CN114488943B - Random multi-area efficient polishing path planning method oriented to matched working conditions - Google Patents
Random multi-area efficient polishing path planning method oriented to matched working conditions Download PDFInfo
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Abstract
The invention discloses a random multi-area efficient polishing path planning method facing to a matching working condition, which comprises the following steps of S100, measuring and obtaining point cloud data of a soft mold matching surface and a hard mold matching surface, carrying out principal component analysis on the point cloud data to correct, fitting the point cloud data of a reinforced wallboard to the surface and taking a complement, matching the complement with the point cloud data of a sacrificial layer, and taking a difference value in the height direction to obtain a height diagram to be processed of the surface of the sacrificial layer; s200, extracting the maximum height and the minimum height of a height map to be processed, and planning a processing sequence and a single processing depth according to the maximum height difference, a polishing head removal depth model and workpiece surface matching precision requirements; s300, optimizing the processing sequence and the polishing path of each region simultaneously by an immune genetic algorithm; s400, dispersing the polishing path and the transition path according to the maximum step length requirement to obtain a series of cutter contacts, and calculating cutter shaft vector and cutter position point data according to the feeding direction and the surface normal vector to obtain a cutter position track planning path.
Description
Technical Field
The invention belongs to the technical field of advanced manufacturing, and particularly relates to a random multi-area efficient polishing path planning method oriented to a matched working condition.
Background
In the process of integrally forming and manufacturing ribs/skins of a traditional large-sized composite material reinforced wallboard, in order to meet the precision of profile matching, a technology of matching hard film and soft film (most rubber materials) molds is often adopted, wherein the precise processing of the soft film is a great difficulty. The method commonly adopted at present is manual measurement and polishing operation. Firstly, red lead powder is smeared on the surface of a hard die, the soft die is used for bonding, a region to be processed is marked, grinding and polishing processing is carried out, and the detection and processing process is repeated until the accurate bonding of the two surfaces is achieved. Because of the need of multiple grinding and polishing and repeated measurement, the quality and efficiency of the manual operation are difficult to ensure, and the manual operation belongs to a low-efficiency manufacturing process, and the machining precision and efficiency are seriously affected. In order to solve the problems, the high-precision non-contact scanning is adopted to replace red lead powder to detect the area to be processed, and the robot grinding and polishing technology is adopted to replace manual grinding and polishing, so that the automation of detection and processing is realized. The region to be processed of the soft mold is obtained by matching with the hard film, and has the characteristics of random quantity, random distribution, random shape and the like. In order to realize the robot rapid measurement-polishing integrated automatic operation on the basis of accurately measuring the area to be processed, an efficient polishing path planning method is required to be provided for the soft mold random multi-piece area to be processed under the matched working condition.
For random shape region polishing path planning, patent document CN112947309a discloses a robot polishing path planning method based on end faces with equal residual heights, constructing bounding boxes of regions to be processed, acquiring initial polishing paths of the bounding boxes and performing dispersion according to given step sizes. Determining a machining line distance according to the radius of the grinding head, the curvature of the workpiece surface and the limit of the residual height, sequentially extrapolating discrete cutter contacts to obtain interpolation points of adjacent grinding paths, and deleting the interpolation points outside the boundary of the bounding box to obtain interpolation point coordinates of the planned grinding paths. In addition, for polishing path planning of a random distribution area, patent document CN107932505a discloses an optimal polishing task path planning method based on an articulated arm robot, and based on a simulated annealing algorithm, an optimal polishing task path is obtained through steps of data input, path generation, path point calculation, path update, iteration control, temperature control processing and the like in sequence, so that compared with an enumeration method, the calculation amount is greatly reduced, the calculation complexity is reduced, the processing speed is accelerated, and a method for meeting the requirements on real-time processing and reducing the requirements on software and hardware performance by means of parallel calculation of multiple CPUs is provided. Patent document CN111203788A discloses a wall surface polishing path planning method, which scans a wall surface, obtains protruding points of the wall surface to be polished through obtained point cloud data, and calculates the processing order of the protruding points through a variation of a greedy algorithm.
However, the method of patent document CN112947309a is only directed to a single region to be processed, and is not applicable to a case of multiple regions, in which the problems of the processing sequence and connection transition of multiple regions are not considered when multiple regions exist. Both the task path planning methods disclosed in patent document CN107932505A and patent document CN111203788A have the problem of abstracting the area to be processed into a point instead of a real area, and when the area to be processed is large, the planning of the processing path in the area has a large influence on the idle stroke length, and the method proposed by the document is difficult to be applied.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a random multi-area efficient polishing path planning method facing to a matched working condition, which is characterized in that a to-be-processed height chart is quickly converted into a to-be-processed area chart corresponding to each processing depth through simple image processing, fine holes and islands of the to-be-processed area caused by fluctuation of measured data are removed, a processing sequence and a polishing path of each area are simultaneously optimized through an immune genetic algorithm, the polishing path and a transition path are discretized according to the maximum step requirement, a series of cutter contacts are obtained, and cutter axis vectors and cutter position data are calculated according to a feeding direction and a surface normal vector, so that a cutter position track planning path is obtained.
In order to achieve the above purpose, the invention provides a random multi-area efficient polishing path planning method oriented to a matched working condition, which comprises the following steps:
s100, measuring and obtaining point cloud data of the matching surfaces of the soft mold and the hard mold, carrying out principal component analysis on the point cloud data to be placed correctly, fitting the point cloud data of the reinforced wallboard to the surface, taking a complement set, matching the complement set with the point cloud data of the sacrificial layer, and taking a difference value in the height direction to obtain a height map to be processed of the surface of the sacrificial layer;
s200, extracting the maximum height and the minimum height of a height map to be processed, and planning a processing sequence and a single processing depth according to the maximum height difference, a polishing head removal depth model and workpiece surface matching precision requirements;
s300, planning a polishing path of each region to be processed, and simultaneously optimizing a processing sequence and a polishing path of each region through an immune genetic algorithm;
s400, dispersing the polishing path and the transition path according to the maximum step length requirement to obtain a series of cutter contacts, and calculating cutter shaft vector and cutter position point data according to the feeding direction and the surface normal vector to obtain a cutter position track planning path.
Further, in step S100, for an n-dimensional random variable
X＝(X 1 ,X 2 ,…,X N ) T The covariance matrix is:
wherein c ij ＝COV(X i ,X j ) I, j, =1, 2, …, n represents a component X of X i And X j Is a covariance of (c).
Further, in step S100, the rotation matrix R of the point cloud normal vector of the mating surfaces of the soft mold and the hard mold to be perpendicular to the yoz plane is:
wherein n= (n x ,n y ,n z ) The normal vector of the plane where the point cloud is located is equal to the unit vector of the eigenvector corresponding to the minimum singular value of the covariance matrix.
Further, step S200 further includes:
s201: and carrying out gray level binarization processing on the height map to be processed by taking the target height during each processing as a threshold value, wherein the gray level binarization processing process comprises the following steps:
wherein Binary (i, j) represents the Gray level of the image corresponding to the Gray level binarized, gray (i, j) represents the Gray level of the original Gray level image corresponding to the position, and Threshold represents the Threshold value used in the Gray level binarization process.
Further, step S200 further includes:
s202: merging islands close to the edge of the area to be processed through expansion corrosion, eliminating fine jitter of the boundary, and then performing corrosion expansion to eliminate independent fine noise points;
the expansion corrosion treatment process comprises the following steps:
where a represents a non-zero set of pixels in the binary image and B represents a structural element, i.e. a structural element.
Further, step S200 further includes:
s203: and carrying out boundary extraction on all gray level binary images to obtain boundary curves of all areas to be processed, placing the boundary curves in the same coordinate system, and judging the inclusion relation of each curve.
Further, the determining the inclusion relation of each curve includes:
s204: let the point taken be P c For the boundary point set { P e Point P in } i 、P i+1 Calculate the included angle and record P c P i And P c P i+1 The included angle between them is theta i Counterclockwise positive and clockwise negative, the cumulative angular increment α is:
s205: when alpha approaches + -360 DEG, then the point is within the curve, and when it approaches 0 DEG, then the point is outside the curve.
Further, step S300 includes:
s301: calculating all characteristic values and characteristic vectors of the covariance matrix of the point set of each region to be processed, and obtaining a direction with the maximum point set scattering by taking the characteristic vector corresponding to the maximum characteristic value, wherein the direction approximates to the processing direction with the minimum number of line cutting paths;
s302: calculating the maximum distance of the area to be processed perpendicular to the processing direction, determining the cutting speed and the applied pressure adopted by the grinding and polishing operation according to the single processing depth, thereby obtaining a polished removal profile, and determining the row spacing by combining the distance of the area to be processed perpendicular to the processing direction;
s303: and generating a series of parallel lines according to the row spacing, and solving intersection points with the boundary curve to serve as path points of the polishing path.
Further, step S300 includes:
s304: through a nested genetic algorithm, the outer layer optimizes the processing sequence of the to-be-processed areas, and the inner layer optimizes the path trend of each area, so that the optimization target of enabling the total length of the to-be-processed path to be the shortest can be achieved.
Further, step S400 includes:
s401: let point P be the knife contact point and the coordinates P (P x ,P y ,P z ) The path O point is the tool center point, and the coordinates are O (O x ,O y ,O z ) Let the motion direction unit vector of the processing track where the processing point is located be r= (r) i ,r j ,r k ) The unit normal vector of the surface is n= (n) i ,n j ,n k ) The diameter of the polishing disc is R, the inclination angle of the axis of the polishing disc is theta, the contact area width is L through simulation after the polishing pressure is set, and therefore the calculation formula of the center point of the polishing disc is obtained:
O＝P+(R-L)(r cosθ+n sinθ) (7)
s402: the corresponding arbor vectors are:
c＝n cosθ-r sinθ (8)
s403: and (3) after the steps are completed, a planned tool position file can be obtained, a rapid program for polishing processing is obtained through post-processing, and random multi-area efficient polishing path planning is completed.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained:
1. according to the method, a to-be-machined height map is quickly converted into a to-be-machined area map corresponding to each machining depth through simple image processing, fine holes and islands of the to-be-machined area caused by fluctuation of measured data are removed, machining sequences and grinding paths of each area are simultaneously optimized through an immune genetic algorithm, the grinding paths and the transition paths are discretized according to the maximum step length requirement, a series of cutter contacts are obtained, cutter shaft vectors and cutter position data are calculated according to the feeding direction and the surface normal vector, and a cutter position track planning path is obtained.
2. According to the method, the direction of the minimum number of the line cutting paths of each area is rapidly calculated through a principal component analysis method, and the parallel lines are intersected with the area to be processed to achieve rapid line cutting path planning of the random area.
3. According to the method, the inclusion relation of the adjacent height layer curves projected on the same plane is judged through the accumulated angle increment, a relation tree among all areas to be processed is established and added to the path planning as constraint, and the layering processing sequence is ensured in the path planning of random multiple areas.
4. According to the method, the path trend in the region is optimized while the random multi-region processing sequence is optimized through the nested immune genetic algorithm, and compared with other methods for abstracting the region to be processed into particles, the method is more suitable for the situation that the area of the random region to be processed is not negligible relative to the total area of a workpiece to be processed.
Drawings
FIG. 1 is a workflow of a random multi-area grinding path planning method according to an embodiment of the invention;
FIG. 2 is a view showing the height to be processed of the hard film surface of the sacrificial layer according to the embodiment of the invention;
FIG. 3 is a height view of the surface of a sacrificial layer rubber pad according to an embodiment of the present invention;
FIG. 4 is a height to be processed view of the surface of a sacrificial layer according to an embodiment of the present invention;
FIG. 5 is a view of removing a depth map according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of marking a region to be processed with a threshold of 1/3 of the maximum removal depth in an embodiment of the present invention;
FIG. 7 is a schematic view of a region to be processed according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of an expansion operation performed in an embodiment of the present invention;
FIG. 9 is a schematic diagram of an etching operation performed in an embodiment of the present invention;
FIG. 10 is a schematic diagram illustrating selecting different starting points for a slicing path according to an embodiment of the present invention;
FIG. 11 is a flow chart of an embodiment of the nested immune genetic algorithm of the present invention;
FIG. 12 is a schematic diagram of a random multi-area cut path plan in accordance with an embodiment of the present invention;
fig. 13 is a schematic view of face grinding with inclination angle according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
As shown in fig. 1, the embodiment of the invention provides a random multi-area efficient polishing path planning method oriented to a matching working condition, taking an IRB6700 type robot as an example, the main steps are as follows:
(1) Before planning a path, measuring is needed, point cloud data of the matching surfaces of the soft mold and the hard mold are obtained through high-precision non-contact scanning, and covariance matrix is obtained through calculation of the point cloud data:
for an n-dimensional random variable x= (X 1 ,X 2 ,…,X N ) T The covariance matrix is:
wherein c ij ＝COV(X i ,X j ) I, j, =1, 2, …, n represents a component X of X i And X j Is a covariance of (c).
And (3) carrying out SVD (singular value decomposition) on the covariance matrix to obtain a minimum singular value and a corresponding eigenvector, wherein the eigenvector is the normal vector of the plane where the point cloud is located. Let the normal vector be n= (n) x ,n y ,n z ) Then the normal vector of the surface point cloud is rotated to be vertical to yoz levelThe rotation matrix R of the facets is:
fitting the point cloud data of the reinforced wallboard to the surface, taking a complement set, matching the point cloud data of the reinforced wallboard with the point cloud data of the sacrificial layer, and taking a difference value in the height direction to obtain a height diagram to be processed of the surface of the sacrificial layer, wherein the height diagram is shown in figures 2-4.
(2) And extracting the maximum height and the minimum height from the height map, and planning the machining times and the single machining depth according to the maximum height difference, the polishing head removal depth model and the workpiece surface matching precision requirement. And respectively carrying out gray level binarization processing on the height map by taking the target height in each processing as a threshold value, setting the point with the height higher than the threshold value as black, obtaining a group of black-white binary images corresponding to different processing heights, and identifying the region to be processed by using black. The gray level binarization processing process comprises the following steps:
where bin (i, j) represents the Gray level of the image corresponding to the Gray level binarized, gray (i, j) represents the Gray level of the original Gray level image corresponding to the position, and Threshold represents the Threshold used in the Gray level binarization process, as shown in fig. 5 and 6.
(3) Because of fluctuation of data obtained by non-contact measurement, a plurality of fine islands or holes exist after the data are converted into gray level binary images, the islands near the edge of the area to be processed can be combined through expansion corrosion, fine shaking of the boundary is eliminated, and then corrosion expansion is carried out to eliminate independent fine noise points. The structural elements of the expansion and corrosion operations adopt the shape and size of the contact area of the polishing head. The calculation processes of expansion and corrosion are respectively as follows:
where a represents a non-zero set of pixels in the binary image and B represents a structural element, i.e. a structural element. Expanding a with B is a set of origin positions of B that all make B intersect a after translating B, and corroding a with B is a set of origin positions of B when B is completely included in a after translating B. Taking the operation process of fig. 6 in step (2) as an example, the original fine holes are removed after the expansion and corrosion operations, and the boundary is slightly smooth, as shown in fig. 7-9.
And carrying out boundary extraction on all gray level binary images to obtain boundary curves of all areas to be processed. These boundary curves are placed in the same coordinate system, and the inclusion relationship of each curve is calculated. In the process of planning a subsequent layered processing path, if two boundary curves have an inclusion relationship, the processing sequence relationship of the two boundary curves corresponding to the region to be processed needs to be determined, and the region with higher height needs to be processed before the region with lower height. When judging the inclusion relation, one point in the curve can be selected, and the cumulative angle increment is calculated with the boundary curve of the upper layer area. Let the point taken be P c For the boundary point set { P e Point P in } i 、P i+1 Calculate the included angle and record P c P i And P c P i+1 The included angle between them is theta i Counterclockwise positive and clockwise negative, the cumulative angular increment α is:
the point is indicated to be within the curve when α is close to ±360°, and the point is indicated to be outside the curve when it is close to 0 °. The distance between the selected point and the curve is determined while the cumulative angle increment is calculated, and the two curves can be determined to have an inclusion relationship when the distance is smaller than the radius of the expansion structural element.
(4) And performing line cutting path planning on each region to be processed, calculating all eigenvalues and eigenvectors of the covariance matrix, and obtaining the direction of maximum point set scattering by taking the eigenvector corresponding to the maximum eigenvalue. The direction in which the point set spreads most can be regarded as the machine direction in which the number of line cutting paths is minimized.
And calculating the maximum distance of the area to be processed perpendicular to the processing direction. And determining the cutting speed and the applied pressure adopted by the grinding and polishing operation according to the single processing depth, so as to obtain a polished removal profile, and determining the row spacing by combining the distance of the area to be processed perpendicular to the processing direction. And generating a series of parallel lines according to the line spacing, and solving intersection points with the boundary curve to serve as path points of the polishing path. Recording two end points of two outermost paths to obtain four selectable processing path starting points in the region. When the area of the area to be machined is not negligible relative to the total surface area, the start and end points of the machining path in the area to be machined have a large effect on the total path length. The selection of the start and end points of the processing path for each region to be processed needs to be taken into consideration in the subsequent path optimization process, as shown in fig. 10.
(5) The processing order and path trend of each region are simultaneously optimized by nested immune genetic algorithm, and the optimization process can be analogous to the Traveler (TSP) problem, the flow of which is shown in fig. 11.
The cost function between each node is a certain value when the original genetic algorithm solves the TSP problem, and for path planning on random multiple areas, the selection of the starting point of the processing path in each area has a larger influence on the idle stroke length between the areas. When the number of areas to be processed is n, the idle stroke between the areas is listed as a consumption matrix, which will have 4 n In the state, when the number of the areas is large, it is difficult to determine the optimal starting point of the processing path of each area through traversal. Through a nested genetic algorithm, the outer layer optimizes the processing sequence of the to-be-processed areas, and the inner layer optimizes the path trend of each area, so that the optimization target of enabling the total length of the to-be-processed path to be the shortest can be achieved. To test the optimization effect of the selected algorithm, a plurality of regions of random shape were randomly generated as shown in the left graph of fig. 12 and optimized by the selected algorithm, and the resulting paths are shown in the right graph of fig. 12.
(6) And dispersing the polishing path and the transition path according to the maximum step length requirement to obtain a series of cutter contacts, and calculating cutter shaft vector and cutter position data according to the feeding direction and the surface normal vector to obtain a cutter position track. Fig. 13 is a schematic view of face grinding with inclination, and the midpoint P defining the boundary line between the grinding disc and the workpiece surface is the tool contact point.
The method for calculating the cutter position data according to the cutter contact points is as follows:
let point P be the knife contact point and the coordinates P (P x ,P y ,P z ) The path O point is the tool center point, and the coordinates are O (O x ,O y ,O z ) Let the motion direction unit vector of the processing track where the processing point is located be r= (r) i ,r j ,r k ) The unit normal vector of the surface is n= (n) i ,n j ,n k ) The diameter of the polishing disc is R, the inclination angle of the axis of the polishing disc is theta, the contact area width is L through simulation after the polishing pressure is set, and therefore the calculation formula of the center point of the polishing disc is obtained:
O＝P+(R-L)(r cosθ+n sinθ) (7)
the corresponding arbor vectors are:
c＝n cosθ-r sinθ (8)
and (3) after the steps are completed, a planned tool position file can be obtained, a rapid program which can be used for actual processing is obtained through post-processing, and the random multi-area efficient polishing path planning is completed.
According to the method, a to-be-machined height map is quickly converted into a to-be-machined area map corresponding to each machining depth through simple image processing, fine holes and islands of the to-be-machined area caused by fluctuation of measured data are removed, machining sequences and grinding paths of each area are simultaneously optimized through an immune genetic algorithm, the grinding paths and the transition paths are discretized according to the maximum step length requirement, a series of cutter contacts are obtained, cutter shaft vectors and cutter position data are calculated according to the feeding direction and the surface normal vector, and a cutter position track planning path is obtained. And judging the inclusion relation of the adjacent height layer curves projected on the same plane through the accumulated angle increment, establishing a relation tree among all areas to be processed, adding the relation tree as constraint to path planning, and ensuring the layering processing sequence in the path planning of random multiple areas. By means of the nested immune genetic algorithm, path trend in the area is optimized while the random multi-area processing sequence is optimized, and compared with other methods for abstracting the area to be processed into particles, the method is more suitable for the situation that the area of the random area to be processed is not negligible relative to the total area of a workpiece to be processed.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A random multi-area efficient polishing path planning method oriented to a matched working condition is characterized by comprising the following steps:
s100, measuring and obtaining point cloud data of the matching surfaces of the soft mold and the hard mold, carrying out principal component analysis on the point cloud data to be straightened, fitting the point cloud data of the reinforced wallboard to the surface, taking a complement set, matching the complement set with the point cloud data of the sacrificial layer, and taking a difference value in the height direction to obtain a height map to be processed of the surface of the sacrificial layer;
s200, extracting the maximum height and the minimum height of a height map to be processed, and planning a processing sequence and a single processing depth according to the maximum height difference, a polishing head removal depth model and workpiece surface matching precision requirements;
s300, planning a polishing path of each region to be processed, and simultaneously optimizing a processing sequence and a polishing path of each region through an immune genetic algorithm;
s400, dispersing the polishing path and the transition path according to the maximum step length requirement to obtain a series of cutter contacts, and calculating cutter shaft vector and cutter position point data according to the feeding direction and the surface normal vector to obtain a cutter position track planning path.
2. The method for efficient random multi-region grinding path planning under the matched condition according to claim 1, wherein in step S100, for n-dimensional random variable x= (X) 1 ,X 2 ,…,X N ) T The covariance matrix is:
wherein c ij ＝COV(X i ,X j ) I, j, =1, 2, …, n represents a component X of X i And X j Is a covariance of (c).
3. The method for planning a random multi-area efficient polishing path under a matching condition according to claim 2, wherein in step S100, a rotation matrix R for rotating a normal vector of a point cloud of a matching surface of a soft mold and a hard mold to be perpendicular to a yoz plane is:
wherein n= (n x ,n y ,n z ) The normal vector of the plane where the point cloud is located is equal to the unit vector of the eigenvector corresponding to the minimum singular value of the covariance matrix.
4. A random multi-area efficient grinding path planning method for a mating condition according to any one of claims 1-3, wherein step S200 further comprises:
s201: and carrying out gray level binarization processing on the height map to be processed by taking the target height during each processing as a threshold value, wherein the gray level binarization processing process comprises the following steps:
wherein Binary (i, j) represents the Gray level of the image corresponding to the Gray level binarized, gray (i, j) represents the Gray level of the original Gray level image corresponding to the position, and Threshold represents the Threshold value used in the Gray level binarization process.
5. The method for efficient random multi-area grinding path planning under matched conditions of claim 4, wherein step S200 further comprises:
s202: merging islands close to the edge of the area to be processed through expansion corrosion, eliminating fine jitter of the boundary, and then performing corrosion expansion to eliminate independent fine noise points;
the expansion corrosion treatment process comprises the following steps:
where a represents a non-zero set of pixels in the binary image and B represents a structural element, i.e. a structural element.
6. The method for efficient random multi-area grinding path planning under matched conditions of claim 5, wherein step S200 further comprises:
s203: and carrying out boundary extraction on all gray level binary images to obtain boundary curves of all areas to be processed, placing the boundary curves in the same coordinate system, and judging the inclusion relation of each curve.
7. The method for efficiently polishing a plurality of random areas under a matched condition according to claim 6, wherein the determining the inclusion relationship of each curve comprises:
s204: let the point taken be P c For the boundary point set { P e Point P in } i 、P i+1 Calculate the included angle and record P c P i And P c P i+1 The included angle between them is theta i Counterclockwise positive and clockwise negative, the cumulative angular increment α is:
s205: when alpha approaches + -360 DEG, then the point is within the curve, and when it approaches 0 DEG, then the point is outside the curve.
8. The method for efficient random multi-area grinding path planning under matched conditions of claim 6, wherein step S300 includes:
s301: for the point set of each area to be processed, taking a covariance matrix to calculate all characteristic values and characteristic vectors of the area to be processed, and taking the characteristic vector corresponding to the maximum characteristic value to obtain the direction of the maximum point set scattering, wherein the direction approximates to the processing direction with the minimum number of line cutting paths;
s302: calculating the maximum distance of the to-be-processed area perpendicular to the feeding direction, determining the cutting speed and the applied pressure adopted by the grinding and polishing operation according to the single processing depth, thereby obtaining a polished removal profile, and determining the processing row spacing by combining the distance of the to-be-processed area perpendicular to the processing direction;
s303: and generating a series of parallel lines according to the row spacing, and solving intersection points with the boundary curve to serve as path points of the polishing path.
9. The method for efficient random multi-area grinding path planning under matched conditions of claim 8, wherein step S300 includes:
s304: through a nested genetic algorithm, the outer layer optimizes the processing sequence of the to-be-processed areas, and the inner layer optimizes the path trend of each area, so that the optimization target of enabling the total length of the to-be-processed path to be the shortest can be achieved.
10. The method for random multi-area efficient grinding path planning under matched conditions according to any one of claims 1-4, wherein step S400 includes:
s401: let point P be the knife contact point and the coordinates P (P x ,P y ,P z ) The path O point is the tool center point, and the coordinates are O (O x ,O y ,O z ) Let the motion direction unit vector of the processing track where the processing point is located be r= (r) i ,r j ,r k ) The unit normal vector of the surface is n= (n) i ,n j ,n k ) The diameter of the polishing disc is R, the inclination angle of the axis of the polishing disc is theta, the contact area width is L through simulation after the polishing pressure is set, and therefore the calculation formula of the center point of the polishing disc is obtained:
O＝P+(R-L)(rcosθ+nsinθ) (7)
s402: the corresponding arbor vectors are:
c＝ncosθ-rsinθ (8)
s403: and (3) after the steps are completed, a planned tool position file can be obtained, a rapid program for polishing processing is obtained through post-processing, and random multi-area efficient polishing path planning is completed.
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