CN116363045A - Intelligent sensing and backtracking repair system for defects in composite manufacturing process of increasing and decreasing materials and control method thereof - Google Patents
Intelligent sensing and backtracking repair system for defects in composite manufacturing process of increasing and decreasing materials and control method thereof Download PDFInfo
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- CN116363045A CN116363045A CN202111626383.XA CN202111626383A CN116363045A CN 116363045 A CN116363045 A CN 116363045A CN 202111626383 A CN202111626383 A CN 202111626383A CN 116363045 A CN116363045 A CN 116363045A
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Abstract
The invention provides an intelligent perception and backtracking repair system for defects in a composite manufacturing process of increasing and decreasing materials and a control method thereof, wherein the intelligent perception and backtracking repair system comprises the following steps: the monitoring subsystem obtains a real-time image of the material adding molten pool and transmits the real-time image to the computing subsystem; the calculation subsystem carries out data processing on the real-time image of the material adding molten pool, deduces the possibility of generating defects and the generation positions of the defects of the current material adding layer in the material adding process according to the defect judging reference model, and outputs the possibility and the generation positions of the defects to the communication subsystem; the communication subsystem is connected with the material increasing and decreasing central control system through a field bus, and the subsystem informs the motion system to enable the material decreasing processing head to move to the position where the defect possibly occurs to decrease the material according to the defect generation possibility and the defect generation position given by the calculation subsystem so as to remove the defect; the intelligent learning subsystem is used for optimizing the accuracy of the defect judgment reference model; the invention realizes the closed-loop management of manufacturing defects in the material adding process, reduces the problem of excessive coupling and improves the quality and the manufacturing precision of products.
Description
Technical Field
The invention belongs to the technical field of composite manufacturing of materials and computer-aided intelligent material processing, and particularly relates to an intelligent sensing and backtracking repair system for defects in a composite manufacturing process of materials and a control method thereof.
Background
The composite manufacturing method for increasing and decreasing the materials is a composite processing manufacturing method which combines high efficiency and high flexibility of additive manufacturing and high precision of material decreasing manufacturing. For large complex components, conventional additive manufacturing often fails to meet geometric accuracy and surface quality requirements. After the composite material is manufactured, the final precision of the additive manufactured part can be obviously improved through subsequent machining or machining in the manufacturing process, so that the additive manufactured part meets the requirement of the service performance, and therefore, the composite manufacturing technology has wide application prospect. However, due to the high coupling between additive manufacturing and subtractive manufacturing in the manufacturing process, although the final precision of the structure is guaranteed by subtractive manufacturing, if the dimension of the part after additive manufacturing is out of tolerance, the difficulty of cutting amount and subtractive path planning is increased, even defect accumulation occurs, and the composite manufacturing process brings adverse effects. Therefore, in the composite manufacturing process of increasing and decreasing materials, on-line identification and perception of forming defects in the material adding process and defect backtracking repair in the manufacturing process have important significance for guaranteeing the quality and the precision of composite manufactured products.
Disclosure of Invention
Aiming at the problems of increasing and decreasing material composite manufacturing, particularly forming and process defect sensitivity in the arc increasing and decreasing material composite manufacturing process and increasing the cutting amount and path planning complexity of the decreasing material, the invention provides an intelligent defect sensing and backtracking repair system in the increasing and decreasing material composite manufacturing process and a control method thereof; the closed-loop management of manufacturing defects in the material adding process is realized, the excessive coupling problem caused by defects in the traditional material adding process and the material subtracting process is weakened, the flexibility degree in the material adding and reducing manufacturing process is further improved, and the quality and the manufacturing precision of the composite manufactured product are ensured.
The invention is realized by the following scheme:
intelligent perception and backtracking repair system for defects in composite manufacturing process of increasing and decreasing materials:
the system specifically comprises: the system comprises a monitoring subsystem, a communication subsystem, a computing subsystem and an intelligent learning subsystem;
the monitoring subsystem comprises a high-dynamic welding camera, a thermal infrared imager and a structural light sensor; the real-time image of the material-adding molten pool is obtained through the high-dynamic welding camera, the infrared thermal imager and the structural light sensor, and is transmitted to the computing subsystem after being integrated;
the computing subsystem performs data processing on the real-time image of the material adding molten pool, deduces the probability of generating defects and the generating positions of the defects of the current material adding layer in the material adding process according to the defect judging reference model, and outputs the probability and the generating positions of the defects to the communication subsystem;
the communication subsystem is connected with the material increasing and decreasing central control system through a field bus, and the subsystem informs the motion system to enable the material decreasing processing head to move to the position where the defect possibly occurs to decrease the material according to the defect generation possibility and the defect generation position given by the calculation subsystem so as to remove the defect;
the intelligent learning subsystem is used for optimizing the accuracy of the defect judgment reference model.
Further, the high-dynamic welding camera is arranged on the additive processing head by utilizing a paraxial, and passive visible light visual images of the additive molten pool are collected in real time; the infrared thermal imager is arranged on the additive processing head by utilizing a paraxial, and acquires infrared visual images of the additive molten pool and temperature field data of the front part and the rear part of the additive molten pool; the structural light sensor acquires an active visible light image visual image of the material adding molten pool in real time, and identifies surface profile information of a current deposition layer of the material adding molten pool;
the monitoring subsystem performs multi-information fusion real-time image processing on the acquired passive visible light visual image and infrared visual image of the additive molten pool and the active visible light visual image to obtain a real-time image of the additive molten pool, integrates the real-time image of the additive molten pool, temperature field data in front and behind the additive molten pool and surface profile information of a current deposition layer of the additive molten pool according to absolute additive time, packages the data and transmits the data to the computing subsystem.
Further, the computing subsystem extracts the shape and size information of the molten pool from the real-time image of the material-increasing molten pool through a computer graphics algorithm or a nerve cell network, filters the information, and accumulates time of the material-increasing process to form a molten pool shape and size data set of the whole material-increasing process; extracting information such as temperature, temperature gradient and the like of the front and rear sides of the molten pool from the temperature field data of the front and rear sides of the molten pool, filtering, and accumulating time of the material adding process to form a temperature field data set of the whole material adding process; reconstructing a surface profile curve from the surface profile point cloud data of the current sedimentary layer, calculating the size of the sedimentary layer, registering the size of the sedimentary layer and the section profile of the sedimentary layer designed in the path planning stage in real time, filtering, and accumulating time of the material adding process to form a surface profile and a sedimentary size data set of the whole material adding process;
the computing subsystem deduces the probability of generating defects and the generating positions of the defects of the current additive layer in the process of adding materials according to the molten pool morphology and size data set of the full-additive process, the temperature field data set of the full-additive process, the surface profile and the deposition size data set of the full-additive process and the defect judging reference model, and outputs the probability and the generating positions of the defects to the communication subsystem.
Further, the defect judging reference model is based on an artificial neuron network, wherein the artificial neuron network takes the shape and size information of a molten pool in a full-additive process, a temperature field data set in the full-additive process, a surface profile and a deposition size data set in the full-additive process as input variables and the defect generation possibility as output variables; and deducing the possibility of defect generation after the end of each layer of additive process, and tracing back to the space coordinates of the defect generation position from the historical data set according to the defect generation time.
Further, the intelligent learning subsystem is used for manufacturing the artificial defects through an increase-decrease material process test, the molten pool morphology and size information of the full-additive process in the artificial defect generation process, the temperature field data set of the full-additive process and the virtual set of the surface profile and the deposition size data set of the full-additive process are integrated into the training data set of the defect judgment reference model, and the accuracy of the defect judgment reference model is improved through iterative learning.
Further, the intelligent learning subsystem obtains a large number of defect generation theoretical data of random processes through increasing and decreasing molten pool flow numerical simulation of the material adding process, a component virtual camera performs data acquisition on the material adding process at the same position in simulation software to obtain molten pool morphology and size information of the full material adding process, a temperature field data set of the full material adding process, a virtual set of a surface profile and deposition size data set of the full material adding process, the virtual set is used as a training data set of a defect judging reference model, and the accuracy of the defect judging reference model is improved through iterative learning.
A control method of an intelligent defect sensing and backtracking repair system in a composite manufacturing process of increasing and decreasing materials comprises the following steps:
the monitoring subsystem obtains real-time images of the material adding molten pool, and the real-time images are transmitted to the computing subsystem after being integrated;
the computing subsystem performs data processing on the real-time image of the material adding molten pool, deduces the probability of generating defects and the generating positions of the defects of the current material adding layer in the material adding process according to the defect judging reference model, and outputs the probability and the generating positions of the defects to the communication subsystem;
the communication subsystem is connected with the material increasing and decreasing central control system through a field bus, and the subsystem informs the motion system to enable the material decreasing processing head to move to the position where the defect possibly occurs to decrease the material according to the defect generation possibility and the defect generation position given by the calculation subsystem so as to remove the defect;
the intelligent learning subsystem is used for optimizing the accuracy of the defect judgment reference model.
The invention has the beneficial effects that
The invention realizes the closed-loop management of manufacturing defects in the material adding process, reduces the excessive coupling problem caused by defects in the traditional material adding process and material subtracting process, further improves the flexibility degree in the material adding and reducing manufacturing process, and ensures the quality and manufacturing precision of composite manufactured products.
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FIG. 1 is a block diagram of the structure of the present invention;
fig. 2 is a workflow of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In combination with figures 1 to 2 of the drawings,
intelligent perception and backtracking repair system for defects in composite manufacturing process of increasing and decreasing materials:
as shown in fig. 1, the system of the present invention specifically includes: the system comprises a monitoring subsystem, a communication subsystem, a computing subsystem and an intelligent learning subsystem;
the monitoring subsystem comprises a high-dynamic welding camera, a thermal infrared imager and a structural light sensor; the high-dynamic welding camera is arranged on the additive processing head by utilizing a paraxial, and passive visible light visual images of the additive molten pool are acquired in real time; the infrared thermal imager is arranged on the additive processing head by utilizing a paraxial, and acquires infrared visual images of the additive molten pool and temperature field data of the front part and the rear part of the additive molten pool; the structural light sensor (a rear active line laser visual sensor) collects active visible light image visual images of the material-adding molten pool in real time, and identifies surface profile information of a current deposition layer of the material-adding molten pool;
the monitoring subsystem performs multi-information fusion real-time image processing on the acquired passive visible light visual image and infrared visual image of the additive molten pool and the active visible light visual image to obtain a real-time image of the additive molten pool, integrates the real-time image of the additive molten pool, temperature field data in front and behind the additive molten pool and surface profile information of a current deposition layer of the additive molten pool according to absolute additive time, packages the data and transmits the data to the computing subsystem.
The computing subsystem extracts the shape and size information of the molten pool from the real-time image of the material-increasing molten pool through a computer graphics algorithm or a nerve cell network, filters the information, and accumulates time of the material-increasing process to form a molten pool shape and size data set of the whole material-increasing process; extracting information such as temperature, temperature gradient and the like of the front and rear sides of the molten pool from the temperature field data of the front and rear sides of the molten pool, filtering, and accumulating time of the material adding process to form a temperature field data set of the whole material adding process; reconstructing a surface profile curve from the surface profile point cloud data of the current sedimentary layer, calculating the size of the sedimentary layer, registering the size of the sedimentary layer and the section profile of the sedimentary layer designed in the path planning stage in real time, filtering, and accumulating time of the material adding process to form a surface profile and a sedimentary size data set of the whole material adding process;
the computing subsystem deduces the probability of generating defects and the generating positions of the defects of the current additive layer in the process of adding materials according to the molten pool morphology and size data set of the full-additive process, the temperature field data set of the full-additive process, the surface profile and the deposition size data set of the full-additive process and the defect judging reference model, and outputs the probability and the generating positions of the defects to the communication subsystem.
The communication subsystem is connected with the material increasing and decreasing central control system through a field bus, and the subsystem informs the motion system to enable the material decreasing processing head to move to the position where the defect possibly occurs to decrease the material according to the defect generation possibility and the defect generation position given by the calculation subsystem so as to remove the defect; since the defect judgment is an experience-based semi-quantitative inference process, it is difficult to conduct forward modeling of an inference model in a mathematical manner, and the judgment model adopts a machine learning algorithm.
The intelligent learning subsystem is used for optimizing the accuracy of the defect judgment reference model. The intelligent learning subsystem serves for optimizing the accuracy of the defect judgment reference model, and provides a training data set for the machine learning algorithm model in a mode of combining numerical simulation and process test, so that the accuracy of the machine learning algorithm model can meet the requirement of using performance.
The defect judging reference model is based on an artificial neural network, the artificial neural network takes the shape and size information of a molten pool in the full-additive process, a temperature field data set in the full-additive process, a surface profile and a deposition size data set in the full-additive process as input variables, and the defect generation possibility as output variables; and deducing the possibility of defect generation after the end of each layer of additive process, and tracing back to the space coordinates of the defect generation position from the historical data set according to the defect generation time.
The intelligent learning subsystem is used for manufacturing the artificial defects through an increase-decrease material process test, the molten pool morphology and size information of the full-additive process in the artificial defect generation process, the temperature field data set of the full-additive process, the surface profile of the full-additive process and the virtual set of the deposition size data set are integrated into the training data set of the defect judgment reference model, and the accuracy of the defect judgment reference model is improved through iterative learning.
The intelligent learning subsystem obtains a large number of theoretical data generated by defects in a random process through increasing and decreasing molten pool flow numerical simulation of the material adding process, a component virtual camera performs data acquisition on the material adding process at the same position in simulation software to obtain molten pool morphology and size information of the full material adding process, a temperature field data set of the full material adding process, a virtual set of a surface profile and deposition size data set of the full material adding process, the virtual set is used as a training data set of a defect judging reference model, and the accuracy of the defect judging reference model is improved through iterative learning.
A control method of an intelligent defect sensing and backtracking repair system in a composite manufacturing process of increasing and decreasing materials comprises the following steps:
the monitoring subsystem obtains real-time images of the material adding molten pool, and the real-time images are transmitted to the computing subsystem after being integrated;
the computing subsystem performs data processing on the real-time image of the material adding molten pool, deduces the probability of generating defects and the generating positions of the defects of the current material adding layer in the material adding process according to the defect judging reference model, and outputs the probability and the generating positions of the defects to the communication subsystem;
the communication subsystem is connected with the material increasing and decreasing central control system through a field bus, and the subsystem informs the motion system to enable the material decreasing processing head to move to the position where the defect possibly occurs to decrease the material according to the defect generation possibility and the defect generation position given by the calculation subsystem so as to remove the defect;
the intelligent learning subsystem is used for optimizing the accuracy of the defect judgment reference model.
The intelligent perception and backtracking repair system for the defects in the composite manufacturing process of the added and reduced materials and the control method thereof are introduced in detail, the principle and the implementation mode of the invention are explained, and the explanation of the above embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (7)
1. An intelligent perception and backtracking repair system for defects in a composite manufacturing process of increasing and decreasing materials is characterized in that:
the system specifically comprises: the system comprises a monitoring subsystem, a communication subsystem, a computing subsystem and an intelligent learning subsystem;
the monitoring subsystem comprises a high-dynamic welding camera, a thermal infrared imager and a structural light sensor; the real-time image of the material-adding molten pool is obtained through the high-dynamic welding camera, the infrared thermal imager and the structural light sensor, and is transmitted to the computing subsystem after being integrated;
the computing subsystem performs data processing on the real-time image of the material adding molten pool, deduces the probability of generating defects and the generating positions of the defects of the current material adding layer in the material adding process according to the defect judging reference model, and outputs the probability and the generating positions of the defects to the communication subsystem;
the communication subsystem is connected with the material increasing and decreasing central control system through a field bus, and the subsystem informs the motion system to enable the material decreasing processing head to move to the position where the defect possibly occurs to decrease the material according to the defect generation possibility and the defect generation position given by the calculation subsystem so as to remove the defect;
the intelligent learning subsystem is used for optimizing the accuracy of the defect judgment reference model.
2. The system according to claim 1, wherein:
the high-dynamic welding camera is arranged on the additive processing head by utilizing a paraxial, and passive visible light visual images of the additive molten pool are acquired in real time; the infrared thermal imager is arranged on the additive processing head by utilizing a paraxial, and acquires infrared visual images of the additive molten pool and temperature field data of the front part and the rear part of the additive molten pool; the structural light sensor acquires an active visible light image visual image of the material adding molten pool in real time, and identifies surface profile information of a current deposition layer of the material adding molten pool;
the monitoring subsystem performs multi-information fusion real-time image processing on the acquired passive visible light visual image and infrared visual image of the additive molten pool and the active visible light visual image to obtain a real-time image of the additive molten pool, integrates the real-time image of the additive molten pool, temperature field data in front and behind the additive molten pool and surface profile information of a current deposition layer of the additive molten pool according to absolute additive time, packages the data and transmits the data to the computing subsystem.
3. The system according to claim 1, wherein:
the computing subsystem extracts the shape and size information of the molten pool from the real-time image of the material-increasing molten pool through a computer graphics algorithm or a nerve cell network, filters the information, and accumulates time of the material-increasing process to form a molten pool shape and size data set of the whole material-increasing process; extracting information such as temperature, temperature gradient and the like of the front and rear sides of the molten pool from the temperature field data of the front and rear sides of the molten pool, filtering, and accumulating time of the material adding process to form a temperature field data set of the whole material adding process; reconstructing a surface profile curve from the surface profile point cloud data of the current sedimentary layer, calculating the size of the sedimentary layer, registering the size of the sedimentary layer and the section profile of the sedimentary layer designed in the path planning stage in real time, filtering, and accumulating time of the material adding process to form a surface profile and a sedimentary size data set of the whole material adding process;
the computing subsystem deduces the probability of generating defects and the generating positions of the defects of the current additive layer in the process of adding materials according to the molten pool morphology and size data set of the full-additive process, the temperature field data set of the full-additive process, the surface profile and the deposition size data set of the full-additive process and the defect judging reference model, and outputs the probability and the generating positions of the defects to the communication subsystem.
4. The system according to claim 1, wherein:
the defect judging reference model is based on an artificial neural network, the artificial neural network takes the shape and size information of a molten pool in the full-additive process, a temperature field data set in the full-additive process, a surface profile and a deposition size data set in the full-additive process as input variables, and the defect generation possibility as output variables; and deducing the possibility of defect generation after the end of each layer of additive process, and tracing back to the space coordinates of the defect generation position from the historical data set according to the defect generation time.
5. The system according to claim 1, wherein:
the intelligent learning subsystem is used for manufacturing the artificial defects through an increase-decrease material process test, the molten pool morphology and size information of the full-additive process in the artificial defect generation process, the temperature field data set of the full-additive process, the surface profile of the full-additive process and the virtual set of the deposition size data set are integrated into the training data set of the defect judgment reference model, and the accuracy of the defect judgment reference model is improved through iterative learning.
6. The system according to claim 1, wherein:
the intelligent learning subsystem obtains a large number of theoretical data generated by defects in a random process through increasing and decreasing molten pool flow numerical simulation of the material adding process, a component virtual camera performs data acquisition on the material adding process at the same position in simulation software to obtain molten pool morphology and size information of the full material adding process, a temperature field data set of the full material adding process, a virtual set of a surface profile and deposition size data set of the full material adding process, the virtual set is used as a training data set of a defect judging reference model, and the accuracy of the defect judging reference model is improved through iterative learning.
7. A control method applied to the system according to any one of claims 1 to 6, characterized in that:
the monitoring subsystem obtains real-time images of the material adding molten pool, and the real-time images are transmitted to the computing subsystem after being integrated;
the computing subsystem performs data processing on the real-time image of the material adding molten pool, deduces the probability of generating defects and the generating positions of the defects of the current material adding layer in the material adding process according to the defect judging reference model, and outputs the probability and the generating positions of the defects to the communication subsystem;
the communication subsystem is connected with the material increasing and decreasing central control system through a field bus, and the subsystem informs the motion system to enable the material decreasing processing head to move to the position where the defect possibly occurs to decrease the material according to the defect generation possibility and the defect generation position given by the calculation subsystem so as to remove the defect;
the intelligent learning subsystem is used for optimizing the accuracy of the defect judgment reference model.
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| CN117020656A (en) * | 2023-07-28 | 2023-11-10 | 南京理工大学 | Complex metal component increase-measurement-decrease integrated forming system and processing method |
| CN117020656B (en) * | 2023-07-28 | 2024-05-07 | 南京理工大学 | Complex metal component increase-measurement-decrease integrated forming system and processing method |
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