CN110969636B - 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 the aggregate particles to obtain thickness data of each point of the aggregate particles, and selecting the most appropriate thickness according to particle shapes 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 voidage based on grading characterization measured by a three-dimensional image method.
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 firstly 1 (ii) a Taking a sample, placing the sample on a flat and clean cement ground (or iron plate), uniformly stirring, using a flat spade to scoop up the sample, keeping the distance of 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 surface of the opening 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 barrel 2 (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 obtain the void fraction of the aggregate particles.
Although the porosity of aggregate particles can be measured by the method, the measurement process is complex, and the probability of errors caused by human factors in the operation process is higher, so that the experiment 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:
volume-weighted average particle diameter:
volume weighted standard deviation:
volume weighted skewness:
in the formula: x 1 、X 2 、X 3 、…、X n Respectively the volume ratio of each particle size interval, wherein X 1 +X 2 +X 3 +…+X n ＝100；D 1 、D 2 、D 3 、…、D n Respectively in each particle size intervalN is the number of particle size intervals.
Preferably, the deep learning algorithm adopts a BP neural network.
A system for predicting the porosity of grading characterization measured based on a three-dimensional image method 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 the porosity of the aggregate is predicted by adopting any one of the three-dimensional image method measurement-based grading characterization and porosity 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; a height data acquisition module 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 diagram of the system components 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 line 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 the in-line 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 grading volume ratios of different particle size intervals; a porosity prediction module 210 for predicting the porosity 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 uses the BP neural network to accurately predict the void fraction of the aggregate 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 invention further provides a method for predicting the porosity based on the grading characterization measured by the three-dimensional image method, which comprises 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 particle is calculated based on the profile data and thickness data obtained in step 1) and step 2), and it is noted that there is a correlation between the particle shape and the particle volume, and therefore the particle shape is different, and the method of calculating the volume of the particle is different, for example, when the particle is close to an ellipsoid, the volume of the particle is calculated by the formula
Wherein 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; and when a particle is proximate to a sphere, the calculation formula is ∑>
d is the diameter of the equivalent sphere, not all particles are simply multiplied by the measured outer contour data and thickness data.
4) Equally dividing the 4.75-31.5mm particle size interval into a plurality of parts, and calculating according to the particle size and volume of the aggregate particles to obtain the grading volume ratio of different particle size intervals. Specifically, canEqually dividing an aggregate particle size interval of 4.75-31.5mm into n equal parts, wherein n can be divided infinitely for an image method, and in the invention, n is 20 and is sequentially marked as x 1 、x 2 、x 3 ...x 20 And 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 aggregate particles, due to the particle thickness, the laser lines generate a staggered phenomenon, 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:
volume weighted average particle size:
volume weighted standard deviation:
volume weighted skewness:
in the formula: x 1 、X 2 、X 3 、…、X n Respectively the volume ratio of each particle size interval, wherein X 1 +X 2 +X 3 +…+X n ＝100；D 1 、D 2 、D 3 、…、D n The 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 input to output, and the weight and threshold are adjusted in the direction from output to 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 distribute the error to all units of each layer, and take the error signal obtained from each layer as the basis for adjusting the weight of each unit.
Input vector X = (X) 1 ,x 2 ,…,x i ,…,x n ) T Hidden layer output vector Y = (Y) 1 ,y 2 ,…,y j ,…,y m ) T
The weight from the input layer to the hidden layer is W ij The threshold is b j (ii) a The weight from the hidden layer to the output layer is W jk The threshold is b k (ii) a The net 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,
hidden layer selecting S-type transfer function>
The output layer selects a linear transfer function f 2 (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 (5)
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 outline of the aggregate particles;
2) The aggregate particles are irradiated by a linear laser to obtain thickness data of each point of the aggregate particles, the most appropriate thickness is selected according to the particle shape to represent the thickness of the particles, and the thickness calculation formula of the particles is as follows:
wherein: theta is the installation angle of the linear laser, and l is the staggered distance of the laser lines;
3) Calculating the volume of the particles according to the outer contour and the thickness data;
4) Equally dividing a 4.75-31.5mm particle size interval into a plurality of parts, calculating grading volume ratio of different particle size intervals according to the particle size and volume of aggregate particles, wherein the grading volume ratio comprises volume weighted average particle size, volume weighted standard deviation and volume weighted skewness, and the calculation formulas are respectively as follows:
volume weighted average particle size:
volume weighted standard deviation:
volume weighted skewness:
in the formula: x 1 、X 2 、X 3 、…、X n Respectively in each particle size intervalIn a volume ratio of (b), wherein X 1 +X 2 +X 3 +…+X n ＝100；D 1 、D 2 、D 3 、…、D n The average particle size of each particle size interval is respectively, and n is the number of the particle size intervals;
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 graded 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 of claim 1, wherein the deep learning algorithm employs a BP neural network.
4. 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 porosity of the aggregate by adopting the method for predicting the porosity based on the grading characterization measured by the three-dimensional image method in any one of claims 1 to 3.
5. The system for predicting the porosity based on the grading characterization measured by the three-dimensional image method as claimed in claim 4, 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 of the aggregate particles; 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 the grading volume ratio 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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