CN116700152A - Real-time defect identification and self-correction control method and device for additive manufacturing - Google Patents
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Abstract
The invention relates to the technical field of intelligent control, in particular to a real-time defect identification and self-correction control method and device for additive manufacturing, which can acquire a model diagram to be manufactured and acquire process control parameters and paths according to the model diagram by using a machine learning algorithm; executing a printing process for manufacturing corresponding products according to the process control parameters and the paths, and identifying defects of the products in real time; updating the model diagram according to the related data obtained in the printing process, constructing a new model diagram, and comparing the new model diagram with the model diagram to obtain difference data; and analyzing the difference data by using a machine learning algorithm, optimizing the process control parameters and paths, and updating the process control parameters and paths in real time. It can be appreciated that the technical scheme provided by the invention can automatically identify the defects of the product in the process of additive manufacturing so as to optimize the control parameters and paths of printing, does not need the whole process participation of staff in tracking the production state, has simple operation and improves the production efficiency and the production precision.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to a real-time defect identification and self-correction control method and device for additive manufacturing.
Background
The additive manufacturing technology is to design, slice, path plan, forming control, post-treatment and other modules according to CAD/CAM (Management Software Computer Aided Design ), generate a program which can be identified by a robot to control the movement of the robot, and enable a welding gun of a welding heat source device to move along with the robot to generate arc heat source melting (wire is added by a wire adding device and simultaneously protected by gas inflation) wire forming. The integrated forming from the digital model to the solid part is realized by adopting a layer-by-layer accumulation method to manufacture the solid part, and compared with the traditional material reduction manufacturing (cutting processing) technology, the integrated forming method is a material accumulation manufacturing method. Additive manufacturing may be combined with welding techniques to achieve more complex part manufacturing. For example, in 3D printing of metallic materials, gaps between printed layers may be filled using a welding technique, thereby improving strength and sealability of components.
However, additive manufacturing and welding production assemblies suffer from a variety of internal defects such as powder agglomeration, ball compaction, porosity, cracking and thermal/cold internal stresses, deformation, etc., which significantly affect the forming quality, mechanical properties and final product safety properties of the part. In the additive manufacturing process at present, in order to solve the problems, the workers usually participate in tracking the production state in the whole process, so that the operation difficulty of the workers is high, and the production precision is insufficient.
Disclosure of Invention
Accordingly, the present invention is directed to a method and apparatus for real-time defect identification and self-correction control in additive manufacturing, which solves the problem that defects easily occur during the additive manufacturing process in the prior art, thereby affecting the quality of the final product.
According to a first aspect of an embodiment of the present invention, there is provided a real-time defect identification and self-correction control method for additive manufacturing, applied to an additive manufacturing system, including:
obtaining a model diagram to be manufactured, and obtaining process control parameters and paths according to the model diagram by using a machine learning algorithm;
executing a printing process for manufacturing a corresponding product according to the process control parameters and the paths, and carrying out defect identification on the product in real time;
updating the model diagram according to the related data obtained in the printing process, constructing a new model diagram, and comparing the new model diagram with the model diagram to obtain difference data;
and analyzing the difference data by using a machine learning algorithm, optimizing the process control parameters and paths, and updating the process control parameters and paths in real time.
Preferably, updating the model map according to the related data obtained in the printing process, and constructing a new model map, including:
and establishing a data set according to the related data obtained in the printing process, and updating the model graph according to the data set to construct a new model graph.
Preferably, the identifying the defects of the product in real time includes:
and performing defect identification including visual inspection and mechanical property test on the product during printing and after product construction of the product to obtain process characterization data, and updating the data set by taking the process characterization data as related data.
Preferably, the method further comprises:
and simulating the printing process according to the data set, the process control parameters and the paths to obtain process simulation data, and updating the data set by taking the process simulation data as related data.
Preferably, the comparing the new model diagram with the model diagram to obtain difference data includes:
taking the original model diagram as first point cloud data and taking the new model diagram as second point cloud data;
and carrying out point cloud data analysis on the first point cloud data and the second point cloud data to obtain difference data, wherein the difference data comprises point position direction coordinate differences, point position distance coordinate differences and point position area volume differences.
Preferably, analyzing the difference data using a machine learning algorithm to optimize the process control parameters and paths, comprising:
and analyzing the difference data, and if the product being manufactured is judged to need to be filled, optimizing the process control parameters and the paths, and assigning a new printing path.
Preferably, the method further comprises:
analyzing the difference data, and if the product being manufactured is judged to need to be cut, judging whether the difference is in a preset controllable range or not;
if yes, optimizing the process control parameters and paths;
if not, the printing is stopped.
According to a second aspect of an embodiment of the present invention, there is provided an additive manufacturing real-time defect identification and self-correction control device, including:
the parameter path generation module is used for acquiring a model diagram to be manufactured and obtaining process control parameters and paths according to the model diagram by utilizing a machine learning algorithm;
the defect identification module is used for executing a printing process for manufacturing corresponding products according to the process control parameters and the paths and carrying out defect identification on the products in real time;
the comparison module is used for updating the model diagram according to the related data obtained in the printing process, constructing a new model diagram, and comparing the new model diagram with the model diagram to obtain difference data;
and the parameter path optimization module is used for analyzing the difference data by utilizing a machine learning algorithm, optimizing the process control parameters and paths and updating the process control parameters and paths in real time.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
it can be understood that the technical scheme provided by the invention can acquire the model diagram to be manufactured, and acquire the process control parameters and the path according to the model diagram by using a machine learning algorithm; executing a printing process for manufacturing corresponding products according to the process control parameters and the paths, and identifying defects of the products in real time; updating the model diagram according to the related data obtained in the printing process, constructing a new model diagram, and comparing the new model diagram with the model diagram to obtain difference data; and analyzing the difference data by using a machine learning algorithm, optimizing the process control parameters and paths, and updating the process control parameters and paths in real time. It can be appreciated that the technical scheme provided by the invention can automatically identify the defects of the product in the process of additive manufacturing so as to optimize the control parameters and paths of printing, does not need the whole process participation of staff in tracking the production state, has simple operation and improves the production efficiency and the production precision.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic diagram illustrating steps of an additive manufacturing real-time defect identification and self-correction control method according to an exemplary embodiment;
FIG. 2 is a schematic diagram of an additive manufacturing system, shown according to an example embodiment;
FIG. 3 is a schematic diagram illustrating a point cloud data analysis versus compensation flow according to an exemplary embodiment;
FIG. 4 is a schematic block diagram illustrating an additive manufacturing real-time defect identification and self-correction control device, according to an example embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Example 1
FIG. 1 is a schematic diagram illustrating steps of an additive manufacturing real-time defect identification and self-correction control method according to an exemplary embodiment, and according to a first aspect of an embodiment of the present invention, an additive manufacturing real-time defect identification and self-correction control method is provided, which is applied to an additive manufacturing system, and includes:
s11, obtaining a model diagram to be manufactured, and obtaining process control parameters and paths according to the model diagram by using a machine learning algorithm;
step S12, executing a printing process for manufacturing corresponding products according to the process control parameters and the paths, and carrying out defect identification on the products in real time;
step S13, updating the model diagram according to the related data obtained in the printing process, constructing a new model diagram, and comparing the new model diagram with the model diagram to obtain difference data;
and S14, analyzing the difference data by using a machine learning algorithm, optimizing the process control parameters and paths, and updating the process control parameters and paths in real time.
In specific practice, preferably, the technical scheme relates to computer software, hardware, external equipment, a communication network, a control system, an automation element, an instrument device, artificial intelligence automation, a robot and the like, integrates real-time monitoring and identifying defects, combines with a database, autonomous learning, algorithm and other automation control process correction parameters and planning paths, so as to ensure that the quality, the precision, the mechanical property and the like of a product are improved.
The additive manufacturing system to which the present solution is applied may be a system as shown in fig. 2. Preferably, the applied additive manufacturing system may comprise the following components: robot system, 3D printing software, CMT welding machine system, monitoring sensor and control system, flexible frock, arm base, running gear, location frock, rotary platform, wherein, robot system: controlling the walking track of a mechanical arm of the robot; 3D printing software: model slicing, path planning and assignment parameters; CMT welder system: controlling welding parameters; monitoring sensor and control system: monitoring surface morphology, crack defects and the like, and monitoring temperature, molten pool, strength, hardness, hot-cold shrinkage and the like; flexible tooling: the disassembly, the displacement and the deformation are convenient, and the printing workpiece is printed on the printing workpiece; mechanical arm base: the base of the robot is supported on the travelling mechanism to accurately position the robot; and a traveling mechanism: the mechanical arm base and the robot form a travelling mechanism through a travelling bracket, and the travelling is controlled by a control system, so that the precision is ensured; positioning tool: the positioning robot is arranged on the mechanical arm base, so that the stability and the precision of the robot are ensured; and (3) rotating a platform: rotatable printing platform, flexible frock is installed on rotary platform. The main equipment capabilities are: part forming size: less than or equal to phi 3400 x 8500mm; maximum load bearing: 4500Kg; weldable materials: aluminum alloys, carbon steels, stainless steels, wear resistant alloys, copper alloys, superalloys, nickel-based alloys, and the like; printable wall thickness: 3.0-15.0 mm; welding machine current range: 3-400A; wire feed speed range: 8mm/s to 250mm/s.
Preferably, the machine learning algorithm in the technical solution shown in this embodiment includes an artificial neural network algorithm, a hierarchical clustering algorithm, a gaussian process regression algorithm, a decision tree algorithm, a random forest algorithm, a logic model tree algorithm, a fuzzy classifier algorithm, a fuzzy clustering algorithm, a k-means algorithm, a deep boltzmann machine learning algorithm, a deep recurrent neural network, a deep convolutional neural network algorithm, or any combination thereof. The machine learning algorithm is capable of automatically classifying object defects, predicting an optimal set or sequence state of process control parameters, and adjusting the production process control change parameter planning path in real time.
It can be understood that, according to the technical scheme provided by the embodiment, a model diagram to be manufactured can be obtained, and a machine learning algorithm is utilized to obtain process control parameters and paths according to the model diagram; executing a printing process for manufacturing corresponding products according to the process control parameters and the paths, and identifying defects of the products in real time; updating the model diagram according to the related data obtained in the printing process, constructing a new model diagram, and comparing the new model diagram with the model diagram to obtain difference data; and analyzing the difference data by using a machine learning algorithm, optimizing the process control parameters and paths, and updating the process control parameters and paths in real time. It can be appreciated that the technical scheme provided by the embodiment can automatically identify defects of the product in the process of additive manufacturing so as to optimize the control parameters and paths of printing, does not need the whole process participation of staff in tracking the production state, is simple to operate, and improves the production efficiency and the production precision.
It should be noted that, updating the model diagram according to the related data obtained in the printing process, and constructing a new model diagram includes:
and establishing a data set according to the related data obtained in the printing process, and updating the model graph according to the data set to construct a new model graph.
In specific practice, the data set includes all data required in the printing process and can be obtained, and the technical scheme shown in the embodiment can integrate relevant data into the data set so as to construct a new model diagram according to the data set and the model diagram. Preferably, the training data set may be updated with process simulation data, process characterization data, in-process inspection data, post-build inspection data, or any combination thereof.
It should be noted that the performing defect recognition on the product in real time includes:
and performing defect identification including visual inspection and mechanical property test on the product during printing and after product construction of the product to obtain process characterization data, and updating the data set by taking the process characterization data as related data.
In specific practice, the defect identification is performed on the product in real time, which can be real-time identification in the printing process of the product or identification after the product is printed. Performing defect identification on the product, wherein the visual inspection comprises visual inspection from surface finish or inspection based on machine vision and mechanical performance test, and the visual inspection is used for obtaining defect data such as surface cracks, pores and the like; mechanical property tests, in turn, are typically tests to obtain strength, hardness, flatness, ductility, fatigue, heat/cold shrinkage ratio, chemical properties, such as composition, separation transformation of tissue material, defect characterization methods, including data acquired by X-ray diffraction or imaging, CT scanning, ultrasound imaging, radar imaging, eddy current sensor array measurements, or thermal imaging, acoustic imaging, etc.
The method also comprises the following steps:
and simulating the printing process according to the data set, the process control parameters and the paths to obtain process simulation data, and updating the data set by taking the process simulation data as related data.
In specific practice, 3D printing can be simulated in advance according to the process control parameters and paths, preferably, a data combination algorithm can be adopted to output a predicted state, the input layer can be respectively matched with different hidden layers according to different input data transmitted to the input layer, and the hidden layers are combined to the output layer to predict a future state. In this embodiment, the data input to the input layer may be the process control parameters and paths obtained in advance, or may be data in a data set including model data, previous simulation data, sensor data, operation data, detection data, and training data.
It should be noted that the comparing the new model diagram with the model diagram to obtain difference data includes:
taking the original model diagram as first point cloud data and taking the new model diagram as second point cloud data;
and carrying out point cloud data analysis on the first point cloud data and the second point cloud data to obtain difference data, wherein the difference data comprises point position direction coordinate differences, point position distance coordinate differences and point position area volume differences.
In specific practice, referring to fig. 3, a comparison may be made using point cloud data analysis. And comparing the new model diagram with the original imported model diagram, analyzing the staggered positions to generate the subsequent expected printing positions, and judging whether filling printing or cutting is needed.
It should be noted that, using a machine learning algorithm to analyze the difference data, the process control parameters and paths are optimized, including:
and analyzing the difference data, and if the product being manufactured is judged to need to be filled, optimizing the process control parameters and the paths, and assigning a new printing path.
The method also comprises the following steps:
analyzing the difference data, and if the product being manufactured is judged to need to be cut, judging whether the difference is in a preset controllable range or not;
if yes, optimizing the process control parameters and paths;
if not, the printing is stopped.
It can be appreciated that the technical scheme shown in the embodiment can reliably finish 3D printing of the workpiece, ensure printing quality, precision and efficiency, reduce waste and reduce rejection rate. The invention aims at real-time defect identification monitoring and self-correction control in the DED additive manufacturing process. The invention combines intelligent iteration and manual operation through the combination of hardware, data and algorithm. The production state is tracked without personnel participating in the whole process, the operation is simple, the method is convenient and quick, the stability and the reliability are realized, the operation difficulty and personnel labor force are greatly reduced, and the production efficiency and the production precision are improved. The maintenance cost is low, and the economic benefit is obvious.
Example two
FIG. 4 is a schematic block diagram of an additive manufacturing real-time defect recognition and self-correction control device, according to an exemplary embodiment, and referring to FIG. 4, there is provided an additive manufacturing real-time defect recognition and self-correction control device, including:
a parameter path generating module 101, configured to obtain a model diagram to be manufactured, and obtain a process control parameter and a path according to the model diagram by using a machine learning algorithm;
a defect identifying module 102, configured to execute a printing process for manufacturing a corresponding product according to the process control parameter and the path, and identify a defect of the product in real time;
a comparison module 103, configured to update the model map according to related data obtained in the printing process, construct a new model map, and compare the new model map with the model map to obtain difference data;
and the parameter path optimization module 104 is configured to analyze the difference data by using a machine learning algorithm, optimize the process control parameters and paths, and update the process control parameters and paths in real time.
It can be understood that, in the technical solution provided in this embodiment, a model diagram to be manufactured can be obtained through the parameter path generating module 101, and a machine learning algorithm is used to obtain a process control parameter and a path according to the model diagram; performing a printing process for manufacturing a corresponding product according to the process control parameters and the path by a defect identification module 102, and identifying the defect of the product in real time; updating the model diagram according to the related data obtained in the printing process through the comparison module 103, constructing a new model diagram, and comparing the new model diagram with the model diagram to obtain difference data; the process control parameters and paths are optimized by the parameter path optimization module 104 using machine learning algorithms to analyze the difference data and update the process control parameters and paths in real time. It can be appreciated that the technical scheme provided by the embodiment can automatically identify defects of the product in the process of additive manufacturing so as to optimize the control parameters and paths of printing, does not need the whole process participation of staff in tracking the production state, is simple to operate, and improves the production efficiency and the production precision.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (8)
1. The real-time defect identification and self-correction control method for additive manufacturing is applied to an additive manufacturing system and is characterized by comprising the following steps of:
obtaining a model diagram to be manufactured, and obtaining process control parameters and paths according to the model diagram by using a machine learning algorithm;
executing a printing process for manufacturing a corresponding product according to the process control parameters and the paths, and carrying out defect identification on the product in real time;
updating the model diagram according to the related data obtained in the printing process, constructing a new model diagram, and comparing the new model diagram with the model diagram to obtain difference data;
and analyzing the difference data by using a machine learning algorithm, optimizing the process control parameters and paths, and updating the process control parameters and paths in real time.
2. The method of claim 1, wherein updating the model map based on the related data obtained during printing to construct a new model map comprises:
and establishing a data set according to the related data obtained in the printing process, and updating the model graph according to the data set to construct a new model graph.
3. The method of claim 2, wherein the identifying defects in the product in real time comprises:
and performing defect identification including visual inspection and mechanical property test on the product during printing and after product construction of the product to obtain process characterization data, and updating the data set by taking the process characterization data as related data.
4. A method according to claim 3, further comprising:
and simulating the printing process according to the data set, the process control parameters and the paths to obtain process simulation data, and updating the data set by taking the process simulation data as related data.
5. The method of claim 1, wherein comparing the new model map to the model map yields difference data, comprising:
taking the original model diagram as first point cloud data and taking the new model diagram as second point cloud data;
and carrying out point cloud data analysis on the first point cloud data and the second point cloud data to obtain difference data, wherein the difference data comprises point position direction coordinate differences, point position distance coordinate differences and point position area volume differences.
6. The method of claim 5, wherein analyzing the difference data using a machine learning algorithm to optimize the process control parameters and paths comprises:
and analyzing the difference data, and if the product being manufactured is judged to need to be filled, optimizing the process control parameters and the paths, and assigning a new printing path.
7. The method as recited in claim 6, further comprising:
analyzing the difference data, and if the product being manufactured is judged to need to be cut, judging whether the difference is in a preset controllable range or not;
if yes, optimizing the process control parameters and paths;
if not, the printing is stopped.
8. An additive manufacturing real-time defect identification and self-correction control device, which is characterized by comprising:
the parameter path generation module is used for acquiring a model diagram to be manufactured and obtaining process control parameters and paths according to the model diagram by utilizing a machine learning algorithm;
the defect identification module is used for executing a printing process for manufacturing corresponding products according to the process control parameters and the paths and carrying out defect identification on the products in real time;
the comparison module is used for updating the model diagram according to the related data obtained in the printing process, constructing a new model diagram, and comparing the new model diagram with the model diagram to obtain difference data;
and the parameter path optimization module is used for analyzing the difference data by utilizing a machine learning algorithm, optimizing the process control parameters and paths and updating the process control parameters and paths in real time.
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2023
- 2023-06-20 CN CN202310732847.8A patent/CN116700152A/en active Pending
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