CN116900448A - Arc additive residual height monitoring method based on molten pool vision and welding temperature field cooperative sensing - Google Patents
Arc additive residual height monitoring method based on molten pool vision and welding temperature field cooperative sensing Download PDFInfo
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- CN116900448A CN116900448A CN202311020108.2A CN202311020108A CN116900448A CN 116900448 A CN116900448 A CN 116900448A CN 202311020108 A CN202311020108 A CN 202311020108A CN 116900448 A CN116900448 A CN 116900448A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 29
- 238000005253 cladding Methods 0.000 claims abstract description 34
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/095—Monitoring or automatic control of welding parameters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/04—Welding for other purposes than joining, e.g. built-up welding
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Abstract
The application discloses an arc additive residual height monitoring method based on molten pool vision and welding temperature field cooperative sensing. Acquiring a square molten pool visual image and a side weldment image through acquisition equipment; processing and calculating a welding part image based on a colorimetric temperature measurement method to obtain a welding temperature field image; and the visual image of the molten pool and the welding temperature field image are cooperated to be used as the input of the trained arc additive residual height prediction model, so that the residual height of the cladding layer corresponding to the current moment is obtained. According to the difference between the predicted cladding layer residual height and the required cladding layer residual height, the application adjusts welding current based on fuzzy proportional integral derivative control, thereby realizing the control of arc material increase residual height. The application provides a new method for online monitoring of the arc additive residual height, and has important significance for further improving the arc additive manufacturing quality.
Description
Technical Field
The application belongs to the technical field of additive manufacturing quality monitoring, and particularly relates to an arc additive residual height monitoring method based on molten pool vision and welding temperature field cooperative sensing.
Background
Arc additive manufacturing is based on a discrete and stacked manufacturing principle, a solid model of a workpiece is established by using three-dimensional design software, metal wires are continuously melted under the action of an arc heat source, and three-dimensional solid workpieces required by layer-by-layer stacked forming are carried out according to a set forming path. The accumulation of materials is carried out in a high-temperature liquid metal solution drop transition mode, the number of layers of a cladding layer is gradually increased in the arc additive manufacturing process, the heat accumulation of an additive manufactured workpiece is serious, the heat dissipation condition is poor, the time required by solidification of a molten pool is gradually prolonged, the shape of the cladding layer is difficult to control, and particularly at the edge of the workpiece, the edge forming precision of the workpiece is more difficult to control due to the existence of a liquid molten pool. All the factors can influence the final forming dimensional precision of the additive manufactured workpiece, so that research on an online monitoring method of the high-precision arc additive manufacturing process has important significance for improving the arc additive forming quality.
The premise of realizing the online control of the residual height of the arc additive is to realize the online monitoring of the residual height of the arc additive, and in the arc additive manufacturing process, the residual height of the cladding layer is affected by a remelting area of the cladding layer and the height of a molten pool are not equal, so that the online monitoring of the residual height of the cladding layer of the arc additive is difficult to directly realize at present. The current arc additive forming quality monitoring method basically uses single information source characteristics such as molten pool vision and the like to establish an additive forming quality prediction model, and has the problems of single utilization of welding information sources, low monitoring precision and the like.
Disclosure of Invention
In order to solve the problems in the prior art, the application provides an arc additive surplus height monitoring method based on cooperative sensing of molten pool vision and welding temperature field. The technical problems to be solved by the application are realized by the following technical scheme:
the application provides an arc additive residual height monitoring method based on molten pool vision and welding temperature field cooperative sensing, which comprises the following steps:
s100, acquiring a square molten pool visual image and a side weldment image through acquisition equipment;
s200, processing and calculating the weldment image based on a colorimetric temperature measurement method to obtain a welding temperature field image;
and S300, the molten pool visual image and the welding temperature field image are cooperated to be used as the input of a trained arc additive residual height prediction model, and the corresponding cladding residual height is output through the arc additive residual height prediction model.
The application discloses an arc additive residual height monitoring method based on molten pool vision and welding temperature field cooperative sensing. Acquiring a square molten pool visual image and a side weldment image through acquisition equipment; processing and calculating the weldment image based on a colorimetric temperature measurement method to obtain a welding temperature field image; and the molten pool visual image and the welding temperature field image are cooperated to be used as the input of a trained arc additive residual height prediction model, so that the residual height of the cladding layer corresponding to the current moment is obtained. According to the difference between the predicted cladding layer residual height and the required cladding layer residual height, the welding current is regulated based on fuzzy proportional integral derivative control, so that the arc material increase residual height is controlled. The application provides a new method for online monitoring of the arc additive residual height, and has important significance for further improving the arc additive manufacturing quality. The present application will be described in further detail with reference to the accompanying drawings and examples.
Drawings
FIG. 1 is a schematic flow chart of an arc additive remainder monitoring method based on molten pool vision and welding temperature field cooperative sensing provided by the application;
FIG. 2 is a diagram of an arc additive residual height monitoring system;
FIG. 3 is a block diagram of an arc additive residual height prediction model based on deep learning;
FIG. 4 is a schematic diagram of arc additive rest height control based on PID control.
Detailed Description
The present application will be described in further detail with reference to specific examples, but embodiments of the present application are not limited thereto.
With reference to fig. 1 and 2, the application provides an arc additive surplus height monitoring method based on cooperative sensing of molten pool vision and welding temperature field, which comprises the following steps:
s100, acquiring a square molten pool visual image and a side weldment image through acquisition equipment;
the method comprises the steps of S100, acquiring a square molten pool visual image through a molten pool visual sensing system, and measuring a temperature field image of a side weldment through a welding temperature field measuring system.
Referring to fig. 2, the puddle vision sensing system includes a light reducing sheet, a filter, and a black-and-white CCD; the light reducing sheet is used for reducing the intensity of incident light; the optical filter is used for selecting light of a characteristic wave band to pass through; the black-and-white CCD is used for imaging the molten pool under a specific wave band to obtain a visual image of the molten pool;
the welding temperature field measurement system comprises a spectroscope, two infrared band filters and two infrared CCDs; the spectroscope is used for dividing the incident light into two identical paths of output; the infrared band filter is used for selecting light passing of a specific infrared band; the infrared CCD is used for imaging the welding piece under a specific infrared band to obtain a welding piece image;
the molten pool visual sensing system and the welding temperature field measuring system are connected to a computer, and the computer is used for processing the molten pool visual image and the welding piece image. The black-white CCD is synchronous with the time of the two infrared CCDs for collecting images, and the collection process is triggered by 3 paths of signals sent out by an external FPGA.
The FPGA module of the application sends out square wave signals with the frequency of 100Hz and simultaneously triggers the molten pool visual sensing system and the welding temperature field measuring system, the cooperation of the molten pool visual image obtained by square acquisition and the welding temperature field image obtained by side measurement is taken as input, the residual height of the cladding layer corresponding to the current moment is taken as output, and an arc additive residual height prediction model is established based on deep learning. After each layer of material is added, the height of the welding part is measured by using a three-dimensional scanner, and the height difference of two adjacent cladding layers is the residual height of the current cladding layer. And the measured residual height value is used as training data of an arc additive residual height prediction model.
S200, processing and calculating a welding part image to obtain a welding temperature field image;
the S200 of the present application includes:
s210, selecting a blackbody furnace, and aligning the input end of the spectroscope with the outlet of the blackbody furnace;
s220, raising the temperature of the blackbody furnace to 100 ℃, and extracting the gray value N of the welding piece image output by each infrared CCD 1 、N 2 ；
S230, gradually increasing the temperature of the blackbody furnace by 20 ℃ to N 1 、N 2 As an input of a preset back propagation neural network, T as an output of the preset back propagation neural network;
s240, adjusting internal parameters according to the output of the back propagation neural network;
s250, repeating the processes from S220 to S240 until the temperature of the blackbody furnace reaches 1600 ℃ to obtain a trained welding temperature field measurement model;
and S260, processing the weldment image by using the trained welding temperature field measurement model to obtain a welding temperature field image.
And S300, the molten pool visual image and the welding temperature field image are cooperated to serve as input of a trained arc additive residual height prediction model, and the corresponding cladding residual height at the current moment is output through the arc additive residual height prediction model.
The training process of the arc additive residual height prediction model comprises the following steps:
measuring the height of a welding part by using a three-dimensional scanner after each layer of material addition is completed, and determining the height difference of two adjacent cladding layers as the residual height of the current cladding layer;
taking a historical molten pool visual image acquired by an acquisition device and a welding temperature field image obtained by processing a historical weldment image as training set data, and determining the residual height of the current cladding layer obtained by measurement as a real residual height;
and carrying out iterative training on the arc additive residual height prediction model through training set data, and adjusting internal parameters of the arc additive residual height prediction model according to a loss value between the real residual height and the residual height predicted by the arc additive residual height prediction model to obtain the trained arc additive residual height prediction model.
As shown in fig. 3, the molten pool visual image and the welding temperature field image are cooperated to be used as the input of a trained arc additive residual height prediction model, and the arc additive residual height prediction model uses a depth residual error network of one path to extract features of the molten pool visual image, so that 1000-dimensional molten pool visual image features are extracted; extracting features of the welding temperature field image by using a depth residual error network of the other path, and extracting 1000-dimensional welding temperature field image features; and fusing and superposing the welding temperature field image features obtained by extracting the two paths and the molten pool visual image features to form new 1000-dimensional features, and obtaining the arc additive residual high value through a convolution layer, a pooling layer and a full connection layer.
As shown in fig. 4, after S300, the arc additive surplus height monitoring method based on the cooperative sensing of the molten pool vision and the welding temperature field further includes:
the method comprises the steps of inputting an output value of an arc additive residual height prediction network model and a required target cladding residual height into a fuzzy controller, comparing the output value of the arc additive residual height prediction network model with the required cladding residual height by the fuzzy controller to obtain deviation, inputting the deviation into a PID (Proportion Integral Differential, proportional integral derivative) controller, determining the welding current according to the coefficient of the PID and the deviation by the PID controller, synchronously correcting the coefficient of the PID controller by the fuzzy controller, and adjusting the coefficient of the PID controller to change the welding current by continuous feedback control, so that the output value of the arc additive residual height prediction network model is close to the target cladding residual height.
After the residual height of the cladding layer corresponding to the current time predicted by the model is obtained, the preparation of the current cladding layer is not completed, so that the welding current can be adjusted in advance, the control effect is achieved, and the error probability is reduced.
The application discloses an arc additive residual height monitoring method based on molten pool vision and welding temperature field cooperative sensing. Acquiring a square molten pool visual image and a side weldment image through acquisition equipment; processing and calculating the weldment image based on a colorimetric temperature measurement method to obtain a welding temperature field image; and the molten pool visual image and the welding temperature field image are cooperated to be used as the input of a trained arc additive residual height prediction model, so that the residual height of the cladding layer corresponding to the current moment is obtained. According to the difference between the predicted cladding layer residual height and the required cladding layer residual height, the welding current is regulated based on fuzzy proportional integral derivative control, so that the arc material increase residual height is controlled. The application provides a new method for online monitoring of the arc additive residual height, and has important significance for further improving the arc additive manufacturing quality.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Although the application is described herein in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality.
The foregoing is a further detailed description of the application in connection with the preferred embodiments, and it is not intended that the application be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the application, and these should be considered to be within the scope of the application.
Claims (8)
1. The arc additive surplus height monitoring method based on the cooperative sensing of molten pool vision and welding temperature field is characterized by comprising the following steps:
s100, acquiring a square molten pool visual image and a side weldment image through acquisition equipment;
s200, processing and calculating the weldment image based on a colorimetric temperature measurement method to obtain a welding temperature field image;
and S300, the molten pool visual image and the welding temperature field image are cooperated to be used as the input of a trained arc additive residual height prediction model, and the corresponding cladding residual height is output through the arc additive residual height prediction model.
2. The arc additive residual height monitoring method based on the cooperative sensing of molten pool vision and welding temperature fields as claimed in claim 1, wherein S100 comprises:
and acquiring a square molten pool visual image through a molten pool visual sensing system, and measuring a temperature field image of the side weldment through a welding temperature field measuring system.
3. The arc additive surplus height monitoring method based on the cooperative sensing of molten pool vision and welding temperature field as claimed in claim 2, wherein,
the molten pool vision sensing system comprises a light reducing sheet, an optical filter and a black-and-white CCD; the light reducing sheet is used for reducing the intensity of incident light; the optical filter is used for selecting light of a characteristic wave band to pass through; the black-and-white CCD is used for imaging the molten pool under a specific wave band to obtain a visual image of the molten pool;
the welding temperature field measurement system comprises a spectroscope, two infrared band filters and two infrared CCDs; the spectroscope is used for dividing incident light into two paths of identical output; the infrared band filter is used for selecting light passing of a specific infrared band; the infrared CCD is used for imaging the welding piece under the specific infrared band to obtain a welding piece image;
wherein the molten pool visual sensing system and the welding temperature field measuring system are both connected to a computer for processing the molten pool visual image and the weldment image.
4. The arc additive residual height monitoring method based on the cooperative sensing of molten pool vision and welding temperature field as claimed in claim 1, wherein S200 comprises:
s210, selecting a blackbody furnace, and aligning the input end of the spectroscope with the outlet of the blackbody furnace;
s220, raising the temperature of the blackbody furnace to 100 ℃, and extracting the gray value N of the welding piece image output by each infrared CCD 1 、N 2 ；
S230, gradually increasing the temperature of the blackbody furnace by 20 ℃ to N 1 、N 2 As an input of a preset back propagation neural network, T as an output of the preset back propagation neural network;
s240, adjusting internal parameters according to the output of the back propagation neural network;
s250, repeating the processes from S220 to S240 until the temperature of the blackbody furnace reaches 1600 ℃ to obtain a trained welding temperature field measurement model;
and S260, processing and calculating the weldment image by using the trained welding temperature field measurement model to obtain a welding temperature field image.
5. The arc additive residual height monitoring method based on molten pool vision and welding temperature field cooperative sensing as claimed in claim 1, wherein the arc additive residual height prediction model training process comprises:
measuring the height of a welding part by using a three-dimensional scanner after each layer of material addition is completed, and determining the height difference of two adjacent cladding layers as the residual height of the current cladding layer;
taking a historical molten pool visual image acquired by an acquisition device and a welding temperature field image obtained by processing a historical weldment image as training set data, and determining the residual height of the current cladding layer obtained by measurement as a real residual height;
and carrying out iterative training on the arc additive residual height prediction model through training set data, and adjusting internal parameters of the arc additive residual height prediction model according to a loss value between the real residual height and the residual height predicted by the arc additive residual height prediction model to obtain the trained arc additive residual height prediction model.
6. The arc additive residual height monitoring method based on the cooperative sensing of molten pool vision and welding temperature field as claimed in claim 1, wherein S300 comprises:
the molten pool visual image and the welding temperature field image are cooperated to be used as input of a trained arc additive residual height prediction model, and the arc additive residual height prediction model uses a depth residual error network of one path to extract features of the molten pool visual image, so that 1000-dimensional molten pool visual image features are extracted; extracting features of the welding temperature field image by using a depth residual error network of another path to extract 1000-dimensional welding temperature field image features; and (3) fusing and superposing the welding temperature field image features obtained by two paths of extraction and the molten pool visual image features to form new 1000-dimensional features, and obtaining an arc additive residual high value through a convolution layer, pooling and a full connection layer.
7. The arc additive surplus height monitoring method based on cooperative sensing of molten pool vision and welding temperature fields according to claim 3, wherein the black-white CCD is synchronous with the time of acquiring images by two infrared CCDs, and the acquisition process is triggered by 3 paths of signals sent by an external FPGA at the same time.
8. The arc additive residual height monitoring method based on the cooperative sensing of the molten pool vision and the welding temperature field according to claim 1, further comprising, after S300:
the method comprises the steps that the output value of an arc additive residual height prediction network model and a required target cladding residual height are input into a fuzzy controller, the fuzzy controller compares the output value of the arc additive residual height prediction network model with the required cladding residual height to obtain deviation, the deviation is input into a PID controller, the PID controller determines the welding current according to the deviation and the coefficient, the fuzzy controller synchronously corrects the coefficient of the PID controller through fuzzy control, and the fuzzy controller adjusts the coefficient of the PID controller through continuous feedback adjustment to change the welding current, so that the output value of the arc additive residual height prediction network model is close to the target cladding residual height.
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| CN117583698A (en) * | 2024-01-19 | 2024-02-23 | 中建材（合肥）粉体科技装备有限公司 | Automatic surfacing device and surfacing control method |
| CN117583698B (en) * | 2024-01-19 | 2024-04-26 | 中建材（合肥）粉体科技装备有限公司 | Automatic surfacing device and surfacing control method |
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