CN110021010B - Surface material inspection method - Google Patents
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- CN110021010B CN110021010B CN201910186591.9A CN201910186591A CN110021010B CN 110021010 B CN110021010 B CN 110021010B CN 201910186591 A CN201910186591 A CN 201910186591A CN 110021010 B CN110021010 B CN 110021010B
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- G—PHYSICS
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Abstract
The method is based on the point cloud data acquired by a mature laser point cloud scanning technology, utilizes an evaluation and inspection method based on a total least square method to quickly, accurately and automatically identify, intercept and classify the point cloud data, and can accurately calculate the surface roughness, the thickness and the side parallelism of a module material, so that effective/quantitative verification and evaluation can be carried out on the module and a production process thereof; the problem of business flow error accumulation caused by material production process errors is effectively solved, and therefore electromagnetic signal distortion and distortion caused by the process errors are eliminated; the automatic program batch processing can be integrated in an automatic checking module of the material production equipment, which is beneficial to improving the production process, improving the product consistency, accurately and efficiently ensuring the quality control of the product, saving the time and the production cost, improving the production quality of the sub-ring and effectively creating economic benefits.
Description
Technical Field
The invention relates to the field of automotive millimeter wave radars, in particular to an evaluation and inspection method for surface materials of a millimeter wave radar.
Background
With the continuous improvement of the intelligent degree of automobiles, particularly the rapid development of intelligent driving and unmanned driving, radar detectors are widely used on automobiles in large quantities for detecting the environment around the automobiles, providing data such as the distance, angle, relative speed, driving track and the like between the automobiles and surrounding obstacles, and being widely applied to systems of automobile reversing early warning, collision early warning, automatic braking, adaptive cruise control, blind spot detection, lane change assistance and the like. And it is anticipated that radar detectors will also play a very important role in unmanned technology.
Common radars on automobiles include ultrasonic radar, infrared radar, laser radar, cameras, millimeter wave radar, and the like, which respectively apply different detection techniques to detect the environment around the automobile. The millimeter wave radar is a radar operating in a millimeter wave band, that is, a radio wave having a wavelength of 1 to 10mm and a frequency range of 30 to 300GHz is used. Compared with ultrasonic radars, infrared radars, cameras and laser radars, the millimeter wave radar has stronger advantages in the aspects of long-distance detection capability, night working capability, all-weather working capability, working capability under poor environmental conditions, temperature stability, measurement accuracy and the like, so that the millimeter wave radar is paid more and more attention to and applied.
In practice, the millimeter wave radar is usually installed at an air intake grille part at the front part of the automobile or a bumper part at the front and rear part of the automobile, and a covering piece made of a hard material is usually further arranged outside an antenna of the millimeter wave radar so as to provide a protection effect for the antenna of the millimeter wave radar. This covering is generally referred to as a second surface hard mold material (hereinafter referred to simply as a hard mold material). Because the radar wave penetrates through the hard mold material, the surface flatness, uniformity and thickness (required to be integral multiples of half wavelength) of the hard mold material have great influence (such as interference, attenuation, reflection and the like) on radar electromagnetic signal transmission; when the surface flatness, uniformity and thickness of the material do not meet the design requirements, serious distortion and distortion of electromagnetic transmission signals are caused, and various detection performance indexes of the millimeter wave radar sensor are further reduced sharply. However, in the actual production process, due to the production process and the like. Due to the influence of factors, the surface flatness, uniformity and thickness of the hard mold material often do not meet the requirements of design indexes. In order to effectively evaluate the quality control of the material and check and optimize the production process of the material, the indexes of flatness, thickness, parallelism and the like of a hard die material sample piece need to be accurately measured so as to evaluate whether the hard die is qualified or not and whether the hard die can be produced in mass production or not.
Generally, an automobile front grid hard mold supplier does not have the hard mold precision acceptance capability, and at present, two methods for evaluating and detecting the second surface material of the automobile front grid millimeter wave radar are mainly adopted:
firstly, Surface point cloud coordinate data (STL format) of a hard die material is accurately acquired through a mature laser point cloud scanning technology, the point cloud data is imported into CATIA software, point cloud data processing is carried out by utilizing a digital Surface editing module DSE (digital profile editor) and a rapid Surface reconstruction module QSR (Quick Surface reconstruction-section) in the CATIA, and then CATIA reverse design is carried out by combining Surface design and entity design to restore a 3D model of the hard die and measure the thickness of the hard die. However, the relative error in the reverse design of the CATIA is large, and the error cannot be well controlled to a required level when a high-precision hard mold is processed, so that the true thickness of the material cannot be reflected by the reverse design method of the CATIA. In fact, due to the fact that the surface of the material is not flat, even if the laser point cloud data of the uniform surface is not completely in the same plane, the accuracy of restoring the 3D model of the hard mold by using the reverse design is low, and various surface characteristic parameters of the hard mold material are difficult to obtain.
Secondly, the hard die materials in the same batch are mailed to a foreign authority to be tested, the electromagnetic transmission characteristics of the millimeter wave radar are measured through an electromagnetic wave darkroom, and the reflection intensity, the attenuation degree of a transmission signal and the distribution of the reflection intensity are analyzed, so that whether the hard die material sample reaches the standard or not is judged. The method directly evaluates the influence of the hard mold material on the transmission of the electromagnetic signals of the millimeter wave radar, the result is more visual, but the actual parameters of the hard mold material cannot be directly given, the detection period is long, the test cost is high, any process adjustment or radar frequency band adjustment of a hard mold material manufacturer needs to be evaluated again, and the cost-effectiveness ratio is extremely low.
For the two existing methods, the first method cannot accurately obtain the geometric parameters of the hard mold material, and the second method can intuitively give the electrical characteristics of the hard mold material, but cannot directly obtain the characteristic parameter characteristics of the hard mold material, and has long test period, low efficiency and high cost.
Disclosure of Invention
In order to solve the technical problems, the invention provides an evaluation and inspection method for a millimeter wave radar surface material, in particular to an evaluation and inspection method for a millimeter wave radar second surface hard film piece material based on a total least square classifier.
A method of inspecting surface material, comprising the steps of:
generating an outer envelope of a point cloud data set based on the obtained laser scanning point cloud of the surface material;
after the vertex of an outer envelope polygon of the point cloud data set is obtained, effectively separating the point cloud data of the surface material according to the relative size parameters of the surface material to obtain an effective surface material laser point cloud data set;
carrying out validity judgment on the obtained surface material laser point cloud data to obtain a point cloud data set to be evaluated;
classifying the point cloud data set to be evaluated to obtain a laser point cloud data set on one side of the surface material and a laser point cloud data set on the other side;
respectively evaluating the point cloud data sets on the two sides to obtain the flatness value of each side surface; respectively calculating the uniformity value and the thickness value of the surface material according to the point cloud data sets on the two sides;
and comparing the obtained flatness value with a preset flatness value, comparing the obtained uniformity value with a preset uniformity value, comparing the obtained thickness value with a preset thickness value to obtain a comparison result, and giving a material inspection and evaluation conclusion.
Further, a convex hull detection algorithm is adopted to generate the point cloud outer envelope.
Further, the effective separation is carried out by:
d) roughly estimating boundary vertex coordinates of the surface material;
e) constructing a circumscribed quadrangular pyramid of the surface material to calculate the point cloud;
f) and separating effective point cloud data.
Further, in the classification of the point cloud data set to be evaluated, a Total Least Square (TLS) method is used, and a Total Least square device is used to separate the point cloud data belonging to the two side surfaces, so as to obtain laser point cloud data sets on the two sides of the surface material respectively.
Further, the validity judgment is to utilize a convex hull detection algorithm to respectively calculate the separated effective point cloud outer envelopes, calculate the proportion of the size of the effective point cloud outer envelopes to the actual size of the surface material, and judge that the effective point cloud is successfully obtained when the proportion is greater than a first proportion; and when the proportion is smaller than the second proportion, judging that the effective point cloud acquisition fails.
Furthermore, the circumscribed quadrangular pyramid is composed of four boundary vertexes of the surface material and a reference point outside the point cloud outer envelope.
Further, an opening angle of the surface material relative to the reference point is less than 10 °.
Further, the surface material is a cover in front of the millimeter wave radar, and the cover is made of a hard mold material.
Further, the flatness of the side surface is evaluated by the mean and mean standard deviation of the vertical distance distribution of each point cloud of the side surface to the fitting plane of the side surface.
Further, the uniformity/thickness of the surface material is evaluated by the vertical distance distribution of the individual point clouds on one side to the fitted plane on the other side.
The implementation of the invention has the following beneficial effects: the invention can accurately calculate the surface roughness, the thickness and the side parallelism of the module material based on the point cloud data acquired by a mature laser point cloud scanning technology and by utilizing an evaluation and inspection method based on a total least square method, thereby effectively/quantitatively verifying and evaluating the module production process; the problem of business flow error accumulation caused by material production process errors is effectively solved, and therefore electromagnetic signal distortion and distortion caused by the process errors are eliminated; the automatic program batch processing can be integrated in an automatic checking module of the material production equipment, which is beneficial to improving the production process, improving the product consistency, accurately and efficiently ensuring the quality control of the product, saving the time and the production cost, improving the production quality of the sub-ring and effectively creating economic benefits.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic view showing the installation position of a millimeter wave radar in a front grille of an automobile according to the present invention;
FIG. 2 is a flow chart of an evaluation test method of the present invention;
FIG. 3 is a schematic diagram of the efficient point cloud data separation of the present invention;
FIG. 4 is a schematic view of a point cloud classification plane obtained by the TLS classifier of the present invention;
FIG. 5 is a schematic of the resulting fitted plane of the present invention;
FIG. 6 is a schematic diagram of the point-to-area spacing distribution obtained by the present invention.
Wherein the reference numerals in the figures correspond to: 1-front grid, 2-millimeter wave radar, and 3-second surface hard mold material.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of the present invention.
As shown in fig. 1, which shows an installation form of a millimeter wave radar that is commonly used in an automobile, a millimeter wave radar 2 is generally installed at a portion of a front grid 1 of the automobile, and the millimeter wave radar 2 is generally installed at a middle position of the front grid 1 of the automobile based on a detection angle that is advantageous to a front side of a vehicle body. The front surface of the millimeter wave radar 2 is provided with a second surface hard mold material 3, and an antenna of the millimeter wave radar 2 is arranged behind the second surface hard mold material 3, so that the antenna is protected. The radar waves transmitted and received by the antenna of the millimeter wave radar 2 need to pass through the second surface hard mask material 3, and thus, the geometrical parameters of the second surface hard mask material 3, such as surface flatness, uniformity, thickness, etc., are important for the normal and efficient propagation of the radar waves.
In order to directly obtain the geometric parameters of the second surface hard mold material 3, such as surface flatness, uniformity and thickness, accurately, in a short time and cheaply, the invention provides an evaluation and inspection method, as shown in fig. 2.
Firstly, based on the obtained laser scanning point cloud of the hard die material, a convex hull detection algorithm is adopted to generate a point cloud outer envelope.
And laser point cloud data of the surfaces of the hard die material and the fixed support thereof can be obtained by a mature laser point cloud scanning technology. Since the hard mold material needs to be placed on the fixing support to obtain the comprehensive laser point cloud data of the geometric shape of the hard mold material, the point cloud data can include point cloud data related to the fixing support, and the point cloud data has certain irregularity, so that the obtained laser point cloud data needs to be separated first, and the effective point cloud data in the middle of the surface of the hard mold material is intercepted.
The conventional interception method comprises the steps of drawing scanned laser point cloud data in MATLAB software and the like, selecting an effective boundary of the hard mold material in a three-dimensional discrete point diagram, and removing invalid point cloud data related to an external support through a geometric relation, so that the effective point cloud data on the surface of the hard mold material can be obtained. However, the method needs manual clicking, is long in time consumption and low in efficiency.
In order to overcome the above problems of the conventional interception method and realize rapid, accurate and automatic separation of the acquired point cloud data, the method is more advantageous in that a convex hull detection algorithm is used for calculating and generating a three-dimensional outer envelope of the point cloud data, that is, the vertex of a polygon formed by the point cloud data is obtained. Common three-dimensional convex hull algorithms include random increment method, Graham scanning method, Jarvis stepping method, divide and conquer method, Quickhull algorithm and the like, and the MATLAB can be realized by calling a convex function.
Secondly, after the vertexes of the outer envelope polygon of the point cloud data are obtained, the phases of the hard die materials are determined
And (4) effectively separating the point cloud data on the surface of the hard die material according to the size parameters.
The separation process can be divided into the following steps:
1. roughly estimating the coordinates of the boundary vertex of the hard mould material: four vertices A, B, C, D are selected at the central flat portion of the hardmask material, which allow for retraction, i.e., not necessarily at the vertex locations. The coordinates of the four vertices can then be calculated from the relative dimensional parameters of the material to form a quadrilateral in three-dimensional space, see fig. 3. It should be noted here that the four selected vertices are generally not in a plane due to the poor flatness of the die material itself and the error of laser scanning in practice.
2. Constructing an external quadrangular pyramid of the hard mold material to calculate the point cloud: a reference point, preferably the origin O of the laser scanning point cloud with respect to the coordinate system, is chosen outside the envelope of the point cloud, as shown in fig. 3. The origin O and the four sides of the quadrangle ABCD in the three-dimensional space form four triangular faces, i.e., Δ OAB, Δ OBC, Δ OCD, and Δ ODA, respectively, and the point O and the four triangular faces form a quadrangular pyramid O-ABCD as shown in fig. 3 when possible deformities of the quadrangle ABCD are ignored. Then calculating plane equations S corresponding to delta OAB, delta OBC, delta OCD and delta ODA respectivelyOAB(x,y,z)＝0，SOBC(x,y,z)＝0，SOCD(x,y,z)＝0，SODA(x,y,z)＝0。
3. Separation of effective point cloud data: and sequentially judging the relative spatial position relations between all the point clouds and the four planes delta OAB, delta OBC, delta OCD and delta ODA based on the plane equations of the delta OAB, the delta OBC, the delta OCD and the delta ODA. The point clouds "wrapped" by the four planes, i.e. the internal point clouds of the quadrangular pyramid O-ABCD (base surface infinite extension), are taken out and constitute the data set of the effective point clouds on the surface of the hard-mould material. For example, as shown in fig. 3, a certain point in the point cloud set is arbitrarily selected and denoted as point E, and the judgment criteria are as follows:
Define bFlag1＝SOAB(xC,yC,zC)·SOAB(xE,yE,zE)＞0
bFlag2＝SOBC(xD,yD,zD)·SOBC(xE,yE,zE)＞0
bFlag3＝SOCD(xA,yA,zA)·SOCD(xE,yE,zE)＞0
bFlag4＝SODA(xB,yB,zB)·SODA(xE,yE,zE)＞0
wherein, bFlag1True indicates that E is ipsilateral to C in face Δ OAB, bFlag1(ii) Flase indicates that E is on the opposite side of plane Δ OAB from C; and so on for the others. When all ipsilateral relationships are simultaneously established, that is:
using the above criteria, point E is inside the quadrangular pyramid O-ABCD when bFlag is True, and outside it otherwise. Through matrix operation in MATLAB, the relative spatial position relationship between all point clouds and the quadrangular pyramid O-ABCD can be rapidly judged, and effective point cloud data can be separated and obtained.
And thirdly, judging the validity of the point cloud data. After the effective point cloud data is obtained through separation, validity check needs to be carried out on the point cloud data obtained through separation. And respectively calculating the outer envelopes of the separated effective point clouds by using a convex hull detection algorithm, and calculating the proportion of the size of the outer envelopes to the actual size of the hard die material. When the proportion is smaller than a certain proportion, for example, 50%, it is determined that the effective point cloud acquisition fails, and verification is required again and the position reasonableness of the reference point is examined. Typically, the reference point is chosen to be sufficiently far from the hard mask material point cloud. Preferably, the opening angle of the die material to the reference point is less than 10 degrees. And if the separated point cloud data passes the validity judgment, entering the next step.
And fourthly, classifying the point cloud data based on a Total Least Square (TLS) classifier.
The correctly separated effective point clouds on the two sides of the hard template have good plane distribution characteristics, and the parallel characteristics of the point clouds on the two sides are obvious. Due to the dense distribution characteristics of the point cloud, a classification plane equation S of the cloud sets of the two side points can be obtained by using a Total Least Square (TLS) methodCAnd (x, y, z) ═ 0, as shown in fig. 4, the point clouds on the surfaces of both sides can be clearly seen to be distributed on both sides of the classification plane. Therefore, based on the classification plane and point cloud data distribution relationship, the TLS classifier can be represented as:
P1＝{(x1,y1,z1)∈P|SC(x1,y1,z1)＜0}
P2＝{(x2,y2,z2)∈P|SC(x2,y2,z2)＜0}
thus, based on the TLS classifier, the point cloud data on two sides can be separated quickly.
And after separating the point cloud data on the two sides, evaluating the flatness of each side and the uniformity/thickness of the material of the hard die material according to the obtained point cloud data on the two sides.
And fifthly, evaluating the flatness of each side surface based on an outlier detection algorithm, and evaluating the uniformity/thickness of the hard die material based on a total least square method.
Two side point cloud data sets P1 are obtained by using the TLS classifier,After P2, performing total least square fitting on the point cloud data sets P1 and P2 respectively to obtain a fitting plane S1(x, y, z) ═ 0 and S2(x, y, z) is 0, the point cloud data sets on each side are respectively distributed near the corresponding fitting planes, and fig. 5 exemplarily shows the point cloud data set P1 and the fitting planes thereof.
Sequentially calculating each point in the point set P1 to the plane S1(x, y, z) is a vertical distance (i.e., outlier) of 0, and points in point set P2 are located on plane S2And (x, y, z) is 0, and the distribution of the outlier distance reflects the degree of deviation of the point cloud from the fitting plane, and the flatness of the surface of the hard die material can be represented by the mean value and the mean square error of the point cloud.
Then, sequentially calculating each point in the point set P1 to the plane S2(x, y, z) is 0, and each point in the set of points P2 is to the plane S1(x, y, z) is a vertical distance of 0, and the dot-plane pitch distribution is shown in fig. 6. The data such as the average distance and the standard deviation can be obtained from all the point-surface distance distribution, and the thickness/uniformity of the hard mold material can be obtained.
Furthermore, the fitted plane equation S according to two sides1(x, y, z) ═ 0 and S2And (x, y, z) is 0, and the included angle of the two side surfaces of the hard die material can be calculated by the normal vector of the (x, y, z).
And sixthly, comparing the obtained flatness value with a preset flatness value, comparing the obtained uniformity value with a preset uniformity value, comparing the obtained thickness value with a preset thickness value to obtain a comparison result, and giving a material inspection and evaluation conclusion.
The parameters of interest for the hard mold material tested are listed in table 1 below.
TABLE 1 reference parameters for testing hard mold materials
The following table 2 lists the evaluation results obtained by the evaluation test method of the present invention.
TABLE 2 evaluation results of the hard mold material tested
The above table 1 is a design index parameter of the hard mold material to be tested, and the table 2 is a test result of the hard mold material to be tested in the testing method of the present invention. From the actual measured data in table 2, it can be seen that the mean outlier distances, i.e., roughnesses, were 0.0584mm and 0.0541mm, respectively, and the standard deviations of the outlier distances were 0.0435mm and 0.0436mm, respectively, showing better flatness of both sides of the hard mold material. Comparing the data of the hard module material obtained by actual detection with the design index parameters thereof, wherein the included angle of two side surfaces of the hard module material to be detected is 0.0431 degrees and is more than 0.0390 degrees of the design requirement; the thickness of the hard module material tested, i.e. the average side spacing, was 2.2273mm, which did not meet the design requirements of 2.4 + -0.1 mm. Therefore, the comparison of numerical analysis shows that the thickness and the parallelism of the hard module material to be tested do not meet the design requirements.
According to the embodiment of the invention, the surface roughness, the thickness and the side surface parallelism of the module material can be accurately calculated, so that effective/quantitative verification and evaluation can be carried out on the module production process; the problem of business flow error accumulation caused by material production process errors is effectively solved, and the electromagnetic signal distortion caused by the process errors is strictly reduced; the automatic program batch processing can be integrated in an automatic checking module of the material production equipment, which is beneficial to improving the production process/improving the product consistency, accurately and efficiently ensuring the quality control of the product, saving the time and the production cost, improving the production quality of the sub-ring and effectively creating economic benefits.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (8)
1. A method of inspecting surface material, comprising the steps of:
generating an outer envelope of a point cloud data set based on the obtained laser scanning point cloud of the surface material;
after the vertex of an outer envelope polygon of the point cloud data set is obtained, effectively separating the point cloud data of the surface material according to the relative size parameters of the surface material to obtain an effective surface material laser point cloud data set;
carrying out validity judgment on the obtained surface material laser point cloud data to obtain a point cloud data set to be evaluated;
classifying the point cloud data set to be evaluated to obtain a laser point cloud data set on one side of the surface material and a laser point cloud data set on the other side;
respectively evaluating the point cloud data sets on the two sides to obtain the flatness value of each side surface; respectively calculating the uniformity value and the thickness value of the surface material according to the point cloud data sets on the two sides;
comparing the obtained flatness value with a preset flatness value, comparing the obtained uniformity value with a preset uniformity value, comparing the obtained thickness value with a preset thickness value to obtain a comparison result, and giving a material inspection and evaluation conclusion;
the effective separation is carried out by the following steps:
a) roughly estimating boundary vertex coordinates of the surface material;
b) constructing an external quadrangular pyramid of the surface material to calculate the point cloud, wherein the external quadrangular pyramid consists of four boundary vertexes of the surface material and a reference point outside an external envelope of the point cloud;
c) and separating effective point cloud data.
2. The method of claim 1, wherein the point cloud outer envelope is generated using a convex hull detection algorithm.
3. The method according to claim 1, wherein in classifying the point cloud data set to be evaluated, a Total Least Squares (TLS) method is used, and a Total Least square device is used to separate the point cloud data belonging to the two sides, so as to obtain laser point cloud data sets on the two sides of the surface material respectively.
4. The method according to claim 1, wherein the validity judgment is to use a convex hull detection algorithm to respectively calculate the separated effective point cloud outer envelopes and calculate the proportion of the size of the effective point cloud outer envelopes to the actual size of the surface material, and when the proportion is greater than a first proportion, the effective point cloud is judged to be successfully obtained; and when the proportion is smaller than the second proportion, judging that the effective point cloud acquisition fails.
5. The method of claim 1, wherein an opening angle of the surface material relative to the reference point is less than 10 °.
6. The method of claim 1, wherein the surface material is a cover in front of the millimeter wave radar, the cover being made of a hard-mold material.
7. The method of claim 1, wherein the flatness of the side surface is evaluated by the mean and mean standard deviation of the vertical distance distribution of each point cloud of the side surface to the fitting plane of the side surface.
8. The method of claim 1, wherein the uniformity/thickness of the surface material is evaluated from the vertical distance distribution of the point clouds on one side to the fitted plane on the other side.
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| CN102393181A (en) * | 2011-09-22 | 2012-03-28 | 南京信息工程大学 | Automatic online detection method and device of angle steel geometric parameters |
| CN102735175A (en) * | 2012-06-07 | 2012-10-17 | 华中科技大学 | Method for determining maximum thickness of blade based on moving least square method |
| CN104613883A (en) * | 2015-02-11 | 2015-05-13 | 南通大学 | Wet film thickness difference measurement and evenness assessment method of metal sheet printing coating |
| CN105627938A (en) * | 2016-01-07 | 2016-06-01 | 厦门大学 | Pavement asphalt thickness detection method based on vehicle-mounted laser scanning spot cloud |
| CN107607064A (en) * | 2017-09-01 | 2018-01-19 | 华南理工大学 | LED fluorescent powder glue coating planeness detection system and method based on a cloud information |
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