CN115082642A - Method, system and device for rapidly predicting surface topography and storage medium - Google Patents
Method, system and device for rapidly predicting surface topography and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a system, a device and a storage medium for rapidly predicting surface topography, wherein the method comprises the following steps: s1, obtaining three-dimensional information of the free-form surface, a polishing track on the free-form surface and polishing processing parameters; s2, dispersing the polishing track into a plurality of knife contacts, and dispersing the free-form surface into a plurality of uniform sampling points; s3, calculating a polishing influence area of the cutter contact according to the surface curvature of the three-dimensional information and the polishing processing parameters, and acquiring sampling points in the polishing influence area; s4, calculating and updating the material removal amount of the sampling points in the polishing influence area by adopting a preset neural network prediction model; and S5, traversing the cutter contact, and repeating the steps S3-S4 to obtain the global material removal amount so as to obtain the polished surface appearance. The method utilizes the neural network prediction model to calculate the removal amount of the polishing material, avoids complex time-consuming integral operation, and can be widely applied to the technical field of machining.
Description
Technical Field
The invention relates to the technical field of machining, in particular to a method, a system and a device for quickly predicting surface topography and a storage medium.
Background
With the development of modern science and technology, high-precision free-form surface parts are widely applied to the field of high-end manufacturing equipment such as aerospace, automobiles, ships, optical precision instruments and the like. These parts all have very high face shape accuracy and low surface roughness requirements. In order to achieve the desired surface quality and functional properties, the polishing process often includes a final finishing operation to finish the surface. Conventionally, polishing of high-precision free-form surface parts is completed in a manual operation mode by depending on experienced workers, time and labor are consumed, machining consistency is not guaranteed, and dust and noise generated by polishing can seriously harm physical health of the workers. To improve such a situation, robots have been widely used in recent years to achieve automated polishing of various components. In order to reduce the resource waste in the automatic polishing process and improve the polishing efficiency, the method has important significance for predicting the surface morphology after polishing in advance.
A key challenge with current multi-directional prediction of polishing surface topography is that the calculation of the amount of polishing material removal involves very complex and time-consuming integral calculations of the polishing contact area, contact pressure, and amount of polishing material removal. Such time-consuming calculations make it difficult to achieve rapid iterative optimization of polishing trajectories and polishing parameters, even if surface topography prediction can be achieved.
Disclosure of Invention
To solve at least one of the technical problems in the prior art to some extent, an object of the present invention is to provide a method, a system, a device and a storage medium for fast surface topography prediction.
The technical scheme adopted by the invention is as follows:
a method for rapidly predicting surface topography comprises the following steps:
s1, obtaining three-dimensional information of the free-form surface, a polishing track on the free-form surface and polishing processing parameters;
s2, dispersing the polishing track into a plurality of knife contacts, and dispersing the free-form surface into a plurality of uniform sampling points;
s3, calculating a polishing influence area of the cutter contact according to the surface curvature of the three-dimensional information and the polishing processing parameters, and acquiring sampling points in the polishing influence area;
s4, calculating and updating the material removal amount of the sampling points in the polishing influence area by adopting a preset neural network prediction model;
s5, traversing the cutter contact, repeating the steps S3-S4, and obtaining the overall material removal amount so as to obtain the polished surface appearance;
wherein the neural network prediction model is used to predict material removal profiles under different surface curvatures and processing conditions.
Further, the polishing parameters include an inclination angle, a pressing amount, a feeding speed and a rotating speed.
Further, the discrete distance of the uniform sampling points in step S2 is defined according to the sampling frequency;
the sampling frequency is a first frequency when surface roughness is predicted, the sampling frequency is a second frequency when surface waviness is predicted, and the sampling frequency is a third frequency when surface profile errors are predicted;
wherein the first frequency > the second frequency > the third frequency.
Further, the surface curvature in step S3 is a normal curvature of the free-form surface perpendicular to the direction of the feed speed at the point of contact of the blade.
Further, the polishing influence region of the blade contact in step S3 is a contact region where the polishing tool makes contact with the free-form surface during the movement of the blade contact to the next blade contact.
Further, the neural network prediction model is constructed and obtained by the following method:
constructing a neural network prediction model according to the distribution rule of the material removal outline;
the material removal profile is distributed in a manner that is specific to the polishing of the inclined polishing disk and follows a quadratic function distribution.
Further, the neural network prediction model is established by taking the normal curvature, the inclination angle, the pressing amount, the feeding speed and the rotating speed of the free-form surface perpendicular to the feeding speed direction at the position of the knife contact as input and taking three coefficients of a quadratic function as output.
The other technical scheme adopted by the invention is as follows:
a system for rapid prediction of surface topography, comprising:
the parameter acquisition module is used for acquiring three-dimensional information of the free-form surface, a polishing track on the free-form surface and polishing processing parameters;
the curved surface discretization module is used for discretizing the polishing track into a plurality of knife contacts and discretizing a free curved surface into a plurality of uniform sampling points;
the influence area calculation module is used for calculating the polishing influence area of the cutter contact according to the surface curvature of the three-dimensional information and the polishing processing parameters and acquiring sampling points in the polishing influence area;
the removal amount calculation module is used for calculating and updating the material removal amount of all sampling points in the polishing influence area by adopting a preset neural network prediction model;
the curved surface traversing module is used for traversing the cutter contact to obtain the global material removal amount so as to obtain the polished surface appearance;
wherein the neural network prediction model is used to predict material removal profiles under different surface curvatures and processing conditions.
The other technical scheme adopted by the invention is as follows:
a surface topography fast prediction device comprises:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method described above.
The other technical scheme adopted by the invention is as follows:
a computer readable storage medium in which a processor executable program is stored, which when executed by a processor is for performing the method as described above.
The invention has the beneficial effects that: the method utilizes the neural network prediction model to calculate the removal amount of the polishing material, avoids complex time-consuming integral operation, and can quickly obtain the surface appearance of the free-form surface.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present invention or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart illustrating steps of a method for fast prediction of surface topography according to an embodiment of the present invention;
FIG. 2 is an exemplary graph of calculating a polishing impact area of a blade contact in an embodiment of the present invention;
FIG. 3 is an exemplary plot of a material removal profile calculated from different surface curvatures and processing conditions in an embodiment of the present invention;
FIG. 4 is an exemplary diagram of a neural network model for predicting a material removal profile in an embodiment of the present invention;
FIG. 5 is an exemplary plot of predicted surface topography in an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
The artificial neural network is an intelligent calculation model for realizing input information processing by simulating the work of a biological neural network, can simply and efficiently perform complex calculation, and provides an idea for solving the problems of complexity and time consumption in the process of calculating and removing polishing materials. By combining the material removal profile distribution rule of the free-form surface polished by the inclined polishing disk and the advantages of the neural network, a rapid and accurate surface topography prediction method can be provided for the free-form surface polishing.
As shown in fig. 1, the present embodiment provides a method for rapidly predicting a surface topography, which is capable of predicting a surface topography of a free-form surface after polishing in advance for polishing of an inclined polishing disk, and simultaneously providing a faster prediction method by means of a neural network algorithm. The method specifically comprises the following steps:
step 1: and obtaining three-dimensional curved surface information, polishing tracks on the curved surface and polishing processing parameters.
Extracting three-dimensional curved surface information and polishing tracks on the curved surface, and acquiring the diameter, the inclination angle, the pressing amount, the feeding speed and the rotation of the polishing disk of the inclined polishing diskSpeed-inside processing parameters. The trace in discrete step 1 is the series of knife contacts P _ Set ═ P 1 ，P 2 ，…，P i ，…，P N-1 ，P N And setting the distance of a sampling point of the free curved surface to be 1mm, and conforming to the sampling frequency of the surface waviness. The amount of material removed at the sample points was initialized to 0.
Step 2: according to the precision requirement, the track in the discrete step 1 is a series of knife contacts, and the discrete free-form surface is a series of uniform sampling points.
And step 3: and (3) calculating the polishing influence area of the tool contact according to the surface curvature and the processing parameters of the three-dimensional curved surface in the step (1) and obtaining sampling points in the polishing influence area.
Calculating and polishing the ith tool contact point P according to the normal curvature and the processing conditions of the tool contact point in the direction perpendicular to the feeding speed i The contact bandwidth of the first blade and the ith blade contact P constructed by the contact bandwidth i Finding a sampling point S _ Set ═ S inside the region 1 ，S 2 ，…，S j ，…，S M-1 ，S M As shown in fig. 2.
Constructional knife contact P i The method of polishing an affected area of (1) is as follows:
passing through each knife contact P i Making a line segment P with the track i P i+1 Straight vertical line segment P' i P″ i ，P′ i P″ i Is equal to the knife contact point P i Of (c) is connected in turn to P' i P′ i+1 ，P″ i P″ i+1 And obtaining the contact belt for polishing the workpiece along the whole contact locus of the cutter. Contact area quadrilateral P' i P″ i P″ i+1 P′ i+1 I.e. the knife contact P i The polishing affected zone.
And 4, step 4: and predicting the material removal contour under different surface curvatures and processing conditions by adopting a neural network according to the distribution rule of the material removal contour.
Calculating a trajectory line segment P i P i+1 The material removal profile perpendicular to the direction of the feed speed is calculated for a plurality of groups of materials under different conditionsThe removal of the profile, resulting in a material removal profile that all approximately follows the law of a quadratic distribution, is shown in fig. 3. The argument of the quadratic function being the distance from the track segment P i P i+1 The minimum distance of (c). Performing quadratic fitting on a plurality of groups of different material removal profiles, taking the processing conditions and the surface curvature as the input of the neural network model, taking the coefficient of the fitted quadratic function as the output, and establishing the neural network model capable of predicting the material removal profiles, as shown in fig. 4.
And 5: and (4) calculating and updating the material removal amount of the sampling points in all polishing influence areas according to the neural network prediction model established in the step (4).
The calculation of the material removal profile comprises the following steps:
based on the Preston equation, namely:
dh＝k p pv r dt
and calculating the material removal depth of the single-point polishing in unit time.
In the formula k p Is a coefficient related to the abrasive wear on the polishing tool; p is the contact point pressure, determined by the contact conditions; v. of r The relative linear velocity is determined by the rotating speed and the feeding speed of the polishing disk; dt is the residence time.
And converting the Preston equation of the first step into a path integral form, wherein the polishing material removal profile vertical to the direction of the feeding speed can be obtained by the integral of the path.
Step 6: traversing the cutter contact, repeating the steps 3-5, and finally obtaining the overall material removal amount so as to obtain the polished surface appearance.
Go through S _ Set, calculate S j The minimum distance from the trajectory, S, can be obtained from the neural network model described above j Material removal amount of dots, and S j The amount of material removed is accumulated.
Judging the current knife contact P i Whether it is the last knife contact P N . If i<N, i is i +1, and steps 3 to 5 are repeated. Otherwise, jumping to the next step.
After the above steps are completed, the material removal amounts of all sampling points can be obtained. The minimum material removal for all the sampling points is found and subtracted from the material removal for all the sampling points, i.e., the surface topography, as shown in fig. 5.
In conclusion, compared with the prior art, the method of the invention can quickly and accurately obtain the distribution of the removal profile of the polishing material by utilizing the distribution rule of the removal profile of the material and the neural network algorithm, thereby accelerating the simulation calculation of the morphology of the polishing surface.
The embodiment also provides a system for rapidly predicting surface topography, which includes:
the parameter acquisition module is used for acquiring three-dimensional information of the free-form surface, a polishing track on the free-form surface and polishing processing parameters;
the curved surface discretization module is used for discretizing the polishing track into a plurality of knife contacts and discretizing a free curved surface into a plurality of uniform sampling points;
the influence area calculation module is used for calculating the polishing influence area of the cutter contact according to the surface curvature of the three-dimensional information and the polishing processing parameters and acquiring sampling points in the polishing influence area;
the removal amount calculation module is used for calculating and updating the material removal amount of all sampling points in the polishing influence area by adopting a preset neural network prediction model;
the curved surface traversing module is used for traversing the cutter contact to obtain the global material removal amount so as to obtain the polished surface appearance;
wherein the neural network prediction model is used to predict material removal profiles under different surface curvatures and processing conditions.
The system for rapidly predicting the surface topography of the embodiment can execute the method for rapidly predicting the surface topography provided by the embodiment of the method of the invention, can execute any combination of implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The embodiment further provides a device for rapidly predicting surface topography, which includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of fig. 1.
The device for rapidly predicting the surface topography of the embodiment can execute the method for rapidly predicting the surface topography provided by the embodiment of the method of the invention, can execute any combination of implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The embodiment of the application also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
The embodiment also provides a storage medium, which stores an instruction or a program capable of executing the method for quickly predicting the surface topography provided by the method embodiment of the present invention, and when the instruction or the program is run, the method can be executed by any combination of the method embodiments, and the method has corresponding functions and advantages.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for rapidly predicting surface topography is characterized by comprising the following steps:
s1, obtaining three-dimensional information of the free-form surface, a polishing track on the free-form surface and polishing processing parameters;
s2, dispersing the polishing track into a plurality of knife contacts, and dispersing the free-form surface into a plurality of uniform sampling points;
s3, calculating a polishing influence area of the cutter contact according to the surface curvature of the three-dimensional information and the polishing processing parameters, and acquiring sampling points in the polishing influence area;
s4, calculating and updating the material removal amount of the sampling points in the polishing influence area by adopting a preset neural network prediction model;
s5, traversing the cutter contact, repeating the steps S3-S4, and obtaining the overall material removal amount so as to obtain the polished surface appearance;
wherein the neural network prediction model is used to predict material removal profiles under different surface curvatures and processing conditions.
2. The method as claimed in claim 1, wherein the polishing parameters include tilt angle, pressing amount, feeding speed, and rotation speed.
3. The method for rapidly predicting surface topography according to claim 1, wherein the discrete distance of the uniform sampling points in step S2 is defined according to the sampling frequency;
when predicting the surface roughness, the sampling frequency is a first frequency, when predicting the surface waviness, the sampling frequency is a second frequency, and when predicting the surface profile error, the sampling frequency is a third frequency;
wherein the first frequency > the second frequency > the third frequency.
4. The method of claim 1, wherein the surface curvature in step S3 is a normal curvature of the free-form surface perpendicular to the direction of the feed speed at the contact point of the blade.
5. The method for rapidly predicting surface morphology according to claim 1, wherein the polishing affected area of the blade contact point in step S3 is a contact area generated by the polishing tool contacting with the free-form surface during the movement from the blade contact point to the next blade contact point.
6. The method for rapidly predicting the surface topography according to claim 1, wherein the neural network prediction model is constructed by the following steps:
constructing a neural network prediction model according to the distribution rule of the material removal outline;
the material removal profile is distributed according to a quadratic function distribution for polishing of the inclined polishing disk.
7. The method of claim 1, wherein the neural network prediction model is built by taking a normal curvature, an inclination angle, a pressing amount, a feeding speed and a rotating speed of a free-form surface at a contact point of the cutter, which are perpendicular to a feeding speed direction, as input and taking three coefficients of a quadratic function as output.
8. A system for rapid prediction of surface topography, comprising:
the parameter acquisition module is used for acquiring three-dimensional information of the free-form surface, a polishing track on the free-form surface and polishing processing parameters;
the curved surface discretization module is used for discretizing the polishing track into a plurality of knife contacts and discretizing a free curved surface into a plurality of uniform sampling points;
the influence area calculation module is used for calculating the polishing influence area of the cutter contact according to the surface curvature of the three-dimensional information and the polishing processing parameters and acquiring sampling points in the polishing influence area;
the removal amount calculation module is used for calculating and updating the material removal amount of all sampling points in the polishing influence area by adopting a preset neural network prediction model;
the curved surface traversing module is used for traversing the cutter contact to obtain the global material removal amount so as to obtain the polished surface appearance;
wherein the neural network prediction model is used to predict material removal profiles under different surface curvatures and processing conditions.
9. An apparatus for rapidly predicting surface topography, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, in which a program executable by a processor is stored, wherein the program executable by the processor is adapted to perform the method according to any one of claims 1 to 7 when executed by the processor.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116533127A (en) * | 2023-07-06 | 2023-08-04 | 浙江晶盛机电股份有限公司 | Polishing pressure adjusting method, polishing pressure adjusting device, computer equipment and storage medium |
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| CN116533127A (en) * | 2023-07-06 | 2023-08-04 | 浙江晶盛机电股份有限公司 | Polishing pressure adjusting method, polishing pressure adjusting device, computer equipment and storage medium |
| CN116533127B (en) * | 2023-07-06 | 2023-10-31 | 浙江晶盛机电股份有限公司 | Polishing pressure adjusting method, polishing pressure adjusting device, computer equipment and storage medium |
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