CN115600366B - Automobile windshield mold profile design method and device based on regression model - Google Patents

Automobile windshield mold profile design method and device based on regression model Download PDF

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CN115600366B
CN115600366B CN202211071854.XA CN202211071854A CN115600366B CN 115600366 B CN115600366 B CN 115600366B CN 202211071854 A CN202211071854 A CN 202211071854A CN 115600366 B CN115600366 B CN 115600366B
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CN115600366A (en
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张明
张儒
路明标
孙自飞
郭震
甘雨
李作东
安旭
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Nanjing Tianfu Software Co ltd
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Abstract

The invention provides a design method and a device for a mold surface of an automobile windshield based on a regression model. And secondly, establishing a regression model according to the geometric feature data of all the discrete points and the normal distance from the discrete points to the corresponding mold surface. And extracting discrete points of the required glass molded surface, and acquiring geometric characteristic data of each discrete point on the required glass molded surface. And finally, predicting the normal distance from each discrete point on the required glass profile to the required mold profile according to the geometric feature data and the regression model of the discrete points on the required glass profile, and generating the profile of the required mold by utilizing the geometric feature data of the discrete points on the required glass profile and the normal distance from the discrete points on the required glass profile to the required mold profile.

Description

Automobile windshield mold profile design method and device based on regression model
Technical Field
The invention belongs to the technical field of glass mold design, and particularly relates to a method and a device for designing a mold surface of an automobile windshield glass mold based on a regression model.
Background
The production process of the automobile windshield comprises the following steps: firstly, the glass raw sheet is conveyed to a forming chamber after cutting, edging, cleaning, drying and heating. Then, the glass mold is sucked onto the suction surface of the suction mold machine in the molding chamber, and the glass mold below is held as the suction mold surface descends. When the glass raw sheet is heated to reach a softening point, the glass raw sheet is downwards bent and attached to the glass die under the influence of gravity, so that the set profile curvature of the glass die is achieved. Finally, the glass raw sheet formed by hot bending is cooled and subjected to subsequent steps to form the automobile windshield meeting production requirements.
It can be seen that the glass mould has a very important role for the shaping of the glass raw sheet. However, since the glass sheet is subject to springback during the molding process, the geometric characteristics of the glass mold profile are not exactly the same as the design curve of the automobile windshield.
At present, the design of the mold surface of the automobile windshield adopts a trial-and-error method, and the general flow is as follows: (1) determining a desired glass profile; (2) preliminarily designing the molding surface of the glass mold according to the molding surface of the glass; (3) trial-manufacturing a glass finished product according to the molded surface of the glass mold; (4) Detecting deviation between the finished product of the trial-produced glass and the required glass profile; (5) And (3) if the deviation is not qualified, returning to the step (2) to adjust the molding surface of the glass mold. However, trial-and-error requires repeated corrections to the glass mold profile, which is not only long in design cycle, heavy in effort, but also costly, resulting in lower efficiency in the production of automotive windshields.
With the continuous development of the automobile industry, the styles of automobile windshields are more and more, and the design of mold surfaces faces new demands and challenges. Trial and error has been difficult to meet the development trend of the automobile windshield manufacturing industry due to its high design cost and long design cycle.
Disclosure of Invention
The embodiment of the invention provides a regression model-based design method and device for the mold surface of an automobile windshield glass mold, which are used for solving the problems of higher design cost and longer design period in the prior art.
In order to solve the technical problems, the embodiment of the invention discloses the following technical scheme:
a design method of a mold surface of an automobile windshield glass mold based on a regression model is applied to generating a required mold surface according to a required glass mold surface, and comprises the following steps:
obtaining multiple groups of molded surfaces from historical design schemes, wherein each group of molded surfaces comprises a glass molded surface and a corresponding mold molded surface;
discrete points of each glass molded surface are extracted respectively, and geometric characteristic data of each discrete point are obtained;
obtaining the normal distance from each discrete point to the corresponding mold surface;
establishing a regression model according to the geometric feature data of all the discrete points and the normal distance from the discrete points to the corresponding mold surfaces, wherein the input variable of the regression model is the geometric feature of the discrete points, and the output variable is the normal distance from the discrete points to the corresponding mold surfaces;
Discrete points of the required glass molded surface are extracted, and geometric feature data of each discrete point are obtained;
according to the geometric feature data of discrete points of the required glass molded surface and the regression model, predicting the normal distance from each discrete point on the required glass molded surface to the required mold molded surface;
and generating the profile of the required mold by utilizing the geometric characteristic data of the discrete points of the required glass profile and the normal distance from the discrete points on the required glass profile to the profile of the required mold.
Optionally, the extracting discrete points of each glass profile and obtaining geometric feature data of each discrete point respectively includes:
for each glass profile, discrete points are extracted as follows:
determining a minimum bounding box of the glass profile;
establishing a three-dimensional coordinate system of the glass molded surface by taking the central point of the minimum bounding box as an origin, wherein the x-axis of the coordinate system is parallel to the longest side of the minimum bounding box, and the z-axis of the coordinate system is parallel to the shortest side of the minimum bounding box;
respectively taking the longest two boundary lines on the glass molded surface as an upper boundary line and a lower boundary line of the glass molded surface;
dividing the upper boundary line and the lower boundary line by p+1 equally, and connecting corresponding equally dividing points on the upper boundary line and the lower boundary line to obtain p equally dividing lines;
Obtaining p planes which are perpendicular to the XOY plane and pass through any one of p bisectors, wherein the planes are in one-to-one correspondence with the bisectors;
obtaining p intersecting lines intersecting p planes on the glass molded surface;
q+1 aliquoting is carried out on each intersecting line; sequentially connecting the equal division points of the corresponding sequences on the p intersecting lines to obtain q connecting lines; the intersection points of the p intersecting lines and the q connecting lines are discrete points of the glass profile.
Optionally, the extracting discrete points of each glass profile, and obtaining geometric feature data of each discrete point, further includes:
(1) Acquiring the coordinates of each discrete point under the coordinate system of the glass molded surface;
(2) For each discrete point, a normal vector N is obtained as follows:
obtaining a tangent vector S with discrete points parallel to the x-axis x And a tangent vector S of the discrete point parallel to the y-axis y ；
The normal vector N is obtained by tangential vector cross multiplication: n=s x ×S y ；
(3) For each discrete point, the average curvature H is obtained as follows:
wherein k is 1 Is the maximum radius of curvature through the discrete points; k (k) 2 Is the minimum radius of curvature through the discrete points;
(4) For each discrete point, a gaussian curvature K is obtained as follows:
K＝k 1 *k 2
Wherein k is 1 Is the maximum radius of curvature through the discrete points; k (k) 2 Is the minimum radius of curvature through the discrete points;
(5) For each discrete point, arch height AH is obtained according to the following method;
the camber is the distance from the discrete point to the corresponding bisector:
AH＝Dist(P,l i ),P∈l i ',i＝1,2,…p
wherein the discrete point P is at the intersection line l i ' on, l i Is with l i ' corresponding bisectors.
Optionally, the obtaining the normal distance from each discrete point to the corresponding mold surface includes:
for each discrete point, the normal distance of the discrete point is obtained in the following way:
acquiring an intersection point of a straight line passing through the discrete point and parallel to the normal vector of the discrete point and a corresponding mold surface;
and taking the distance between the discrete point and the intersection point as the normal distance from the discrete point to the corresponding mold surface.
Optionally, the establishing a regression model according to the geometric feature data of all the discrete points and the normal distance between the discrete points and the corresponding mold surface includes:
preprocessing the coordinates of all the discrete points by adopting a MinMax (maximum minimum) normalization mode;
preprocessing normal vectors, average curvatures, gaussian curvatures and camber of all discrete points by adopting a Robust (Robust Normalization ) normalization mode;
Constructing a data set by utilizing the geometric feature data of the preprocessed discrete points and the normal distance from the discrete points to the corresponding mold surfaces, wherein the coordinates, normal vectors, average curvature, gaussian curvature and camber of the discrete points are used as input variables of the data set, and the normal distance from the discrete points to the corresponding mold surfaces is used as output variables of the data set;
and obtaining a regression model between the input variable and the output variable by using all data in the data set and adopting a random forest algorithm.
Optionally, the preprocessing is performed on the coordinates of all the discrete points in a MinMax normalization mode, including:
the coordinates of each discrete point are preprocessed according to the following method:
wherein f_max is the maximum value of all the discrete point coordinates; f_min is the minimum value in all the discrete point coordinates, and f is the original discrete point coordinate; f' is the preprocessed discrete point coordinates.
Optionally, the preprocessing is performed on the normal vector, the average curvature, the gaussian curvature and the camber of all the discrete points by adopting a robustnormalization mode, including:
the normal vector, average curvature, gaussian curvature and camber of each discrete point are all pre-processed as follows:
wherein f is an original geometric feature data value, and the geometric feature data value is a data value of one geometric feature of normal vector, average curvature, gaussian curvature and camber of the discrete points; f' is the preprocessed geometrical characteristic data value corresponding to f; f_mean is the median of the geometric feature data values corresponding to f of all the discrete points on the glass molded surface to which the discrete points belong; IQR is the interval length between the 1 st quartile and the 3 rd quartile in the geometric feature data values corresponding to f and all discrete points on the glass profile to which the discrete points belong.
Optionally, using all data in the data set, a random forest algorithm is used to obtain a regression model between the input variable and the output variable, including:
any one of all the glass molded surfaces is selected as a test glass molded surface, and the rest glass molded surfaces are all used as training glass molded surfaces;
constructing a test data set by utilizing the geometric characteristic data of the discrete points after the pretreatment of the test glass molded surface and the normal distance from the discrete points on the test glass molded surface to the corresponding mold molded surface, wherein the coordinates, the normal vector, the average curvature, the Gaussian curvature and the camber of the discrete points are used as input variables, and the normal distance from the discrete points to the corresponding mold molded surface is used as output variables;
constructing a training data set by utilizing the geometric feature data of the discrete points after the pretreatment of the training glass molded surface and the normal distance from the discrete points on the training glass molded surface to the corresponding mold molded surface, wherein the coordinates, the normal vector, the average curvature, the Gaussian curvature and the camber of the discrete points are used as input variables, and the normal distance from the discrete points to the corresponding mold molded surface is used as output variables;
and establishing a regression model by adopting a random forest algorithm according to the training data set and the test data set.
Optionally, the establishing a regression model according to the training data set and the test data set by adopting a random forest algorithm includes:
Constructing a plurality of training data sets by utilizing different combination modes of training glass molded surfaces;
establishing a regression model for each training data set; one training data set corresponds to one regression model;
adopting different regression models to fuse into a new regression model;
performing error testing on each regression model and the fused regression model by using a testing data set;
and taking the regression model with the minimum error as the finally established regression model.
Optionally, the predicting, according to the geometric feature data of discrete points of the required glass profile and the regression model, a normal distance from each discrete point on the required glass profile to the required mold profile includes:
preprocessing the coordinates of all discrete points on the required glass molded surface in a MinMax normalization mode;
preprocessing normal vectors, average curvatures, gaussian curvatures and camber of all discrete points on a required glass molded surface in a Robust normalization mode;
substituting the geometric feature data of all the discrete points on the required glass molded surface after pretreatment into a regression model, and calculating to obtain the normal distance from each discrete point on the required glass molded surface to the required mold molded surface.
Optionally, the generating the profile of the demand mold by using the geometric feature data of the discrete point of the profile of the demand glass and the normal distance from the discrete point of the profile of the demand glass to the profile of the demand mold includes:
Calculating coordinates of each discrete point of the required mold surface by utilizing geometric feature data of the discrete points on the required glass surface and normal distance from the discrete point of the required glass surface to the required mold surface, wherein the discrete points of the required mold surface correspond to the discrete points on the required glass surface one by one and are assumed discrete points on the required mold surface;
the coordinates of each discrete point of the desired mold profile are calculated as follows:
wherein: (x) i ',y i ',z i ') is the coordinates of discrete points of the required mold surface; (x) i ,y i ,z i ) Coordinates of corresponding discrete points on the required glass profile; (n) xi ,n yi ,n zi ) Normal vector of corresponding discrete point on the required glass profile; d, d i For the predicted demand of glass on-mold pairsThe normal distance from the discrete point to the required mold surface is needed;
constructing a spline curve network wire frame connected with discrete points according to the coordinates of the discrete points of the required mold surface;
and (3) carrying out curved surface reconstruction on the spline curve network wire frame by using a geometric modeling engine to generate the molded surface of the required mold.
Another aspect of the present invention provides a regression model-based automotive windshield mold profile design apparatus for generating a desired mold profile from a desired glass profile, comprising:
The glass profile and mould profile acquisition unit is used for acquiring a plurality of groups of profiles from the historical design scheme, wherein each group of profiles comprises a glass profile and a corresponding mould profile;
the glass profile discrete point data acquisition unit is used for respectively extracting discrete points of each glass profile and acquiring geometric characteristic data of each discrete point;
the normal distance acquisition unit is used for acquiring the normal distance from each discrete point to the corresponding mold surface;
the regression model building unit is used for building a regression model according to the geometric feature data of all the discrete points and the normal distance between the discrete points and the corresponding mold surfaces, wherein the input variable of the regression model is the geometric feature of the discrete points, and the output variable is the normal distance between the discrete points and the corresponding mold surfaces;
the glass profile discrete point data acquisition unit is also used for extracting discrete points requiring the glass profile and acquiring geometric characteristic data of each discrete point;
the normal distance prediction unit is used for predicting the normal distance from each discrete point on the required glass profile to the required mold profile according to the geometric feature data of the discrete points of the required glass profile and the regression model;
The required mold profile generating unit is used for generating the profile of the required mold by utilizing the geometric characteristic data of the discrete points of the required glass profile and the normal distance from the discrete points on the required glass profile to the required mold profile.
Optionally, the regression model building unit includes:
the preprocessing module is used for preprocessing the coordinates of all the discrete points in a MinMax (maximum minimum) normalization mode;
the preprocessing module is also used for preprocessing normal vectors, average curvatures, gaussian curvatures and arch heights of all discrete points by adopting a Robust (Robust Normalization ) normalization mode;
the data set construction module is used for constructing a data set by utilizing the geometric feature data of the preprocessed discrete points and the normal distance from the discrete points to the corresponding mold surface, wherein the coordinates, normal vector, average curvature, gaussian curvature and camber of the discrete points are used as input variables of the data set, and the normal distance from the discrete points to the corresponding mold surface is used as output variables of the data set;
and the regression model construction module is used for obtaining a regression model between the input variable and the output variable by using all data in the data set and adopting a random forest algorithm.
Optionally, the regression model building module includes:
the training data set submodule is used for constructing a plurality of training data sets by utilizing different combination modes of the training glass molded surface;
the regression model building sub-module is used for building a regression model for each training data set; one training data set corresponds to one regression model;
the regression model fusion submodule is used for fusing different regression models into a new regression model;
the error testing sub-module is used for carrying out error testing on each regression model and the fused regression model by utilizing the testing data set;
and the regression model determination submodule is used for taking the regression model with the minimum error as the finally established regression model.
As can be seen from the above technical solutions, according to the design method and apparatus for a mold surface of an automotive windshield glass based on a regression model provided by the embodiments of the present invention, first, multiple sets of corresponding glass mold surfaces and mold surfaces are obtained from a historical design scheme, discrete points of each glass mold surface are respectively extracted, geometric feature data of each discrete point is obtained, and a normal distance from each discrete point to the corresponding mold surface is obtained.
And secondly, establishing a regression model according to the geometric feature data of all the discrete points and the normal distance from the discrete points to the corresponding mold surface. And extracting discrete points of the required glass molded surface, and acquiring geometric characteristic data of each discrete point on the required glass molded surface.
And finally, predicting the normal distance from each discrete point on the required glass profile to the required mold profile according to the geometric feature data and the regression model of the discrete points on the required glass profile, and generating the profile of the required mold by utilizing the geometric feature data of the discrete points on the required glass profile and the normal distance from the discrete points on the required glass profile to the required mold profile.
Therefore, the method and the device provided by the embodiment of the invention can greatly improve the design efficiency of the mold surface of the automobile windshield and effectively reduce the iteration cost in the design process.
Drawings
FIG. 1 is a schematic flow chart of a design method of a mold surface of an automobile windshield based on a regression model according to an embodiment of the invention;
FIG. 2 is a flowchart illustrating implementation of step S102 in FIG. 1 according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a minimum bounding box of an automobile windshield according to an embodiment of the present invention;
FIG. 4 is a schematic view of discrete points on a glass surface according to one embodiment of the present invention;
FIG. 5 is a flowchart illustrating implementation of step S104 in FIG. 1 according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating implementation of step S107 in FIG. 1 according to an embodiment of the present invention;
Fig. 7 is a schematic structural diagram of an apparatus for designing a mold surface of an automobile windshield mold based on a regression model according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail below with reference to the drawings and detailed description for the purpose of better understanding of the technical solution of the present invention to those skilled in the art.
Fig. 1 is a schematic flow chart of a regression model-based design method for a mold surface of an automobile windshield glass, which is applied to generating a required mold surface according to a required glass surface. As shown in fig. 1, the method can be implemented according to the following steps:
step S101: multiple sets of profiles are obtained from the historical design, each set of profiles including a glass profile and a corresponding mold profile.
The glass molded surfaces in each set of molded surfaces and the mold molded surfaces correspond to each other, and each glass molded surface is manufactured by the corresponding mold molded surface.
Step S102: and respectively extracting discrete points of each glass molded surface, and acquiring geometric characteristic data of each discrete point.
In one embodiment of the present disclosure, for each glass profile, as shown in FIG. 2, discrete points are extracted as follows:
Step S1021: a minimum bounding box of the glass profile is determined.
The minimum bounding box is the minimum bounding rectangle of the glass profile, which in the disclosed embodiments of the invention can be obtained using existing algorithms.
Step S1022: and establishing a three-dimensional coordinate system of the glass molded surface by taking the central point of the minimum bounding box as an origin.
As shown in fig. 3, the x-axis of the three-dimensional coordinate system is parallel to the longest side of the minimum bounding box, the z-axis of the coordinate system is parallel to the shortest side of the minimum bounding box, and the y-axis of the coordinate system is parallel to the middle length side of the minimum bounding box.
Step S1023: the longest two borderlines on the glass profile are respectively taken as an upper borderline and a lower borderline of the glass profile.
The glass profile is generally an irregular curved surface of approximately rectangular shape, and the boundary of the glass profile is formed by an outwardly curved curve. When the glass profile is upright, its boundary lines are divided into an upper boundary line, a lower boundary line, a left boundary line, and a right boundary line. Wherein, the upper and lower boundary lines of the glass profile are longest.
Step S1024: and respectively carrying out p+1 equal division on the upper boundary line and the lower boundary line, and connecting corresponding equal division points on the upper boundary line and the lower boundary line to obtain p equal division lines.
Dividing the upper boundary line and the lower boundary line by p+1 equally, thereby obtaining p equally divided points up on the upper boundary line i I=1, 2, … p, and p bisectors lp on the lower boundary line i ,i＝1,2,…p。
Connecting corresponding bisectors on the upper and lower border lines, e.g. up 1 Connection lp 1 ，up 2 Connection lp 2 . After connecting the corresponding p equal-dividing points on the upper and lower boundary lines, obtaining p equal-dividing lines l i ,i＝1,2,…p。
Step S1025: p planes perpendicular to the XOY plane and passing through any of the p bisectors are obtained.
With a bisector line l 1 For example, obtain a line of bisection l 1 At the same time, the plane is perpendicular to the XOY plane on the three-dimensional coordinate system, which plane can intersect the glass profile. In the above manner, p planes are obtained, each plane passing through a bisector l i And perpendicular to the XOY plane. Each plane passing through only one bisector line l i The planes are in one-to-one correspondence with the bisectors.
Step S1026: p intersecting lines on the glass profile intersecting p planes are obtained.
The p planes obtained are intersected with the glass profile, so that p intersecting lines l on the glass profile can be obtained i ' i=1, 2, … p, the intersection line being a curve.
Step S1027: q+1 aliquoting is carried out on each intersection line; sequentially connecting the equal division points of the corresponding sequences on the p intersecting lines to obtain q connecting lines; the intersection of the p intersecting lines and the q connecting lines is a discrete point of the glass profile.
For each intersection line l on the glass profile i ' q+1 aliquoting is performed on each of i=1, 2, … p, thereby obtaining each intersection line l i ' q bisectors on i=1, 2, … p.
Sequentially connecting corresponding sequential bisectors of p intersecting lines, e.g. connecting intersecting line l' 1 The first bisecting point on the line of intersection l' 2 The first bisecting point on the first line is connected in turn until it is connected to the intersection line l' p The first of the points of bisection, thereby obtaining a connecting line connecting the first of the points of bisection on each of the intersecting lines. After the connection is completed at the q-th bisector on each intersection line, q connection lines on the glass profile can be obtained.
As shown in fig. 4, the intersections of the p intersecting lines with the q connecting lines are taken as discrete points corresponding to the glass profile.
In the disclosed embodiments, the geometric features required to obtain discrete points are discrete point coordinates, normal vectors, average curvature, gaussian curvature, and camber.
The reason for extracting the characteristic parameters is as follows:
combining the hot bending forming and rebound processes of the glass, and lifting the glass raw sheet and attaching the glass raw sheet to the suction mold under the combined action of vacuum adsorption and blowing of the suction film after the glass raw sheet is heated at high temperature; then the suction mold and the glass descend to press the glass together with the hot ring; the glass falls on the cooling ring after being pressed and formed, and is rapidly supported and delivered to the quenching cold air grid area for quenching tempering.
The glass sheet is formed by pressing a suction mold surface and a hot ring surface, and then falls on a cold ring by means of gravity. The stress distribution conditions at each stage are as follows: when in press forming, the surface of the suction mold is firstly contacted with the middle area of the glass raw sheet, and gradually attached to the boundary, so that the stress is small and uneven. After the glass edge is extruded by the surface of the suction mold and the hot ring surface, boundary stress is increased and the distribution is more uniform. When the mould falls, the glass falls onto the cold ring at a certain speed under the action of self gravity, so that impact load is generated, and the stress at the edge of the glass suddenly increases.
Therefore, the stress distribution of the windshield in different positions during the molding process has a certain difference, which can cause the deviation between the different positions on the molded glass surface and the molded surface of the mold to be different. Based on this, positional information of discrete points on the glass surface, i.e., discrete point coordinates (x/y/z), is extracted.
In the hot bending forming process of the glass raw sheet, when the glass raw sheet is subjected to an external bending moment, the shape of the glass raw sheet is changed. When the glass is bent, the inner glass raw sheet of the deformation zone is subjected to tangential compressive stress to generate compression deformation, the outer glass raw sheet is subjected to tangential tensile stress to generate tensile deformation, and the tangential direction also influences the deformation of the glass profile, so that unit normal vectors (vector_x/vector_y/vector_z) perpendicular to the tangential direction are extracted for characterizing the tangential characteristics of the glass profile.
Meanwhile, the radius of curvature of the mold surface is a main factor determining the glass surface, the degree of curvature of the glass surface depends on the shape of the mold surface, and the degree of rebound is related to the radius of curvature of the mold surface. This is because the smaller the radius of curvature of the mold surface, the deeper the relative degree of bending, and the larger the proportion of elastic deformation in the total deformation increases, and the larger the rebound after the external force is removed. Thus, the curvature characteristics of the glass profile, including the average curvature H and gaussian curvature K, are extracted.
In addition, to characterize the degree of curvature of the glass profile, the camber AH of the discrete points was also extracted.
The data values of each geometrical feature of the discrete points are obtained respectively in the following way:
(1) The coordinates of each discrete point are obtained under the coordinate system of the glass profile.
(2) For each discrete point, a normal vector N is obtained as follows:
obtaining a tangent vector S with discrete points parallel to the x-axis x And a tangent vector S of the discrete point parallel to the y-axis y 。
The normal vector N is obtained by tangential vector cross multiplication: n=s x ×S y 。
(3) For each discrete point, the average curvature H is obtained as follows:
wherein k is 1 Is the maximum radius of curvature through the discrete points; k (k) 2 Is the smallest radius of curvature through the discrete points.
(4) For each discrete point, a gaussian curvature K is obtained as follows:
K＝k 1 *k 2
wherein k is 1 Is the maximum radius of curvature through the discrete points; k (k) 2 Is the smallest radius of curvature through the discrete points.
(5) For each discrete point, the arch height AH is obtained according to the following method, wherein the arch height is from the discrete point P to the corresponding bisector l i Distance of (2):
AH＝Dist(P,l i ),P∈l i ',i＝1,2,…p
wherein the discrete point P is at the intersection line l i ' i=1, 2, …, p, l i Is equal to l i ' corresponding bisectors.
Step S103: and obtaining the normal distance from each discrete point to the corresponding mold surface.
In the disclosed embodiment of the invention, the normal distance of each discrete point to the corresponding mold profile is obtained as follows:
for each discrete point, the normal distance of the discrete point is obtained in the following manner:
(1) And acquiring the intersection point of the straight line passing through the discrete point and parallel to the normal vector of the discrete point and the corresponding mold surface.
(2) And taking the distance between the discrete point and the intersection point as the normal distance from the discrete point to the corresponding mold surface.
Step S104: and establishing a regression model according to the geometric characteristic data of all the discrete points and the normal distance from the discrete points to the corresponding mold surface.
The input variable of the regression model is the geometric characteristic of the discrete point, and the output variable is the normal distance from the discrete point to the corresponding mold surface.
In one embodiment of the present disclosure, as shown in FIG. 5, a regression model is built in the following manner:
the geometric feature data of the discrete points are preprocessed before the regression model is built.
Step S1041: and preprocessing the coordinates of all the discrete points by adopting a MinMax normalization mode.
In one embodiment of the present disclosure, the coordinates of each discrete point are pre-processed as follows:
wherein f_max is the maximum value of all the discrete point coordinates; f_min is the minimum value in all the discrete point coordinates, and f is the original discrete point coordinate; f' is the preprocessed discrete point coordinates.
Step S1042: and preprocessing the normal vector, the average curvature, the Gaussian curvature and the camber of all the discrete points by adopting a Robust normalization mode.
In one embodiment of the present disclosure, the normal vector, average curvature, gaussian curvature, and camber of each discrete point are all pre-processed as follows:
wherein f is an original geometric feature data value, and the geometric feature data value is a geometric feature data value of one of normal vector, average curvature, gaussian curvature and camber of discrete points; f' is the preprocessed geometrical characteristic data value corresponding to f; f_mean is the median of the geometric feature data values corresponding to f and all the discrete points on the glass molded surface to which the discrete points belong; IQR is the interval length between the 1 st quartile and the 3 rd quartile in the geometric feature data values corresponding to f and all discrete points on the glass profile to which the discrete points belong. For example, f is the original data value of the camber of the discrete point, f' is the data value of the camber after the pretreatment of the discrete point, f_mean is the median of the camber data values of all the discrete points on the glass surface to which the discrete point belongs, and IQR is the interval length between the 1 st quartile and the 3 rd quartile of the camber data values of all the discrete points on the glass surface to which the discrete point belongs.
Step S1043: and constructing a data set by utilizing the geometric characteristic data of the preprocessed discrete points and the normal distance from the discrete points to the corresponding mold surfaces.
The coordinates, normal vector, average curvature, gaussian curvature and camber of the discrete points are used as input variables in the data set, and the normal distance from the discrete points to the corresponding mold surface is used as output variables in the data set.
Step S1044: and obtaining a regression model between the input variable and the output variable by using all data in the data set and adopting a random forest algorithm.
In one embodiment of the present disclosure, this step may be implemented by the sub-steps of:
(1) Any one of all the glass profiles is selected as a test glass profile, and all the glass profiles except the test glass profile are used as training glass profiles.
(2) And constructing a test data set by utilizing the geometric characteristic data of the discrete points of the test glass molded surface after pretreatment and the normal distance from the discrete points on the test glass molded surface to the corresponding mold molded surface. The coordinates, normal vectors, average curvature, gaussian curvature and camber of the discrete points are used as input variables, and the normal distance from the discrete points to the corresponding mold surface is used as an output variable.
(3) And constructing a training data set by utilizing the geometric characteristic data of the discrete points of the training glass molded surface after pretreatment and the normal distance from the discrete points on the training glass molded surface to the corresponding mold molded surface. The coordinates, normal vectors, average curvature, gaussian curvature and camber of the discrete points are used as input variables, and the normal distance from the discrete points to the corresponding mold surface is used as an output variable.
(4) And establishing a regression model by adopting a random forest algorithm according to the training data set and the test data set.
In one embodiment of the present disclosure, the regression model may be established in the following manner:
(1) And constructing a plurality of training data sets by utilizing different combination modes of the training glass molded surface.
The training data sets are constructed in a plurality, each training data set can be composed of data corresponding to different training glass molded surfaces, and the data corresponding to the training glass molded surfaces are discrete point geometric feature data after glass molded surface pretreatment and normal distance between the discrete points and the corresponding mold molded surfaces. The test dataset contains only data corresponding to the test glass profile.
In a specific embodiment of the present disclosure, the data of 6 groups of glass profiles and corresponding mold profiles are respectively a group a, a group D, a group H, a group K, a group N and a group U, wherein the group U is used as a test group, the glass profiles therein are used as test glass profiles, and the group a, the group D, the group H, the group K and the group N are used as training glass profiles.
The training data sets of 5 different combination forms are constructed by using the 5 groups of training groups, wherein independent training data sets, namely an H training data set, an H+A training data set, an H+A+K training data set, an H+A+K+D training data set and an H+A+K+D+N training data set are respectively constructed according to the 5 different combination forms of H, H + A, H +A+ K, H +A+K+ D, H +A+K+D+N. And constructing a test data set according to the U group.
(2) A regression model is built for each training dataset.
And respectively utilizing different training data sets and the same test data set to establish a plurality of regression models, wherein one training data set corresponds to one regression model.
For example, a regression model M1 is built using the H training dataset and the U-set test dataset; establishing a regression model M2 by using the H+A training data set and the U group test data set; establishing a regression model M3 by using the H+A+K training data set and the U group test data set; establishing a regression model M4 by using the H+A+K+D training data set and the U-group test data set; a regression model M5 was established using the h+a+k+d+n training dataset and the U-set test dataset.
(3) And adopting different regression models to fuse into a new regression model.
And fusing the established regression model into a new regression model. For example, regression models M1 and M2 are fused to obtain a regression model M6, and regression models M1 to M5 are fused to obtain a regression model M7. The fusion method can be to take the average value of the data values of the output variables of the regression models in the fusion combination, namely the average value of the predicted values, as the data value of the output variables of the regression models after fusion, namely the predicted values. For example, the input variable data value of the same discrete point p is substituted into M1 and M2 to obtain predicted values M1-p and M2-p, and the input variable data of the discrete point p is substituted into a regression model M6 fused by M1 and M2 to obtain predicted values M6-p, so that the predicted values M6-p= (M1-p+m2-p)/2 outputted by M6.
(4) And carrying out error test on each regression model and the fused regression model by using the test data set.
In the disclosed embodiment of the invention, for each regression model and the fused regression model, the single point maximum error is calculated as follows:
substituting the geometric feature data of each discrete point in the test data set into a regression model, wherein the regression model predicts a predicted value corresponding to each discrete point, namely the normal distance from the discrete point to the corresponding mold surface. And obtaining the absolute value of the difference between the predicted value and the true value of each discrete point, and taking the largest absolute value as the single-point maximum error of the regression model.
(5) And taking the regression model with the minimum error as the finally established regression model.
After single-point maximum errors of all regression models and the fused regression models are respectively obtained, single-point maximum errors of several regression models are compared, and the regression model corresponding to the minimum single-point maximum error is used as the finally established regression model.
For example, the single point maximum error for M1 is 4.19; the single point maximum error of M2 is 3.25; the single point maximum error of M3 is 3.41; the single point maximum error of M4 is 3.43; the single point maximum error of M5 is 2.18; the single point maximum error of M6 is 2.47; the single point maximum error for M7 is 2.04. The single-point maximum error value of M7 is the smallest, so the regression model M7 is used as the final regression model.
Step S105: and extracting discrete points requiring the glass profile, and acquiring geometric characteristic data of each discrete point.
The required glass profile is a glass profile which is required to be designed corresponding to the mold profile and is a glass profile which is designed in advance.
The three-dimensional coordinate system of the required glass profile is constructed according to the method of extracting the discrete points of the glass profile in the foregoing embodiment, and the discrete points of the required glass profile are extracted, which will not be described herein.
According to the method for acquiring the geometric feature data of each discrete point on the glass surface in the foregoing embodiment, the geometric feature data of each discrete point on the glass surface is acquired, which is not described herein.
Step S106: and predicting the normal distance from each discrete point on the required glass profile to the required mold profile according to the geometric feature data of the discrete points of the required glass profile and the regression model.
In one embodiment of the present disclosure, this step may be implemented by the sub-steps of:
(1) The coordinates of all discrete points on the desired glass profile were pre-processed in a MinMax normalization manner, in the manner employed in the previous examples.
(2) The normal vector, average curvature, gaussian curvature and camber of all discrete points on the desired glass profile were pretreated in the manner described in the previous examples using the Robust normalization.
(3) Substituting the geometric feature data of all the discrete points on the required glass molded surface after pretreatment into a finally established regression model, and calculating the normal distance from each discrete point on the required glass molded surface to the required mold molded surface by the regression model.
Step S107: and generating the profile of the required mold by utilizing the geometric characteristic data of the discrete points of the required glass profile and the normal distance from the discrete points on the required glass profile to the profile of the required mold.
In the disclosed embodiment of the invention, as shown in fig. 6, step S107 may be accomplished using the following sub-steps:
step S1071: and calculating the coordinates of each discrete point of the required mold surface by utilizing the geometric characteristic data of the discrete points on the required glass surface and the normal distance from the discrete point of the required glass surface to the required mold surface.
Wherein, the discrete points of the required mold profile are in one-to-one correspondence with the discrete points on the required glass profile, and are assumed discrete points on the required mold profile.
The coordinates of each discrete point of the desired mold profile are calculated as follows:
wherein: (x) i ',y i ',z i ') is the coordinates of discrete points of the required mold surface; (x) i ,y i ,z i ) Coordinates of corresponding discrete points on the required glass profile; (n) xi ,n yi ,n zi ) Normal vector of corresponding discrete point on the required glass profile; d, d i The normal distance from the corresponding discrete point on the predicted required glass profile to the required mold profile is set.
Step S1072: and constructing a spline curve network wire frame connected with the discrete points according to the coordinates of the discrete points of the required mold surface.
In one embodiment of the present disclosure, step S1072 may be implemented by:
(1) The discrete points of the required mold surface are transposed into a p multiplied by q two-dimensional matrix by using the existing geometric modeling engine.
(2) And randomly selecting a plurality of groups of preset number of discrete points from all discrete points of the required mold surface, and respectively generating a plurality of detection surfaces of the required mold by using a geometric modeling engine. Wherein each detection profile is generated from a preset number of discrete points.
(3) According to the coordinates of discrete points of the required mold surface and the detection surfaces of the multiple groups of required molds, the average error between all discrete points of the required mold surface and the detection surfaces of each required mold is calculated respectively, namely, the average error between all discrete points of the required mold surface and the detection surfaces of each required mold is calculated according to the detection surfaces of each required mold. The average error may be the average of the normal distances of all discrete points to the detection profile.
(4) And determining a detection profile with the minimum average error, and taking the corresponding discrete point as the selected discrete point.
(5) Connecting the selected discrete points to form a spline curve network wire frame.
Step S1073: and (3) carrying out curved surface reconstruction on the spline curve network wire frame by using a geometric modeling engine to generate the molded surface of the required mold.
And (3) carrying out curved surface reconstruction on the spline curve network wire frame by using the conventional geometric modeling engine, and constructing a final required mold surface.
Fig. 7 is a schematic structural diagram of a regression model-based design device for molding a mold surface of an automobile windshield, which is applied to generating a desired mold surface according to a desired glass surface, as shown in fig. 7, and comprises the following units:
a glass profile and mold profile acquiring unit 11 configured to acquire a plurality of sets of profiles each including one glass profile and a corresponding one of the mold profiles from the historical design;
a glass profile discrete point data acquisition unit 12 configured to extract discrete points of each glass profile, respectively, and acquire geometric feature data of each discrete point;
a normal distance acquisition unit 13 configured to acquire a normal distance from each discrete point to a corresponding mold surface;
a regression model establishing unit 14 configured to establish a regression model according to the geometric feature data of all the discrete points and the normal distances from the discrete points to the corresponding mold surfaces, wherein the input variable of the regression model is the geometric feature of the discrete points, and the output variable is the normal distance from the discrete points to the corresponding mold surfaces;
A glass profile discrete point data acquisition unit 12 further configured to extract discrete points requiring a glass profile and acquire geometric feature data of each discrete point;
a normal distance predicting unit 15 configured to predict a normal distance from each discrete point on the required glass profile to the required mold profile according to the geometric feature data of the discrete points of the required glass profile and the regression model;
the required mold profile generating unit 16 is configured to generate a profile of the required mold using geometric feature data of discrete points of the required glass profile and a normal distance from the discrete points on the required glass profile to the required mold profile.
In one embodiment of the disclosure, the regression model building unit 14 in the foregoing embodiment includes the following modules:
the preprocessing module is configured to preprocess the coordinates of all the discrete points in a MinMax normalization mode;
the preprocessing module is further configured to preprocess normal vectors, average curvatures, gaussian curvatures and camber of all discrete points in a Robust normalization mode;
the data set construction module is configured to construct a data set by utilizing the geometric characteristic data of the preprocessed discrete points and the normal distance from the discrete points to the corresponding mold surface, wherein the coordinates, the normal vector, the average curvature, the Gaussian curvature and the camber of the discrete points are used as input variables of the data set, and the normal distance from the discrete points to the corresponding mold surface is used as output variables of the data set;
The regression model construction module is configured to obtain a regression model between the input variable and the output variable by using a random forest algorithm by using all data in the data set.
In an embodiment of the disclosure, the regression model building module in the foregoing embodiment includes the following submodules:
a training data set sub-module configured to construct a plurality of training data sets using different combinations of training glass profiles;
the regression model building sub-module is configured to build a regression model for each training data set; one training data set corresponds to one regression model;
the regression model fusion submodule is configured to fuse different regression models into a new regression model;
an error testing sub-module configured to utilize the test dataset to perform error testing on each regression model and the fused regression model;
the regression model determination submodule is configured to take the regression model with the smallest error as a finally established regression model.
It is to be understood that the above embodiments are merely illustrative of the application of the principles of the present invention, but not in limitation thereof. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.

Claims (10)

1. The design method for the mold surface of the automobile windshield glass mold based on the regression model is applied to generating a required mold surface according to the required glass mold surface and is characterized by comprising the following steps of:
obtaining multiple groups of molded surfaces from historical design schemes, wherein each group of molded surfaces comprises a glass molded surface and a corresponding mold molded surface;
extracting discrete points of each glass profile, respectively, comprising:
for each glass profile, discrete points are extracted as follows:
determining a minimum bounding box of the glass profile;
establishing a three-dimensional coordinate system of the glass molded surface by taking the central point of the minimum bounding box as an origin, wherein the x-axis of the coordinate system is parallel to the longest side of the minimum bounding box, and the z-axis of the coordinate system is parallel to the shortest side of the minimum bounding box;
respectively taking the longest two boundary lines on the glass molded surface as an upper boundary line and a lower boundary line of the glass molded surface;
dividing the upper boundary line and the lower boundary line by p+1 equally, and connecting corresponding equally dividing points on the upper boundary line and the lower boundary line to obtain p equally dividing lines;
obtaining p planes which are perpendicular to the XOY plane and pass through any one of p bisectors, wherein the planes are in one-to-one correspondence with the bisectors;
Obtaining p intersecting lines intersecting p planes on the glass molded surface;
q+1 aliquoting is carried out on each intersecting line; sequentially connecting the equal division points of the corresponding sequences on the p intersecting lines to obtain q connecting lines; the intersection points of the p intersecting lines and the q connecting lines are discrete points of the glass profile;
obtaining geometric feature data of each discrete point, wherein the geometric feature data comprises coordinates, normal vectors, average curvature, gaussian curvature and camber;
obtaining the normal distance from each discrete point to the corresponding mold surface;
establishing a regression model according to geometric feature data of all discrete points and normal distances from the discrete points to corresponding mold surfaces, wherein the regression model comprises the following steps:
preprocessing the coordinates of all the discrete points by adopting a MinMax normalization mode;
preprocessing normal vectors, average curvatures, gaussian curvatures and arch heights of all discrete points by adopting a Robust normalization mode;
constructing a data set by utilizing the geometric feature data of the preprocessed discrete points and the normal distance from the discrete points to the corresponding mold surfaces, wherein the coordinates, normal vectors, average curvature, gaussian curvature and camber of the discrete points are used as input variables of the data set, and the normal distance from the discrete points to the corresponding mold surfaces is used as output variables of the data set;
Obtaining a regression model between an input variable and an output variable by using all data in the data set and adopting a random forest algorithm, wherein the input variable of the regression model is the geometric characteristic of a discrete point, and the output variable is the normal distance from the discrete point to a corresponding mold surface;
discrete points of the required glass molded surface are extracted, and geometric feature data of each discrete point are obtained;
according to the geometric feature data of discrete points of the required glass molded surface and the regression model, predicting the normal distance from each discrete point on the required glass molded surface to the required mold molded surface;
generating a profile of the demand mold using geometric feature data of discrete points of the profile of the demand glass and a normal distance from the discrete points of the profile of the demand glass to the profile of the demand mold, comprising:
calculating coordinates of each discrete point of the required mold surface by utilizing geometric feature data of the discrete points on the required glass surface and normal distance from the discrete point of the required glass surface to the required mold surface, wherein the discrete points of the required mold surface correspond to the discrete points on the required glass surface one by one and are assumed discrete points on the required mold surface;
the coordinates of each discrete point of the desired mold profile are calculated as follows:
Wherein: (x' i ,y' i ,z' i ) Coordinates of discrete points of the required mold surface; (x) i ,y i ,z i ) Coordinates of corresponding discrete points on the required glass profile; (n) xi ,n yi ,n zi ) Normal vector of corresponding discrete point on the required glass profile; d, d i The normal distance from the corresponding discrete point on the predicted required glass molded surface to the required mold molded surface is set;
constructing a spline curve network wire frame connected with discrete points according to the coordinates of the discrete points of the required mold surface;
and (3) carrying out curved surface reconstruction on the spline curve network wire frame by using a geometric modeling engine to generate the molded surface of the required mold.
2. The method of claim 1, wherein the separately extracting discrete points of each glass profile and obtaining geometric feature data for each of the discrete points further comprises:
(1) Acquiring the coordinates of each discrete point under the coordinate system of the glass molded surface;
(2) For each discrete point, a normal vector N is obtained as follows:
obtaining a tangent vector S with discrete points parallel to the x-axis x And a tangent vector S of the discrete point parallel to the y-axis y ；
The normal vector N is obtained by tangential vector cross multiplication: n=s x ×S y ；
(3) For each discrete point, the average curvature H is obtained as follows:
wherein k is 1 Is the maximum radius of curvature through the discrete points; k (k) 2 Is the minimum radius of curvature through the discrete points;
(4) For each discrete point, a gaussian curvature K is obtained as follows:
K＝k 1 *k 2
wherein k is 1 Is the maximum radius of curvature through the discrete points; k (k) 2 Is the minimum radius of curvature through the discrete points;
(5) For each discrete point, arch height AH is obtained according to the following method;
the camber is the distance from the discrete point to the corresponding bisector:
AH＝Dist(P,l i ),P∈l' i ,i＝1,2,…p
wherein the discrete point P is at the intersection line l' i Upper, l i Is equal to l' i Corresponding bisectors.
3. The method of claim 1, wherein the obtaining a normal distance of each discrete point to a corresponding mold profile comprises:
for each discrete point, the normal distance of the discrete point is obtained in the following way:
acquiring an intersection point of a straight line passing through the discrete point and parallel to the normal vector of the discrete point and a corresponding mold surface;
and taking the distance between the discrete point and the intersection point as the normal distance from the discrete point to the corresponding mold surface.
4. The method according to claim 1, wherein the preprocessing of the coordinates of all the discrete points by the MinMax normalization method includes:
the coordinates of each discrete point are preprocessed according to the following method:
Wherein f_max is the maximum value of all the discrete point coordinates; f_min is the minimum value in all the discrete point coordinates, and f is the original discrete point coordinate; f' is the preprocessed discrete point coordinates.
5. The method of claim 1, wherein the preprocessing of the normal vector, the average curvature, the gaussian curvature and the camber of all the discrete points by the Robust normalization method comprises:
the normal vector, average curvature, gaussian curvature and camber of each discrete point are all pre-processed as follows:
wherein f is an original geometric feature data value, and the geometric feature data value is a data value of one geometric feature of normal vector, average curvature, gaussian curvature and camber of the discrete points; f' is the preprocessed geometrical characteristic data value corresponding to f; f_mean is the median of the geometric feature data values corresponding to f of all the discrete points on the glass molded surface to which the discrete points belong; IQR is the interval length between the 1 st quartile and the 3 rd quartile in the geometric feature data values corresponding to f and all discrete points on the glass profile to which the discrete points belong.
6. The method of claim 1, wherein using a random forest algorithm to obtain a regression model between the input variable and the output variable using all data in the dataset comprises:
Any one of all the glass molded surfaces is selected as a test glass molded surface, and the rest glass molded surfaces are all used as training glass molded surfaces;
constructing a test data set by utilizing the geometric characteristic data of the discrete points after the pretreatment of the test glass molded surface and the normal distance from the discrete points on the test glass molded surface to the corresponding mold molded surface, wherein the coordinates, the normal vector, the average curvature, the Gaussian curvature and the camber of the discrete points are used as input variables, and the normal distance from the discrete points to the corresponding mold molded surface is used as output variables;
constructing a training data set by utilizing the geometric feature data of the discrete points after the pretreatment of the training glass molded surface and the normal distance from the discrete points on the training glass molded surface to the corresponding mold molded surface, wherein the coordinates, the normal vector, the average curvature, the Gaussian curvature and the camber of the discrete points are used as input variables, and the normal distance from the discrete points to the corresponding mold molded surface is used as output variables;
and establishing a regression model by adopting a random forest algorithm according to the training data set and the test data set.
7. The method of claim 6, wherein said establishing a regression model using a random forest algorithm based on said training data set and test data set comprises:
Constructing a plurality of training data sets by utilizing different combination modes of training glass molded surfaces;
establishing a regression model for each training data set; one training data set corresponds to one regression model;
adopting different regression models to fuse into a new regression model;
performing error testing on each regression model and the fused regression model by using a testing data set;
and taking the regression model with the minimum error as the finally established regression model.
8. The method of claim 7, wherein predicting the normal distance of each discrete point on the desired glass profile to the desired mold profile based on the geometric feature data of the discrete points of the desired glass profile and the regression model comprises:
preprocessing the coordinates of all discrete points on the required glass molded surface in a MinMax normalization mode;
preprocessing normal vectors, average curvatures, gaussian curvatures and camber of all discrete points on a required glass molded surface in a Robust normalization mode;
substituting the geometric feature data of all the discrete points on the required glass molded surface after pretreatment into a regression model, and calculating to obtain the normal distance from each discrete point on the required glass molded surface to the required mold molded surface.
9. An automobile windshield mold profile design device based on a regression model, which is applied to generating a required mold profile according to a required glass profile, and is characterized by comprising:
the glass profile and mould profile acquisition unit is used for acquiring a plurality of groups of profiles from the historical design scheme, wherein each group of profiles comprises a glass profile and a corresponding mould profile;
a glass profile discrete point data acquisition unit for respectively extracting discrete points of each glass profile, comprising:
for each glass profile, discrete points are extracted as follows:
determining a minimum bounding box of the glass profile;
establishing a three-dimensional coordinate system of the glass molded surface by taking the central point of the minimum bounding box as an origin, wherein the x-axis of the coordinate system is parallel to the longest side of the minimum bounding box, and the z-axis of the coordinate system is parallel to the shortest side of the minimum bounding box;
respectively taking the longest two boundary lines on the glass molded surface as an upper boundary line and a lower boundary line of the glass molded surface;
dividing the upper boundary line and the lower boundary line by p+1 equally, and connecting corresponding equally dividing points on the upper boundary line and the lower boundary line to obtain p equally dividing lines;
obtaining p planes which are perpendicular to the XOY plane and pass through any one of p bisectors, wherein the planes are in one-to-one correspondence with the bisectors;
Obtaining p intersecting lines intersecting p planes on the glass molded surface;
q+1 aliquoting is carried out on each intersecting line; sequentially connecting the equal division points of the corresponding sequences on the p intersecting lines to obtain q connecting lines; the intersection points of the p intersecting lines and the q connecting lines are discrete points of the glass profile;
obtaining geometric feature data of each discrete point, wherein the geometric feature data comprises coordinates, normal vectors, average curvature, gaussian curvature and camber;
the normal distance acquisition unit is used for acquiring the normal distance from each discrete point to the corresponding mold surface;
the regression model building unit is used for building a regression model according to the geometric feature data of all the discrete points and the normal distance between the discrete points and the corresponding mold surfaces, wherein the input variable of the regression model is the geometric feature of the discrete points, and the output variable is the normal distance between the discrete points and the corresponding mold surfaces;
the regression model building unit includes:
the preprocessing module is used for preprocessing the coordinates of all the discrete points in a MinMax normalization mode;
the preprocessing module is also used for preprocessing normal vectors, average curvatures, gaussian curvatures and camber of all discrete points in a Robust normalization mode;
The data set construction module is used for constructing a data set by utilizing the geometric feature data of the preprocessed discrete points and the normal distance from the discrete points to the corresponding mold surface, wherein the coordinates, normal vector, average curvature, gaussian curvature and camber of the discrete points are used as input variables of the data set, and the normal distance from the discrete points to the corresponding mold surface is used as output variables of the data set;
the regression model construction module is used for obtaining a regression model between an input variable and an output variable by using all data in the data set and adopting a random forest algorithm;
the glass profile discrete point data acquisition unit is also used for extracting discrete points requiring the glass profile and acquiring geometric characteristic data of each discrete point;
the normal distance prediction unit is used for predicting the normal distance from each discrete point on the required glass profile to the required mold profile according to the geometric feature data of the discrete points of the required glass profile and the regression model;
the demand mould profile generation unit is used for generating the profile of the demand mould by utilizing the geometric characteristic data of the discrete points of the demand glass profile and the normal distance from the discrete points on the demand glass profile to the demand mould profile, and comprises:
Calculating coordinates of each discrete point of the required mold surface by utilizing geometric feature data of the discrete points on the required glass surface and normal distance from the discrete point of the required glass surface to the required mold surface, wherein the discrete points of the required mold surface correspond to the discrete points on the required glass surface one by one and are assumed discrete points on the required mold surface;
the coordinates of each discrete point of the desired mold profile are calculated as follows:
wherein: (x' i ,y' i ,z' i ) Coordinates of discrete points of the required mold surface; (x) i ,y i ,z i ) Coordinates of corresponding discrete points on the required glass profile; (n) xi ,n yi ,n zi ) Normal vector of corresponding discrete point on the required glass profile; d, d i The normal distance from the corresponding discrete point on the predicted required glass molded surface to the required mold molded surface is set;
constructing a spline curve network wire frame connected with discrete points according to the coordinates of the discrete points of the required mold surface;
and (3) carrying out curved surface reconstruction on the spline curve network wire frame by using a geometric modeling engine to generate the molded surface of the required mold.
10. The apparatus of claim 9, wherein the regression model building module comprises:
the training data set submodule is used for constructing a plurality of training data sets by utilizing different combination modes of the training glass molded surface;
The regression model building sub-module is used for building a regression model for each training data set; one training data set corresponds to one regression model;
the regression model fusion submodule is used for fusing different regression models into a new regression model;
the error testing sub-module is used for carrying out error testing on each regression model and the fused regression model by utilizing the testing data set;
and the regression model determination submodule is used for taking the regression model with the minimum error as the finally established regression model.
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李贵 ; 柳玉起 ; 孙世岩 ; 文伏灵 ; .基于UV线的几何旋转变形回弹补偿算法.华中科技大学学报(自然科学版).2012,(09),全文. *

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