CN115431527B - 3D printing management optimization method and system - Google Patents
3D printing management optimization method and system Download PDFInfo
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- CN115431527B CN115431527B CN202211057684.XA CN202211057684A CN115431527B CN 115431527 B CN115431527 B CN 115431527B CN 202211057684 A CN202211057684 A CN 202211057684A CN 115431527 B CN115431527 B CN 115431527B
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- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000010146 3D printing Methods 0.000 title claims abstract description 27
- 238000005457 optimization Methods 0.000 title claims abstract description 26
- 238000007639 printing Methods 0.000 claims abstract description 61
- 238000012797 qualification Methods 0.000 claims abstract description 14
- 230000005764 inhibitory process Effects 0.000 claims description 14
- 239000000463 material Substances 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 10
- 238000004806 packaging method and process Methods 0.000 claims description 6
- 238000003062 neural network model Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 description 5
- 238000005070 sampling Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 230000033228 biological regulation Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 230000009191 jumping Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 229910001220 stainless steel Inorganic materials 0.000 description 1
- 239000010935 stainless steel Substances 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
- B29C64/30—Auxiliary operations or equipment
- B29C64/386—Data acquisition or data processing for additive manufacturing
- B29C64/393—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
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- 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
- B33Y50/00—Data acquisition or data processing for additive manufacturing
- B33Y50/02—Data acquisition or data processing for additive manufacturing for controlling or regulating additive manufacturing processes
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- Optics & Photonics (AREA)
Abstract
The invention discloses a 3D printing management optimization method and a system, wherein the method comprises the following steps: acquiring point cloud data of an object model to be printed; identifying the point cloud data by using a pre-built object identification model to obtain a category corresponding to the object model to be printed; when the category is a controlled article, judging whether a user has printing qualification for the article model to be printed; if yes, slicing the object model to be printed to generate a target printing file, and then sending the target printing file to a 3D printer for printing; if not, generating printing alarm information, wherein the printing alarm information is used for prompting the user to reject printing. According to the invention, the user printing authority is checked before the slicing operation is implemented, and slicing is refused when the checking result is failed, so that the potential safety hazard problem existing in the current 3D printing field can be effectively solved, and unnecessary workload is avoided.
Description
Technical Field
The invention relates to the technical field of 3D printing management, in particular to a 3D printing management optimization method and system.
Background
Any object model searched on the internet can generate a format file which can be identified by a 3D printer through 3D printing slicing software when the application safety of the object model is not checked, and then the format file is input into the 3D printer for finished product production without any obstacle. That is, users are completely likely to illegally use the household 3D printer to make illicit controlled articles such as firearms, daggers, spring knives and the like, and the current 3D printing field has a great potential threat.
Disclosure of Invention
The invention provides a 3D printing management optimization method and system, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
The embodiment of the invention provides a 3D printing management optimization method, which comprises the following steps:
Acquiring point cloud data of an object model to be printed;
Identifying the point cloud data by using a pre-built object identification model to obtain a category corresponding to the object model to be printed;
when the category is a controlled article, judging whether a user has printing qualification for the article model to be printed;
If yes, slicing the object model to be printed to generate a target printing file, and then sending the target printing file to a 3D printer for printing;
if not, generating printing alarm information, wherein the printing alarm information is used for prompting the user to reject printing.
Further, the construction process of the article identification model is as follows:
Acquiring a training data set, wherein the training data set comprises a plurality of first point cloud data and a plurality of second point cloud data, the plurality of first point cloud data represent different controlled object models, and the plurality of second point cloud data represent different common object models;
and inputting the training data set into a neural network model for training to obtain the article identification model.
Further, the determining whether the user qualifies for printing the to-be-printed item model includes:
Acquiring a first image, wherein the first image contains business license of a user;
Judging whether the business license is forged or not; if yes, generating the printing alarm information;
if not, acquiring a second image, wherein the second image is an identity card image of the user;
Generating a printing model file, wherein the printing model file internally records the to-be-printed object model and 3D printing materials appointed by a user;
Compressing and packaging the first image, the second image and the printing model file, and transmitting the packaged file formed after packaging to a background server for checking;
Determining that a user has printing qualification to the to-be-printed object model according to the license information fed back by the background server;
Or determining that the user does not have the printing qualification of the to-be-printed object model according to the forbidden information fed back by the background server.
Further, the determining whether the business license has forgery includes:
scanning and identifying the two-dimensional code in the business license to obtain a first data set;
invoking an OCR recognition tool to extract information of the business license to obtain a second data set;
Judging whether the first data set is completely matched with the second data set; if yes, determining that the business license is not forged; if not, determining that the business license is forged.
Further, the first data set and the second data set each include a uniform social credit code, name, and legal representative.
Further, the checking process of the background server is as follows:
calling an OCR recognition tool to extract information of the identity card image to obtain a third data set;
acquiring identification information registered by the business license during handling to obtain a fourth data set;
Judging whether the third data set is completely matched with the fourth data set; if not, generating first inhibition information, wherein the first inhibition information represents authentication failure;
If yes, judging whether the to-be-printed object model accords with the operation range described on the business license or not by combining the 3D printing material; if yes, generating the permission information; if the model is not matched with the operation range, generating second inhibition information, wherein the second inhibition information represents that the model exceeds the operation range;
wherein the first prohibition information and the second prohibition information are collectively referred to as the prohibition information.
Further, the third data set and the fourth data set each include a name, a date of birth, a home address, and an identification number.
Further, the method further comprises:
And when the category is a common article, slicing the article model to be printed to generate a target printing file, and then sending the target printing file to a 3D printer for printing.
Further, before slicing the to-be-printed object model to generate a target print file, the method further comprises:
acquiring construction feature information contained in the to-be-printed object model, and carrying out query matching in a preset model feature database according to the construction feature information to acquire optimal placement information;
and readjusting the current posture of the object model to be printed according to the optimal placement information.
In addition, an embodiment of the present invention provides a 3D print management optimization system, including:
At least one processor;
At least one memory for storing at least one program;
The at least one program, when executed by the at least one processor, causes the at least one processor to implement the 3D print management optimization method of any one of the above.
The invention has at least the following beneficial effects: the object model which is wanted to be printed by the user is subjected to point cloud identification by setting up the object identification model, and when the object model is identified to be of a control type, after the business license and the identification information provided by the user are subjected to double verification, the object model can be sliced so as to facilitate the 3D printer to perform the next printing operation, so that the potential safety hazard problem in the current 3D printing field can be effectively solved, and the method has good practical value. The slicing operation is an indispensable link in the 3D printing process, that is, a step before the 3D printer performs the normal printing operation, by performing the print authority check of the user before the slicing operation is performed, and rejecting slicing when the check result is failed, thereby avoiding unnecessary workload. By adjusting the placement position of the input object model before slicing it, support material is saved and print quality is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate and do not limit the invention.
FIG. 1 is a flow chart of a 3D print management optimization method in an embodiment of the invention;
Fig. 2 is a flowchart of a method for determining print qualification of a user according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It should be noted that although functional block diagrams are depicted as block diagrams, and logical sequences are shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the block diagrams in the system. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Referring to fig. 1, fig. 1 is a flow chart of a 3D print management optimization method according to an embodiment of the present invention, where the method includes the following steps:
S100, acquiring point cloud data of an object model to be printed;
S200, identifying the point cloud data by using a pre-built object identification model to obtain a category corresponding to the object model to be printed;
S300, judging whether the category is a controlled article or not; if yes, go to step S400; if not, the category is indicated as a common object, and the step S500 is executed in a jumping manner;
s400, judging whether a user has printing qualification to the to-be-printed object model; if yes, executing step S500; if not, executing step S600;
S500, slicing the object model to be printed to generate a target printing file, and then sending the target printing file to a 3D printer for printing;
And S600, generating printing alarm information, wherein the printing alarm information is used for prompting a user to reject printing.
It should be noted that the above-mentioned 3D print management optimization method is mainly applied to 3D print slicing software, where the 3D print slicing software is loaded in a user terminal device, and the user terminal device is remotely connected with a background server.
In the embodiment of the present invention, the implementation process of the step S100 is as follows: processing the object model to be printed, which is imported by a user, by utilizing CloudCompare software to obtain corresponding point cloud data; wherein CloudCompare software is mature three-dimensional point cloud editing processing software.
In the embodiment of the present invention, the process of building the object identification model mentioned in the step S200 includes the following steps:
S210, acquiring a training data set, wherein the training data set comprises a plurality of first point cloud data and a plurality of second point cloud data; wherein the first point cloud data characterize different controlled article models, and the second point cloud data characterize different common article models; the different controlled article models include a gun model, a dagger model and a spring knife model, and the different common article models are actually other article models except for the type of the controlled article;
S220, inputting the training data set into a neural network model for training to obtain the article identification model; the neural network model can be PointNet ++ model, and comprises a feature extraction network, a feature propagation network and a fully-connected network which are connected in sequence.
More specifically, the feature extraction network includes a sampling layer, a grouping layer and a PointNeting layer, where the sampling layer is configured to perform FPS (Farthest Point Sampling, furthest point sampling) operation on point cloud data input by a model to obtain a plurality of sampling center points, the grouping layer is configured to determine a plurality of center point neighbors according to the plurality of sampling center points, and the PointNeting layer is configured to perform feature extraction on the plurality of center point neighbors to correspondingly obtain a plurality of local feature data; the feature propagation network comprises an interpolation layer and an MLP (Multilayer Perceptron, multi-layer perceptron) layer, wherein the interpolation layer is used for splicing the local feature data and the respectively associated low-layer feature data to obtain a plurality of combined feature data, the low-layer feature data associated with each local feature data is obtained by weighting all sampling point data in the neighborhood of a central point corresponding to the local feature data, and the MLP layer is used for dimension integrating the combined feature data to obtain global feature data; the fully connected network is used for carrying out label classification on the global characteristic data.
In the embodiment of the present invention, as shown in fig. 2, the implementation process of step S400 includes the following steps:
S410, acquiring a first image, wherein the first image contains business licenses of a user;
S420, judging whether the business license is forged or not; if yes, directly executing the step S600; if not, executing step S430;
s430, acquiring a second image, wherein the second image is an identity card image of the user;
s440, generating a printing model file, wherein the printing model file internally records the to-be-printed object model and 3D printing materials appointed by a user;
S450, compressing and packaging the first image, the second image and the printing model file, and transmitting the packaged file formed after packaging to a background server for checking;
S460, receiving information fed back by the background server, and judging the information as permission information or inhibition information; when the license information is fed back by the background server, executing step S470; when the background server feeds back the forbidden information, executing step S480;
S470, determining that the user has the printing qualification of the to-be-printed object model;
s480, determining that the user does not have the printing qualification of the to-be-printed object model.
It should be noted that, when the above step S410 or the above step S430 is not performed, that is, when the user refuses to provide the first image or the second image, the step S600 is directly performed.
In the above step S420, the implementation process includes the following steps:
s421, scanning and identifying the two-dimensional code in the business license to obtain a first data set;
S422, invoking an OCR recognition tool to extract information of the business license to obtain a second data set;
S433, judging whether the first data set is completely matched with the second data set; if yes, determining that the business license is not forged; if not, determining that the business license is forged.
It should be noted that, the first data set and the second data set each include a unified social credit code, a name and a legal representative, and it is necessary to ensure that the above information completely matches when executing the above step S433.
In the above-described step S440, the to-be-printed article model is stored in the print model file in the 3DS format or the STL format, and the 3D printed material is registered in the form of a note and stored in the print model file.
After executing the step S450, when the background server receives the package file, the first image, the second image and the print model file are preferentially decompressed from the package file, and then the above files are synthesized for inspection, and the specific implementation process includes the following steps:
(1) Calling an OCR recognition tool to extract information of the identity card image to obtain a third data set;
(2) Acquiring identification information registered by the business license during handling to obtain a fourth data set;
(3) Judging whether the third data set is completely matched with the fourth data set; if yes, jumping to execute the step (5); if not, directly executing the step (4);
(4) Generating first inhibition information, wherein the first inhibition information represents authentication failure;
(5) Judging whether the to-be-printed object model accords with the operation range described on the business license or not by combining the 3D printing material; if yes, executing the step (6); if not, executing the step (7);
(6) Generating the license information, wherein the license information represents that the model meets the printing requirements;
(7) Generating second inhibition information, wherein the second inhibition information represents that the model exceeds the operation range;
wherein the first prohibition information and the second prohibition information are collectively referred to as the prohibition information.
It should be noted that, the third data set and the fourth data set each include a name, a birth date, a home address, and an identification card number, and it is necessary to ensure that the above information completely matches when executing the step (3).
It should be noted that, when executing the step (5), the step is mainly dependent on a manual checking mode, after opening the print model file, the manager of the background server presumes that the object model to be printed is a gun model and the 3D printing material is plastic, and continuously checks whether the toy manufacturing field is mentioned in the operation range described on the business license, if yes, the permission information is issued, and if not, the second prohibition information is issued; or if the manager checks that the object model to be printed is a dagger model and the 3D printing material is stainless steel, continuously checking whether the cutter manufacturing field is mentioned in the operation range described on the business license, if so, issuing the permission information, and if not, issuing the second inhibition information; and so on.
In an embodiment of the present invention, before executing the step S500, the method further includes: firstly, acquiring construction characteristic information contained in the to-be-printed article model, and carrying out query matching in a preset model database according to the construction characteristic information to acquire optimal placement information; and secondly, readjusting the current posture of the object model to be printed according to the optimal placement information.
The preset model feature database is created by a user in advance, and the preset model feature database stores the construction feature information and the associated optimal placement information of each of a plurality of controlled object models and the construction feature information and the associated optimal placement information of each of a plurality of common object models in the controlled object models, wherein the construction feature information can be all plane features and all curved surface features of the composition model, and the optimal placement information actually characterizes which surface of the models is used as the final model placement bottom surface, so that the use amount of supporting materials can be reduced, and the printing quality can be improved.
In the embodiment of the present invention, the print warning information mentioned in the step S600 is actually accompanied by a reason for rejecting the user to print (i.e. rejecting slicing the model of the article to be printed), that is, identity verification fails or the model exceeds the operating range; in addition, the printed alert information may actually be accompanied by a hyperlink for popularizing laws and regulations regarding weapon regulation to the user.
In the embodiment of the invention, the object model to be printed by the user is subjected to point cloud identification by building the object identification model, and when the object model is identified to be of a control type, after the business license and the identification information provided by the user are subjected to double verification, the object model can be allowed to be sliced so as to facilitate the 3D printer to perform the next printing operation, so that the problem of potential safety hazard in the current 3D printing field can be effectively solved, and the method has good practical value. The slicing operation is an indispensable link in the 3D printing process, that is, a step before the 3D printer performs the normal printing operation, by performing the print authority check of the user before the slicing operation is performed, and rejecting slicing when the check result is failed, thereby avoiding unnecessary workload. By adjusting the placement position of the input object model before slicing it, support material is saved and print quality is improved.
In addition, the embodiment of the invention also provides a 3D printing management optimization method system, which comprises the following steps:
At least one processor;
At least one memory for storing at least one program;
The at least one program, when executed by the at least one processor, causes the at least one processor to implement the 3D print management optimization method described in the above method embodiment.
The content in the method embodiment is applicable to the system embodiment, the functions achieved by the system embodiment are the same as those achieved by the method embodiment, and the achieved beneficial effects are the same as those achieved by the method embodiment.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, digital-Signal-Processor (DSP), application-Specific-Integrated-Circuit (ASIC), field-Programmable-Gate Array (FPGA), or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the 3D print management optimization system, and connects various parts of the whole 3D print management optimization system operable device using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the 3D print management optimization system by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a memory program area and a memory data area, wherein: the storage program area is used for storing an operating system, application programs (such as a sound playing function, an image playing function and the like) required by at least one function and the like; the storage data area is used to store data (such as audio data, phonebook, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart-Media-Card (SMC), secure-Digital (SD) Card, flash Card (Flash-Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
While the present application has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiments or any particular embodiment, but is to be considered as providing a broad interpretation of such claims by reference to the appended claims in light of the prior art and thus effectively covering the intended scope of the application. Furthermore, the foregoing description of the application has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the application that may not be presently contemplated, may represent an equivalent modification of the application.
Claims (9)
1. A method of 3D print management optimization, the method comprising:
Acquiring point cloud data of an object model to be printed;
Identifying the point cloud data by using a pre-built object identification model to obtain a category corresponding to the object model to be printed;
when the category is a controlled article, judging whether a user has printing qualification for the article model to be printed;
if the user has the printing qualification of the to-be-printed object model, slicing the to-be-printed object model to generate a target printing file, and then sending the target printing file to a 3D printer for printing;
If the user does not have the printing qualification to the to-be-printed article model, generating printing alarm information, wherein the printing alarm information is used for prompting the user to reject printing;
Wherein the judging whether the user has the print qualification to the to-be-printed article model comprises:
Acquiring a first image, wherein the first image contains business license of a user;
Judging whether the business license is forged or not; if the business license is forged, generating the printing alarm information;
if the business license is not forged, a second image is acquired, wherein the second image is an identity card image of the user;
Generating a printing model file, wherein the printing model file internally records the to-be-printed object model and 3D printing materials appointed by a user;
Compressing and packaging the first image, the second image and the printing model file, and transmitting the packaged file formed after packaging to a background server for checking;
Determining that a user has printing qualification to the to-be-printed object model according to the license information fed back by the background server;
Or determining that the user does not have the printing qualification of the to-be-printed object model according to the forbidden information fed back by the background server.
2. The 3D printing management optimization method according to claim 1, wherein the building process of the article identification model is as follows:
Acquiring a training data set, wherein the training data set comprises a plurality of first point cloud data and a plurality of second point cloud data, the plurality of first point cloud data represent different controlled object models, and the plurality of second point cloud data represent different common object models;
and inputting the training data set into a neural network model for training to obtain the article identification model.
3. The 3D printing management optimization method according to claim 1, wherein the judging whether the business license is forged or not includes:
scanning and identifying the two-dimensional code in the business license to obtain a first data set;
invoking an OCR recognition tool to extract information of the business license to obtain a second data set;
Judging whether the first data set is completely matched with the second data set; if yes, determining that the business license is not forged; if not, determining that the business license is forged.
4. The 3D print management optimization method according to claim 3, wherein the first data set and the second data set each comprise a uniform social credit code, name, and legal representatives.
5. The 3D printing management optimization method according to claim 1, wherein the checking process of the background server is:
calling an OCR recognition tool to extract information of the identity card image to obtain a third data set;
acquiring identification information registered by the business license during handling to obtain a fourth data set;
Judging whether the third data set is completely matched with the fourth data set; if not, generating first inhibition information, wherein the first inhibition information represents authentication failure;
If yes, judging whether the to-be-printed object model accords with the operation range described on the business license or not by combining the 3D printing material; if yes, generating the permission information; if the model is not matched with the operation range, generating second inhibition information, wherein the second inhibition information represents that the model exceeds the operation range;
wherein the first prohibition information and the second prohibition information are collectively referred to as the prohibition information.
6. The 3D printing management optimization method according to claim 5, wherein the third data set and the fourth data set each include a name, a birth date, a home address, and an identification card number.
7. The 3D print management optimization method according to claim 1, further comprising:
And when the category is a common article, slicing the article model to be printed to generate a target printing file, and then sending the target printing file to a 3D printer for printing.
8. The 3D print management optimization method according to claim 7, further comprising, before slicing the to-be-printed item model to generate a target print file:
acquiring construction feature information contained in the to-be-printed object model, and carrying out query matching in a preset model feature database according to the construction feature information to acquire optimal placement information;
and readjusting the current posture of the object model to be printed according to the optimal placement information.
9. A 3D print management optimization system, the system comprising:
At least one processor;
At least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the 3D print management optimization method according to any one of claims 1 to 8.
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