CN116727691A - Metal 3D printing method and system based on digital management - Google Patents
Metal 3D printing method and system based on digital management Download PDFInfo
- Publication number
- CN116727691A CN116727691A CN202310840912.9A CN202310840912A CN116727691A CN 116727691 A CN116727691 A CN 116727691A CN 202310840912 A CN202310840912 A CN 202310840912A CN 116727691 A CN116727691 A CN 116727691A
- Authority
- CN
- China
- Prior art keywords
- powder cleaning
- target
- preset
- cleaning
- track
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000002184 metal Substances 0.000 title claims abstract description 93
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000010146 3D printing Methods 0.000 title claims abstract description 57
- 238000004140 cleaning Methods 0.000 claims abstract description 316
- 239000000843 powder Substances 0.000 claims abstract description 294
- 230000000694 effects Effects 0.000 claims description 15
- 230000004927 fusion Effects 0.000 claims description 15
- 238000013507 mapping Methods 0.000 claims description 12
- 238000005070 sampling Methods 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 5
- 229920000742 Cotton Polymers 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 2
- 238000010926 purge Methods 0.000 claims 3
- 238000007639 printing Methods 0.000 abstract description 17
- 238000012545 processing Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 238000002844 melting Methods 0.000 description 2
- 230000008018 melting Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
- B22F10/20—Direct sintering or melting
- B22F10/28—Powder bed fusion, e.g. selective laser melting [SLM] or electron beam melting [EBM]
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
- B22F10/80—Data acquisition or data processing
- B22F10/85—Data acquisition or data processing for controlling or regulating additive manufacturing processes
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F12/00—Apparatus or devices specially adapted for additive manufacturing; Auxiliary means for additive manufacturing; Combinations of additive manufacturing apparatus or devices with other processing apparatus or devices
- B22F12/90—Means for process control, e.g. cameras or sensors
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y10/00—Processes of additive manufacturing
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B33—ADDITIVE MANUFACTURING TECHNOLOGY
- B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
- B33Y30/00—Apparatus for additive manufacturing; Details thereof or accessories therefor
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/25—Process efficiency
Abstract
The invention discloses a metal 3D printing method and a system based on digital management, which are applied to the technical field of data processing, wherein the method comprises the following steps: and constructing a target three-dimensional model of the target metal workpiece through the target image set. And carrying out structural division on the target three-dimensional model to obtain a target structure set. A first curvature of a first structural face in the first structure is detected, and it is determined whether the first curvature satisfies a predetermined curvature constraint. If the first curvature is satisfied, analyzing the first curvature to obtain a first preset powder cleaning track, and if the first curvature is not satisfied, calling the preset powder cleaning track. And carrying out powder cleaning treatment on the first structural surface, and collecting a first powder cleaning result image through the intelligent camera. And analyzing the first powder cleaning result image to obtain first powder cleaning data of the first structure body. The technical problem that in the prior art, the powder cleaning treatment efficiency in the metal 3D printing process is low, and then the metal printing process is influenced, so that the metal 3D printing efficiency is low is solved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a metal 3D printing method and system based on digital management.
Background
The metal 3D printing is to fuse metal powder by utilizing a laser melting technology, so that a solid part is formed, and after printing at a certain height, the printed finished product is required to be subjected to powder cleaning treatment, so that the influence on the subsequent printing process is avoided. However, in the prior art, the powder cleaning process is mainly performed manually, so that the labor cost is high, the powder cleaning efficiency is low, the powder cleaning process cannot work with a metal 3D printer in a matching manner, and the problem of low printing efficiency of the metal 3D printer is caused.
Therefore, in the prior art, the powder cleaning treatment efficiency in the metal 3D printing process is lower, so that the metal printing process is influenced, and the technical problem of lower metal 3D printing efficiency is caused.
Disclosure of Invention
The application provides a metal 3D printing method and a system based on digital management, which solve the technical problems that in the prior art, the powder cleaning treatment efficiency in the metal 3D printing process is low, and the metal printing process is influenced, so that the metal 3D printing efficiency is low.
The application provides a metal 3D printing method based on digital management, which is applied to a metal 3D printing system, and the metal 3D printing system is in communication connection with a powder cleaning manipulator, and the metal 3D printing method comprises the following steps: carrying an intelligent camera by the powder cleaning manipulator, and acquiring multi-angle image information of a target metal workpiece to obtain a target image set; analyzing the target image set and constructing a target three-dimensional model of the target metal workpiece; performing structure division on the target three-dimensional model to obtain a target structure body set, wherein the target structure body set comprises a first structure body; detecting a first curvature of a first structural surface in the first structural body, and judging whether the first curvature meets a preset curvature constraint; if the first curvature is satisfied, analyzing the first curvature to obtain a first preset powder cleaning track, and if the first curvature is not satisfied, calling the preset powder cleaning track; the powder cleaning manipulator controls a preset powder cleaning brush head to perform powder cleaning treatment on the first structural surface based on the first preset powder cleaning track or the preset powder cleaning track, and a first powder cleaning result image is acquired through the intelligent camera; and analyzing the first powder cleaning result image to obtain first powder cleaning data of the first structure, wherein the first powder cleaning data refers to preliminary powder cleaning result data of the first structure.
The application also provides a metal 3D printing system based on digital management, wherein the metal 3D printing system is in communication connection with the powder cleaning manipulator, and the metal 3D printing system comprises: the image acquisition module is used for carrying an intelligent camera through the powder cleaning manipulator, and acquiring multi-angle image information of a target metal workpiece to obtain a target image set; the three-dimensional model construction module is used for analyzing the target image set and constructing a target three-dimensional model of the target metal workpiece; the target structure body acquisition module is used for carrying out structure division on the target three-dimensional model to obtain a target structure body set, wherein the target structure body set comprises a first structure body; the curvature detection module is used for detecting a first curvature of a first structural surface in the first structural body and judging whether the first curvature meets a preset curvature constraint; the powder cleaning track acquisition module is used for analyzing the first curvature to obtain a first preset powder cleaning track if the first curvature is met, and calling the preset powder cleaning track if the first curvature is not met; the powder cleaning result image module is used for controlling a preset powder cleaning brush head to perform powder cleaning treatment on the first structural surface based on the first preset powder cleaning track or the preset powder cleaning track by the powder cleaning manipulator, and collecting a first powder cleaning result image by the intelligent camera; the powder cleaning result acquisition module is used for analyzing the first powder cleaning result image to obtain first powder cleaning data of the first structure body, wherein the first powder cleaning data refer to preliminary powder cleaning result data of the first structure body.
The application also provides an electronic device, comprising:
a memory for storing executable instructions;
and the processor is used for realizing the metal 3D printing method based on digital management when executing the executable instructions stored in the memory.
The embodiment of the application provides a computer readable storage medium which stores a computer program, and when the program is executed by a processor, the metal 3D printing method based on digital management provided by the embodiment of the application is realized.
The metal 3D printing method and system based on digital management, which are proposed by the application, construct a target three-dimensional model of a target metal workpiece through a target image set. And carrying out structural division on the target three-dimensional model to obtain a target structure set. A first curvature of a first structural face in the first structure is detected, and it is determined whether the first curvature satisfies a predetermined curvature constraint. If the first curvature is satisfied, analyzing the first curvature to obtain a first preset powder cleaning track, and if the first curvature is not satisfied, calling the preset powder cleaning track. And carrying out powder cleaning treatment on the first structural surface, and collecting a first powder cleaning result image through the intelligent camera. And analyzing the first powder cleaning result image to obtain first powder cleaning data of the first structure body. And then realize the automatic deashing to the metal 3D in-process of printing, and then improve the clear powder treatment effeciency of printing process, reduce the human cost of use, improve metal 3D printing efficiency. The technical problem that in the prior art, the powder cleaning treatment efficiency in the metal 3D printing process is low, and then the metal printing process is influenced, so that the metal 3D printing efficiency is low is solved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
Fig. 1 is a schematic flow chart of a metal 3D printing method based on digital management according to an embodiment of the present application;
fig. 2 is a schematic flow chart of obtaining a target three-dimensional model by a metal 3D printing method based on digital management according to an embodiment of the present application;
fig. 3 is a schematic flow chart of acquiring a powder cleaning track by a metal 3D printing method based on digital management according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a system of a metal 3D printing method based on digital management according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a system electronic device based on a digital management-based metal 3D printing method according to an embodiment of the present application.
Reference numerals illustrate: the device comprises an image acquisition module 11, a three-dimensional model construction module 12, a target structure body acquisition module 13, a curvature detection module 14, a powder cleaning track acquisition module 15, a powder cleaning result image module 16, a powder cleaning result acquisition module 17, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
Example 1
The present application will be further described in detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present application more apparent, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
In the following description, the terms "first", "second", "third" and the like are merely used to distinguish similar objects and do not represent a particular ordering of the objects, it being understood that the "first", "second", "third" may be interchanged with a particular order or sequence, as permitted, to enable embodiments of the application described herein to be practiced otherwise than as illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only.
While the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server, the modules are merely illustrative, and different aspects of the system and method may use different modules.
A flowchart is used in the present application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
As shown in fig. 1, an embodiment of the present application provides a metal 3D printing method based on digital management, where the metal 3D printing method is applied to a metal 3D printing system, and the metal 3D printing system is in communication connection with a powder cleaning manipulator, and the metal 3D printing method includes:
S10: carrying an intelligent camera by the powder cleaning manipulator, and acquiring multi-angle image information of a target metal workpiece to obtain a target image set;
s20: analyzing the target image set and constructing a target three-dimensional model of the target metal workpiece;
s30: performing structure division on the target three-dimensional model to obtain a target structure body set, wherein the target structure body set comprises a first structure body;
s40: detecting a first curvature of a first structural surface in the first structural body, and judging whether the first curvature meets a preset curvature constraint;
specifically, metal 3D printing fuses metal powder by using a laser melting technology, so that a solid part is formed, and after printing at a certain height, the printed finished product is required to be subjected to powder cleaning treatment, so that the influence on the subsequent printing process is avoided. However, in the prior art, the powder cleaning treatment is mainly performed manually, so that the powder cleaning efficiency is low, the powder cleaning treatment cannot work with a metal 3D printer in a matching manner, and the printing efficiency of the metal 3D printer is low. In order to solve the existing problems, an intelligent camera is mounted on a powder cleaning manipulator, and multi-angle image information acquisition is carried out on a target metal workpiece to obtain a target image set. And then, constructing a target three-dimensional model of the target metal workpiece according to the target image set. After the target three-dimensional model is obtained, carrying out structural division on the target three-dimensional model, and dividing the target three-dimensional model into different three-dimensional structures such as cylinders, cubes and the like to obtain a target structure body set, wherein the target structure body set comprises a first structure body. The first structure is the uppermost structure in the structure set sorting result from top to bottom according to the target structure set. Further, detecting a first curvature of the first structural surface in the first structural body, and then judging whether the first curvature meets a preset curvature constraint, wherein the first structural surface corresponding to the cone comprises a curved surface with a certain curvature, and the preset curvature constraint is a preset constraint condition for judging whether the first structural surface is the curved surface.
As shown in fig. 2, the method S20 provided by the embodiment of the present application further includes:
s21: constructing a workpiece registration fusion model by utilizing a random sampling consistency algorithm principle;
s22: obtaining a point cloud data set of the target image set, and taking the point cloud data set as input information of the workpiece registration fusion model to obtain model output information;
s23: extracting a workpiece parameter estimation result in the model output information, and obtaining a preset point cloud model based on the workpiece parameter estimation result;
s24: obtaining basic characteristic parameters of the intelligent camera, and performing texture mapping on the preset point cloud model based on the basic characteristic parameters to obtain a texture mapping result;
s25: and taking the texture mapping result as the target three-dimensional model.
Specifically, when analyzing the target image set and constructing a target three-dimensional model of the target metal workpiece, constructing a workpiece registration fusion model by utilizing a random sampling consistency algorithm principle. The method comprises the steps of obtaining a point cloud data set of a target image set, wherein the point cloud data set of the target image set is a point cloud data set formed by sampling points of pixel two-dimensional coordinates. And taking the point cloud data set as input information of the workpiece registration fusion model to obtain model output information. And then, extracting a workpiece parameter estimation result in the model output information, and obtaining a preset point cloud model based on the workpiece parameter estimation result. The method comprises the steps of extracting model parameter estimation results in output information, obtaining distances between a plurality of point clouds and a target model, and arranging the positions of the plurality of point clouds to obtain a preset point cloud model of the target model. And performing texture mapping on the preset point cloud model based on the basic characteristic parameters of the intelligent camera to obtain a texture mapping result, and finally taking the texture mapping result as the target three-dimensional model.
The method S22 provided by the embodiment of the application further comprises the following steps:
s221: the workpiece registration fusion model randomly samples the point cloud data set to obtain a first sample set, and obtains a first parameter estimation result of the target three-dimensional model based on the first sample set;
s222: removing the first sample set from the point cloud data set to obtain a first non-sample set, wherein the first non-sample set comprises a plurality of non-sample point cloud data;
s223: sequentially calculating the distances from the plurality of non-sample point cloud data to the target three-dimensional model, and screening the plurality of non-sample point cloud data by combining a preset distance threshold value to obtain a first consistency point set;
s224: calculating a first data volume in the first consistency point set, and judging whether the first data volume meets a preset quantity threshold;
s225: if the first data volume meets the preset quantity threshold, a first re-estimation instruction is obtained, and a second parameter estimation result of the target three-dimensional model is obtained by combining the first consistency point set according to the first re-estimation instruction;
s226: and replacing the first parameter estimation result with the second parameter estimation result to serve as the model output information.
Specifically, the obtaining the point cloud data set of the target image set, and taking the point cloud data set as input information of the workpiece registration fusion model, to obtain model output information, includes: randomly sampling the workpiece registration fusion model from the point cloud data set to obtain a first sample set, and obtaining a first parameter estimation result of the target three-dimensional model based on the first sample set. The workpiece registration fusion model randomly samples the point cloud data set to obtain a first sample set, wherein the first sample set comprises a plurality of point cloud data, a first parameter estimation result of the target model is obtained based on the first sample set according to the corresponding relation between the pixel distance and the actual distance in the first sample set, and if the plurality of point clouds in the point cloud data set are randomly selected and connected, the distance in the first sample set is calculated, and the first parameter estimation result can be obtained by adopting a least square method.
And then, the first sample set is removed from the point cloud data set to obtain a first non-sample set, wherein the first non-sample set comprises a plurality of non-sample point cloud data. Further, distances from the plurality of non-sample point cloud data to the target three-dimensional model are sequentially calculated, and the plurality of non-sample point cloud data are screened by combining a preset distance threshold value, so that a first consistency point set is obtained. The distance between each point in the first consistency point set and the target three-dimensional model is within a preset distance threshold range. And calculating a first data volume in the first consistency point set, and judging whether the first data volume meets a preset quantity threshold. The preset distance threshold is a distance threshold set by a person skilled in the art, and the preset number threshold is a number threshold set by a person skilled in the art. If the first data volume meets the preset quantity threshold, a first re-estimation instruction is obtained, wherein the first re-estimation instruction is a control instruction for carrying out parameter estimation on the first consistency point set, and a second parameter estimation result of the target three-dimensional model is obtained by combining the first consistency point set according to the first re-estimation instruction. And when the first data quantity does not meet the preset quantity threshold, acquiring point cloud data again, and increasing the quantity of sampling points until the first data quantity meets the preset quantity threshold. And finally, replacing the first parameter estimation result with the second parameter estimation result to serve as the model output information. The model output information comprises a workpiece parameter estimation result.
S50: if the first curvature is satisfied, analyzing the first curvature to obtain a first preset powder cleaning track, and if the first curvature is not satisfied, calling the preset powder cleaning track;
s60: the powder cleaning manipulator controls a preset powder cleaning brush head to perform powder cleaning treatment on the first structural surface based on the first preset powder cleaning track or the preset powder cleaning track, and a first powder cleaning result image is acquired through the intelligent camera;
s70: and analyzing the first powder cleaning result image to obtain first powder cleaning data of the first structure, wherein the first powder cleaning data refers to preliminary powder cleaning result data of the first structure.
Specifically, when a predetermined curvature constraint is met, and at the moment, the first structural surface of the first structural body is a curved surface, analyzing the first curvature to obtain a first preset powder cleaning track, and if the first curvature is not met, calling the preset powder cleaning track, wherein the preset powder cleaning track is a linear track. And then, controlling a preset powder cleaning brush head to perform powder cleaning treatment on the first structural surface by using a powder cleaning manipulator based on the first preset powder cleaning track or the preset powder cleaning track, and collecting a first powder cleaning result image by using the intelligent camera. And analyzing the first powder cleaning result image to obtain first powder cleaning data of the first structure, acquiring the first powder cleaning result image and evaluating similarity between the first powder cleaning result image and the image of the same acquisition position of the first structure when the first powder cleaning result image is analyzed, acquiring the similarity between the image of the same acquisition position of the first structure and the first powder cleaning result image, and when the similarity between the image of the same acquisition position of the first structure and the first powder cleaning result image is higher, the better the powder cleaning effect is, otherwise, the worse the powder cleaning effect is. Further, different powder cleaning effect grades are set according to different similarity, so that corresponding powder cleaning effect grades are obtained according to image similarity, and first powder cleaning data are obtained. The first powder cleaning data refer to preliminary powder cleaning result data of the first structure body. And then realize the automatic deashing to the metal 3D in-process of printing, and then improve the clear powder treatment effeciency of printing process, improve metal 3D printing efficiency.
As shown in fig. 3, the method S60 provided by the embodiment of the present application further includes:
s61: acquiring an included angle between the first structural surface and the ground, and recording the included angle as a first included angle;
s62: performing angle adjustment on the powder cleaning manipulator according to the first included angle to obtain a first angle adjustment manipulator;
s63: acquiring size information of the first structural surface, and recording the size information as the size of the first structural surface;
s64: generating a first arch-shaped track based on the first structural surface size model, and taking the first arch-shaped track as the preset powder cleaning track;
s65: the first angle adjusting manipulator performs powder cleaning treatment on the first structural surface based on the preset powder cleaning track.
Specifically, when the predetermined cleaning brush head is controlled to perform cleaning treatment on the first structural surface, an included angle between the first structural surface and the ground is obtained and is recorded as a first included angle. And carrying out angle adjustment on the powder cleaning manipulator according to the first included angle to obtain a first angle adjustment manipulator. And acquiring size information of the first structural surface, and recording the size information as the size of the first structural surface, such as the plane size of the conical sector. Further, a first arch-shaped track is generated based on the first structural surface size model, and the first arch-shaped track is used as the preset powder cleaning track. The first angle adjusting manipulator performs powder cleaning treatment on the first structural surface based on the preset powder cleaning track.
The method S65 provided by the embodiment of the application further comprises the following steps:
s651: obtaining an actual powder cleaning track of the powder cleaning manipulator through the track tracking model;
s652: comparing the actual powder cleaning track with the first preset powder cleaning track to obtain first track comparison data, and analyzing the first track comparison data to obtain a first powder cleaning deviation;
s653: comparing the actual powder cleaning track with the preset powder cleaning track to obtain second track comparison data, and analyzing the second track comparison data to obtain a second powder cleaning deviation;
s654: if the first powder cleaning deviation or the second powder cleaning deviation accords with a preset deviation threshold, a secondary powder cleaning instruction is sent out;
s655: and replacing the preset cleaning brush head with a preset cleaning cotton head according to the secondary cleaning instruction, and performing cleaning treatment on the first structural surface to obtain second cleaning data.
Specifically, a track tracking model is embedded in the powder cleaning manipulator, and the actual powder cleaning track of the powder cleaning manipulator is obtained through the track tracking model. When the curvature meets the preset curvature constraint, comparing the actual powder cleaning track with the first preset powder cleaning track to obtain first track comparison data, and analyzing the first track comparison data to obtain first powder cleaning deviation, namely, performing overlap ratio comparison on the actual powder cleaning track and the first preset powder cleaning track to obtain an overlap ratio, and obtaining a track non-overlap ratio to obtain the first powder cleaning deviation. And when the curvature does not meet the preset curvature constraint, comparing the actual powder cleaning track with the preset powder cleaning track to obtain second track comparison data, and analyzing the second track comparison data to obtain second powder cleaning deviation, wherein the acquisition mode of the second powder cleaning deviation is consistent with that of the first powder cleaning deviation. Further, if the first powder cleaning deviation or the second powder cleaning deviation accords with a preset deviation threshold, a secondary powder cleaning instruction is sent out. The preset deviation threshold value is a preset deviation proportion threshold value. And replacing the preset cleaning brush head with a preset cleaning brush head according to the secondary cleaning instruction, wherein the preset cleaning brush head is a specific cleaning brush head for cleaning metal powder, and the preset cleaning brush head is a specific cleaning brush head for cleaning metal powder, so that cleaning treatment is carried out on the first structural surface to obtain second cleaning data.
The method S651 provided by the embodiment of the application further comprises the following steps:
s6511: the track tracking model controls the intelligent camera to acquire images of the preset cleaning brush head at preset frequency to obtain a brush head cleaning image sequence;
s6512: extracting a first powder cleaning image in the brush head powder cleaning image sequence;
s6513: positioning a first pixel area of the preset cleaning head in the first cleaning image, and collecting a first longest-diameter pixel number of the first pixel area;
s6514: reading the number of pre-aiming pixels, and drawing a target circle by taking the center of the first longest path pixel number as a circle center and the number of pre-aiming pixels as a radius;
s6515: taking the intersection point of the target circle and the target theoretical track as a pre-aiming pixel point;
s6516: acquiring a first actual movement direction of the first pixel region, and taking an included angle between the first actual movement direction and the pre-aiming pixel point as a first deviation angle;
s6517: calculating a first actual rotation angle of the powder cleaning manipulator based on the first longest path pixel number, the pretightening pixel number and the first deviation angle;
s6518: extracting a second clear powder image in the clear powder image sequence of the brush head, and analyzing the first clear powder image to obtain a second actual corner, wherein the first clear powder image and the second clear powder image are adjacent images;
S6519: and generating the actual powder cleaning track of the powder cleaning manipulator based on the first actual rotation angle and the second actual rotation angle.
Specifically, when the actual powder cleaning track is acquired, the track tracking model controls the intelligent camera, and the image acquisition is carried out on the preset powder cleaning brush head at preset frequency to obtain a brush head powder cleaning image sequence. And extracting a first clear image in the clear image sequence of the brush head, wherein the first clear image is the first image in the clear image sequence. Positioning a first pixel area of the preset cleaning head in the first cleaning image, and collecting a first longest-diameter pixel number of the first pixel area. And reading the number of the pre-aiming pixels, and drawing a target circle by taking the center of the first longest path pixel number as a circle center and the number of the pre-aiming pixels as a radius. And taking the intersection point of the target circle and the target theoretical track as a pre-aiming pixel point, acquiring a first actual movement direction of the first pixel area, and taking an included angle between the first actual movement direction and the pre-aiming pixel point as a first deviation angle. And calculating a first actual rotation angle of the powder cleaning manipulator based on the first longest path pixel number, the pretightening pixel number and the first deviation angle. And continuously extracting a second clear powder image in the clear powder image sequence of the brush head, and analyzing the first clear powder image to obtain a second actual corner, wherein the first clear powder image and the second clear powder image are adjacent images. And continuously traversing all image training based on the first actual rotation angle and the second actual rotation angle, and generating the actual powder cleaning track of the powder cleaning manipulator.
The method S65 provided by the embodiment of the application further comprises the following steps:
s656: analyzing the second powder cleaning data to obtain a powder cleaning effect index;
s657: if the powder cleaning effect index meets a preset index threshold, acquiring a second structure body in the target structure body set, and performing powder cleaning treatment on the second structure body;
s658: wherein the second structure is a structure which is mutually connected with the first structure.
Specifically, the second powder cleaning data is analyzed to obtain a powder cleaning effect index, wherein the acquisition mode of the powder cleaning effect index is consistent with the acquisition mode of the powder cleaning effect grade. And if the powder cleaning effect index meets a preset index threshold, acquiring a second structure body in the target structure body set, and carrying out powder cleaning treatment on the second structure body. The preset index threshold is a preset cleaning effect grade threshold, and the second structure body is a structure body which is mutually connected with the first structure body.
According to the technical scheme provided by the embodiment of the application, the intelligent camera is carried by the powder cleaning manipulator, and the multi-angle image information acquisition is carried out on the target metal workpiece, so that a target image set is obtained. And analyzing the target image set and constructing a target three-dimensional model of the target metal workpiece. And carrying out structural division on the target three-dimensional model to obtain a target structure body set, wherein the target structure body set comprises a first structure body. A first curvature of a first structural face in the first structural body is detected, and whether the first curvature meets a predetermined curvature constraint is judged. If the first curvature is satisfied, analyzing the first curvature to obtain a first preset powder cleaning track, and if the first curvature is not satisfied, calling the preset powder cleaning track. The powder cleaning manipulator controls a preset powder cleaning brush head to perform powder cleaning treatment on the first structural surface based on the first preset powder cleaning track or the preset powder cleaning track, and a first powder cleaning result image is acquired through the intelligent camera. And analyzing the first powder cleaning result image to obtain first powder cleaning data of the first structure, wherein the first powder cleaning data refers to preliminary powder cleaning result data of the first structure. And then realize the automatic deashing to the metal 3D in-process of printing, and then improve the clear powder treatment effeciency of printing process, reduce the human cost of use, improve metal 3D printing efficiency. The technical problem that in the prior art, the powder cleaning treatment efficiency in the metal 3D printing process is low, and then the metal printing process is influenced, so that the metal 3D printing efficiency is low is solved.
Example two
Based on the same inventive concept as the metal 3D printing method based on digital management in the foregoing embodiment, the present invention also provides a system based on the metal 3D printing method based on digital management, where the system may be implemented by hardware and/or software, and may be generally integrated in an electronic device, for executing the method provided by any embodiment of the present invention. As shown in fig. 4, the metal 3D printing system is in communication connection with the powder cleaning manipulator, and the metal 3D printing system comprises
The image acquisition module 11 is used for carrying an intelligent camera through the powder cleaning manipulator, and acquiring multi-angle image information of a target metal workpiece to obtain a target image set;
a three-dimensional model construction module 12 for analyzing the set of target images and constructing a target three-dimensional model of the target metal workpiece;
a target structure body acquisition module 13, configured to perform structural division on the target three-dimensional model to obtain a target structure body set, where the target structure body set includes a first structure body;
a curvature detection module 14, configured to detect a first curvature of a first structural surface in the first structural body, and determine whether the first curvature meets a predetermined curvature constraint;
The powder cleaning track acquisition module 15 is configured to analyze the first curvature to obtain a first preset powder cleaning track if the first curvature is satisfied, and call the preset powder cleaning track if the first curvature is not satisfied;
the powder cleaning result image module 16 is configured to control a predetermined powder cleaning brush head to perform powder cleaning treatment on the first structural surface based on the first preset powder cleaning track or the preset powder cleaning track, and collect a first powder cleaning result image through the intelligent camera;
the powder cleaning result obtaining module 17 is configured to analyze the first powder cleaning result image to obtain first powder cleaning data of the first structure, where the first powder cleaning data refers to preliminary powder cleaning result data of the first structure.
Further, the three-dimensional model building module 12 is further configured to:
constructing a workpiece registration fusion model by utilizing a random sampling consistency algorithm principle;
obtaining a point cloud data set of the target image set, and taking the point cloud data set as input information of the workpiece registration fusion model to obtain model output information;
extracting a workpiece parameter estimation result in the model output information, and obtaining a preset point cloud model based on the workpiece parameter estimation result;
Obtaining basic characteristic parameters of the intelligent camera, and performing texture mapping on the preset point cloud model based on the basic characteristic parameters to obtain a texture mapping result;
and taking the texture mapping result as the target three-dimensional model.
Further, the three-dimensional model building module 12 is further configured to:
the workpiece registration fusion model randomly samples the point cloud data set to obtain a first sample set, and obtains a first parameter estimation result of the target three-dimensional model based on the first sample set;
removing the first sample set from the point cloud data set to obtain a first non-sample set, wherein the first non-sample set comprises a plurality of non-sample point cloud data;
sequentially calculating the distances from the plurality of non-sample point cloud data to the target three-dimensional model, and screening the plurality of non-sample point cloud data by combining a preset distance threshold value to obtain a first consistency point set;
calculating a first data volume in the first consistency point set, and judging whether the first data volume meets a preset quantity threshold;
if the first data volume meets the preset quantity threshold, a first re-estimation instruction is obtained, and a second parameter estimation result of the target three-dimensional model is obtained by combining the first consistency point set according to the first re-estimation instruction;
And replacing the first parameter estimation result with the second parameter estimation result to serve as the model output information.
Further, the powder cleaning result image module 16 is further configured to:
acquiring an included angle between the first structural surface and the ground, and recording the included angle as a first included angle;
performing angle adjustment on the powder cleaning manipulator according to the first included angle to obtain a first angle adjustment manipulator;
acquiring size information of the first structural surface, and recording the size information as the size of the first structural surface;
generating a first arch-shaped track based on the first structural surface size model, and taking the first arch-shaped track as the preset powder cleaning track;
the first angle adjusting manipulator performs powder cleaning treatment on the first structural surface based on the preset powder cleaning track.
Further, the powder cleaning result image module 16 is further configured to:
obtaining an actual powder cleaning track of the powder cleaning manipulator through the track tracking model;
comparing the actual powder cleaning track with the first preset powder cleaning track to obtain first track comparison data, and analyzing the first track comparison data to obtain a first powder cleaning deviation;
comparing the actual powder cleaning track with the preset powder cleaning track to obtain second track comparison data, and analyzing the second track comparison data to obtain a second powder cleaning deviation;
If the first powder cleaning deviation or the second powder cleaning deviation accords with a preset deviation threshold, a secondary powder cleaning instruction is sent out;
and replacing the preset cleaning brush head with a preset cleaning cotton head according to the secondary cleaning instruction, and performing cleaning treatment on the first structural surface to obtain second cleaning data.
Further, the powder cleaning result image module 16 is further configured to:
the track tracking model controls the intelligent camera to acquire images of the preset cleaning brush head at preset frequency to obtain a brush head cleaning image sequence;
extracting a first powder cleaning image in the brush head powder cleaning image sequence;
positioning a first pixel area of the preset cleaning head in the first cleaning image, and collecting a first longest-diameter pixel number of the first pixel area;
reading the number of pre-aiming pixels, and drawing a target circle by taking the center of the first longest path pixel number as a circle center and the number of pre-aiming pixels as a radius;
taking the intersection point of the target circle and the target theoretical track as a pre-aiming pixel point;
acquiring a first actual movement direction of the first pixel region, and taking an included angle between the first actual movement direction and the pre-aiming pixel point as a first deviation angle;
Calculating a first actual rotation angle of the powder cleaning manipulator based on the first longest path pixel number, the pretightening pixel number and the first deviation angle;
extracting a second clear powder image in the clear powder image sequence of the brush head, and analyzing the first clear powder image to obtain a second actual corner, wherein the first clear powder image and the second clear powder image are adjacent images;
and generating the actual powder cleaning track of the powder cleaning manipulator based on the first actual rotation angle and the second actual rotation angle.
Further, the powder cleaning result image module 16 is further configured to:
analyzing the second powder cleaning data to obtain a powder cleaning effect index;
if the powder cleaning effect index meets a preset index threshold, acquiring a second structure body in the target structure body set, and performing powder cleaning treatment on the second structure body;
wherein the second structure is a structure which is mutually connected with the first structure.
The included units and modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Example III
Fig. 5 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 5, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 5, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 5, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to the digital management-based metal 3D printing method in the embodiment of the present invention. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e., implements the above-described digital management-based metal 3D printing method.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. The metal 3D printing method based on digital management is characterized in that the metal 3D printing method is applied to a metal 3D printing system, the metal 3D printing system is in communication connection with a powder cleaning manipulator, and the metal 3D printing method comprises the following steps:
carrying an intelligent camera by the powder cleaning manipulator, and acquiring multi-angle image information of a target metal workpiece to obtain a target image set;
analyzing the target image set and constructing a target three-dimensional model of the target metal workpiece;
performing structure division on the target three-dimensional model to obtain a target structure body set, wherein the target structure body set comprises a first structure body;
Detecting a first curvature of a first structural surface in the first structural body, and judging whether the first curvature meets a preset curvature constraint;
if the first curvature is satisfied, analyzing the first curvature to obtain a first preset powder cleaning track, and if the first curvature is not satisfied, calling the preset powder cleaning track;
the powder cleaning manipulator controls a preset powder cleaning brush head to perform powder cleaning treatment on the first structural surface based on the first preset powder cleaning track or the preset powder cleaning track, and a first powder cleaning result image is acquired through the intelligent camera;
and analyzing the first powder cleaning result image to obtain first powder cleaning data of the first structure, wherein the first powder cleaning data refers to preliminary powder cleaning result data of the first structure.
2. The method of claim 1, wherein said analyzing the set of target images and constructing a target three-dimensional model of the target metal workpiece comprises:
constructing a workpiece registration fusion model by utilizing a random sampling consistency algorithm principle;
obtaining a point cloud data set of the target image set, and taking the point cloud data set as input information of the workpiece registration fusion model to obtain model output information;
Extracting a workpiece parameter estimation result in the model output information, and obtaining a preset point cloud model based on the workpiece parameter estimation result;
obtaining basic characteristic parameters of the intelligent camera, and performing texture mapping on the preset point cloud model based on the basic characteristic parameters to obtain a texture mapping result;
and taking the texture mapping result as the target three-dimensional model.
3. The method according to claim 2, wherein obtaining the point cloud data set of the target image set and using the point cloud data set as input information of the workpiece registration fusion model to obtain model output information includes:
the workpiece registration fusion model randomly samples the point cloud data set to obtain a first sample set, and obtains a first parameter estimation result of the target three-dimensional model based on the first sample set;
removing the first sample set from the point cloud data set to obtain a first non-sample set, wherein the first non-sample set comprises a plurality of non-sample point cloud data;
sequentially calculating the distances from the plurality of non-sample point cloud data to the target three-dimensional model, and screening the plurality of non-sample point cloud data by combining a preset distance threshold value to obtain a first consistency point set;
Calculating a first data volume in the first consistency point set, and judging whether the first data volume meets a preset quantity threshold;
if the first data volume meets the preset quantity threshold, a first re-estimation instruction is obtained, and a second parameter estimation result of the target three-dimensional model is obtained by combining the first consistency point set according to the first re-estimation instruction;
and replacing the first parameter estimation result with the second parameter estimation result to serve as the model output information.
4. The method of claim 1, wherein controlling the predetermined cleaning head to perform the cleaning process on the first structural surface comprises:
acquiring an included angle between the first structural surface and the ground, and recording the included angle as a first included angle;
performing angle adjustment on the powder cleaning manipulator according to the first included angle to obtain a first angle adjustment manipulator;
acquiring size information of the first structural surface, and recording the size information as the size of the first structural surface;
generating a first arch-shaped track based on the first structural surface size model, and taking the first arch-shaped track as the preset powder cleaning track;
the first angle adjusting manipulator performs powder cleaning treatment on the first structural surface based on the preset powder cleaning track.
5. The method of claim 4, wherein the purge manipulator has a trajectory tracking model embedded therein, comprising:
obtaining an actual powder cleaning track of the powder cleaning manipulator through the track tracking model;
comparing the actual powder cleaning track with the first preset powder cleaning track to obtain first track comparison data, and analyzing the first track comparison data to obtain a first powder cleaning deviation;
comparing the actual powder cleaning track with the preset powder cleaning track to obtain second track comparison data, and analyzing the second track comparison data to obtain a second powder cleaning deviation;
if the first powder cleaning deviation or the second powder cleaning deviation accords with a preset deviation threshold, a secondary powder cleaning instruction is sent out;
and replacing the preset cleaning brush head with a preset cleaning cotton head according to the secondary cleaning instruction, and performing cleaning treatment on the first structural surface to obtain second cleaning data.
6. The method of claim 5, wherein the obtaining, by the trajectory tracking model, an actual purging trajectory of the purging robot comprises:
the track tracking model controls the intelligent camera to acquire images of the preset cleaning brush head at preset frequency to obtain a brush head cleaning image sequence;
Extracting a first powder cleaning image in the brush head powder cleaning image sequence;
positioning a first pixel area of the preset cleaning head in the first cleaning image, and collecting a first longest-diameter pixel number of the first pixel area;
reading the number of pre-aiming pixels, and drawing a target circle by taking the center of the first longest path pixel number as a circle center and the number of pre-aiming pixels as a radius;
taking the intersection point of the target circle and the target theoretical track as a pre-aiming pixel point;
acquiring a first actual movement direction of the first pixel region, and taking an included angle between the first actual movement direction and the pre-aiming pixel point as a first deviation angle;
calculating a first actual rotation angle of the powder cleaning manipulator based on the first longest path pixel number, the pretightening pixel number and the first deviation angle;
extracting a second clear powder image in the clear powder image sequence of the brush head, and analyzing the first clear powder image to obtain a second actual corner, wherein the first clear powder image and the second clear powder image are adjacent images;
and generating the actual powder cleaning track of the powder cleaning manipulator based on the first actual rotation angle and the second actual rotation angle.
7. The method of claim 5, wherein after said replacing said predetermined cleaning head with said predetermined cleaning cotton head, performing cleaning treatment on said first structured surface to obtain second cleaning data, comprising:
analyzing the second powder cleaning data to obtain a powder cleaning effect index;
if the powder cleaning effect index meets a preset index threshold, acquiring a second structure body in the target structure body set, and performing powder cleaning treatment on the second structure body;
wherein the second structure is a structure which is mutually connected with the first structure.
8. A metal 3D printing system based on digital management, wherein the metal 3D printing system performs the method of any one of claims 1 to 7, the metal 3D printing system is communicatively connected to a powder cleaning robot, the metal 3D printing system comprises:
the image acquisition module is used for carrying an intelligent camera through the powder cleaning manipulator, and acquiring multi-angle image information of a target metal workpiece to obtain a target image set;
the three-dimensional model construction module is used for analyzing the target image set and constructing a target three-dimensional model of the target metal workpiece;
The target structure body acquisition module is used for carrying out structure division on the target three-dimensional model to obtain a target structure body set, wherein the target structure body set comprises a first structure body;
the curvature detection module is used for detecting a first curvature of a first structural surface in the first structural body and judging whether the first curvature meets a preset curvature constraint;
the powder cleaning track acquisition module is used for analyzing the first curvature to obtain a first preset powder cleaning track if the first curvature is met, and calling the preset powder cleaning track if the first curvature is not met;
the powder cleaning result image module is used for controlling a preset powder cleaning brush head to perform powder cleaning treatment on the first structural surface based on the first preset powder cleaning track or the preset powder cleaning track by the powder cleaning manipulator, and collecting a first powder cleaning result image by the intelligent camera;
the powder cleaning result acquisition module is used for analyzing the first powder cleaning result image to obtain first powder cleaning data of the first structure body, wherein the first powder cleaning data refer to preliminary powder cleaning result data of the first structure body.
9. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
A processor for implementing the digitally managed metal 3D printing method of any one of claims 1 to 7 when executing executable instructions stored in the memory.
10. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a digitally managed metal 3D printing method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310840912.9A CN116727691B (en) | 2023-07-11 | 2023-07-11 | Metal 3D printing method and system based on digital management |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310840912.9A CN116727691B (en) | 2023-07-11 | 2023-07-11 | Metal 3D printing method and system based on digital management |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116727691A true CN116727691A (en) | 2023-09-12 |
| CN116727691B CN116727691B (en) | 2023-11-17 |
Family
ID=87902763
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310840912.9A Active CN116727691B (en) | 2023-07-11 | 2023-07-11 | Metal 3D printing method and system based on digital management |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116727691B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116985525A (en) * | 2023-09-25 | 2023-11-03 | 深圳正实自动化设备有限公司 | Intelligent management method and system for full-automatic vision printer |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107175329A (en) * | 2017-04-14 | 2017-09-19 | 华南理工大学 | A kind of 3D printing successively detects reverse part model and positioning defect apparatus and method |
| US20180297113A1 (en) * | 2017-04-14 | 2018-10-18 | Desktop Metal, Inc. | Calibration of 3d printer via computer vision |
| EP3581379A1 (en) * | 2018-06-13 | 2019-12-18 | XYZprinting, Inc. | Three-dimensional printing device |
| CN113255706A (en) * | 2021-07-16 | 2021-08-13 | 南京奕荣芯科技有限公司 | Target identification method and system based on convolutional neural network algorithm |
| CN114102274A (en) * | 2021-11-12 | 2022-03-01 | 苏州大学 | 3D printing part processing method |
| CN114274501A (en) * | 2021-11-26 | 2022-04-05 | 深圳市创想三维科技股份有限公司 | 3D printer continuous printing method and device, computer equipment and storage medium |
| CN115138877A (en) * | 2022-06-27 | 2022-10-04 | 季华实验室 | Powder cleaning device and powder cleaning method for 3D printing part |
| CN217777801U (en) * | 2022-08-12 | 2022-11-11 | 浙江拓博环保科技有限公司 | Platform rotary mechanism and powder cleaning equipment |
| CN115453570A (en) * | 2022-09-13 | 2022-12-09 | 北京踏歌智行科技有限公司 | Multi-feature fusion mining area dust filtering method |
| CN115578523A (en) * | 2022-11-21 | 2023-01-06 | 慧医谷中医药科技(天津)股份有限公司 | Tongue three-dimensional modeling method and system for multi-angle image fusion |
| KR20230045290A (en) * | 2021-09-28 | 2023-04-04 | 주식회사 테라웍스 | Apparatus for manufacturing products and method for manufacturing products using therewith |
| CN116141126A (en) * | 2023-02-16 | 2023-05-23 | 中国铁建重工集团股份有限公司 | Automatic robot polishing method and system for frog castings |
-
2023
- 2023-07-11 CN CN202310840912.9A patent/CN116727691B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107175329A (en) * | 2017-04-14 | 2017-09-19 | 华南理工大学 | A kind of 3D printing successively detects reverse part model and positioning defect apparatus and method |
| US20180297113A1 (en) * | 2017-04-14 | 2018-10-18 | Desktop Metal, Inc. | Calibration of 3d printer via computer vision |
| EP3581379A1 (en) * | 2018-06-13 | 2019-12-18 | XYZprinting, Inc. | Three-dimensional printing device |
| CN113255706A (en) * | 2021-07-16 | 2021-08-13 | 南京奕荣芯科技有限公司 | Target identification method and system based on convolutional neural network algorithm |
| KR20230045290A (en) * | 2021-09-28 | 2023-04-04 | 주식회사 테라웍스 | Apparatus for manufacturing products and method for manufacturing products using therewith |
| CN114102274A (en) * | 2021-11-12 | 2022-03-01 | 苏州大学 | 3D printing part processing method |
| CN114274501A (en) * | 2021-11-26 | 2022-04-05 | 深圳市创想三维科技股份有限公司 | 3D printer continuous printing method and device, computer equipment and storage medium |
| CN115138877A (en) * | 2022-06-27 | 2022-10-04 | 季华实验室 | Powder cleaning device and powder cleaning method for 3D printing part |
| CN217777801U (en) * | 2022-08-12 | 2022-11-11 | 浙江拓博环保科技有限公司 | Platform rotary mechanism and powder cleaning equipment |
| CN115453570A (en) * | 2022-09-13 | 2022-12-09 | 北京踏歌智行科技有限公司 | Multi-feature fusion mining area dust filtering method |
| CN115578523A (en) * | 2022-11-21 | 2023-01-06 | 慧医谷中医药科技(天津)股份有限公司 | Tongue three-dimensional modeling method and system for multi-angle image fusion |
| CN116141126A (en) * | 2023-02-16 | 2023-05-23 | 中国铁建重工集团股份有限公司 | Automatic robot polishing method and system for frog castings |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116985525A (en) * | 2023-09-25 | 2023-11-03 | 深圳正实自动化设备有限公司 | Intelligent management method and system for full-automatic vision printer |
| CN116985525B (en) * | 2023-09-25 | 2024-01-09 | 深圳正实自动化设备有限公司 | Intelligent management method and system for full-automatic vision printer |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116727691B (en) | 2023-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10430951B2 (en) | Method and device for straight line detection and image processing | |
| CN116727691B (en) | Metal 3D printing method and system based on digital management | |
| CN111844101B (en) | Multi-finger dexterous hand sorting planning method | |
| CN109633676A (en) | A kind of method and system based on the laser radar obstruction detection direction of motion | |
| CN111523414A (en) | Face recognition method and device, computer equipment and storage medium | |
| WO2016068869A1 (en) | Three dimensional object recognition | |
| CN107993488A (en) | A kind of parking stall recognition methods, system and medium based on fisheye camera | |
| CN115240149A (en) | Three-dimensional point cloud detection and identification method and device, electronic equipment and storage medium | |
| CN111008994A (en) | Moving target real-time detection and tracking system and method based on MPSoC | |
| Liang et al. | An extraction and classification algorithm for concrete cracks based on machine vision | |
| CN111008956A (en) | Beam bottom crack detection method, system, device and medium based on image processing | |
| CN109313708B (en) | Image matching method and vision system | |
| Chen et al. | Geometry-based optimized point cloud compression methodology for construction and infrastructure management | |
| CN112232248A (en) | Method and device for extracting plane features of multi-line LiDAR point cloud data | |
| CN113643544B (en) | Intelligent detection method and system for illegal parking in parking lot based on Internet of things | |
| CN110824451A (en) | Processing method and device of radar echo map, computer equipment and storage medium | |
| CN115984759A (en) | Substation switch state identification method and device, computer equipment and storage medium | |
| WO2015168362A1 (en) | Method and apparatus for processing block to be processed of urine sediment image | |
| Cai et al. | Unfeatured weld positioning technology based on neural network and machine vision | |
| Wu et al. | A novel least square and image rotation based method for solving the inclination problem of license plate in its camera captured image | |
| CN110399783A (en) | Traffic action triggers platform, method and storage medium based on image analysis | |
| CN107392209A (en) | A kind of method, module, device and storage device for extracting line segment | |
| Wang et al. | Obstacle recognition method for ship based on 3D lidar | |
| CN114723748B (en) | Detection method, device and equipment of motor controller and storage medium | |
| Hamzah et al. | Developed an Automated Design Sorting System According to Outer Edges of Object |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |