A multi-source thermal tomography monitoring system and method for additive manufacturing processes

Abstract

This invention discloses a multi-source thermal imaging monitoring system and method for additive manufacturing processes. The system includes a computer (1), an infrared thermal imager (6), a high-power laser power supply (17), a laser galvanometer (25), an SLM device (27), and a high-performance function generator (29). This invention integrates multiple technical fields such as single-mirror multi-laser multiplexing technology, high background thermal environment noise suppression, and extraction of DC and AC multi-quantity feature information. Compared with traditional passive thermal imaging, it has the advantages of high resolution and large detection depth. Compared with traditional active thermal imaging, it has the advantage of high efficiency. At the same time, the method improves equipment utilization by sharing the galvanometer optical path.

Description

Technical Field

[0001] This invention relates to a multi-source thermal wave tomography monitoring system and method for additive manufacturing processes, belonging to the field of photothermal science and detection technology. Background Technology

[0002] While additive manufacturing technologies, such as Selective Laser Melting (SLM), offer advantages like high forming accuracy, good surface quality, and fewer defects, quality control during the additive manufacturing process remains a bottleneck limiting its application. Metal SLM additive manufacturing involves drastic temperature changes and complex material behavior, making it difficult to control forming quality, internal stress, and microstructure uniformity. This inevitably leads to defects of various shapes and sizes, such as surface cracks, incomplete fusion, and voids, which severely compromise the performance of additively manufactured parts. Currently, methods and technologies for online in-situ non-destructive testing and quality evaluation in SLM are relatively limited. Therefore, effective and accurate online in-situ non-destructive testing of defects in the formed layer during SLM additive manufacturing is crucial. In 2020, a commentary published by Professor Reed R.'s team at the University of Oxford, UK, pointed out that additive manufacturing technology will become a disruptive technology in manufacturing, and also considered non-destructive testing technology in metal additive manufacturing to be one of the major key technological challenges currently facing the industry. Research on online in-situ inspection technology for metal SLM additive manufacturing is an inherent requirement for ensuring forming quality, expanding the application scope of SLM, and realizing quality traceability. Therefore, it has become a cutting-edge research hotspot in this field.

[0003] For non-destructive testing (NDT) technologies targeting metal additive manufacturing structural parts, it is necessary to meet requirements such as efficient and accurate testing, adaptability to complex structures and poor surface quality, and handling of various types of defects. Developed countries such as the United States, Germany, and the United Kingdom have all formulated detailed development roadmaps for quality control in metal additive manufacturing, particularly focusing on research into NDT online monitoring / inspection, offline testing methods, and in-situ and post-processing quality evaluation systems for complex metal additive manufacturing structural parts. Currently, there are various methods to ensure the quality of metal additive manufacturing parts, including online monitoring, online NDT, and offline testing.

[0004] Online monitoring primarily utilizes principles of optics, heat transfer, and spectroscopy to monitor surface morphology, temperature field, and radiation intensity during additive manufacturing. This approach helps to promptly identify unstable factors in the additive manufacturing process, allowing for real-time adjustments to relevant process parameters. However, the defect generation mechanism in metal additive manufacturing is not yet fully understood, and there is no clear correlation between monitored features and defects. Therefore, it is difficult to rely solely on online monitoring for defect identification. Consequently, non-destructive testing (NDT) that directly reflects defect information is more crucial for ensuring the quality of metal SLM additive manufacturing. Offline NDT does not need to consider the environmental impact of metal additive manufacturing, and traditional NDT methods for metal materials can be directly applied to additively manufactured structural parts. However, offline NDT methods lack real-time capability, making it impossible to perform real-time defect detection and process adjustments during manufacturing. This further highlights the importance of online in-situ NDT in metal additive manufacturing. Online in-situ NDT in metal additive manufacturing can detect defects during the manufacturing process and then repair them by changing process parameters, significantly improving the quality of metal additively manufactured structural parts. Current methods for non-destructive online / offline inspection and quality evaluation of metal additive manufacturing structural parts mainly focus on optical inspection, eddy current inspection, ultrasonic inspection, X-ray inspection and infrared inspection, and scholars at home and abroad have carried out a lot of research on these methods.

[0005] (1) Optical Inspection Methods. Currently, many foreign scholars have conducted research on online optical non-destructive testing of metal additive manufacturing processes. Jacobsmühlen JZ et al. from RWTH Aachen University in Germany integrated a CCD camera into an SLM additive manufacturing device for online inspection of the surface morphology of the metal SLM forming layer. This system was used to record and detect defects in the metal SLM additive forming process. To achieve online detection of small-scale unfused defects, Bamberg J. et al. from MTU GmbH in Germany proposed an online optical tomography inspection technology for surface quality in metal additive manufacturing. This technology integrates a high-resolution CMOS camera into the SLM additive manufacturing device and achieves defect detection by long-term exposure imaging of the additive manufacturing process. Experimental results show that this technology can effectively detect unfused defects as small as 0.15 mm. Optical inspection devices can be easily embedded into SLM devices and have good detection results for surface defects in the additive manufacturing process, but they face the challenge of not being able to detect deep or interlayer defects.

[0006] (2) Eddy Current Detection Method. Eddy current detection is based on the principle of electromagnetic induction, detecting surface and shallow surface defects by probing changes in induced eddy currents within the workpiece. American scholars Todorov et al. proposed an eddy current array technology for defect detection in the SLM additive manufacturing process. They integrated the array eddy current detection device with the SLM additive manufacturing equipment and conducted experimental research on the detection of unfused defects in Inconel 625 within a sealed SLM forming cavity. The experimental results showed that eddy current detection technology has high sensitivity for surface and shallow surface discontinuous structural defects. Ehlers H. et al. from the German Federal Institute for Materials and Testing used a combination of a giant magnetoresistive detector and a single excitation coil to detect defects in 316L stainless steel SLM additive manufacturing. The experimental results showed that metal powder does not affect eddy current detection, and this method can effectively detect pre-fabricated surface defects with a width of 100 μm. Wang Longqun et al. from Dalian University of Technology conducted simulation and experimental research on eddy current testing methods, establishing the relationship between eddy current testing depth, excitation frequency, and lift-off amount. This research provides a theoretical basis for the integration of eddy current testing with additive manufacturing equipment. Eddy current testing yields good results for detecting surface and shallow defects in additively manufactured structural parts. However, the high temperature within the forming cavity of additive manufacturing affects the electromagnetic properties of the material, thus impacting the accuracy of eddy current defect detection. Furthermore, this method has certain requirements regarding the surface quality of the material.

[0007] (3) Ultrasonic and Laser Ultrasonic Testing Methods. The principle of ultrasonic testing is that internal defects in the specimen affect the propagation of ultrasonic waves, and thus the defects can be detected through echo characteristics. Lopez A. et al. from the University of Lisbon, Portugal, used ultrasonic testing to detect internal defects in arc-manufactured aluminum alloy and low-carbon steel specimens. Due to the irregular surface geometry of the specimens, the ultrasonic device was placed under the substrate. The experimental results showed that the ultrasonic testing results were in good agreement with the X-ray testing results. Traditional ultrasonic testing is a contact testing method, which has good detection results for micro-cracks or incomplete fusion defects. However, ultrasonic testing is easily affected by the surface quality, geometric complexity, and high temperature of the additive manufacturing process. Laser ultrasonic testing technology is a method that uses pulsed laser to excite ultrasonic waves on the surface of the specimen, and then uses the laser beam to detect the propagation of ultrasonic waves to detect internal defects in the specimen. Everton S. et al. from the University of Nottingham, UK, used laser ultrasonic testing to test Ti-6Al-4V additive manufacturing specimens containing defects. The study found that the surface quality of the material had a significant impact on the detection of laser ultrasonic signals. Yuan Jiuxin et al. from Wuhan University of Technology used laser ultrasonic testing technology to detect internal defects in arc additive manufacturing specimens. Their research showed that this method can detect internal defects with an inner diameter of 1 mm within a depth of 10 mm. Laser ultrasonic testing overcomes the shortcomings of traditional ultrasonic testing, achieving non-contact detection and enabling the detection of minute subsurface defects. However, this method has low detection efficiency, is susceptible to external interference, and requires high surface quality from the tested specimen.

[0008] (4) X-ray inspection method. Due to its advantages of being non-contact, highly efficient, and having a large detection depth, the X-ray inspection method has been widely used in the offline inspection of metal additive manufacturing parts in recent years. Gordon JV et al. from Carnegie Mellon University in the United States used synchrotron radiation micro-X-ray tomography to analyze the influence of different process parameters on the formation of pores in SLM-formed Ti-6Al-4V specimens. The study found that synchrotron radiation micro-X-ray tomography had good detection results for pores, lack of fusion, cracks, and inclusions. Zhang Xiangchun et al. from the China Aeronautical Integrated Technology Research Institute used X-ray computational tomography (X-CT) to conduct offline inspection experiments on typical pore and crack defects in SLM. The results showed that X-CT can detect pores with a diameter of 0.3 mm and cracks with an opening of 0.05 mm. The X-ray inspection method has significant advantages for detecting pores, cracks, lack of fusion, and inclusions in SLM additive manufacturing specimens, especially for small defects. However, the X-ray source is expensive, the equipment is complex, and integration with additive manufacturing equipment is difficult, making online inspection currently challenging.

[0009] (5) Infrared Detection Methods. Infrared detection methods have advantages such as being non-contact, intuitive, and having a large detection area. Depending on the presence or absence of an external excitation source, infrared detection methods can be divided into passive infrared detection and active infrared detection. Passive infrared detection monitors the temperature and temperature gradient of the metal melting and solidification process in real time through a non-contact method, enabling quality detection and evaluation during the additive manufacturing process. Bartlett JL et al. from the University of Virginia integrated a long-wave infrared thermal imager with an SLM additive manufacturing device to acquire infrared images of the AlSi10Mg additive manufacturing process. Defect detection was achieved by analyzing the location of temperature anomalies after single-layer sintering. The results showed that this method could detect 80% of unfused defects, with complete detection of unfused defects larger than 0.5 mm. However, the detection rate for internal keyhole bubble defects was only 33%. Currently, passive detection methods in metal additive manufacturing are mainly used for monitoring the molten pool temperature and measuring the temperature field distribution.

[0010] Active infrared detection employs an external excitation source to actively heat-load the specimen, thereby amplifying the temperature difference between defective and defect-free locations and achieving effective defect detection. Currently, several mature active infrared detection methods exist, such as pulsed infrared detection, phase-locked infrared detection, and linear frequency modulated radar detection. This technology has been widely applied in the non-destructive testing of composite materials, biological materials / tissues, and metallic materials. Mandelis A. et al. from the University of Toronto, Canada, proposed a truncated correlation thermal tomography (TC-PCT) active infrared detection method, achieving detection and three-dimensional tomographic imaging of stainless steel and biological tissues with a depth range of up to 3.2 mm and a depth resolution of 25 μm. Vavilov VP from Tomsk Polytechnic University, Russia, used dynamic thermal tomography to achieve tomographic imaging of internal corrosion in steel storage tanks. (Applicant)

[22] Radar thermal tomography based on the frequency domain characteristics of deep-correlation thermal waves has achieved effective detection and three-dimensional tomographic imaging of defects such as debonding (or inclusions) and cracks within layered materials. Domestic scholars have also conducted research on active infrared detection of defects in additively manufactured structural components. Wang Kedian et al. from Xinjiang University...

[23] Offline inspection of iron-based additive manufacturing specimens was conducted using active infrared detection (AIR). The study found that AIR technology can quantitatively characterize the quality of cladding additive materials, thus laying the foundation for online inspection in additive manufacturing using AIR. Xie Huimin et al. from Tsinghua University proposed a coupled neural network and point-source excitation infrared detection method. This method uses a point-source laser to actively thermally excite the laser near-net-shape forming (SLM) additive manufacturing coating, and establishes a relationship between thermal image data and crack size through a neural network. Results show that for cracks with a width within 68 μm, the average absolute error of this method is 2 μm (maximum absolute error 6.6 μm). Among typical defects in SLM formed layers, cracks and lack of fusion are macroscopic in size, generally large, while pore defects have a smaller equivalent diameter, typically 100 μm. Therefore, AIR has significant potential advantages in achieving online inspection of typical defects in SLM formed layers. However, currently, this method is mostly used for offline inspection, and research on applying AIR nondestructive testing to online inspection of defects in SLM additive manufacturing has not yet been reported.

[0011] Passive infrared thermal imaging technology can achieve real-time monitoring of the thermal processing in additive manufacturing, offering high efficiency, but its detection resolution and depth are relatively shallow. Active infrared thermal imaging, on the other hand, can achieve higher image resolution and deeper defect detection, but its efficiency is lower. The organic integration of active and passive infrared thermal imaging can solve the pressing challenges in additive manufacturing processes, such as real-time monitoring, high-resolution imaging, and deep defect detection. Summary of the Invention

[0012] To address the technical problems of low resolution and shallow detection depth in commonly used passive thermal imaging for online monitoring of additive manufacturing, this invention proposes a multi-source thermal wave tomography monitoring system and method for additive manufacturing processes.

[0013] The technical solution adopted by this invention to solve the above problems is as follows: This invention proposes a multi-source thermal wave tomography monitoring system for additive manufacturing processes, comprising:

[0014] Computer (1), infrared thermal imager (6), high-power laser power supply (17), laser galvanometer (25), SLM device (27) and high-performance function generator (29);

[0015] The first signal output terminal of the computer (1) is connected to the signal input terminal of the high-power laser power supply (17) via the high-power laser power supply control line (2). The second signal output terminal of the computer (1) is connected to the signal input terminal of the laser galvanometer (25) via the laser galvanometer control line (3). The third signal output terminal of the computer (1) is connected to the signal input terminal of the SLM device (27) via the SLM control line (30). The fourth signal output terminal of the computer (1) is connected to the signal input terminal of the high-performance function generator (29) via the B-type USB data cable (31). The fifth signal output terminal of the computer (1) is connected to the signal output terminal of the infrared thermal imager (6) via the Ethernet cable / thermal image sequence transmission line (4).

[0016] Optionally, the high-performance function generator (29) has two signal output terminals. One signal output terminal is connected to the signal input terminal of the infrared thermal imager (6) through the pulse trigger line (5), and the other signal output terminal is connected to the low-power detection laser power supply (22) through the low-power detection laser control line (21).

[0017] The low-power detection laser power supply (22) has two power output terminals. One power output terminal is connected to the low-power detection laser (18) through the low-power detection laser power supply line (19), and the other power output terminal is connected to the second heat dissipation module (23) through the second heat dissipation module power supply line (20).

[0018] Optionally, the high-power laser power supply (17) is provided with two power output terminals. One power output terminal is connected to the high-power laser (13) through the high-power laser power supply line (15), and the other power output terminal is connected to the first heat dissipation module (14) through the first heat dissipation module power supply line (16).

[0019] The high-power laser (13) is connected to the laser collimation and focusing system (9) through the first transmission fiber (11), and the low-power detection laser (18) is connected to the laser collimation module (10) through the second transmission fiber (12). The laser output from the laser collimation and focusing system (9) and the laser collimation module (10) is irradiated onto the entrance port of the laser galvanometer (25) through the second polarizer (24).

[0020] Optionally, the infrared thermal imager (6) is equipped with a bandpass filter (7) and a first polarizer (8).

[0021] Optionally, the printed sample (28) and powder doctor blade (26) are placed on top of the SLM device (27).

[0022] A method for monitoring additive manufacturing processes using multi-source thermal wave tomography includes:

[0023] Step 1: The three-dimensional model slice data of the selected workpiece is transmitted to the SLM device (27) via the SLM control line (30) through the computer (1);

[0024] Step 2: Inspect the SLM equipment (27) and start the layer-by-layer sintering process using the inspected SLM equipment (27);

[0025] Step 3: Use an infrared thermal imager (6) to monitor the sintering process, obtain temperature data cloud map, and obtain suspected damage areas based on the set damage threshold;

[0026] Step 4: Turn off the SLM device (27) and use the active infrared thermal imaging method to perform depth detection on the suspected damaged area to obtain single-layer tomographic image data;

[0027] Step 5: Repeat steps 2-4, turn on the SLM device (27), and acquire the tomographic image data of each layer;

[0028] Step 6: Based on the obtained tomographic data of each layer, use the ImageJ reconstruction program to perform three-dimensional tomographic reconstruction to obtain the workpiece of the specified design.

[0029] Optionally, the step of testing the SLM device (27) in step 2 includes:

[0030] Check if there is enough metal powder in the SLM equipment (27) to make the selected workpiece; if not, add more metal powder.

[0031] Check if the powder scraper (26) is working properly. If it is not working properly, adjust the powder scraper (26).

[0032] Optionally, step 2, which involves initiating the layer-by-layer sintering process, includes:

[0033] Using a powder scraper (26), metal powder is evenly spread onto the printed sample (28), while the high-power laser (13) is turned on to the processing state, and the printed sample is subjected to predetermined selective laser sintering through the laser galvanometer (25).

[0034] Optionally, step 4, which involves obtaining single-layer tomographic image data, includes:

[0035] Step 4.1: Use active infrared thermal imaging to perform depth detection on the suspected damaged area and turn off the SLM device (27);

[0036] Step 4.2: The computer (1) generates a DC signal and controls the low-power detection laser power supply (22) to output a constant power laser through the B-type USB data cable (31);

[0037] Step 4.3: A constant power laser passes through the laser galvanometer (25) to quickly determine the suspected damage location of the printed sample (28);

[0038] Step 4.4: Using the suspected damage location as the center, quickly move 30 positions with equal arcs within a circular area with a radius of 5mm, and obtain 30 cross-sectional temperature data for each of the 30 positions.

[0039] The expression for the single-layer data after accurate 3D tomography reconstruction is:

[0040]

[0041] In formula (1), u, v, w are the three coordinates of the reconstructed data, x, y, z are the three coordinates of the original image sequence, k is the direction vector, j is the imaginary number, Us is the cutoff temperature data, and G is the Fourier transform domain data.

[0042] The beneficial effects of this invention are:

[0043] 1. This invention integrates multiple technical fields such as single-mirror multi-laser multiplexing technology, high background thermal environment noise suppression, and DC and AC multi-quantity feature information extraction. Compared with traditional passive thermal imaging, it has the advantages of high resolution and large detection depth. Compared with traditional active thermal imaging, it has the advantage of high efficiency. At the same time, the method improves equipment utilization by sharing the galvanometer optical path.

[0044] 2. This invention utilizes the residual heat from the material's processing to achieve passive infrared measurement and monitor suspected damage locations. Subsequently, the system employs an ultrafast laser galvanometer to perform rapid, multi-type (point / line) scanning of the detection laser, enabling active infrared detection of suspected damage locations. Simultaneously, based on the thermal wave transmission dispersion characteristics, it achieves multi-interface tomographic imaging with a three-dimensional tomographic imaging depth of 1-2 mm and a resolution of ~5 μm. It can detect cracks in metallic materials with a width of up to 50 μm, with a mean absolute error of 2 μm. It can detect 90% of non-fusion defects, achieving complete detection of non-fusion defects larger than 0.5 mm, and increasing the detection rate of internal keyhole bubbles to over 50%. Attached Figure Description

[0045] Figure 1 This is a structural diagram of a multi-source thermal wave tomography monitoring system for additive manufacturing processes provided in an embodiment of the present invention;

[0046] Figure 2 A flowchart of a multi-source thermal wave tomography monitoring method for additive manufacturing processes provided in this embodiment of the invention;

[0047] Figure 3A schematic diagram of a multi-source thermal wave tomography monitoring method for additive manufacturing processes provided in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of multi-position cut-off data acquisition provided in an embodiment of the present invention;

[0049] Figure 5 and Figure 6 All images are physical pictures of the multi-source thermal wave tomography monitoring system for additive manufacturing processes provided in the embodiments of the present invention.

[0050] Figure 7 This is a multi-source thermal tomography monitoring result image provided in an embodiment of the present invention;

[0051] In the diagram, 1-computer, 2-high-power laser power supply control line, 3-laser galvanometer control line, 4-Ethernet cable / thermal image sequence transmission line, 5-pulse trigger line, 6-infrared thermal imager, 7-bandpass filter, 8-first polarizer, 9-laser collimation and focusing system, 10-laser collimation module, 11-first conductive fiber, 12-second conductive fiber, 13-high-power laser, 14-first heat dissipation module, 15-high-power laser power supply line, 16-first heat dissipation module power supply line 17-High-power laser power supply, 18-Low-power detection laser, 19-Low-power detection laser power supply line, 20-Second heat dissipation module power supply line, 21-Low-power detection laser control line, 22-Low-power detection laser power supply, 23-Second heat dissipation module, 24-Second polarizer, 25-Laser galvanometer, 26-Powder scraper, 27-SLM equipment, 28-Printed sample, 29-High-performance function generator, 30-SLM control line, 31-Type B USB data cable. Detailed Implementation

[0052] Combination Figure 1-7 This embodiment will be described as follows: Figure 1 , Figure 5 and Figure 6 As shown in the figure, the structure of a multi-source thermal tomography monitoring system for additive manufacturing processes provided in this embodiment includes:

[0053] Computer (1), infrared thermal imager (6), high-power laser power supply (17), laser galvanometer (25), SLM device (27) and high-performance function generator (29);

[0054] The first signal output terminal of the computer (1) is connected to the signal input terminal of the high-power laser power supply (17) via the high-power laser power supply control line (2). The second signal output terminal of the computer (1) is connected to the signal input terminal of the laser galvanometer (25) via the laser galvanometer control line (3). The third signal output terminal of the computer (1) is connected to the signal input terminal of the SLM device (27) via the SLM control line (30). The fourth signal output terminal of the computer (1) is connected to the signal input terminal of the high-performance function generator (29) via the B-type USB data cable (31). The fifth signal output terminal of the computer (1) is connected to the signal output terminal of the infrared thermal imager (6) via the Ethernet cable / thermal image sequence transmission line (4).

[0055] The high-performance function generator (29) has two signal output terminals. One signal output terminal is connected to the signal input terminal of the infrared thermal imager (6) through the pulse trigger line (5), and the other signal output terminal is connected to the low-power detection laser power supply (22) through the low-power detection laser control line (21).

[0056] The low-power detection laser power supply (22) has two power output terminals. One power output terminal is connected to the low-power detection laser (18) through the low-power detection laser power supply line (19), and the other power output terminal is connected to the second heat dissipation module (23) through the second heat dissipation module power supply line (20).

[0057] The high-power laser power supply (17) has two power output terminals. One power output terminal is connected to the high-power laser (13) through the high-power laser power supply line (15), and the other power output terminal is connected to the first heat dissipation module (14) through the first heat dissipation module power supply line (16).

[0058] The high-power laser (13) is connected to the laser collimation and focusing system (9) through the first transmission fiber (11), and the low-power detection laser (18) is connected to the laser collimation module (10) through the second transmission fiber (12). The laser output from the laser collimation and focusing system (9) and the laser collimation module (10) is irradiated onto the entrance port of the laser galvanometer (25) through the second polarizer (24).

[0059] The infrared thermal imager (6) is equipped with a bandpass filter (7) and a first polarizer (8), and the printed sample (28) and powder scraper (26) are placed on top of the SLM device (27).

[0060] like Figure 2 and Figure 3 As shown, the steps of a multi-source thermal wave tomography monitoring method for additive manufacturing processes provided in this embodiment include:

[0061] Step S1: Equipment inspection;

[0062] Furthermore, step S1 includes: checking whether there is enough metal powder in the SLM equipment 27 to make a sample, and checking whether the powder scraper 26 is working properly;

[0063] Step S2: Turn on the device;

[0064] Furthermore, step S2 includes: turning on the computer (1), infrared thermal imager (6), high-power laser power supply (17), low-power detection laser power supply (22), laser galvanometer (25), SLM device (27) and high-performance function generator (29).

[0065] Step S3: Sinter the printed sample and obtain the suspected damaged area;

[0066] Furthermore, step S3, which involves sintering the printed sample, includes:

[0067] The computer (1) transmits the three-dimensional model slice data of the selected workpiece to the SLM device (27) via (30). The SLM device (27) begins the layer-by-layer sintering process. The powder scraper (26) spreads the metal powder evenly on the printed sample 28. The high-power laser (13) is turned on to the processing state, and the predetermined selective laser sintering is achieved through the laser galvanometer (25).

[0068] Step S3, which involves obtaining the suspected damaged area, includes:

[0069] The real-time infrared thermal imager (6) monitors the sintering process in real time and forms a temperature data cloud map of the layer. Based on the set damage threshold (temperature fluctuation range > 5% in this embodiment), it can promptly detect suspected damage areas.

[0070] Step S4: Perform a depth examination on the suspected damaged area to obtain single-layer tomographic image data;

[0071] Further, such as Figure 4 As shown, step S4, which involves acquiring tomographic image data for each layer, includes:

[0072] Step S401: Use active infrared thermal imaging to perform depth detection on the suspected damaged area and turn off the SLM device (27);

[0073] Step S402: The computer (1) generates a DC signal and controls the low-power detection laser power supply (22) to output a constant power laser through the B-type USB data cable (31). In this embodiment, the constant power is 5W.

[0074] Step S403: A constant power laser passes through the laser galvanometer (25) to quickly determine the suspected damage location of the printed sample (28);

[0075] Step S404: Using the suspected damage location as the center, quickly move 30 positions with equal arcs within a circular area with a radius of 5mm, and acquire 30 cross-sectional temperature data for each of the 30 positions, i.e., single-layer tomographic image data.

[0076] The expression for single-layer tomographic image data is:

[0077]

[0078] In formula (1), u, v, w are the three coordinates of the reconstructed data, x, y, z are the three coordinates of the original image sequence, k is the direction vector, j is the imaginary number, Us is the sectional temperature data, and G is the Fourier transform domain number.

[0079] Step S5: Repeat steps S3-S4, turn on the SLM device (27), acquire the tomographic image data of each layer, and use the ImageJ reconstruction program to realize three-dimensional tomographic reconstruction based on the acquired tomographic data of each layer to obtain the workpiece of the specified design.

[0080] Step S6: Turn off the device;

[0081] Furthermore, step S6 includes: shutting down the computer (1), infrared thermal imager (6), high-power laser power supply (17), low-power detection laser power supply (22), laser galvanometer (25), SLM device (27), and high-performance function generator (29).

[0082] like Figure 7 As shown, the three-dimensional tomographic imaging of this invention has a depth of 1-2 mm and a resolution of ~5 μm. It can detect cracks in metallic materials with a width of less than 50 μm, with an average absolute error of 2 μm. It can detect 90% of unfused defects, and can completely detect unfused defects larger than 0.5 mm. The detection rate for internal keyhole bubbles is increased to over 50%.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A method for monitoring additive manufacturing processes using multi-source thermal wave tomography, characterized in that, The monitoring system used in the multi-source thermal tomography monitoring method includes: Computer (1), infrared thermal imager (6), high-power laser power supply (17), laser galvanometer (25), SLM device (27) and high-performance function generator (29); The first signal output terminal of the computer (1) is connected to the signal input terminal of the high-power laser power supply (17) via the high-power laser power supply control line (2), the second signal output terminal of the computer (1) is connected to the signal input terminal of the laser galvanometer (25) via the laser galvanometer control line (3), the third signal output terminal of the computer (1) is connected to the signal input terminal of the SLM device (27) via the SLM control line (30), the fourth signal output terminal of the computer (1) is connected to the signal input terminal of the high-performance function generator (29) via the B-type USB data cable (31), and the fifth signal output terminal of the computer (1) is connected to the signal output terminal of the infrared thermal imager (6) via the Ethernet cable / thermal image sequence transmission line (4). The high-power laser power supply (17) has two power output terminals. One power output terminal is connected to the high-power laser (13) through the high-power laser power supply line (15), and the other power output terminal is connected to the first heat dissipation module (14) through the first heat dissipation module power supply line (16). The high-power laser (13) is connected to the laser collimation and focusing system (9) through the first transmission fiber (11), and the low-power detection laser (18) is connected to the laser collimation module (10) through the second transmission fiber (12). The laser output from the laser collimation and focusing system (9) and the laser collimation module (10) is irradiated onto the entrance port of the laser galvanometer (25) through the second polarizer (24). The multi-source thermal tomography monitoring method specifically includes: Step 1: The three-dimensional model slice data of the selected workpiece is transmitted to the SLM device (27) via the SLM control line (30) through the computer (1); Step 2: Inspect the SLM equipment (27) and start the layer-by-layer sintering process using the inspected SLM equipment (27); Step 3: Use an infrared thermal imager (6) to monitor the sintering process, obtain temperature data cloud map, and obtain suspected damage areas based on the set damage threshold; Step 4: Use active infrared thermal imaging to perform depth detection on the suspected damaged area to obtain single-layer tomographic image data; Step 4, which involves obtaining single-layer tomographic image data, includes the following steps: Step 4.1: Use active infrared thermal imaging to perform depth detection on the suspected damaged area and turn off the SLM device (27). Step 4.2: The computer (1) generates a DC signal and controls the low-power detection laser power supply (22) to output a constant power laser through the B-type USB data cable (31); Step 4.3: A constant power laser passes through the laser galvanometer (25) to quickly determine the suspected damage location of the printed sample (28); Step 4.4: Using the suspected damage location as the center, quickly move 30 positions with equal arcs within a circular area with a radius of 5mm, and obtain 30 cross-sectional temperature data for each of the 30 positions. The expression for the single-layer data after accurate 3D tomography reconstruction is: （1） In formula (1), u,v,w For the three coordinates of the reconstructed data, x,y,z The three coordinates of the original image sequence, k It is a direction vector. j It is an imaginary number. Us For the cut-off temperature data, G Data in the Fourier transform domain; Step 5: Repeat steps 2-4, turn on the SLM device (27), and acquire tomographic image data for each layer; Step 6: Based on the obtained tomographic data of each layer, use the ImageJ reconstruction program to perform three-dimensional tomographic reconstruction to obtain the workpiece of the specified design.

2. The multi-source thermal wave tomography monitoring method for additive manufacturing processes according to claim 1, characterized in that, The high-performance function generator (29) has two signal output terminals. One signal output terminal is connected to the signal input terminal of the infrared thermal imager (6) through the pulse trigger line (5), and the other signal output terminal is connected to the low-power detection laser power supply (22) through the low-power detection laser control line (21). The low-power detection laser power supply (22) has two power output terminals. One power output terminal is connected to the low-power detection laser (18) through the low-power detection laser power supply line (19), and the other power output terminal is connected to the second heat dissipation module (23) through the second heat dissipation module power supply line (20).

3. The multi-source thermal tomography monitoring method for additive manufacturing processes according to claim 1, characterized in that, The infrared thermal imager (6) is equipped with a bandpass filter (7) and a first polarizer (8).

4. The multi-source thermal tomography monitoring method for additive manufacturing processes according to claim 1, characterized in that, The SLM device (27) has a printed sample (28) and a powder scraper (26) placed on top.

5. The multi-source thermal wave tomography monitoring method for additive manufacturing processes according to claim 4, characterized in that, Step 2, which involves testing the SLM device (27), includes the following steps: Check if there is enough metal powder in the SLM equipment (27) to make the selected workpiece; if not, add more metal powder. Check if the powder scraper (26) is working properly. If it is not working properly, adjust the powder scraper (26).

6. The multi-source thermal tomography monitoring method for additive manufacturing processes according to claim 4, characterized in that, Step 2, which involves starting the layer-by-layer sintering process, includes: Using a powder scraper (26), metal powder is evenly spread on the printed sample (28), while the high-power laser (13) is turned on to the processing state, and the printed sample is subjected to predetermined selective laser sintering through the laser galvanometer (25).

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