CN110969636A - Method and system for predicting porosity by grading characterization based on three-dimensional image method measurement - Google Patents
Method and system for predicting porosity by grading characterization based on three-dimensional image method measurement Download PDFInfo
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Abstract
The method and the system for predicting the porosity based on the grading characterization measured by the three-dimensional image method comprise the following steps of: 1) shooting an aggregate particle image, and performing image processing to obtain the outer contour of the aggregate particles; 2) utilizing a linear laser to irradiate aggregate particles to obtain thickness data of each point of the aggregate particles, and selecting the most appropriate thickness according to the particle shape to represent the thickness of the particles; 3) calculating the volume of the particles according to the outer contour and the thickness data; 4) equally dividing the 4.75-31.5mm particle size interval into a plurality of parts, and calculating the grading volume ratio of different particle size intervals according to the particle size and the volume of the aggregate particles; 5) and (4) predicting the void ratio of the aggregate by combining the grading volume ratio with a deep learning algorithm. The invention can accurately predict the porosity among multiple particles.
Description
Technical Field
The invention relates to the field of engineering machinery, in particular to a method and a system for predicting void ratio by grading characterization based on three-dimensional image method measurement.
Background
The current method for measuring the void fraction of aggregate is mainly a container weighing method, and the mass m of a container is weighed firstly1(ii) a Taking a sample, placing the sample on a flat and clean cement ground (or an iron plate), uniformly stirring, shoveling the sample by using a flat-head shovel, keeping the distance between the sample and the upper opening of the container barrel to be about 50mm, enabling the stones to freely fall into the container barrel, filling the container barrel, removing particles protruding out of the opening surface of the container barrel, filling proper particles into a concave gap, enabling the volumes of a slightly convex part and a concave part on the surface to be approximately equal, and weighing the total mass m of the sample and the container barrel2(ii) a The mass of the sample can be obtained by subtracting the mass of the container barrel from the total mass, the density of the used sample is consulted, and the volume of the sample is calculated; void volume is determined by subtracting the sample volume from the container volume to give the void fraction of the aggregate particles.
Although the method can measure the void ratio of the aggregate particles, the measurement process is complex, and the probability of errors caused by human factors in the operation process is higher, so that the experimental repeatability is low, and the reliability of the measurement result is not high.
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art, and provides a method and a system for predicting the porosity based on the grading characterization measured by a three-dimensional image method.
The invention adopts the following technical scheme:
a method for predicting porosity based on grading characterization measured by a three-dimensional image method is characterized by comprising the following steps:
1) shooting an aggregate particle image, and performing image processing to obtain the outer contour of the aggregate particles;
2) utilizing a linear laser to irradiate aggregate particles to obtain thickness data of each point of the aggregate particles, and selecting the most appropriate thickness according to the particle shape to represent the thickness of the particles;
3) calculating the volume of the particles according to the outer contour and the thickness data;
4) equally dividing the 4.75-31.5mm particle size interval into a plurality of parts, and calculating the grading volume ratio of different particle size intervals according to the particle size and the volume of the aggregate particles;
5) and (4) predicting the void ratio of the aggregate by combining the grading volume ratio with a deep learning algorithm.
Preferably, in step 1), the image processing includes image filtering, image binarization, image filling and image contour extraction.
Preferably, the thickness of the particles is calculated as follows:
wherein: theta is the installation angle of the linear laser, and l is the staggered distance of the laser.
Preferably, in step 4), the characterization manner of the graded volume fraction includes volume weighted average particle diameter, volume weighted standard deviation and volume weighted skewness, and the calculation formulas are respectively:
in the formula: x1、X2、X3、…、XnRespectively the volume ratio of each particle size interval, wherein X1+X2+X3+…+Xn=100;D1、D2、D3、…、DnThe average particle size of each particle size interval, and n is the number of particle size intervals.
Preferably, the deep learning algorithm adopts a BP neural network.
A system for representing and predicting porosity of grading based on three-dimensional image measurement is characterized by comprising a conveying device, a CCD camera, a linear laser, a vibratory feeder and a computer; the vibratory feeder is used for feeding and dispersing aggregate particles; the conveying device is used for conveying aggregate particles; the CCD camera is used for shooting an aggregate particle image; the linear laser is used for irradiating aggregate particles to obtain thickness data of each point of the aggregate particles; the computer is connected with the conveying device, the CCD camera, the linear laser and the vibratory feeder, and predicts the void ratio of the aggregate by adopting any one of the three-dimensional image method measurement-based grading characterization and void ratio prediction methods.
Preferably, the computer is provided with an image acquisition module for acquiring an aggregate particle image and performing image processing to obtain the outer contour of the aggregate particles; the height data acquisition module is used for acquiring the height profile; the motor driving module is used for controlling the speed and the running direction of the conveying device; the grading volume ratio measuring module is used for calculating grading volume ratios of different particle size intervals; the grain shape measuring module is used for calculating grain shape parameters; and the porosity prediction module is used for predicting the porosity of the aggregate.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
in the invention, the particle profile is obtained by an image method, the particle thickness data is obtained by a linear laser, the most appropriate thickness is selected according to the particle shape to represent the thickness of the particles, the particle volume is obtained according to the profile area and the particle thickness, and then the void ratio of the aggregate is accurately predicted according to the grading of the particles under the condition of not considering the particle shape, so that the void ratio among multiple particles can be accurately predicted.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a system diagram of the present invention;
the invention is described in further detail below with reference to the figures and specific examples.
Detailed Description
The invention is further described below by means of specific embodiments.
Referring to fig. 2, a system for predicting the porosity based on grading characterization measured by a three-dimensional image method comprises a linear laser 201, a conveying device, a CCD camera 202, a vibratory feeder 203, a computer 207 and the like. The conveying device comprises a transparent conveying belt 204 and a stepping motor 206, wherein the stepping motor 206 controls the conveying belt 204 to move to convey aggregate particles, and an LED backlight source 205 is arranged at the bottom of the conveying belt to enable the aggregate particles to sequentially pass through a backlight source region.
A vibratory feeder 203 is mounted at one end of the conveyor belt 204 for feeding and dispersing aggregate particles. A line laser 201 and CCD camera 202 are mounted above the conveyor belt 204. The CCD camera 202 is used to capture an image of the aggregate particles. The aggregate particles are irradiated by a linear laser 201 to obtain thickness data of each point of the aggregate particles. The computer 207 is connected with the conveying device, the CCD camera 202, the linear laser 201 and the vibration feeder 203 and is used for predicting the porosity of aggregate by adopting an aggregate stacking porosity prediction method by adopting an image method.
Specifically, the computer 207 is provided with an image acquisition module 208 for acquiring the aggregate particle image to perform image processing to obtain the outer contour of the aggregate particles; a height data acquisition module 212 for acquiring the height profile; a motor driving module 211 for controlling the speed and the running direction of the conveyor; a grading volume ratio measuring module 209 for calculating the grading volume ratio of different particle size intervals; a void fraction prediction module 210 for predicting a void fraction of the aggregate.
Specifically, the void fraction predicting module 210 obtains the volume of the particles according to the thickness and the contour area of the particles without considering the particle shape of the aggregate particles, and then accurately predicts the void fraction of the aggregate by using a BP neural network according to the grading (volume weighted average particle size, volume weighted skewness and volume weighted standard deviation) of the particles.
Referring to fig. 1, the present invention further provides a method for predicting a porosity based on a grading characterization measured by a three-dimensional image method, including the following steps:
1) starting the vibration feeder 203 to disperse aggregate particles, and simultaneously driving the stepping motor 206 to work by the motor driver module 211; the backlight 205 is adjusted and an image of the aggregate particles is taken directly above the CCD camera 202.
The picture taken by the camera is sent to an image acquisition module 208 of the computer 207, and image processing including image filtering, image binarization, image filling, image contour extraction and the like is performed to obtain the outer contour of the aggregate particles.
2) The aggregate particles are irradiated by a linear laser 201 to obtain thickness data of each point of the aggregate particles, and the height data acquisition module 212 selects the most appropriate thickness according to the particle shape to represent the thickness of the particles. For example, the thickness data of each point may be fitted to a curved surface, the lowest point and the highest point of the curved surface are found, and the particle thickness may be selected as the median value or expressed by the average value of all the thickness data.
3) The volume of the particles is calculated from the outer contour and thickness data. Specifically, the volume of the particles is calculated according to the profile data and the thickness data obtained in the step 1) and the step 2), and it is noted that the particle shape and the particle volume are related, so the shape of the particles is different, the volume calculation method of the particles is also different,for example, when the particle is close to an ellipsoid, the volume of the particle is calculated asWherein a, b and c are respectively the long axis, the middle axis and the short axis of the particle, namely the length, the width and the thickness of the particle; when the particles are close to the sphere, the calculation formula isd is the diameter of the equivalent sphere, not all particles are simply multiplied by the measured outer contour data and the thickness data.
4) Equally dividing the 4.75-31.5mm particle size interval into a plurality of parts, and calculating the grading volume ratio of different particle size intervals according to the particle size and the volume of the aggregate particles. Specifically, the aggregate grain size interval of 4.75-31.5mm can be equally divided into n equal parts, n can be infinitely divided for an image method, and n is 20 in the invention and is sequentially marked as x1、x2、x3...x20And counting the volume ratio of each particle size interval.
5) And (4) predicting the void ratio of the aggregate by combining the grading volume ratio with a deep learning algorithm. Specifically, the void fraction prediction module 210 accurately predicts the void fraction using a BP neural network based on the aggregate grading (volume weighted average particle size, volume weighted skewness, and volume weighted standard deviation) as input, without considering the particle shape.
Further, in step 2), when the linear laser irradiates the aggregate particles, due to the particle thickness, the laser lines are staggered, the thickness of the particles can be calculated by using a corresponding algorithm according to the laser installation angle theta and the staggered distance l of the laser lines, and the thickness calculation formula of the particles is as follows:
in step 4), the characterization mode of the graded volume fraction includes volume weighted average particle size, volume weighted standard deviation and volume weighted skewness, and the calculation formulas are respectively:
in the formula: x1、X2、X3、…、XnRespectively the volume ratio of each particle size interval, wherein X1+X2+X3+…+Xn=100;D1、D2、D3、…、DnThe average particle size of each particle size interval, and n is the number of particle size intervals.
The BP algorithm includes two processes of forward propagation of a signal and back propagation of an error. That is, the error output is calculated in the direction from the input to the output, and the weight and the threshold are adjusted in the direction from the output to the input. During forward propagation, an input signal acts on an output node through a hidden layer, an output signal is generated through nonlinear transformation, and if actual output does not accord with expected output, the process of backward propagation of errors is carried out. The error back transmission is to back transmit the output error to the input layer by layer through the hidden layer, and to distribute the error to all units of each layer, and to use the error signal obtained from each layer as the basis for adjusting the weight of each unit.
Input vector X ═ X1,x2,…,xi,…,xn)THidden layer output vector Y ═ Y1,y2,…,yj,…,ym)T
The weight from the input layer to the hidden layer is WijThe threshold is bj(ii) a The weight from the hidden layer to the output layer is WjkThe threshold is bk(ii) a The network output value is denoted by o and the desired output value is denoted by d.
For the output layer or layers, the number of layers,in the case of a hidden layer or layers,s-shaped transfer function is selected for hidden layer
The output layer selects a linear transfer function f2(x)=x。
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.
Claims (7)
1. A method for predicting porosity based on grading characterization measured by a three-dimensional image method is characterized by comprising the following steps:
1) shooting an aggregate particle image, and performing image processing to obtain the outer contour of the aggregate particles;
2) utilizing a linear laser to irradiate aggregate particles to obtain thickness data of each point of the aggregate particles, and selecting the most appropriate thickness according to the particle shape to represent the thickness of the particles;
3) calculating the volume of the particles according to the outer contour and the thickness data;
4) equally dividing the 4.75-31.5mm particle size interval into a plurality of parts, and calculating the grading volume ratio of different particle size intervals according to the particle size and the volume of the aggregate particles;
5) and (4) predicting the void ratio of the aggregate by combining the grading volume ratio with a deep learning algorithm.
2. The method for predicting the porosity based on the grading characterization measured by the three-dimensional image method as claimed in claim 1, wherein in the step 1), the image processing comprises image filtering, image binarization, image filling and image contour extraction.
3. The method for predicting porosity based on grading characterization measured by three-dimensional imaging method according to claim 1, wherein the thickness of the particles is calculated by the following formula:
wherein: theta is the installation angle of the linear laser, and l is the staggered distance of the laser.
4. The method for predicting the porosity of the grading characterization based on the three-dimensional image method as recited in claim 1, wherein in the step 4), the characterization mode of the grading volume ratio comprises volume weighted average particle diameter, volume weighted standard deviation and volume weighted skewness, and the calculation formulas are respectively:
in the formula: x1、X2、X3、…、XnRespectively the volume ratio of each particle size interval, wherein X1+X2+X3+…+Xn=100;D1、D2、D3、…、DnThe average particle size of each particle size interval, and n is the number of particle size intervals.
5. The method of claim 1, wherein the deep learning algorithm employs a BP neural network.
6. A system for representing and predicting porosity of grading based on three-dimensional image measurement is characterized by comprising a conveying device, a CCD camera, a linear laser, a vibratory feeder and a computer; the vibratory feeder is used for feeding and dispersing aggregate particles; the conveying device is used for conveying aggregate particles; the CCD camera is used for shooting an aggregate particle image; the linear laser is used for irradiating aggregate particles to obtain thickness data of each point of the aggregate particles; the computer is connected with a conveying device, a CCD camera, a linear laser and a vibratory feeder and predicts the void ratio of the aggregate by adopting the method for predicting the void ratio based on the grading characterization measured by the three-dimensional image method in any one of claims 1 to 5.
7. The system for predicting the porosity based on the grading characterization measured by the three-dimensional image method as claimed in claim 6, wherein the computer is provided with an image acquisition module for acquiring the aggregate particle image and performing image processing to obtain the outer contour of the aggregate particles; the height data acquisition module is used for acquiring the height profile; the motor driving module is used for controlling the speed and the running direction of the conveying device; the grading volume ratio measuring module is used for calculating grading volume ratios of different particle size intervals; the grain shape measuring module is used for calculating grain shape parameters; and the porosity prediction module is used for predicting the porosity of the aggregate.
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CN112611690B (en) * | 2020-12-04 | 2022-07-08 | 华侨大学 | Coarse aggregate equivalent particle size grading method based on three-dimensional image |
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