CN107886464B - Method for generating point cloud model by two-phase composite material mesoscopic model - Google Patents
Method for generating point cloud model by two-phase composite material mesoscopic model Download PDFInfo
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- CN107886464B CN107886464B CN201711098346.XA CN201711098346A CN107886464B CN 107886464 B CN107886464 B CN 107886464B CN 201711098346 A CN201711098346 A CN 201711098346A CN 107886464 B CN107886464 B CN 107886464B
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Abstract
The invention discloses a method for generating a point cloud model by a two-phase composite material mesoscopic model, and relates to a method for generating a point cloud model by a two-phase composite material mesoscopic model. The invention aims to solve the problems that when computer simulation is carried out on the composite material, information contained in a mesoscopic model obtained by sound wave detection or X-ray detection is not complete enough, and storage space occupied by related files of part of software is large. The invention comprises the following steps: the method comprises the following steps: initializing a point cloud model and material information data; step two: determining a blank area of an input two-phase composite material mesoscopic model; step three: processing the mesoscopic model according to the geometric information of each detection point of the mesoscopic model and the information carried by the mesoscopic model; step four: inputting material information to the points in the point cloud model initialized in the step one to obtain the point cloud model with the material information. The method is used in the field of composite material computer simulation.
Description
Technical Field
The invention is applied to the field of computer simulation of composite materials, and particularly relates to a method for constructing a two-phase composite material mesoscale point cloud model.
Background
Along with the increasing popularization of hybrid composite materials with reinforced phase inclusions, such as concrete, ceramic reinforced aluminum materials and other materials in various fields, the precision requirements of people on the production and design of the hybrid composite materials are higher and higher. Therefore, researchers are required to understand the response law and the nature of the composite material under a certain working condition. Therefore, many researchers pay attention to the mesomechanics of the composite material with the major research scale in the range of 10 nm to millimeter, because the mesomechanics can reasonably and comprehensively explain the mechanical behavior of the composite material with millimeter magnitude or more, i.e. the macroscopic scale.
A two-phase composite is a basic, more commonly used composite, which is a two-phase mixture of only two materials, a matrix and inclusions (reinforcement phase material), such as ceramic reinforced aluminum. Meanwhile, models of two-phase composite materials are representative, and some composite materials (such as fiber reinforced concrete, fiber reinforced ceramic aluminum materials and the like) composed of three or more components can be simplified into two-phase composite materials according to conditions to be researched. Therefore, establishing a numerical model of the two-phase composite material on a computer has considerable necessity and significance.
At present, a method for establishing a two-phase composite material mesoscopic model mainly detects an actual composite material by means of nondestructive detection, such as sound wave detection, X-ray detection, laser scanning and the like, so as to obtain the mesoscopic model which is consistent with the actual situation.
However, the method of establishing the numerical model of the two-phase composite material by using the method has a problem that: the obtained model only has geometric attributes and detection information (namely color or reflected sound wave information), and has no other information, even some appearance point cloud models obtained by a laser scanning mode contain only the geometric attributes of the outer surface of the material. When the model is used as an analysis object, a user needs to call the numerical model with the geometric attributes in other software and input other information of the composite material, such as mechanical properties, thermodynamic parameters, electromagnetic property parameters and the like, so as to perform subsequent analysis. That is, the information contained in the model in a single software is incomplete, so that the related information of the composite material is often required to be respectively called or read on a plurality of different pieces of software, and because a general information transmission means is not available, the operation of transmitting information among the pieces of software at the present stage is often realized in a manual operation mode of a user, and the operation is complicated and tedious, has high error rate and affects the overall working efficiency. In addition, the volume of the related files of part of the software is large, and the occupied storage space is large.
Therefore, on the basis of a microscopic model obtained by sound wave detection or X-ray detection, a two-phase composite material microscopic scale model which contains more complete information and occupies a storage space as small as possible is generated, and the two-phase composite material microscopic scale model becomes one of the problems to be solved in the field of composite material computer simulation.
Disclosure of Invention
The invention aims to solve the problems that when a composite material is subjected to computer simulation, the information contained in a mesoscopic model obtained by sound wave detection or X-ray detection is incomplete, and part of related files of software occupy a large storage space, and provides a method for generating a point cloud model by using a two-phase composite material mesoscopic model.
A method for generating a point cloud model from a two-phase composite mesoscopic model comprises the following steps:
the method comprises the following steps: initializing a point cloud model and material information data;
step two: determining a blank area of an input two-phase composite material mesoscopic model;
step three: processing the mesoscopic model according to the geometric information of each detection point of the mesoscopic model and the information (color or sound wave reflection result) carried by the mesoscopic model;
step four: inputting material information to the points in the point cloud model initialized in the step one to obtain the point cloud model with the material information.
The invention has the beneficial effects that:
the invention is based on a point cloud model, which is actually a collection of points with data. The information carried by each point includes the spatial position of the point and the information related to the composite material input by a user, such as mechanical property, thermodynamic parameter, electromagnetic property parameter and the like. Therefore, the information carried by the point cloud model is complete, and all requirements of users can be met. Therefore, the invention has the following effects: the amount of information contained in a single model is greater.
The point cloud model generated by the method has universality, is not limited by software, is suitable for transmitting the relevant information of the composite material among a plurality of different pieces of software, reduces the complexity of transmitting the information among the pieces of software, and reduces the manual operation of a user, thereby reducing the risk of manual error.
The storage space occupied by the point cloud model is related to the file format and the space between points input by a user, the storage space occupied by the part in each software is basically the same, but the additional information of the user is related to limited data only through a number. Compared with other methods for directly adding information, the information storage mode of the invention can save the storage space by more than 50 percent, and the more data information input by a user, the more space is saved. Therefore, the invention occupies smaller storage space on the premise of containing the same information quantity.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flow chart of the method of the present invention for dequeuing single-column data.
FIG. 3 is an illustration of objects processed by the method of the present invention.
FIG. 4 is an initialized point cloud model of the method of the present invention (with purposely increased spacing for ease of illustration).
Fig. 5 is a point in the method of the present invention where a value is to be assigned (the spacing is purposely increased for ease of illustration).
Detailed Description
The first embodiment is as follows: as shown in fig. 1, a method for generating a point cloud model from a two-phase composite mesoscopic model comprises the following steps:
the method comprises the following steps: initializing a point cloud model and material information data;
step two: determining a blank area of an input two-phase composite material mesoscopic model;
step three: processing the mesoscopic model according to the geometric information of each detection point of the mesoscopic model and the information (color or sound wave reflection result) carried by the mesoscopic model;
step four: inputting material information to the points in the point cloud model initialized in the step one to obtain the point cloud model with the material information.
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is: the specific process of initializing the point cloud model and the material information data in the first step is as follows:
initializing a point cloud model:
according to the distance between each point input by the user, the space input by the user is filled with points to form an initialized point cloud model, and the points in the point cloud model do not contain any other information except the self-contained position information;
initialization material information data:
the material information data are divided into N groups according to types (such as mechanical property, thermodynamic property, electromagnetic property and the like), N is one half of the number of input data, the data types in each group are the same, and only two data are contained and respectively correspond to the matrix and the included material information.
Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the present embodiment differs from the first or second embodiment in that: the specific process of determining the blank area of the input two-phase composite material mesoscopic model in the step two is as follows:
the blank area can be determined by different methods according to different acquisition modes of the mesoscopic model;
the area, obtained by sound wave detection, of the relative error between the reflection frequency of the mesoscopic model and the environment white noise frequency is smaller than a threshold set by a user, and the area is regarded as a blank area; the threshold set by the user is less than 10%;
regarding an area, in which the relative error between the gray value of the mesoscopic model obtained by X-ray detection and the gray value of the background is smaller than a threshold set by a user, as a blank area, wherein the threshold set by the user is smaller than 10%;
the points in the blank area do not need to be subsequently processed.
Other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the difference between this embodiment mode and one of the first to third embodiment modes is: the specific process of processing the mesoscopic model according to the geometric information of each detection point of the mesoscopic model and the information carried by the mesoscopic model in the third step is as follows:
in a coordinate system (a coordinate system of a detection instrument) where the detection result is located, detecting points containing the same Y coordinate and the same Z coordinate in the mesoscopic model are regarded as the same row; the type of information carried by the detection points and the distance between the detection points (namely, the detection precision) are different according to the acquisition mode of the mesoscopic model,
the detection points in the mesoscopic model obtained by sound wave detection are sound wave reflection points, the information carried by the detection points is sound wave reflection frequency, and the distance between the sound wave reflection points and the detection points is related to the wavelength of the detected sound wave;
the detection points in the mesoscopic model obtained by X-ray detection are pixel points, the information carried by the detection points is color information, and the distance between the detection points is related to the precision of the detection machine.
Other steps and parameters are the same as those in one of the first to third embodiments.
The fifth concrete implementation mode: the difference between this embodiment and one of the first to fourth embodiments is: the fourth step is to input material information to the points in the point cloud model initialized in the first step, and the specific process of obtaining the point cloud model with the material information is as follows:
information is input to the points in the point cloud model column by column, and the processing method of each column is shown in fig. 2. Suppose that the information currently required to be input is { M }1,M2Is (possibly one of many pairs of information), numbered m respectively1And m2Representing the two information, only inputting the number when inputting the information to the point in the point cloud model, and searching the actual information according to the number when calling, thereby greatly saving the storage space. The point defining the current column is in turn A1，A2，…,An；
Step four, firstly: giving the first point A of the current column according to the actual situation1Giving an initial value a1，a1∈{m1,m2}；
Step four and step two: judging the midpoint A of the mesoscopic model2Whether the gray value or the acoustic wave reflection frequency of (A) is equal to1The same (related to the manner of obtaining the mesoscopic model), if the same, the point A is given2Assigning a point A1Same value of a1Executing the fourth step and the third step; if not, giving point A2Assignment not (a)1) Wherein not (a)1) Represents m1And m2Is not equal to a1Step four and step three are executed;
step four and step three: repeatedly executing the step four or two processing points A3,A4Until the processing is finished and all the points are listed, executing a fourth step;
step four: and repeating the step four to the step four, and processing the next row of data until the processing of all rows of data is finished.
Other steps and parameters are the same as in one of the first to fourth embodiments.
The first embodiment is as follows:
the object treated in this example is shown in FIG. 3, which is a concrete X-ray inspection chart of crushed stone aggregate, since the thickness of the interface between concrete aggregate and cement mortar is generally 10-1On the micron scale, so that the micron scale is negligible, and the concrete is regarded as a two-phase composite material consisting of aggregate and cement mortar.
The method comprises the following steps: and (5) initializing. The initialization operation is to prepare for subsequent operations, and specifically comprises two parts:
1. initializing a point cloud model: according to the distance between each point input by the user, the points are fully distributed in the designated space to form an initialized point cloud model as shown in fig. 4, and at the moment, the points in the point cloud model do not contain any other information except the self-contained position information;
2. initializing material information data, wherein the input information of the example is the mechanical property of the material, and the information is respectively the elastic modulus { E }1,E2And Poisson's ratio [ mu ]1,μ2And numbers 1 and 2 respectively correspond to material information of the aggregate and the cement mortar.
Step two: a blank area of the input numerical model is determined. The blank area in this example is determined according to the color saturation of the mesoscopic model, and the blank area (the area marked by the highlight border in fig. 3) is regarded as a blank area, and the dots in the blank area do not need to be processed. The point cloud model after the blank area is screened out is shown in fig. 5.
Step three: and processing the numerical model according to the geometric information of the numerical model. As shown in fig. 2, the detection points are processed according to the carried information of the detection points of the mesoscopic model, in this example, the carried information of the detection points is color information, and the detection points containing the same Y coordinate (because of being a two-dimensional detection image, no Z coordinate) in the mesoscopic model are regarded as the same column.
Step four: material information is input to points in the point cloud model. Specifically, the column-wise processing inputs information to the points in the point cloud model, and the processing method for each column is shown in fig. 2.
The information which needs to be input at present is the elastic modulus { E of cement mortar and aggregate1,E2And Poisson's ratio [ mu ]1,μ2And numbers 1 and 2 represent the two pieces of information respectively, only the number is input when the information is input to the point in the point cloud model, and the actual information can be searched according to the number when the information is called, so that the storage space can be greatly saved. Specifically, the elastic modulus E of cement mortar can be called by the number 11And poisson ratio mu1(ii) a The modulus of elasticity E of the aggregate can be called by number 22And poisson ratio mu2。
Taking a certain column in this example as an example, the method for processing single-column data in step three is specifically described:
first according to the actual situation, i.e. the first point A1If the brightness of the position is lower and the position can be determined to be cement mortar, A is given1Giving an initial value a1，a11, then judge the point A in the mesoscopic model2Whether the color of (A) is equal to1Same, same in this example, point A is given2Assigning a point A1Same value, i.e. a11. Next, the processing of point A is continued with reference to the same method3,A4… in this example, up to test point A16Then, the brightness and A are found15Obviously different, point A16Assigned 2, and point A2～A15All are assigned a value of 1; to test point A67Then, the brightness and A are found66Obviously different, point A67Assigned 1, point A16～A66All are assigned a value of 2; and so on until the processing is completed when all points in the column, and then processing the next column of data until the processing is completed.
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.
Claims (4)
1. A method for generating a point cloud model by a two-phase composite material mesoscopic model is characterized by comprising the following steps: the method for generating the point cloud model by the two-phase composite material mesoscopic model comprises the following steps of:
the method comprises the following steps: initializing a point cloud model and material information data;
step two: determining a blank area of an input two-phase composite material mesoscopic model;
step three: processing the mesoscopic model according to the geometric information of each detection point of the mesoscopic model and the information carried by the mesoscopic model;
step four: inputting material information to the points in the point cloud model initialized in the step one to obtain the point cloud model with the material information, wherein the specific process is as follows:
inputting information to points in the point cloud model column by column, and assuming that the information needing to be input currently is { M }1,M2Are numbered m respectively1And m2Representing the two information, only inputting the number when inputting the information to the point in the point cloud model, and defining the points of the current column as A in sequence1，A2，…,An；
Step four, firstly: to the first point A of the current column1Giving an initial value a1，a1∈{m1,m2}；
Step four and step two: judging the midpoint A of the mesoscopic model2Whether the gray value or the acoustic wave reflection frequency of (A) is equal to1If the same, give point A2Assigning a point A1Same value of a1Executing the fourth step and the third step; if not, giving point A2Assignment not (a)1) Wherein not (a)1) Represents m1And m2Is not equal to a1Step four and step three are executed;
step four and step three: repeatedly executing the step four or two processing points A3,A4Until the processing is finished and all the points are listed, executing a fourth step;
step four: and repeating the step four to the step four, and processing the next row of data until the processing of all rows of data is finished.
2. The method of generating a point cloud model from a two-phase composite mesoscopic model according to claim 1, wherein: the specific process of initializing the point cloud model and the material information data in the first step is as follows:
initializing a point cloud model:
according to the distance between each point input by the user, the space input by the user is filled with points to form an initialized point cloud model;
initialization material information data:
and dividing the material information data into N groups according to types, wherein N is one half of the number of input data, the data types in each group are the same, and only two data are contained and respectively correspond to the matrix and the included material information.
3. The method of claim 2, wherein the point cloud model is generated from a two-phase composite mesoscopic model, and the method comprises the following steps: the specific process of determining the blank area of the input two-phase composite material mesoscopic model in the step two is as follows:
determining the blank area by applying different methods according to different acquisition modes of the mesoscopic model;
the area, obtained by sound wave detection, of the relative error between the reflection frequency of the mesoscopic model and the environment white noise frequency is smaller than a threshold set by a user, and the area is regarded as a blank area; the threshold set by the user is less than 10%;
regarding an area, in which the relative error between the gray value of the mesoscopic model obtained by X-ray detection and the gray value of the background is smaller than a threshold set by a user, as a blank area, wherein the threshold set by the user is smaller than 10%;
the points in the blank area do not need to be subsequently processed.
4. The method of claim 3, wherein the point cloud model is generated from a two-phase composite mesoscopic model, and the method comprises the following steps: the specific process of processing the mesoscopic model according to the geometric information of each detection point of the mesoscopic model and the information carried by the mesoscopic model in the third step is as follows:
in a coordinate system where the detection result is located, the detection points containing the same Y coordinate and the same Z coordinate in the mesoscopic model are regarded as the same row;
detecting points in the mesoscopic model obtained by sound wave detection are sound wave reflection points, and information carried by the detecting points is sound wave reflection frequency;
and detecting points in the mesoscopic model obtained by X-ray detection are pixel points, and information carried by the detecting points is color information.
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