G j ＝[g(i-1,j-1)+2g(i-1,j)+g(i-1,j+1)]-[g(i+1,j-1)+2g(i+1,j)+g(i+1,j+1)]Wherein g (i, j) represents the gray scale value of the preprocessed image g at (i, j);
after being filtered by Sobel operator, the filter is processed by formula
Obtaining an amplitude value to obtain a gradient amplitude value image h;
marking a foreground object, namely performing morphological reconstruction on the image obtained by preprocessing, and obtaining a foreground marking image by obtaining a local maximum value of the processed gray image, wherein the method specifically comprises the following steps:
performing open operation reconstruction on the image obtained by preprocessing, specifically as follows:
corroding the image g obtained by preprocessing to obtain a marked image g required by the open operation reconstruction 1 ，g 1 ＝(gΘb)(s,t)＝min{g(s+i,t+j)-b(s,t)|(s+i),(t+j)∈D g ；(s,t)∈D b Wherein b is a structural element, D g 、D b Definition domains of the gray level image g and the structural element b respectively;
repeating iterative operations
Up to m a+1 ＝m a Obtaining an opening operation reconstructed image g 2 And g is 2 ＝m a+1 ＝m a Wherein m is 1 Initialized to marker image g 1 A is a positive integer;
performing closed operation reconstruction on the image obtained by the open operation reconstruction, specifically as follows:
image g reconstructed by bisection operation 2 Swelling is carried out to obtain an image g 3 ，
Respectively to the image g 2 、g 3 Performing a negation operation to obtain an image g 2 ’、g 3 ', in g 2 ' is a mask image, g 3 ' for marking the image, performing the open operation reconstruction again, and performing the negation operation on the reconstructed result to obtain the image g reconstructed by the closed operation 4 ；
Obtaining a local maximum value of the image reconstructed based on the opening and closing operation by using an eight-neighborhood method to obtain a local maximum image u;
marking a background object, namely further processing the reconstructed image to obtain a background marked image;
modifying the gradient amplitude image, namely modifying the segmentation function, so that the gradient amplitude image only has local minimum values at the positions of the foreground mark and the background mark;
segmenting the modified gradient amplitude image by using a traditional watershed algorithm;
and (5) carrying out edge extraction on the segmented image by using a canny operator to obtain an interface of the oxide skin on the surface of the sample.
2. The method for detecting the peeling performance of the hot-rolled steel bar scale as claimed in claim 1, wherein the image preprocessing comprises the following steps:
gray level conversion, converting the steel crystal grain image into a gray level image by using a gray level conversion formula;
and (4) bilateral filtering denoising is performed, and noise points in the steel grain image are reduced.
3. The method for detecting the peeling property of a hot-rolled steel bar scale as claimed in claim 1, wherein the peeling property index D = ml/m, m is a weight of a peeled scale, and ml is a maximum size of the scale.
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