Molten pool temperature online monitoring method and system in laser additive manufacturing process

Abstract

A molten pool temperature online monitoring method and system in a laser additive manufacturing process. The method comprises: using a beam splitter (110) to split a molten pool optical signal into a first light beam and a second light beam having the same intensity, wherein the first light beam reaches a first reflector (130) after passing through a first filter (120), passes through the first filter (120) again after being reflected by the first reflector (130), and is focused by a lens group (160) to a detector (170) for imaging; the second light beam reaches a second reflector (150) after passing through a second filter (140), passes through the second filter (140) again after being reflected by the second reflector (150), and is focused by the lens group (160) to the detector (170) for imaging; and the center wavelengths of the first filter (120) and the second filter (140) are different, and the detector (170) obtains a dual-wavelength molten pool image; and using an image processing unit (180) to calculate a molten pool temperature field on the basis of the dual-wavelength molten pool image. The measurement accuracy of a molten pool temperature in the laser additive manufacturing process can be improved.

Description

A method and system for online monitoring of molten pool temperature during laser additive manufacturing. Technical Field

[0001] This invention belongs to the field of additive manufacturing technology, and more specifically, relates to a method and system for online monitoring of molten pool temperature during laser additive manufacturing. Background Technology

[0002] Laser powder bed fusion (LPBF), as a mainstream technology in additive manufacturing (AM), plays a crucial role in key fields such as aerospace. During AM, key variables such as the molten pool temperature field distribution directly affect the performance of the components. For example, defects such as porosity, lack of fusion, microcracks, and contour deformation may occur. Therefore, the lack of quality assurance and performance control severely hinders the widespread application of LPBF technology. To improve the stability and repeatability of components, real-time monitoring of the molten pool temperature field has gradually gained attention.

[0003] Currently, methods for monitoring the temperature field of LPBF molten pool mainly include photodiodes, infrared thermal imagers, and dual-wavelength colorimetric thermometry systems. However, commercial equipment such as photodiodes and infrared thermal imagers have complex emissivity calibration, small temperature measurement ranges that make them difficult to meet monitoring requirements, and high costs. Furthermore, traditional infrared equipment is difficult to integrate in limited spaces due to its large system size. While current dual-wavelength colorimetric thermometry systems based on single or dual cameras can monitor higher molten pool temperatures without emissivity calibration, the frame rate consistency error, large size, and high cost of dual-camera systems limit their application. While current single-camera systems reduce costs, their complex optical paths are difficult to integrate, and they do not consider the impact of coaxial imaging systems on temperature measurement. Therefore, there is an urgent need to develop a high-precision, wide-range, and compact temperature measurement system for coaxial online monitoring of molten pool temperature. Summary of the Invention

[0004] This invention provides a method and system for online monitoring of molten pool temperature during laser additive manufacturing, thereby solving the problem of low measurement accuracy of molten pool temperature in existing technologies.

[0005] In a first aspect, the present invention provides a method for online monitoring of the molten pool temperature during laser additive manufacturing, comprising the following steps:

[0006] A beam splitter is used to split the molten pool optical signal into a first beam and a second beam of equal intensity. The first beam passes through a first filter and reaches a first reflecting mirror. After being reflected by the first reflecting mirror, it passes through the first filter again and is imaged by a detector. The second beam passes through a second filter and reaches a second reflecting mirror. After being reflected by the second reflecting mirror, it passes through the second filter again and is imaged by the detector. The center wavelengths of the first filter and the second filter are different, and the detector obtains a dual-wavelength molten pool image.

[0007] The temperature field of the molten pool is calculated using the image processing unit based on the dual-wavelength molten pool image.

[0008] Preferably, the image processing unit performs preliminary segmentation on the dual-wavelength molten pool image, and matches and aligns the first molten pool image and the second molten pool image obtained after preliminary segmentation; the first molten pool image corresponds to the first beam, and the second molten pool image corresponds to the second beam.

[0009] After matching and alignment, the image processing unit calculates the molten pool temperature using a dual-wavelength colorimetric thermometry formula.

[0010] Preferably, the preliminary segmentation of the dual-wavelength fused pool image using the image processing unit includes the following sub-steps:

[0011] The dual-wavelength molten pool image is binarized using an adaptive threshold binarization method, and the molten pool boundary position information is obtained based on the binary image corresponding to the dual-wavelength molten pool image.

[0012] Based on the molten pool boundary location information and the prior size information of the molten pool, the first molten pool image and its binary image, as well as the second molten pool image and its binary image, are obtained by extracting the region of interest.

[0013] Preferably, matching and aligning the first molten pool image and the second molten pool image includes the following sub-steps:

[0014] Based on the molten pool boundary position information, the center position of the first molten pool image in the i-th iteration and the center position of the second molten pool image in the i-th iteration are calculated; where i = 1, 2, ..., n; n is the set number of iterations;

[0015] Based on the center position of the first molten pool image in the i-th iteration and the center position of the second molten pool image in the i-th iteration, the center offset value of the two images in the i-th iteration is calculated;

[0016] After completing n iterations, calculate the average of the n center offset values ​​to obtain the final offset value;

[0017] The final offset value is used to correct and align the two images.

[0018] Preferably, the molten pool optical signal comes from a coaxial test optical path system.

[0019] Preferably, the dual-wavelength colorimetric temperature measurement formula is as follows:

[0020]

[0021] In the formula, C2 represents the temperature of the molten pool in the coaxial test optical path system, C2 is the second radiation constant, λ1 is the center wavelength of the first filter, and λ2 is the center wavelength of the second filter. R(λ1) represents the relationship coefficient between gray values ​​under dual-wavelength radiation after coaxial calibration, R(λ2) represents the detector response to wavelength λ1, R(λ2) represents the detector response to wavelength λ2, and k represents the coefficient obtained from non-coaxial calibration of the blackbody furnace.

[0022] Preferably, the relationship coefficient between the grayscale values ​​under coaxial calibration dual-wavelength radiation We obtain it from the following formula:

[0023]

[0024] In the formula, w is the relationship coefficient between gray values ​​under dual-wavelength radiation before coaxial calibration, and A is the calibration coefficient of the beam splitter in the non-coaxial test optical path system. is the intensity ratio of the two wavelengths in a non-coaxial test optical path system, and m is the intensity ratio of the two wavelengths in a coaxial test optical path system. This is the white image corresponding to wavelength λ1 in a non-coaxial test optical path system. This is the white image corresponding to wavelength λ2 in a non-coaxial test optical path system. This is the white image corresponding to wavelength λ1 in a coaxial test optical path system. This is the white image corresponding to wavelength λ2 in the coaxial test optical path system.

[0025] In a second aspect, the present invention provides an online monitoring system for the temperature of the molten pool during laser additive manufacturing, comprising: a beam splitter, a first filter, a first reflector, a second filter, a second reflector, a detector, and an image processing unit;

[0026] The online monitoring system for molten pool temperature during laser additive manufacturing is used to perform the steps in the above-described online monitoring method for molten pool temperature during laser additive manufacturing.

[0027] Preferably, the online monitoring system for molten pool temperature during laser additive manufacturing further includes a focusing lens group; both beams of light are focused by the focusing lens group before entering the detector.

[0028] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for online monitoring of molten pool temperature during laser additive manufacturing.

[0029] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0030] (1) In this invention, the molten pool light signal is first split into a first beam and a second beam of equal intensity using a beam splitter. The first beam passes through a first filter and reaches a first reflecting mirror. After being reflected by the first reflecting mirror, it passes through the first filter again and is imaged by a detector. The second beam passes through a second filter and reaches a second reflecting mirror. After being reflected by the second reflecting mirror, it passes through the second filter again and is imaged by a detector. The center wavelengths of the first and second filters are different, and the detector obtains a dual-wavelength molten pool image. Then, the image processing unit obtains the molten pool temperature field based on the dual-wavelength molten pool image. In this invention, each beam of light after being split by the beam splitter is filtered twice by the corresponding filter, which can effectively remove the interference of stray wavelengths and improve the accuracy of molten pool temperature measurement.

[0031] (2) This invention utilizes an image processing unit to perform preliminary segmentation of the dual-wavelength molten pool image (including using an adaptive threshold binarization method to binarize the dual-wavelength molten pool image, obtaining the molten pool boundary position information based on the binary image, and extracting the two molten pool images and their corresponding binary images through region of interest extraction). The first and second molten pool images obtained after preliminary segmentation are then matched and aligned (including calculating the center position of the molten pool image through multiple iterations, calculating the center offset value and obtaining the final offset value, and correcting based on the final offset value to match and align the two images). In other words, this invention proposes an adaptive threshold dynamic online matching method for the center position of molten pool images. This method calculates the center deviation of two images under dual wavelengths and corrects it to make them overlap, achieving sub-pixel-level matching of molten pool images. This high-precision matching technology can solve the problem of inaccurate matching in traditional methods when facing dynamic changes in the shape and position of the molten pool, achieving high-precision molten pool temperature field measurement results even under different laser scanning conditions.

[0032] (3) In this invention, when the molten pool optical signal comes from the coaxial test optical path system and the molten pool temperature is calculated using the dual-wavelength colorimetric thermometry formula, the grayscale ratio under coaxial dual-wavelength radiation is also calibrated. That is, this invention addresses the influence of the coaxial system on dual-wavelength thermometry by calibrating the dual-wavelength intensity of the coaxial test optical path system and deriving the calibration formula. This avoids the need for additional hardware calibration, reduces the complexity and cost of calibration, and further improves the accuracy of coaxial measurement of molten pool temperature.

[0033] (4) The online monitoring system for molten pool temperature in the laser additive manufacturing process provided by the present invention mainly includes a beam splitter, a first filter, a first reflector, a second filter, a second reflector, a focusing lens group, a detector and an image processing unit. It uses only five core components—the beam splitter, the first filter, the first reflector, the second filter and the second reflector—to achieve beam splitting and filtering. The structure is compact, and the two reflectors can flexibly adjust the position of the dual-wavelength image to be suitable for various processes, such as coaxial and off-axis systems. It is also easy to integrate into coaxial devices such as LPBF equipment. Attached Figure Description

[0034] Figure 1 is a schematic diagram showing the positional relationship between an online monitoring system for molten pool temperature and a coaxial test optical path system during laser additive manufacturing, according to an embodiment of the present invention.

[0035] Figure 2 is a schematic diagram of an online monitoring system for melt pool temperature during laser additive manufacturing provided in an embodiment of the present invention.

[0036] Figure 3 is a schematic diagram of the process of matching dual-wavelength molten pool images in an online monitoring method for molten pool temperature during laser additive manufacturing provided by an embodiment of the present invention; wherein, Figure 3(a) is a schematic diagram of the process of segmenting dual-wavelength molten pool images by prior adaptive threshold, and Figure 3(b) is a schematic diagram of the process of iterative matching and aligning dual-wavelength molten pool images.

[0037] Figure 4 is a schematic diagram of the change in the center distance of the molten pool before and after matching in the dual-wavelength molten pool image; wherein, (a) in Figure 4 is the center distance of the molten pool in the vertical and horizontal directions in the dual-wavelength molten pool image before matching, and (b) in Figure 4 is the center offset error of the molten pool in the dual-wavelength molten pool image after matching.

[0038] Figure 5 is a schematic diagram of dual-wavelength intensity radiation calibration in a coaxial online monitoring method for molten pool temperature during laser additive manufacturing provided by an embodiment of the present invention;

[0039] Figure 6 shows the relative error distribution of 40,000 pixels of the blackbody furnace at five representative temperatures;

[0040] Figure 7 shows the molten pool temperature field results obtained using an online monitoring method for molten pool temperature during laser additive manufacturing provided by an embodiment of the present invention.

[0041] Among them, the 100-online monitoring system for melt pool temperature during laser additive manufacturing;

[0042] 110-beam splitter, 120-first filter, 130-first reflector, 140-second filter, 150-second reflector, 160-focusing lens group, 170-detector, 180-image processing unit;

[0043] 201-Processed laser, 202-Beam expander, 203-Long-pass dichroic mirror, 204-Galvanometer, 205-Field mirror, 206-Substrate, 207-Molten pool. Embodiments of the present invention

[0044] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0045] Example 1:

[0046] Example 1 provides a method for online monitoring of the molten pool temperature during laser additive manufacturing, as shown in Figure 2, including the following steps:

[0047] Step 1: The molten pool light signal (i.e., molten pool radiation light) is split into a first beam and a second beam of equal intensity using a beam splitter 110. The first beam passes through the first filter 120 and reaches the first reflector 130. After being reflected by the first reflector 130, it passes through the first filter 120 again and is focused by the focusing lens group 160 onto the detector 170 for imaging. The second beam passes through the second filter 140 and reaches the second reflector 150. After being reflected by the second reflector 150, it passes through the second filter 140 again and is focused by the focusing lens group 160 onto the detector 170 for imaging. The center wavelengths of the first filter 120 and the second filter 140 are different, and the detector 170 obtains a dual-wavelength molten pool image.

[0048] Step 2: The image processing unit 180 calculates the molten pool temperature field based on the dual-wavelength molten pool image.

[0049] Specifically, the image processing unit 180 performs preliminary segmentation on the dual-wavelength molten pool image, and matches and aligns the first molten pool image and the second molten pool image obtained after preliminary segmentation; the first molten pool image corresponds to the first beam, and the second molten pool image corresponds to the second beam; after matching and alignment, the image processing unit 180 calculates the temperature of the molten pool using the dual-wavelength colorimetric thermometry formula.

[0050] Specifically, the preliminary segmentation of the dual-wavelength molten pool image using the image processing unit 180 includes the following sub-steps: performing binarization processing on the dual-wavelength molten pool image using an adaptive threshold binarization method, obtaining molten pool boundary position information based on the binary image corresponding to the dual-wavelength molten pool image; and extracting the first molten pool image and its binary image, as well as the second molten pool image and its binary image, through the region of interest based on the molten pool boundary position information and the prior size information of the molten pool.

[0051] Specifically, matching and aligning the first molten pool image and the second molten pool image includes the following sub-steps: Calculating the center position of the first molten pool image in the i-th iteration and the center position of the second molten pool image in the i-th iteration based on the molten pool boundary position information; where i = 1, 2, ..., n; n is the set number of iterations; calculating the center offset value of the two images in the i-th iteration based on the center position of the first molten pool image and the center position of the second molten pool image in the i-th iteration; after completing n iterations, calculating the average of the n center offset values ​​to obtain the final offset value; and correcting based on the final offset value to match and align the two images.

[0052] The molten pool optical signal can originate from a coaxial test optical path system. That is, the online monitoring method provided by this invention can be used for coaxial online monitoring of the LPBF molten pool temperature field, acquiring dual-wavelength information of the molten pool, and then calculating the molten pool temperature field.

[0053] The dual-wavelength colorimetric temperature measurement formula is as follows:

[0054]

[0055] In the formula, C2 represents the temperature of the molten pool in the coaxial test optical path system, C2 is the second radiation constant, λ1 is the center wavelength of the first filter, and λ2 is the center wavelength of the second filter. R(λ1) represents the relationship coefficient between gray values ​​under dual-wavelength radiation after coaxial calibration, R(λ2) represents the detector response to wavelength λ1, R(λ2) represents the detector response to wavelength λ2, and k represents the coefficient obtained from non-coaxial calibration of the blackbody furnace.

[0056] The relationship coefficient between gray values ​​under coaxial calibration dual-wavelength radiation We obtain it from the following formula:

[0057]

[0058] In the formula, w is the relationship coefficient between gray values ​​under dual-wavelength radiation before coaxial calibration, and A is the calibration coefficient of the beam splitter in the non-coaxial test optical path system. is the intensity ratio of the two wavelengths in a non-coaxial test optical path system, and m is the intensity ratio of the two wavelengths in a coaxial test optical path system. This is the white image corresponding to wavelength λ1 in a non-coaxial test optical path system. This is the white image corresponding to wavelength λ2 in a non-coaxial test optical path system. This is the white image corresponding to wavelength λ1 in a coaxial test optical path system. This is the white image corresponding to wavelength λ2 in the coaxial test optical path system.

[0059] Example 2:

[0060] Example 2 provides an online monitoring system for molten pool temperature during laser additive manufacturing, as shown in Figure 2. It mainly includes: a beam splitter 110, a first filter 120, a first reflector 130, a second filter 140, a second reflector 150, a detector 170, and an image processing unit 180. Furthermore, it may include a focusing lens group 160; both beams of light are focused by the focusing lens group 160 before entering the detector 170.

[0061] The online monitoring system for melt pool temperature in the laser additive manufacturing process provided in Example 2 is used to perform the steps in the online monitoring method for melt pool temperature in the laser additive manufacturing process as described in Example 1.

[0062] Since the functions of each device in the monitoring system provided in Example 2 correspond to the steps in the monitoring method provided in Example 1, they can be understood by referring to the description in Example 1, and will not be repeated here.

[0063] Example 3:

[0064] Example 3 provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the online monitoring method for molten pool temperature during laser additive manufacturing as described in Example 1.

[0065] The processor primarily executes tasks related to obtaining the molten pool temperature field based on dual-wavelength molten pool images using the image processing unit.

[0066] The main parts of the present invention will be further described below.

[0067] (1) Design of an online monitoring system for molten pool temperature during laser additive manufacturing.

[0068] Referring to Figure 1, the online molten pool temperature monitoring system 100 provided by this invention during laser additive manufacturing is preferably used in conjunction with a coaxial testing optical path system (such as an LPBF system) to capture dual-wavelength molten pool images in real time. The configuration of the coaxial imaging device ensures that the molten pool remains centered in the image when the laser scans the powder bed, thereby maintaining good spatial resolution of the molten pool throughout the monitoring area. Referring to Figure 1, the coaxial testing optical system mainly includes a processing laser 201, a beam expander 202, a long-pass dichroic mirror 203, a galvanometer 204, and a field mirror 205, and also involves a substrate 206 and a molten pool 207. The light emitted from the molten pool 207 passes through the field mirror 205, the galvanometer 204, and the long-pass dichroic mirror 203, and finally reaches the online molten pool temperature monitoring system 100 designed in this invention. The function of the long-pass dichroic mirror 203 is to allow light with the same wavelength as the laser to pass through, while shorter wavelength light, such as part of the light radiated from the molten pool, is reflected to the filter to prevent damage to the imaging equipment by the high-power laser.

[0069] Referring to Figure 2, in the online molten pool temperature monitoring system provided by this invention, the light emitted from the molten pool is split into two beams at a 1:1 ratio by the beam splitter 110, namely transmitted light and reflected light. The reflected light passes through the first filter 120 with a center wavelength of λ1 and reaches the first reflecting mirror 130. After being reflected by the first reflecting mirror 130, it passes through the first filter 120 again and is focused by the focusing lens group 160, and finally imaged by the detector 170. The transmitted light passes through another second filter 140 with a center wavelength of λ2, and is then reflected by the second reflecting mirror 150. It then passes through the second filter 140 again and is focused by the same focusing lens group 160, and finally imaged by the same detector 170. That is, the two beams of light split by the beam splitter 110 pass through filters of two different wavelengths and emit two beams of light with different intensities. These two beams of light then pass through the same focusing lens group and are transmitted to the detector 170 to obtain two images of the molten pool (i.e., a dual-wavelength molten pool image). Then, the image processing unit 180 calculates the molten pool temperature field based on the dual-wavelength molten pool image, including calculating the center deviation of the two images through an adaptive threshold dynamic matching algorithm and correcting it to make them overlap, and calculating the molten pool temperature field based on the dual-wavelength colorimetric thermometry method.

[0070] The online molten pool temperature monitoring system designed in this invention has a compact structure and is easy to integrate. The reflector can be flexibly adjusted to adapt to the molten pool temperature field monitoring requirements of different additive manufacturing equipment. Furthermore, in this invention, the molten pool radiation light is split into two beams by the beam splitter 110, and both beams undergo two filtering operations. If one beam is reflected and filtered once by the first filter 120 to obtain a light signal with wavelength λ1, due to the filter's inherent error, this λ1 light signal contains a small amount of stray light of other wavelengths, leading to a significant error in molten pool temperature detection. In the structure designed in this invention, if the molten pool light signal with wavelength λ1 after the first filtering is reflected by the first reflector 130 and then filtered again by the first filter 120, the interference of stray wavelengths can be effectively filtered out. Similarly, wavelength λ2 can also be filtered twice to improve the accuracy of molten pool temperature detection.

[0071] (2) Calculation method for dual-wavelength colorimetric temperature.

[0072] The dual-wavelength colorimetric temperature calculation method uses Wien's approximation formula to calculate Planck's law in order to determine the intensity of the object to be measured.

[0073] (1)

[0074] Where L(λ,T) represents the intensity at wavelength λ and temperature T, h represents Planck's constant, c represents the speed of light, and k B Represents the Boltzmann constant; Emissivity is used to describe the degree to which an object emits thermal radiation relative to an ideal blackbody, and its value is between 0 and 1.

[0075] The emissivity of wavelengths λ1 and λ2 is similar. The grayscale value detected by the detector at wavelength λ and temperature T is G(λ,T), and it is assumed that G(λ,T) = R(λ)L(λ,T), where R(λ) represents the relative spectral response that needs to be calibrated. The temperature can be solved by the grayscale ratio of the detector at the two wavelengths.

[0076] (2)

[0077] (3)

[0078] Where, C1=2hc 2 C2 = 1.44 × 10 -2 m·K.

[0079] In the formula, R(λ) is the detector's response to wavelength λ, w=G(λ1,T) / G(λ2,T), and k=R(λ2) / R(λ1). w is defined as the gray value ratio of the dual-wavelength radiation intensity, and the coefficient k is a constant.

[0080] In summary, firstly, G(λ1,T) and G(λ2,T) in formula (2) are matched and aligned by dual-wavelength image matching, then the coefficient k is obtained by blackbody furnace calibration, and finally the distribution of the molten pool temperature field is calculated.

[0081] The blackbody furnace is calibrated as follows: the blackbody furnace is set to T. i (i=1,2,…n), record w at each temperature, and based on formula (3), combine multiple T i k can be obtained by linear fitting.

[0082] (3) Subpixel-level adaptive threshold dynamic online matching algorithm.

[0083] This invention designs an adaptive threshold dynamic online matching method for the center position of molten pool images, as shown in Figure 3. First, the dual-wavelength molten pool image in a single image is coarsely segmented into two images. Then, an adaptive threshold dynamic online matching algorithm is used to match the center positions of the two segmented images. The details are explained below.

[0084] (3.1) The dual-wavelength image is segmented using a priori adaptive thresholding method.

[0085] Referring to Figure 3(a), the dual-wavelength image of the molten pool acquired by the online monitoring system for molten pool temperature shown in the left image of the first row is binarized according to an adaptive threshold to obtain its binary image, thereby determining the boundary position on the molten pool image (the left and right boundaries in Figure 3, see the right image of the first row). Then, based on the prior size of the molten pool, the two images of the molten pool and their corresponding two binary images are extracted through the region of interest (ROI) (see the image in the second row).

[0086] (3.2) Iterative dual-wavelength molten pool image dynamic matching

[0087] Referring to Figure 3(b), based on the initially segmented dual-wavelength image of the molten pool, this invention designs an iterative method for dual-wavelength image matching and alignment, defined as follows:

[0088] (4)

[0089] In formula (4), T li B li L li and R li Let and represent the top, bottom, left, and right boundary positions of the left image in the i-th iteration, and T represent the top, bottom, left, and right boundary positions of the left image in the i-th iteration. ri B ri L ri and R ri This indicates the positions of the top, bottom, left, and right boundaries in the right image. Lci and R ci Let represent the center position of the left and right images calculated in the i-th iteration, and n represent the iteration number. Taking the binary image shown on the right side of the second row in Figure 3(a) as an example, in the first iteration, the upper, lower, left, and right boundaries of the left image of the molten pool are marked as T. l 1. B l 1. L l 1. R l 1. Obtain the center position L of the first iteration by calculating the average value. c 1. Similarly, the center position R of the right image in the first iteration can be obtained. c 1. Based on the center positions of the left and right images of the molten pool, obtain the offset T of the center of the dual-wavelength image in the first iteration. d 1. However, the center offset calculated in a single operation is susceptible to matching errors due to image boundary noise.

[0090] To address the issue of large matching errors in single calculations, this invention designs an iterative method. To emphasize boundaries, it is assumed that the dual-wavelength image boundary is set to 1, while other areas are set to 0. The upper, lower, left, and right boundary indices marked in the first iteration are set to 0, and the calculation is repeated to obtain the molten pool dual-wavelength image center offset T for the second iteration. d 2. By iterating n times consecutively, n center offsets T can be obtained. dn Calculate the average of n center offsets to obtain the final offset T. d This enables dual-wavelength image matching and alignment of the molten pool.

[0091] As shown in Figure 4(a), in coaxial additive manufacturing processes such as LPBF, the relative distance between the centers of the two-wavelength images dynamically changes during the calculation of the molten pool temperature based on the dual-wavelength colorimetric theory. Figure 4(a) shows the center distances of the two-wavelength images in the vertical and horizontal directions. It can be observed that the vertical distance variation (i.e., row error) of the molten pool center distance is relatively small, while the horizontal distance fluctuation (i.e., column error) is large, making traditional static matching methods difficult to apply.

[0092] To quantitatively demonstrate the accuracy of the dual-wavelength image matching method proposed in this invention, the center offset error after molten pool image matching was measured. Figure 4(b) shows the offset error range. Specifically, the upper part of Figure 4(b) shows the matched dual-wavelength molten pool contour image, and the bottom two rows of Figure 4(b) show the matched molten pool dual-wavelength image and binary image, where the two images are superimposed pixel by pixel. By magnification, it can be seen that the molten pool dual-wavelength image and binary image in this invention achieve sub-pixel level matching.

[0093] In summary, this invention offers significant advantages in monitoring the temperature field of a molten pool. Unlike traditional methods that require pre-calibration of the dual-wavelength center positions and static matching, static matching methods struggle to achieve stable and accurate molten pool image matching during long-term processing. Therefore, this invention innovatively proposes an adaptive threshold dynamic online matching algorithm. This algorithm dynamically adjusts the matching threshold based on real-time image data, achieving sub-pixel-level accurate online matching of pixel positions in the dual-wavelength image of the molten pool and extracting the dual-wavelength intensity information for each pixel. This dynamic matching method effectively overcomes the limitations of static matching, significantly improving the accuracy and real-time performance of temperature field monitoring.

[0094] (4) Calibration of coaxial dual-wavelength imaging in the online monitoring system for molten pool temperature.

[0095] In coaxial systems such as LPBF, field lenses and galvanometers are coated with special thin film layers to achieve efficient manufacturing, but this may cause aberrations in the imaging system in the visible light range. For systems without field lenses and galvanometers, i.e., non-coaxial systems, no aberrations occur. However, when the two wavelengths of radiation maintain the same processing distance, for systems with field lenses and galvanometers, i.e., coaxial systems, the dual-wavelength radiation image may exhibit varying degrees of blurring. Furthermore, according to formula (3), k is usually obtained through non-coaxial calibration using a blackbody furnace. When the online molten pool temperature monitoring system provided by this invention is used in conjunction with a coaxial system, the intensity ratio of the dual wavelengths in the coaxial and non-coaxial systems cannot be kept consistent, resulting in k obtained from the non-coaxial system being unsuitable for direct coaxial measurement of the molten pool temperature.

[0096] To address the issue that the field mirror and galvanometer in coaxial systems such as LPBF can increase the intensity error of dual-wavelength temperature measurement systems, leading to decreased measurement accuracy, this invention performs coaxial calibration and derives a calibration formula. This invention uses a halogen lamp to calibrate the dual-wavelength intensity of the coaxial system and derives a dual-wavelength temperature measurement calibration formula applicable to coaxial systems. Specifically, as shown in Figure 5, a broadband light source with a wavelength radiation range similar to the molten pool emitted from the halogen lamp is used to calibrate the dual-wavelength radiation intensity of both the non-coaxial (reference) optical path system and the coaxial test optical path system. In the embodiment, in the coaxial device, this invention acquires the λ1 and λ2 light signals reflected by the reflector at the same working distance, and captures and displays white images of different brightness levels through the system. Based on the white images, the dual-wavelength ratio of the non-coaxial and coaxial systems is obtained respectively. Let g be the dual-wavelength white image of the coaxial system. c (λ1) and g c (λ2), g in non-coaxial systems c '(λ1) and g c '(λ2). The intensity ratio of the two wavelengths for both non-coaxial and coaxial systems can be obtained:

[0097] (5)

[0098] Therefore, we can define: , where A represents the calibration coefficient of the spectroscope, used to ensure a spectral ratio of 1:1. According to formulas (3) and (5), the dual-wavelength colorimetric thermometry formulas can be rewritten as formulas (6) and (7) in non-coaxial and coaxial systems, respectively.

[0099] (6)

[0100] (7)

[0101] Among them, T a and T co These represent the temperatures in the off-axis (i.e., non-coaxial) optical path testing system and the coaxial optical path testing system, respectively.

[0102] For example, as shown in Figure 5, the present invention uses filters with center wavelengths of 550nm and 630nm for coaxial system calibration and molten pool temperature measurement.

[0103] In summary, this invention proposes a dual-wavelength calibration method for coaxial systems, which can avoid the use of additional hardware for calibration, reduce calibration complexity and cost, and further improve the temperature measurement accuracy of the molten pool in coaxial systems.

[0104] Finally, dual-wavelength images were acquired using a blackbody furnace, and the accuracy of the online molten pool temperature monitoring system provided by this invention was verified by comparing the difference between the measured temperature field and the set temperature. This invention uses a relative error E... r Analysis of the accuracy of the online monitoring system for molten pool temperature:

[0105] (8)

[0106] Among them, T m T represents the temperature measured by the online monitoring system for the molten pool temperature. t This indicates the actual temperature of the blackbody furnace.

[0107] Specifically, as shown in Figure 6, this invention acquired blackbody furnace images at wavelengths of 550 nm and 630 nm at temperatures of 1273.15 K, 1773.15 K, 2273.15 K, 2773.15 K, and 3273.15 K (the five temperatures cover the metal molten pool from solidification to boiling). A 200-pixel × 200-pixel inscribed square region was extracted from each image, totaling 40,000 pixels. Within these 40,000 pixels, the temperature monitoring system of this invention exhibited a maximum error of less than 3% across the five representative temperature fields. Furthermore, Table 1 quantitatively represents the relative errors at the five representative temperatures in Figure 6.

[0108] Table 1. Relative error of 40,000 pixels at five representative temperatures.

[0109]

[0110] As shown in Table 1, the average relative error at each temperature point is less than 1.9%, demonstrating excellent measurement accuracy across the entire temperature range. At 2273.15 K, the minimum error is 0.01%, and the maximum error is 2.98%, indicating that the invention maintains reliable performance even under the highest heat load conditions. Notably, the average error decreases from 1.89% at 1273.15 K to 1.26% at 3273.15 K, demonstrating higher measurement accuracy at high temperatures. These results validate the robustness and reliability of the proposed method for real-time temperature monitoring during laser powder bed melting (LPBF).

[0111] In addition, this invention also uses high-melting-point alloys as an example to monitor the temperature field and analyzes the temperature distribution and size changes of the molten pool to further verify the accuracy of the online molten pool temperature monitoring system proposed in this invention.

[0112] Specifically, the online molten pool temperature monitoring system provided by this invention is used to monitor the temperature of high-melting-point alloys during the LPBF manufacturing process. Figure 7 shows the molten pool temperature field distribution sequence from left to right at 10ms intervals, where the horizontal axis represents the molten pool width and the vertical axis represents the molten pool length, in μm. This result shows that the temperature is consistent with the laser scanning direction, indicating that this invention can achieve high-precision online monitoring of the molten pool temperature field.

[0113] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for online monitoring of molten pool temperature during laser additive manufacturing, characterized in that, Includes the following steps: A beam splitter is used to split the molten pool optical signal into a first beam and a second beam of equal intensity. The first beam passes through a first filter and reaches a first reflecting mirror. After being reflected by the first reflecting mirror, it passes through the first filter again and is imaged by a detector. The second beam passes through a second filter and reaches a second reflecting mirror. After being reflected by the second reflecting mirror, it passes through the second filter again and is imaged by the detector. The center wavelengths of the first filter and the second filter are different, and the detector obtains a dual-wavelength molten pool image. The temperature field of the molten pool is obtained using the image processing unit based on the dual-wavelength molten pool image.

2. The method for online monitoring of molten pool temperature during laser additive manufacturing according to claim 1, characterized in that, The image processing unit performs preliminary segmentation on the dual-wavelength molten pool image, and then matches and aligns the first molten pool image and the second molten pool image obtained after preliminary segmentation; the first molten pool image corresponds to the first beam, and the second molten pool image corresponds to the second beam. After matching and alignment, the image processing unit calculates the temperature of the molten pool using a dual-wavelength colorimetric thermometry formula.

3. The method for online monitoring of molten pool temperature during laser additive manufacturing according to claim 2, characterized in that, The preliminary segmentation of the dual-wavelength fused pool image using the image processing unit includes the following sub-steps: The dual-wavelength molten pool image is binarized using an adaptive threshold binarization method, and the molten pool boundary position information is obtained based on the binary image corresponding to the dual-wavelength molten pool image. Based on the molten pool boundary location information and the prior size information of the molten pool, the first molten pool image and its binary image, as well as the second molten pool image and its binary image, are obtained by extracting the region of interest.

4. The method for online monitoring of molten pool temperature during laser additive manufacturing according to claim 3, characterized in that, Matching and aligning the first and second molten pool images includes the following sub-steps: Based on the molten pool boundary position information, the center position of the first molten pool image in the i-th iteration and the center position of the second molten pool image in the i-th iteration are calculated; where i = 1, 2, ..., n; n is the set number of iterations; Based on the center position of the first molten pool image in the i-th iteration and the center position of the second molten pool image in the i-th iteration, the center offset value of the two images in the i-th iteration is calculated; After completing n iterations, calculate the average of the n center offset values ​​to obtain the final offset value; The final offset value is used to correct and align the two images.

5. The method for online monitoring of molten pool temperature during laser additive manufacturing according to claim 2, characterized in that, The molten pool optical signal comes from the coaxial test optical path system.

6. The method for online monitoring of molten pool temperature during laser additive manufacturing according to claim 5, characterized in that, The dual-wavelength colorimetric temperature measurement formula is as follows: In the formula, C2 represents the temperature of the molten pool in the coaxial test optical path system, C2 is the second radiation constant, λ1 is the center wavelength of the first filter, and λ2 is the center wavelength of the second filter. R(λ1) represents the relationship coefficient between gray values ​​under dual-wavelength radiation after coaxial calibration, R(λ2) represents the detector response to wavelength λ1, R(λ2) represents the detector response to wavelength λ2, and k represents the coefficient obtained from non-coaxial calibration of the blackbody furnace.

7. The method for online monitoring of molten pool temperature during laser additive manufacturing according to claim 6, characterized in that, The relationship coefficient between gray values ​​under coaxial calibration dual-wavelength radiation We obtain it from the following formula: In the formula, w is the relationship coefficient between gray values ​​under dual-wavelength radiation before coaxial calibration, and A is the calibration coefficient of the beam splitter in the non-coaxial test optical path system. is the intensity ratio of the two wavelengths in a non-coaxial test optical path system, and m is the intensity ratio of the two wavelengths in a coaxial test optical path system. This is the white image corresponding to wavelength λ1 in a non-coaxial test optical path system. This is the white image corresponding to wavelength λ2 in a non-coaxial test optical path system. This is the white image corresponding to wavelength λ1 in a coaxial test optical path system. This is the white image corresponding to wavelength λ2 in the coaxial test optical path system.

8. An online monitoring system for molten pool temperature during laser additive manufacturing, characterized in that, include: The system comprises a beam splitter, a first filter, a first reflector, a second filter, a second reflector, a detector, and an image processing unit. The online monitoring system for molten pool temperature during laser additive manufacturing is used to perform the steps in the online monitoring method for molten pool temperature during laser additive manufacturing as described in any one of claims 1 to 7.

9. The online monitoring system for molten pool temperature during laser additive manufacturing according to claim 8, characterized in that, Also includes: Focusing lens group; Before the two beams of light are incident on the detector, they are both focused by the focusing lens group.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for online monitoring of the molten pool temperature during laser additive manufacturing as described in any one of claims 1 to 7.

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