CN115582559A - Online monitoring system and online monitoring method for powder-laying additive manufacturing defects - Google Patents
Online monitoring system and online monitoring method for powder-laying additive manufacturing defects Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F12/00—Apparatus or devices specially adapted for additive manufacturing; Auxiliary means for additive manufacturing; Combinations of additive manufacturing apparatus or devices with other processing apparatus or devices
- B22F12/90—Means for process control, e.g. cameras or sensors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
- B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
- B22F10/80—Data acquisition or data processing
- B22F10/85—Data acquisition or data processing for controlling or regulating additive manufacturing processes
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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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
An online monitoring system and an online monitoring method for powder-laying additive manufacturing defects can avoid monitoring the influence of external factors in the additive manufacturing process such as vibration generated by powder laying and the like, and can improve monitoring precision. The powder-laying additive manufacturing defect online monitoring system is used for online monitoring of defects of formed parts manufactured by powder-laying additive manufacturing equipment, and the powder-laying additive manufacturing equipment comprises a forming cavity, a powder conveying bin, a base plate, a lifting rod, a scraper and a laser. The online monitoring system comprises an acoustic sensor and an acoustic signal processing module. The acoustic sensor is disposed in an upper space higher than the substrate in the molding chamber, and acquires an acoustic signal during melting of the powder for melt molding in the substrate. The acoustic signal processing module performs a wavelet packet analysis on the acoustic signals acquired by the acoustic sensor to acquire manufacturing defect characteristics from the acoustic signals that are indicative of surface defects or sub-surface defects of the formed part.
Description
Technical Field
The invention relates to an online monitoring system and an online monitoring method for powder paving additive manufacturing defects.
Background
In recent years, laser additive manufacturing technology is rapidly developed, and compared with the traditional forming technology, the laser additive manufacturing technology is more suitable for manufacturing metal parts with complex shapes, and can shorten the production period of the parts and reduce the production cost. Currently, powder-laid laser additive manufacturing techniques have had a wide range of applications and impacts in the aerospace industry, and many aerospace manufacturers have incorporated additive manufacturing techniques into their manufacturing business to produce structurally complex aircraft parts that are difficult to manufacture by traditional manufacturing processes. However, due to the influence of the powder characteristics, the process parameters, the external environment and other factors, the powder-spread laser additive manufacturing part is prone to have defects such as holes, incomplete fusion, cracks and the like, so that the finished product rate and the process stability are influenced. According to the airworthiness clause requirement, 100% of nondestructive testing must be carried out on the civil aircraft additive manufacturing complex structural parts. Currently, a common nondestructive testing method is mainly offline testing, and the offline testing method comprises the following steps: ultrasonic, X-ray, eddy current, CT inspection, etc., but offline inspection results in long part delivery cycle and high cost, and due to the complex structure shielding, not all offline inspection methods can achieve 100% inspection of parts. Therefore, in order to improve the precision of nondestructive testing and manufacture a high-quality aircraft structural part, it is very important to perform online monitoring on the powder-laying laser additive manufacturing process. The online monitoring can timely find defects, when the defects are located on the outer surface of the semi-finished workpiece, the defects are remelted to eliminate local defects in the manufacturing process, so that the receiving rate of the parts is improved, the manufacturing period is shortened, the requirements of airworthiness terms can be met, and the airworthiness machine is realized.
At present, the main ways of online monitoring of powder-laying laser additive manufacturing include: on-line monitoring based on visual imaging, on-line monitoring based on a temperature field, on-line monitoring based on spectral analysis, on-line monitoring based on an acoustic principle, and on-line monitoring based on an electric signal. Due to the limitation of the size of the forming bin and the interference of high-intensity laser and dust inert gas in the printing process, most researches only adopt a high-speed industrial camera to record the manufacturing process, complete monitoring through image processing, or use an acoustic sensor or an acoustic emission sensor to record acoustic signals in the printing process, and perform data analysis and processing.
For example, patent document 1 (chinese patent publication CN 108788153A) discloses an on-line monitoring method for a selective laser melting process, which integrates optical measurement devices such as an industrial camera, a thermal infrared imager, a high-speed camera and a photodiode, and collects molten pool data, monitors the melting and solidification processes of metal powder, observes the geometric morphology of the molten pool, and captures the radiation intensity of the molten pool by combining off-axis on-line monitoring and on-axis on-line monitoring, and the method better integrates optical monitoring.
However, in the research, it is found that as the processing process is carried out, more and more smoke is generated in the processing chamber, smoke particles are easy to adhere to a filter lens or a CCD sensor, the splash defect is difficult to be completely and truly expressed in the environment with high dust and smoke, and a signal processing algorithm of high-frequency image data is difficult to meet the requirement of real-time monitoring.
On the other hand, patent document 2 (chinese patent publication CN 111351863A) discloses an acoustic emission online monitoring system for a selective laser melting manufacturing process, which deduces a position suitable for installing an acoustic emission sensor through theoretical calculation, and collects an acoustic signal in a printing process through the acoustic sensor.
For another example, patent document 3 (chinese patent CN 109269985B) discloses a high-frequency ultrasonic online monitoring method for internal defects of a metal moving molten pool, which includes setting focus points at different depths along the central line of the molten pool, obtaining signals of all focus points according to a delay rule, and performing a drawing analysis by using a two-dimensional matrix, wherein one or more crescent ultrasonic signals exist in an image evaluation range, that is, diffraction signals of inclusions and air holes.
Because the on-line monitoring based on the acoustic principle is insensitive to dust emission and smoke environment of the processing chamber, the method is suitable for real-time dynamic monitoring of the splash defect in the Selective Laser Melting (SLM) material increase manufacturing process.
Documents of the prior art
Patent literature
Patent document 1: chinese patent publication CN108788153A
Patent document 2: chinese patent publication CN111351863A
Patent document 3: chinese patent CN109269985B
Disclosure of Invention
Technical problem to be solved by the invention
However, the method disclosed in patent document 2 requires calculation to determine the mounting position of the acoustic emission sensor, and ensures that the longitudinal waves generated by the acoustic emission signal can reach the acoustic emission sensor along a straight line, which is cumbersome to implement. In addition, in the printing process, the collection work and the precision of the acoustic signals are adversely affected by large vibration generated by the powder spreading action.
Further, the method of patent document 3 described above is heavy in workload, limited in depth to which ultrasonic waves can penetrate, incapable of recording print data for a long time, and limited in types of defects that can be detected.
The present invention has been made in view of the above problems, and an object of the present invention is to provide an on-line monitoring system and an on-line monitoring method for a powder additive manufacturing defect, which can avoid monitoring from being affected by external factors such as laser light, dust, and vibration generated by powder application, and can ensure that a sufficient number of defect types are detected, thereby improving monitoring accuracy.
Technical scheme for solving technical problem
A first technical aspect of the present invention provides an online monitoring system for powder-spreading additive manufacturing defects, which is used for online monitoring of defects of a molded part manufactured by a powder-spreading additive manufacturing apparatus, the powder-spreading additive manufacturing apparatus including: a forming chamber; a powder feeding bin which is arranged in a lower space in the molding chamber and stores powder for melt molding; a base plate disposed in the lower space in parallel with the powder feeding bin, and in which powder for melt molding from the powder feeding bin for manufacturing the molded component is placed; a lift bar connected to the substrate so as to be movable in a vertical direction; a scraper disposed above the powder hopper and configured to be movable from the powder hopper toward the substrate to supply powder for melt-forming in the powder hopper to the substrate; and a laser device disposed above the substrate, the laser device irradiating the substrate on which the powder for melt molding is placed with laser light to melt the powder for melt molding,
wherein, the on-line monitoring system includes: an acoustic sensor disposed in an upper space of the molding chamber above the substrate, the acoustic sensor acquiring an acoustic signal during melting of the powder for melt-molding in the substrate; and an acoustic signal processing module that performs wavelet packet analysis on the acoustic signals acquired by the acoustic sensor to acquire manufacturing defect features from the acoustic signals, the manufacturing defect features being features that represent surface defects or sub-surface defects of the formed part during additive manufacturing.
On the basis of the first technical solution, in a second technical solution of the present invention, the online monitoring system further includes a dust cover for the acoustic sensor, and the dust cover for the acoustic sensor covers the acoustic sensor from the outside.
On the basis of the first technical solution, in a third technical solution of the present invention, the online monitoring system further includes: a high-speed camera disposed in the upper space in the forming chamber, the high-speed camera acquiring image data of a molten pool and contour image data of the formed part; and the image data processing module is used for processing the image data of the molten pool and the contour image data of the formed part, which are acquired by the high-speed camera, so as to at least extract the defect characteristics of the molten pool.
In a fourth aspect of the present invention based on the third aspect, the online monitoring system further includes a high-speed camera protective cover that covers the high-speed camera from an outside.
In a fifth aspect of the present invention, on the basis of the first or third aspect, the online monitoring system further includes: a near-infrared camera disposed in the upper space in the forming chamber, the near-infrared camera acquiring a molten pool temperature and a near-infrared image of the molten pool; and the temperature and near-infrared image processing module is used for processing the molten pool temperature and the near-infrared image acquired by the near-infrared camera so as to extract plasma plume information and splashing dynamic characteristics.
On the basis of the fifth technical solution, in a sixth technical solution of the present invention, the on-line monitoring system further includes a shield for the near-infrared camera, and the shield for the near-infrared camera covers the near-infrared camera from an outer side.
A seventh technical solution of the present invention further provides an online monitoring method for defects in powder-spreading additive manufacturing, which uses the online monitoring system for defects in powder-spreading additive manufacturing according to the first or second technical solution to perform online monitoring on defects in a molded part manufactured by a powder-spreading additive manufacturing apparatus, including: acquiring an acoustic signal in a melting process of a powder for melt-forming in the substrate; and performing wavelet packet analysis on the acquired acoustic signals to acquire manufacturing defect features from the acoustic signals, the manufacturing defect features being features representing surface defects or sub-surface defects of the formed part during an additive manufacturing process.
An eighth technical means of the present invention is to provide an online monitoring method for defects in additive manufacturing by powder deposition, the online monitoring method using the online monitoring system for defects in additive manufacturing by powder deposition according to the third or fourth technical means, the online monitoring method for defects in additive manufacturing by powder deposition, the online monitoring method comprising: acquiring an acoustic signal in a melting process of a powder for melt-forming in the substrate; performing wavelet packet analysis on the acquired acoustic signals to acquire manufacturing defect features from the acoustic signals, the manufacturing defect features being features representing surface defects or sub-surface defects of the formed part in an additive manufacturing process; acquiring image data of a molten pool and contour image data of a formed part; and processing the acquired image data of the molten pool and the contour image data of the formed part to extract at least molten pool defect characteristics.
The ninth technical means of the present invention further provides an online monitoring method for defects in powder-laying additive manufacturing, which uses the online monitoring system for defects in powder-laying additive manufacturing according to the fifth or sixth technical means to perform online monitoring on defects in a molded part manufactured by a powder-laying additive manufacturing apparatus, and the online monitoring method comprises: acquiring an acoustic signal in a melting process of a powder for melt-forming in the substrate; performing wavelet packet analysis on the acquired acoustic signals to acquire manufacturing defect features from the acoustic signals, the manufacturing defect features being features representing surface defects or sub-surface defects of the formed part in an additive manufacturing process; acquiring image data of a molten pool and contour image data of a formed part; processing the acquired image data of the molten pool and the contour image data of the formed part so as to at least extract the defect characteristics of the molten pool; acquiring a molten pool temperature and a near-infrared image of the molten pool; and processing the acquired molten pool temperature and the near-infrared image to extract plasma plume information and splashing dynamic characteristics.
Effects of the invention
According to the first aspect, the acoustic sensor is disposed in the upper space higher than the substrate in the molding chamber, and the acoustic sensor is disposed apart from the substrate. Thus, the process of acquiring the acoustic signal in the melting process of the powder for melt molding in the substrate is not affected by the vibration of the substrate caused by the powder spreading operation, and a more accurate acoustic signal can be acquired, thereby improving the monitoring accuracy. Meanwhile, unlike the prior art which employs high-frequency ultrasonic online monitoring, the present application employs wavelet packet analysis of acquired acoustic signals to acquire manufacturing defect characteristics representing surface defects or subsurface defects of a formed part in an additive manufacturing process from the acoustic signals. Therefore, the technical problems that the workload is large, the depth which can be penetrated by ultrasonic waves is limited, printing data for a long time cannot be recorded, and the types of defects which can be detected are limited under the condition of adopting a high-frequency ultrasonic online monitoring technology can be solved, and the online monitoring precision can be further improved.
According to the second technical scheme, the acoustic sensor is covered from the outside by using the dust cover for the acoustic sensor, so that the influence of dust in the printing process can be avoided.
According to the third technical scheme, the image data of the molten pool and the contour image data of the formed part are acquired by using the high-speed camera while the online monitoring is carried out based on the acoustic sensor, and the defect characteristics of the molten pool are extracted based on the acquired image data. Therefore, the printing process can be monitored more comprehensively, and the online monitoring precision can be further improved.
According to the fourth technical means, by covering the high-speed camera from the outside with the protective cover for the high-speed camera, it is possible to prevent metal powder, molten pool splash, and laser from damaging the high-speed camera during printing.
According to the fifth technical scheme, the molten pool temperature information and the near-infrared image of the molten pool are acquired by using the near-infrared camera while the molten pool image data and the formed part profile image data are monitored on line based on the acoustic sensor and/or acquired in real time by using the high-speed camera, and the plasma plume information and the spatter dynamic characteristics are extracted based on the acquired molten pool temperature information and the acquired near-infrared image of the molten pool. Therefore, the printing process can be monitored more comprehensively, and the online monitoring precision can be further improved.
According to the sixth technical scheme, the near-infrared camera is covered from the outer side by the protective cover for the near-infrared camera, so that the damage to the near-infrared camera caused by metal powder, molten pool splashing and laser in the printing process can be prevented.
According to any one of the seventh to ninth aspects, the online monitoring accuracy can be further improved.
Drawings
Fig. 1 is a schematic diagram of an online monitoring system for powder-laying additive manufacturing defects according to an embodiment of the present invention.
Fig. 2 is a diagram showing the analysis result of wavelet packet analysis of an acoustic signal.
Description of the symbols
S powder spreading additive manufacturing equipment
1 shaped cavity
2 powder feeding bin
3 Forming platform
4 powder collecting bin
5 lifting rod
6 base plate
7 formed part
8 scraper
9 laser
10 acoustic sensor
11 dust cover for acoustic sensor
12 data communication line
13 acquisition card (Acoustic signal acquisition card)
14 Acoustic data processing device (computer)
15 high speed industrial camera (high speed camera)
16 high-speed camera protective cover
17 near infrared camera
18 near-infrared camera protective cover
19 image data processing equipment (computer)
Detailed Description
First, a main configuration of an online monitoring system for a powder deposition additive manufacturing defect (hereinafter, simply referred to as "online monitoring system") according to an embodiment of the present invention will be described with reference to fig. 1. In the present embodiment, the term "powder coating additive manufacturing" refers to additive manufacturing technology based on Selective Laser Melting (Selective Laser Melting).
The online monitoring system of the present embodiment online monitors the defects of the molded part 7 manufactured by the powder coating additive manufacturing apparatus S. As shown in fig. 1, the powder spreading additive manufacturing apparatus S includes a forming chamber 1, a powder feeding bin 2, a powder collecting bin 4, a forming platform 3, a lifting rod 5, a substrate 6, a scraper 8, and a laser 9. The forming chamber 1 is a hollow chamber for accommodating the powder feeding bin 2, the powder collecting bin 4, the forming platform 3, the lifting rod 5, the substrate 6, the scraper 8, the laser 9 and the like, and is, for example, rectangular parallelepiped. The powder hopper 2 is disposed in a lower space in the molding cavity 1, and stores powder for melt molding for selective laser melt molding. The molding platform 3 is arranged in parallel with the powder hopper 2 and similarly arranged in the lower space of the molding cavity 1, and includes a substrate 6 on which powder for melt molding from the powder hopper 2 is placed for manufacturing a molded part 7. The lift pins 5 are disposed below the base plate 6 of the forming table 3 and connected to the base plate 6. The substrate 6 can be moved in the vertical direction by the lift pins 5. The scraper 8 is disposed above the powder hopper 2, and as an initial position, the scraper 8 is located at an end of the powder hopper 2 facing the substrate 6. When the powder spreading operation is required, the scraper 8 moves from the end of the powder feeding hopper 2 facing the substrate 6 toward the substrate 6, and supplies the powder for melt molding stored in the powder feeding hopper 2 to the substrate 6. The powder receiving bin 4 is disposed on the opposite side of the powder feeding bin 2 with respect to the molding platform 3 (and the substrate 6), and receives the excess powder for melt molding supplied by the scraper 8 and not received by the substrate 6. The laser 9 is disposed above the substrate 6, and irradiates the substrate 6 on which the powder for melt molding is placed with laser light to melt the powder for melt molding, thereby producing the molded component 7 layer by layer.
As also shown in fig. 1, in the present embodiment, the online monitoring system includes an acoustic sensor 10, a data communication line 12, an acquisition card (acoustic signal acquisition card) 13, a mathematical data processing device (computer) 14, a high-speed industrial camera (high-speed camera) 15, a near-infrared camera 17, and an image data processing device (computer) 19.
The acoustic sensor 10 is disposed in an upper space higher than the substrate 6 in the molding chamber 1, specifically, for example, on an inner wall of the molding chamber 1 which is higher than the substrate 6 by 2 cm. In the additive manufacturing process, the acoustic sensor 10 acquires an acoustic signal in the melting process of the powder for melt-forming within the substrate 6, which is a time-domain signal reflecting the degree of the melting state of the powder. The response frequency of the acoustic sensor 10 is 0-100KHz. The acquisition card 13 is connected to the acoustic sensor 10 via a data communication line 12, and acquires an acoustic signal acquired by the sensor 10 and transmits the acquired acoustic signal to an acoustic data processing device (computer) 14. The acoustic data processing device 14 has an acoustic signal processing module that processes the acquired acoustic signals. Specifically, when the acoustic data processing device 14 is a computer, signal processing software such as Labview is installed in the computer. Signal processing software such as Labview carries out time-frequency analysis on the acoustic signal, converts the acoustic signal serving as a time domain signal into a frequency domain signal through power spectral density to analyze the characteristics of the acoustic signal, and distinguishes the noise signal from the frequency range of the acoustic signal distribution in the melting process. And, the signal processing software performs wavelet packet analysis on the acoustic signal. The wavelet analysis overcomes the defects of the short-time Fourier method and is suitable for processing non-stationary signals. Wavelet analysis is sensitive to different melting states but insensitive to changes in operating conditions and noise, which allows the wavelet analysis to capture important features in the melting process. Then, the characteristics of the acoustic signal are analyzed, and the melting degree is detected and diagnosed by analyzing the characteristics of the acquired acoustic signal in different frequency bands. Then, the decomposed acoustic signals of different frequency bands are compared with the acoustic signal characteristics of different defects for analysis, and characteristics capable of representing surface or subsurface defects of the formed part 7 in the printing process, namely manufacturing defect characteristics, are extracted. Fig. 2 shows a schematic diagram of the analysis result of a wavelet packet analysis of an acoustic signal. Further, it is preferable to dispose an acoustic sensor dust cover 11 that covers the acoustic sensor 10 from the outside. By covering the acoustic sensor 10 from the outside, the influence of dust during printing can be avoided.
The high-speed industrial camera 15 is also disposed in the upper space of the forming chamber 1 above the substrate 6, specifically, on the right side of the laser 9. Thereby, the position of the high-speed industrial camera 15 is calibrated. In the additive manufacturing process, image data of the melt pool within the substrate 6 is acquired with the high-speed industrial camera 15 while acquiring acoustic signals with the acoustic sensor 10. Specifically, the high-speed industrial camera 15 acquires and collects image data of the molten pool and contour image data of the formed part at a speed of 500fps, and then buffers the acquired image data of the molten pool and contour image data of the formed part to the image data processing apparatus (computer) 19 via the data communication line 12. The image data processing device 19 has an image data processing module that processes the image data of the molten pool and the contour image data of the formed part acquired by the high-speed industrial camera 15 to extract at least a molten pool defect feature. In the case where the image data processing apparatus 19 is a computer, the computer is installed with data processing software such as MATLAB. Data processing software such as MATLAB performs the following processing: carrying out image preprocessing and image gray processing on the cached image data, and converting the original intensity image into a binary image; image filtering and noise reduction are carried out, and powder splashing interference outside a molten pool area is removed; and (4) carrying out feature extraction on the melting state based on a neural network recognition algorithm to realize the feature extraction of the molten pool track and the molten pool defect. Further, a high-speed camera protective cover 16 that covers the high-speed industrial camera 15 from the outside is preferably provided. By covering the high-speed industrial camera 15 from the outside, it is possible to prevent metal powder, molten pool splash, and laser from damaging the high-speed industrial camera 15 during printing. In addition, it is also preferable that the high-speed industrial camera 15 is provided with a high-magnification magnifying lens to realize high-definition imaging of the local part of the molten pool, and further ensure the monitoring precision.
The near-infrared camera 17 is also disposed in the upper space of the molding chamber 1 above the substrate 6, specifically, on the left side of the laser 9. In detail, the near-infrared camera 17 is disposed along the capturing direction of the vertical scanning orbit, and the camera capturing speed is 5000fps. To avoid too bright visible light covering all target areas, an external band pass filter of 700nm to 1000nm is set to capture the image. Thereby, the position of the near-infrared camera 17 is calibrated. In the additive manufacturing process, the temperature of the molten pool and the near infrared image of the molten pool are acquired by the near infrared camera 17 while the acoustic sensor 10 is used for acquiring acoustic signals and/or the high-speed industrial camera 15 is used for acquiring image data of the molten pool and the contour image data of the formed part. Then, the acquired molten pool temperature and the near-infrared image of the molten pool are transmitted to the image data processing apparatus 19 via the data communication line 12. The image data processing device 19 has a temperature and near-infrared image processing module that processes the bath temperature and the near-infrared image acquired by the near-infrared camera 17 to extract the plasma plume information and the spatter dynamic characteristics. In the case where the image data processing apparatus 19 is a computer, the computer is installed with data processing software such as MATLAB. And (3) identifying the plasma plume and the dynamic splashing characteristics in the cached near-infrared image by data processing software such as MATLAB (matrix laboratory), extracting the characteristics of the melting state based on a neural network identification algorithm, and extracting the plasma plume and the dynamic splashing characteristics which play a determining role in the melting process. Further, a near-infrared camera shield 18 for shielding the near-infrared camera 17 from the outside is preferably provided. By covering the near-infrared camera 17 from the outside, it is possible to prevent metal powder, molten pool splashing, and laser from damaging the near-infrared camera 17 during printing.
This embodiment has the following advantageous effects:
(1) The method is used for carrying out powder-laying laser additive manufacturing defect on-line monitoring based on optical and acoustic principles, judging whether defects such as unfused, holes and cracks appear in the interior or on the surface of a part in the manufacturing process in real time, and is high in finding speed, high in identification precision, stable and reliable, capable of being used as an additive manufacturing defect on-line detection method and also capable of being used as a necessary precondition for a future additive manufacturing defect feedback control method;
(2) The monitoring system used by the invention has low cost, has low requirements on the acoustic sensor and the industrial camera hardware, and can save the production cost to a great extent;
(3) The invention has high integration degree, can be further fused and monitored by adding hardware such as a stress strain gauge and the like, and does not need too many complicated operation processes. In addition, more deeper analysis can be performed on the visual image of the molten pool, such as on-line monitoring of the lap joint rate;
(4) The monitoring system used by the invention has the advantages of simple installation process, good portability and strong applicability, is not limited by the size of a forming bin, and is not limited by the problems of the property, the size, the surface state and the like of materials or base materials, has better adaptability, and can be used for different powder laying laser additive manufacturing equipment;
(5) The dust cover and the protective cover are used for protecting the monitoring equipment from the influence of dust and laser intensity, so that the monitoring system has the advantages of high control precision, clear local imaging, high monitoring quality and the like, and has higher potential application value.
More specifically, the monitoring system and method used by the invention are based on the acoustic and optical principles, realize the fusion monitoring of the acoustic sensor, the industrial high-speed camera and the near-infrared camera in the powder paving laser additive manufacturing process, perform heterogeneous data fusion calculation on the extracted acoustic signal characteristic, the visible light image characteristic and the infrared image characteristic, identify the defects of non-fusion, cracks and holes in the melting process, realize the online monitoring of the powder paving laser additive manufacturing, and have more complete and accurate acquired data.
Further, the above-described embodiments show the case where the acoustic sensor, the high-speed industrial camera, and the near-infrared camera are arranged at the same time. However, the present invention is not limited to this, and only the acoustic sensor may be provided, and either or both of the high-speed industrial camera and the near-infrared camera may be provided in addition to the acoustic sensor.
The present disclosure has been described above based on one embodiment, but it should be understood that the present disclosure is not limited to the above-described embodiments and configurations. The present disclosure also includes various modifications and variations within the equivalent scope. In addition, various combinations and modes, including only one element, one or more other combinations and modes, also belong to the scope and the idea of the present disclosure.
Claims (9)
1. An on-line monitoring system for powder additive manufacturing defects for on-line monitoring of defects of a formed part manufactured by a powder additive manufacturing apparatus, the powder additive manufacturing apparatus comprising:
a forming chamber;
a powder feeding bin which is arranged in a lower space in the molding chamber and stores powder for melt molding;
a base plate disposed in the lower space side by side with the powder feeding bin, and on which powder for fusion molding from the powder feeding bin is placed for manufacturing the molded part;
a lift bar connected to the substrate so as to be movable in a vertical direction;
a scraper disposed above the powder feed bin and configured to be movable from the powder feed bin toward the substrate to supply powder for melt-forming within the powder feed bin to the substrate; and
a laser device disposed above the substrate, the laser device irradiating the substrate on which the powder for melt molding is placed with laser light to melt the powder for melt molding,
it is characterized in that the preparation method is characterized in that,
the online monitoring system comprises:
an acoustic sensor disposed in an upper space of the molding chamber above the substrate, the acoustic sensor acquiring an acoustic signal during melting of the powder for melt-molding in the substrate; and
an acoustic signal processing module that performs a wavelet packet analysis on the acoustic signals acquired by the acoustic sensor to acquire manufacturing defect features from the acoustic signals, the manufacturing defect features being features that represent surface defects or sub-surface defects of the shaped part during an additive manufacturing process.
2. The on-line monitoring system for powder-laying additive manufacturing defects of claim 1,
the online monitoring system further comprises a dust cover for the acoustic sensor, and the dust cover for the acoustic sensor covers the acoustic sensor from the outer side.
3. The on-line monitoring system of powder additive manufacturing defects of claim 1,
the online monitoring system further comprises:
a high-speed camera disposed in the upper space in the forming chamber, the high-speed camera acquiring image data of a molten pool and contour image data of the formed part; and
an image data processing module which processes the image data of the weld pool and the contour image data of the formed part acquired by the high-speed camera to extract at least weld pool defect characteristics.
4. The on-line monitoring system of powder additive manufacturing defects of claim 3,
the on-line monitoring system further comprises a protective cover for the high-speed camera, and the protective cover for the high-speed camera covers the high-speed camera from the outer side.
5. The on-line monitoring system for powder-laying additive manufacturing defects of claim 1 or 3,
the online monitoring system further comprises:
a near-infrared camera disposed in the upper space in the forming chamber, the near-infrared camera acquiring a molten pool temperature and a near-infrared image of the molten pool; and
and the temperature and near-infrared image processing module is used for processing the molten pool temperature and the near-infrared image acquired by the near-infrared camera so as to extract plasma plume information and splashing dynamic characteristics.
6. The on-line monitoring system for powder-laying additive manufacturing defects of claim 5,
the on-line monitoring system further comprises a protective cover for the near-infrared camera, and the protective cover for the near-infrared camera covers the near-infrared camera from the outer side.
7. An online monitoring method for powder-laying additive manufacturing defects, which uses the online monitoring system for powder-laying additive manufacturing defects of claim 1 or 2 to perform online monitoring on defects of a formed part manufactured by a powder-laying additive manufacturing device, and is characterized by comprising the following steps:
acquiring an acoustic signal in a melting process of a powder for melt-forming in the substrate; and
performing a wavelet packet analysis on the acquired acoustic signals to acquire manufacturing defect features from the acoustic signals, the manufacturing defect features being features representing surface defects or sub-surface defects of the formed part during an additive manufacturing process.
8. An online monitoring method for powder-laying additive manufacturing defects, which uses the online monitoring system for powder-laying additive manufacturing defects of claim 3 or 4 to perform online monitoring on the defects of a formed part manufactured by a powder-laying additive manufacturing device, and is characterized by comprising the following steps:
acquiring an acoustic signal in a melting process of a powder for melt-forming in the substrate;
performing wavelet packet analysis on the acquired acoustic signals to acquire manufacturing defect features from the acoustic signals, the manufacturing defect features being features representing surface defects or sub-surface defects of the formed part in an additive manufacturing process;
acquiring image data of a molten pool and contour image data of a formed part; and
and processing the acquired image data of the molten pool and the contour image data of the formed part so as to at least extract the defect characteristics of the molten pool.
9. An online monitoring method for powder-laying additive manufacturing defects, which uses the online monitoring system for powder-laying additive manufacturing defects of claim 5 or 6 to perform online monitoring on the defects of a formed part manufactured by a powder-laying additive manufacturing device, and is characterized by comprising the following steps:
acquiring an acoustic signal in a melting process of a powder for melt-forming in the substrate;
performing wavelet packet analysis on the acquired acoustic signals to acquire manufacturing defect features from the acoustic signals, the manufacturing defect features being features representing surface defects or sub-surface defects of the formed part in an additive manufacturing process;
acquiring image data of a molten pool and contour image data of a formed part;
processing the acquired image data of the molten pool and the contour image data of the formed part so as to at least extract the defect characteristics of the molten pool;
acquiring a molten pool temperature and a near-infrared image of the molten pool; and
and processing the acquired molten pool temperature and the near-infrared image to extract plasma plume information and splashing dynamic characteristics.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117058154A (en) * | 2023-10-13 | 2023-11-14 | 西安空天机电智能制造有限公司 | Defect identification method, system and medium for 3DP metal printing powder spreading process |
| CN117392178A (en) * | 2023-12-08 | 2024-01-12 | 武汉纺织大学 | Method and device for extracting motion characteristics of molten pool in powder spreading and material adding manufacturing process |
| CN117805248A (en) * | 2024-02-29 | 2024-04-02 | 云耀深维(江苏)科技有限公司 | Method and system for realizing additive manufacturing quality monitoring by utilizing acoustic measurement |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117058154A (en) * | 2023-10-13 | 2023-11-14 | 西安空天机电智能制造有限公司 | Defect identification method, system and medium for 3DP metal printing powder spreading process |
| CN117058154B (en) * | 2023-10-13 | 2024-03-12 | 西安空天机电智能制造有限公司 | Defect identification method, system and medium for 3DP metal printing powder spreading process |
| CN117392178A (en) * | 2023-12-08 | 2024-01-12 | 武汉纺织大学 | Method and device for extracting motion characteristics of molten pool in powder spreading and material adding manufacturing process |
| CN117805248A (en) * | 2024-02-29 | 2024-04-02 | 云耀深维(江苏)科技有限公司 | Method and system for realizing additive manufacturing quality monitoring by utilizing acoustic measurement |
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