Abnormality monitoring method, system and apparatus for additive manufacturing beam

Abstract

The invention relates to an anomaly monitoring method, system and apparatus for additive manufacturing beams. According to the method, triggering parameters sent to at least one beam generator when a scanning task is executed, beam control parameters after the beam generator receives a triggering signal and actual emission parameters obtained based on an operation signal are synchronously collected, and a paraxial monitoring system is used for obtaining corresponding beam monitoring parameters; comparing the trigger parameter, the control parameter and the actual emission parameter, and if any parameter deviates from a preset tolerance threshold, determining that beam emission is abnormal; according to the method and the device, the abnormal beam parameters are further constructed according to the abnormal triggering, control and actual emission parameters, spatial registration is carried out on the abnormal beam parameters and the beam monitoring parameters, and finally a feedback result containing abnormal point position information is generated. And therefore, accurate identification and positioning of the abnormal beam can be realized.

Description

Technical Field

[0001] This application relates to the field of additive manufacturing technology, and more specifically to a method, system, and device for anomaly monitoring of additive manufacturing beams. Background Technology

[0002] Additive manufacturing technology is a manufacturing process that uses the layer-by-layer deposition of materials to rapidly form complex three-dimensional components. It offers advantages such as high design freedom, high material utilization, and integrated component forming, and has been widely applied in manufacturing fields such as aerospace, medical devices, and the automotive industry. With the increasing use of high-energy laser beams and electron beams in additive manufacturing equipment, their high-precision and high-dynamic-response control has become a key factor in ensuring molten pool stability, continuous microstructure, and uniform component performance.

[0003] In beam-driven additive manufacturing, control commands are typically generated based on a preset scanning path and process parameters. These commands are then sent to the beam generator via intermediate devices such as control cards, driving it to emit energy at a precise time and according to a set power and wavelength, achieving instantaneous melting and deposition of the material. However, in actual manufacturing, the beam generator may exhibit abnormal behaviors such as response delays, emission intensity deviations, wavelength drift, non-response, or false triggering due to multiple factors including control system issues, hardware response, energy transmission efficiency, and environmental disturbances. These anomalies directly affect the energy coupling relationship between the beam and the material, easily leading to molten pool fluctuations, deposition defects, metallurgical discontinuities, and even structural deformation or functional failure, severely impacting the manufacturing quality of three-dimensional components.

[0004] Currently, existing methods for monitoring beam anomalies mostly rely on indirect visual signals such as molten pool images, spatter characteristics, and temperature field changes for post-processing analysis. While these methods can reflect manufacturing anomalies to a certain extent, they lack real-time diagnostic monitoring methods for the critical issue of whether the beam is being fired accurately, making it difficult to meet the real-time control requirements of high-precision manufacturing processes. Summary of the Invention

[0005] This application provides a method, system, and device for anomaly monitoring of additive manufacturing beams, which can comprehensively monitor the entire process from beam trigger signal transmission and control signal execution to actual beam output. This enables multi-parameter comparison and anomaly diagnosis of the entire beam emission process, accurately identifies beam anomalies, and achieves spatial positioning of anomaly points by combining image monitoring.

[0006] In a first aspect, this application provides an anomaly monitoring method for additive manufacturing beams. The method includes: acquiring trigger parameters of trigger signals sent to at least one beam generator during the execution of a scanning task; simultaneously acquiring beam control parameters of the at least one beam generator upon receiving the trigger signals and acquiring actual beam emission parameters based on the operating signals of the at least one beam generator; and simultaneously using a paraxial monitor to monitor beam monitoring parameters during the execution of the scanning task; comparing the trigger parameters, beam control parameters, and actual beam emission parameters and determining parameters deviating from a preset tolerance threshold as beam emission anomalies based on the comparison results; constructing abnormal beam parameters based on the trigger parameters, beam control parameters, and actual beam emission parameters determined to be emission anomalies; registering the abnormal beam parameters with the beam monitoring parameters; and generating and feeding back the corresponding anomaly point locations.

[0007] In one alternative embodiment of the first aspect, when determining a beam emission anomaly, the method includes: extracting the trigger timestamp of the trigger signal, the beam control timestamp of the beam control parameters, and the actual emission timestamp of the actual emission parameters, respectively; constructing a time difference parameter for the beam emission process based on the trigger timestamp, the beam control timestamp, and the actual emission timestamp; comparing the time difference parameter with a time tolerance threshold and determining, based on the comparison result, a time difference parameter exceeding the time tolerance threshold as a beam emission anomaly.

[0008] In one alternative of the first aspect, when constructing the time difference parameters of the beam emission process, the method includes: constructing the trigger-to-control delay time difference, the control-to-actual emission delay time difference, the actual emission-to-energy activation delay time difference, and the total response delay time difference during the beam emission process.

[0009] In one alternative of the first aspect, when determining a beam emission anomaly, the method further includes: comparing the trigger-to-control delay time difference, the control-to-actual-emission delay time difference, and the total response delay time difference sequentially with a time tolerance threshold; when any time difference exceeds the time tolerance threshold, it is determined to be a beam emission anomaly.

[0010] In one alternative embodiment of the first aspect, when determining a beam emission anomaly, the method further includes: extracting beam trigger data of the trigger signal, beam control data of the beam control parameters, and actual beam emission data of the actual emission parameters, respectively; constructing a beam characteristic difference parameter for the beam emission process based on the beam trigger data, beam control data, and actual beam emission data; comparing the beam characteristic difference parameter with a characteristic tolerance threshold and determining a beam characteristic difference parameter that deviates from the characteristic tolerance threshold as an emission anomaly based on the comparison result.

[0011] In one alternative embodiment of the first aspect, when extracting the beam control data and the actual beam transmission data, the method includes: extracting the beam trigger intensity and / or band of the trigger signal, the beam control intensity and / or band of the beam control parameters, and the actual beam transmission intensity and / or band of the actual transmission parameters, respectively; and constructing the difference between trigger and control intensity and / or band, the difference between control and actual transmission intensity and / or band, and the difference between trigger and actual transmission intensity and / or band during the beam transmission process.

[0012] In one alternative of the first aspect, when generating and feeding back the corresponding anomaly location, the method includes: associating the abnormal beam parameters with the corresponding scanning path of the scanning task and matching the spatial coordinates of the actual target component according to the scanning path index; using the beam monitoring parameters to pair image frames with the time period of the beam emission anomaly and combining them with the spatial coordinates of the actual target component to generate the anomaly location; and transmitting beam anomaly information, which includes at least a timestamp, anomaly control parameters, error amplitude, and anomaly location, to the target terminal.

[0013] In one alternative embodiment of the first aspect, when generating the location of an anomaly, the method includes: extracting the anomaly timestamp of the occurrence of the abnormal beam and obtaining the scanning path segment number corresponding to the anomaly in the scanning task based on the anomaly timestamp; querying the geometric mapping in the three-dimensional model of the target component based on the path index information and obtaining the spatial coordinates of the scanning path segment in the three-dimensional model of the target component, and performing Z-axis interpolation or calibration by combining the spatial coordinates with the construction layer thickness and layer sequence number of the scanning path segment; searching for a matching frame in the image data of the beam monitoring parameters based on the anomaly timestamp and extracting the intersection region with the scanning path segment in the matching frame image, thereby converting the intersection region into a physical coordinate system through image calibration; registering the spatial coordinates of the intersection region to generate the location of the anomaly.

[0014] In one alternative embodiment of the first aspect, when determining a beam emission anomaly, the method further includes: synchronously acquiring trigger parameters, beam control parameters, and actual beam emission parameters corresponding to multiple beams under the scanning task, and constructing independent time difference parameters and / or beam characteristic difference parameters for each beam, and sequentially aligning each time difference parameter and / or beam characteristic difference parameter on the time axis; comparing each time difference parameter and / or beam characteristic difference parameter with a tolerance threshold, and determining the time difference parameter and / or beam characteristic difference parameter deviating from the tolerance threshold as a beam emission anomaly based on the comparison result; when at least two beams are detected to generate beam emission anomalies in a preset time interval and / or the same scanning path, the beam emission anomaly is marked as a regional beam anomaly, thereby triggering a preset control strategy based on the regional beam anomaly.

[0015] In one alternative embodiment of the first aspect, when determining a beam emission anomaly, the method further includes: synchronously acquiring trigger parameters, beam control parameters, and actual beam emission parameters corresponding to multiple beams under any scanning path and constructing independent time difference parameters and / or beam characteristic difference parameters for each beam; sequentially aligning each time difference parameter and / or beam characteristic difference parameter on the time axis and constructing a corresponding beam parameter matrix; when any set of diagonal elements in the beam parameter matrix deviates from the cooperative tolerance threshold, it is determined to be a synchronous beam emission anomaly, thereby triggering a preset control strategy based on the synchronous beam emission anomaly.

[0016] In one alternative of the first aspect, during the multi-beam comparison and anomaly determination process, the method includes: setting corresponding characteristic tolerance thresholds according to different beam types and comparing the constructed independent time difference parameters and / or beam characteristic difference parameters of each beam with the characteristic tolerance thresholds matching the beam type.

[0017] Secondly, this application provides an anomaly monitoring method for additive manufacturing beams. Before parameter acquisition, the method includes: irradiating multiple correction points in at least a portion of a correction substrate with at least one visible light and acquiring visible light irradiation signals at each correction point; registering the visible light reflection signals of each correction point with the correction substrate to construct a visible light correction matrix; and applying the visible light correction matrix to the spatial mapping and deviation correction of beam monitoring parameters and anomaly point locations acquired during subsequent beam monitoring to complete data calibration.

[0018] In one alternative embodiment of the second aspect, when multiple correction points of at least a portion of a correction substrate are irradiated with multiple beams of visible light, the method includes: simultaneously or alternately irradiating multiple correction points of at least a portion of a correction substrate with multiple beams of visible light of the same parameters and different positions and / or angles, and periodically switching the multiple beams of visible light on and off according to a preset switching frequency to form a time-segmented controllable irradiation frame sequence; constructing a mapping matrix between the multiple beams of visible light and image coordinates by utilizing the boundary differences between multiple shadow areas and bright areas formed during the irradiation process of the multiple beams of visible light, and using the mapping matrix to assist in data calibration during subsequent beam monitoring.

[0019] Thirdly, this application provides an anomaly monitoring method for additive manufacturing beams. Before parameter acquisition, the method further includes: heating multiple correction points in at least a portion of a correction substrate and acquiring infrared light signals generated by each correction point; registering the infrared light signals of each correction point with the correction substrate to construct an infrared light correction matrix; and applying the infrared light correction matrix to the spatial mapping and deviation correction of beam monitoring parameters and anomaly point locations acquired during subsequent beam monitoring to complete data calibration.

[0020] Fourthly, this application provides an anomaly monitoring system for additive manufacturing beams, the anomaly monitoring system comprising: a beam generator for generating and emitting a beam; a galvanometer control unit for connecting to the galvanometer and the beam generator respectively, for triggering and adjusting the beam parameters generated and emitted by the beam generator; a coaxial monitoring unit for acquiring signal parameters during the galvanometer's scanning task; a paraxial monitoring unit for acquiring timestamp parameters, image data, spectral data, and thermal imaging data of the beam acting on the surface of a target component during at least one scanning task; and a control device connected to and controlling the beam generator, the galvanometer control unit, the coaxial monitoring unit, and the paraxial monitoring unit to perform the method described in any one of the first aspects.

[0021] Fifthly, this application provides an anomaly monitoring system for additive manufacturing beams, the anomaly monitoring system comprising: a beam generator for generating and emitting a beam; a galvanometer control unit connected to the galvanometer and the beam generator respectively, for triggering and adjusting the beam parameters generated and emitted by the beam generator; a coaxial monitoring unit for acquiring signal parameters during the galvanometer's scanning task; a paraxial monitoring unit for acquiring timestamp parameters, image data, spectral data, and thermal imaging data of the beam acting on the surface of a target component during at least one scanning task; at least one calibration substrate disposed in the forming area and having multiple calibration points with unique spatial codes for providing data calibration; a light source disposed within the forming chamber for generating visible light of a preset wavelength band; and a control device connected to and controlling the beam generator, the galvanometer control unit, the coaxial monitoring unit, the paraxial monitoring unit, and the light source to perform the method described in any of the second aspects.

[0022] In a sixth aspect, this application provides an anomaly monitoring system for additive manufacturing beams, the anomaly monitoring system comprising: a beam generator for generating and emitting a beam; a galvanometer control unit connected to the galvanometer and the beam generator respectively, for triggering and adjusting the beam parameters generated and emitted by the beam generator; a coaxial monitoring unit for acquiring signal parameters during the galvanometer's scanning task; a paraxial monitoring unit for acquiring timestamp parameters, image data, spectral data, and thermal imaging data of the beam acting on the surface of a target component during at least one scanning task; at least one calibration substrate disposed in the forming area and having multiple calibration points with unique spatial codes for providing data calibration; a heating unit disposed at the calibration points on the calibration substrate for providing independent heating to the calibration points; and a control device connected to and controlling the beam generator, the galvanometer control unit, the coaxial monitoring unit, the paraxial monitoring unit, and the at least one heating unit to perform the method described in the third aspect.

[0023] In a seventh aspect, this application provides an additive manufacturing apparatus, including the anomaly monitoring system described in any one of the second to fourth aspects.

[0024] Eighthly, this application provides an electronic device comprising: at least one processor; at least one memory; said at least one memory being coupled to said at least one processor and for storing instructions executable by said at least one processor, said instructions, when executed by said at least one processor, causing the electronic device to perform a method according to any one of the first, second and third aspects.

[0025] Ninthly, this application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method according to any one of the first, second, and third aspects.

[0026] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0027] The accompanying drawings, which are incorporated herein and form part of this specification, illustrate one or more embodiments of the present application and, together with the description, serve to explain the principles of the present application and to enable those skilled in the art to make and use the present application.

[0028] Figure 1 This is a schematic diagram of the structural connections of an exemplary anomaly monitoring system according to some embodiments of this application.

[0029] Figure 2 This is a flowchart illustrating an exemplary anomaly monitoring method according to some embodiments of this application.

[0030] Figure 3 This is a timing diagram of an exemplary anomaly monitoring method according to some embodiments of this application.

[0031] Figure 4 This is a flowchart illustrating an exemplary method for generating anomaly locations according to some embodiments of this application.

[0032] Figure 5 This is a flowchart illustrating an exemplary time difference delay determination method according to some embodiments of this application.

[0033] Figure 6 This is a flowchart illustrating an exemplary characteristic deviation determination method according to some embodiments of this application.

[0034] Figure 7This is a flowchart illustrating an exemplary multi-beam parameter deviation determination method according to some embodiments of this application.

[0035] Figure 8 This is a flowchart illustrating an exemplary multi-beam coordinated parameter deviation determination method according to some embodiments of this application.

[0036] Figure 9 This is a schematic flowchart of an exemplary beam type threshold allocation method according to some embodiments of this application.

[0037] Figure 10 This is a schematic diagram of an exemplary calibration substrate, light source, and parietal monitoring unit according to some embodiments of this application.

[0038] Figure 11 This is a schematic flowchart of an exemplary light source illumination data calibration method according to some embodiments of this application.

[0039] Figure 12 This is a schematic diagram of an exemplary calibration substrate, multi-light source, and paraxial monitoring structure according to some embodiments of this application.

[0040] Figure 13 This is a flowchart illustrating an exemplary multi-source periodic on / off data calibration method according to some embodiments of this application.

[0041] Figure 14 This is a schematic diagram of an exemplary calibration substrate, heating unit, and parietal monitoring structure according to some embodiments of this application.

[0042] Figure 15 This is a schematic flowchart of an exemplary calibration point heating data calibration method according to some embodiments of this application.

[0043] Figure 16 This is a schematic diagram of the connection of an exemplary electronic device according to some embodiments of this application. Detailed Implementation

[0044] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments may be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, the description of these embodiments is intended to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to provide a deeper understanding of embodiments of this application.

[0045] In the additive manufacturing technology system, laser beams and electron beams, as high-energy-density beam forms, are widely used in the melting and deposition of materials, representing the mainstream technology path of high-performance additive manufacturing. These energy beams concentrate energy in a very small area to achieve rapid melting and cooling solidification of materials, thereby constructing complex three-dimensional components layer by layer. Among them, additive manufacturing materials include, but are not limited to, metal powder materials and non-metal powder materials. In this application, metal powder materials are used as an example.

[0046] Laser beam additive manufacturing typically includes selective laser melting (SLM), laser powder bed fusion (LPBF), and laser direct energy deposition (L-DED). In SLM and LPBF, a laser beam irradiates a pre-laid material substrate point-by-point or line-by-line along a scanning path, locally melting the material, which then rapidly solidifies to form a solid structure. These layers are stacked to create a three-dimensional component. In L-DED, the laser beam melts and deposits the material in real time and bonds it to the substrate through synchronous material feeding, offering high deposition efficiency and controllability.

[0047] Electron beam additive manufacturing is mainly represented by electron beam melting (EBM). This additive manufacturing process uses a high-speed electron beam to bombard materials in a high vacuum environment, causing them to melt and be deposited layer by layer to form parts. Unlike lasers, electron beams have higher energy density and stronger penetration capabilities, making them suitable for processing high-melting-point alloys (such as titanium-based, nickel-based, tantalum, etc.). They also have fast forming speeds and lower internal residual stress.

[0048] Currently, existing beam anomaly monitoring systems suffer from poor real-time performance, delayed anomaly detection, and inaccurate spatial positioning. They lack real-time diagnostic methods for the crucial issue of whether the beam is outputting the correct power at the correct time and location.

[0049] Therefore, for reference Figure 1 As shown, Figure 1 This illustration shows a structural connection diagram of an exemplary anomaly monitoring system according to some embodiments of this application. To address the aforementioned technical problems of beams, this application relates to an anomaly monitoring system 1 for additive manufacturing beams, comprising:

[0050] The beam generator 100 is used to generate and emit an energy beam with a set intensity, wavelength and duration based on the received trigger signal. In this application, the energy beam is described as a laser beam, but in actual applications, the energy beam can also be an electron beam, plasma beam or other energy beams. The beam generator 100 outputs an operation feedback signal containing the actual emission power, voltage, current, wavelength and emission timestamp while emitting.

[0051] The galvanometer control unit 101 is connected to the beam generator 100 on one hand, sending trigger signals and beam control parameters (including power, voltage, current, band, switching time and other set values) to it. On the other hand, it is connected to the galvanometer 105 for synchronous control of the scanning path, deflection angle and scanning speed. The galvanometer control unit 101 has timing accuracy control function and can output trigger timestamp and control parameter timestamp.

[0052] The coaxial monitoring unit 102 is connected to the galvanometer control unit 101 and is set in the coaxial path of the beam. It is used to collect feedback information on triggering, beam control and actual beam emission.

[0053] The off-axis monitoring unit 103 is located in the forming chamber of the additive manufacturing equipment. It includes, but is not limited to, an industrial camera, a multispectral imaging device, and a thermal imaging device. It is used to collect image data, spectral data, thermal imaging data, and response timestamps of the beam acting on the surface of the target component 401 during the scanning task. The industrial camera is used to collect image data during the beam action process, the multispectral imaging device is used to collect spectral data during the beam action process, and the thermal imaging device is used to collect thermal imaging data during the beam action process.

[0054] The control device 104 connects to and controls the beam generator 100, the galvanometer control unit 101, the coaxial monitoring unit 102, and the off-axis monitoring unit 103 to perform abnormal beam monitoring operations.

[0055] Specifically, during the beam emission process, the galvanometer control unit 101 first sends a trigger signal containing trigger parameters to the beam generator 100. After receiving the trigger signal, the beam generator 100 generates beam control parameters according to the trigger signal and simultaneously emits a beam towards the target point in the forming area. At the same time as the beam is emitted, an operating signal containing the actual beam emission parameters is fed back. Then, the off-axis monitoring unit 103 set at the off-axis position of the beam path records the response image or spot characteristics of the beam acting on the surface of the target component 401.

[0056] Therefore, for reference Figure 2 and Figure 3 As shown, Figure 2 The diagram illustrates a flowchart of an exemplary anomaly monitoring method according to some embodiments of this application. Figure 3 A timing diagram illustrating an exemplary anomaly monitoring method according to some embodiments of this application is shown. This application also relates to an anomaly monitoring method 2 for an additive manufacturing beam using the aforementioned anomaly monitoring system 1, the method comprising the following steps:

[0057] 201: Acquire the trigger parameters of the trigger signal sent to at least one beam generator 100 to execute the scanning task, and simultaneously acquire the beam control parameters of the at least one beam generator 100 receiving the trigger signal and acquire the actual beam emission parameters according to the operation signal of the at least one beam generator 100, while using a paraxial monitor to monitor the beam monitoring parameters of the scanning task.

[0058] Specifically, in step 201, the trigger signal is a TTL trigger signal from the galvanometer control unit 101, which typically includes set values ​​for beam power, voltage, current, band, switching time, etc., and corresponding trigger timestamps. After receiving the trigger signal, the beam generator 100 generates corresponding control parameters and corresponding beam control timestamps based on the beam power set value, band, switching time, etc. The running signal is the internal sampling power feedback signal of the beam generator 100, which typically includes the actual emission power, voltage, current, band, and corresponding actual emission timestamps of the beam. At the same time, the off-axis monitoring unit 103, which is installed in the forming chamber of the additive manufacturing equipment, collects monitoring images, spectral response, and thermal imaging data of the beam acting on the surface of the target component 401 and records the monitoring timestamps simultaneously. That is, the off-axis monitoring unit 103 can not only synchronously monitor the monitoring images, spectral response, and thermal imaging data between the trigger signal and the running signal, but also synchronously record the monitoring timestamp information between the trigger signal and the running signal, so as to facilitate the registration of the corresponding abnormal point position when an abnormal beam is generated in the following steps.

[0059] 202: Compare the trigger parameters, beam control parameters and actual beam emission parameters, and determine the parameters that deviate from the preset tolerance threshold as beam emission abnormalities based on the comparison results.

[0060] Specifically, in step 202, the comparison of abnormal beam emission includes, but is not limited to, comparison of emission time difference, comparison of beam intensity difference, and comparison of beam band difference, so as to determine the beam with abnormal behavior based on at least one of the above comparison methods.

[0061] The preset tolerance thresholds include, but are not limited to, time tolerance thresholds, intensity tolerance thresholds, and band tolerance thresholds. These are the maximum tolerance ranges for each time difference, intensity difference, and band difference, which are determined by the commissioning personnel based on factory static tolerances, process commissioning experience values ​​set based on different materials or scanning paths, etc.

[0062] 203: Construct abnormal beam parameters based on the trigger parameters, beam control parameters, and actual beam emission parameters that are determined to be abnormal, and register the abnormal beam parameters with the beam monitoring parameters to generate and feed back the corresponding abnormal point location.

[0063] Specifically, in step 203, for a transmission event determined to be abnormal, the corresponding trigger parameters, control parameters, and actual transmission parameters are extracted to construct an abnormal beam parameter set. Then, this abnormal beam parameter set is registered with at least one of the image data, spectral data, and thermal imaging data collected by the off-axis monitoring unit 103. The registration process includes: matching the off-axis monitoring data according to the corresponding monitoring timestamp of the abnormality, extracting the beam state at the corresponding moment, then querying the geometric mapping of the three-dimensional model of the target component 401 according to the scanning path index, obtaining the spatial coordinates corresponding to the abnormal scanning points on the scanning path, and using calibration parameters to convert the abnormal points during beam scanning into physical spatial coordinates consistent with the actual target component 401 coordinate system, ultimately determining the location of the abnormal point. By adopting the above technical solution, synchronous acquisition and comparison of data throughout the entire beam transmission process can be achieved, accurately identifying parameter offsets such as beam power, band, and response timing caused during the transmission process. Furthermore, through abnormal point registration, the corresponding spatial location of the abnormal beam can be located, enhancing real-time perception of the additive manufacturing beam.

[0064] In some embodiments of this application, the method of this application can be integrated into an artificial intelligence system to achieve intelligent data collection, parameter comparison, anomaly registration and anomaly identification of related processes and objects, so as to improve the overall application efficiency.

[0065] refer to Figure 4 As shown, Figure 4 The diagram illustrates a flowchart of an exemplary method for generating anomaly locations according to some embodiments of this application. In some embodiments of this application, when generating and reporting the corresponding anomaly locations, the method (specifically step 203) includes the following steps 2031-2033.

[0066] 2031: Associate the abnormal beam parameters with the corresponding scanning path of the scanning task and match the spatial coordinates of the actual target component 401 according to the scanning path index.

[0067] Specifically, in step 2031, the abnormal beam parameters corresponding to the beam emission anomaly are bound to the corresponding scanning path segment of the scanning task. This scanning path segment is provided by the scanning task file in the additive manufacturing equipment and typically includes information such as path number, path point sequence, path layer number, and layer thickness. To achieve anomaly localization, based on the timestamp corresponding to the beam emission anomaly and combined with the execution time index of the scanning path segment in the scanning task, the scanning path number corresponding to the moment when the beam emission anomaly was determined is matched. Then, the geometric entity information corresponding to the above scanning path number is extracted from the 3D CAD model of the target component 401 associated with the scanning task. Based on the index information of the scanning path segment, its projection trajectory in the 3D CAD model of the target component 401 is queried, and Z-axis interpolation or calibration is performed in combination with the layer thickness information and layer sequence number to obtain the complete spatial coordinates (X, Y, Z) of the path in the 3D physical space.

[0068] 2032: Using the beam monitoring parameters and the time period when the beam emission anomaly occurred, image frames were paired and combined with the spatial coordinates of the actual target component 401 to generate the location of the anomaly point.

[0069] Specifically, in step 2032, while determining the beam emission anomaly, the corresponding image frame sequence is synchronously extracted from the image data of the beam monitoring parameters based on the timestamp corresponding to the beam emission anomaly. This image data is usually equipped with a calibrated intrinsic and extrinsic parameter matrix. Then, by accurately matching the abnormal timestamp with the image frame time axis, the image frame closest to the abnormal event is extracted, and the region intersecting with the above-mentioned scanning path segment is found in the image frame. Then, based on the calibration parameters between the image and the forming platform (such as camera intrinsic parameters, distortion coefficients, pose matrix), the pixel coordinates of the image intersection region are back-projected to the physical coordinate system to realize the coordinate mapping from the image domain to the three-dimensional space of the target component 401. Then, the generated path space coordinates are registered with the projected physical coordinates. The least squares method or the Iterative Closest Point (ICP) algorithm is used for coordinate alignment to identify the abnormal point set in the intersection region. Finally, the three-dimensional coordinates of the center point of the registered region or the point with the maximum pixel intensity are extracted as representatives and recorded as the abnormal point position.

[0070] The projection transformation can be based on the following model: ,in For the mapped physical coordinate points, Let be the pose matrix. For the camera intrinsic parameter matrix, For image pixels.

[0071] 2033: Transmit beam anomaly information, including at least a timestamp, anomaly control parameters, error amplitude, and anomaly location, to the target terminal.

[0072] Specifically, in step 2033, information such as abnormal timestamp, abnormal control parameters, abnormal error amplitude, abnormal point location, abnormal image frame, abnormal status code and response suggestion are encapsulated to form a complete beam abnormal information data packet and sent to the target terminal such as the host computer or manufacturing terminal, so as to be used for subsequent process tracing, defect prediction, compensation reconstruction or quality assessment.

[0073] By adopting the above technical solution, this application achieves accurate positioning of beam anomalies and spatial coordinate calibration of anomaly points. Compared with traditional anomaly detection methods based solely on images or control parameters, this application has stronger spatial mapping capabilities and temporal consistency, effectively avoiding misjudgment or omission of anomaly locations. At the same time, by using registration of the intersection area of ​​image frames and path segments, the resolution and accuracy of anomaly point extraction can be improved.

[0074] refer to Figure 5 As shown, Figure 5 A flowchart illustrating an exemplary time difference delay determination method according to some embodiments of this application is shown. In some embodiments of this application, when determining a beam emission anomaly, the method (specifically step 202) includes the following steps 2021-2023.

[0075] 2021: Extract the trigger timestamp of the trigger signal, the beam control timestamp of the beam control parameters, and the actual transmission timestamp of the actual transmission parameters, respectively.

[0076] Specifically, in step 2021, the trigger timestamp recorded when the galvanometer control unit 101 sends a TTL trigger signal to the beam generator 100 is extracted sequentially, the beam control timestamp recorded when the beam generator 100 receives the trigger signal and completes the control parameter generation process is extracted, and the actual transmission timestamp recorded when the beam is actually emitted is extracted.

[0077] 2022: Construct time difference parameters for the beam launch process based on the trigger timestamp, beam control timestamp, and actual launch timestamp.

[0078] Specifically, in step 2022, the recorded trigger timestamp is set to T1, the beam control timestamp to T2, and the actual launch timestamp or monitoring timestamp to T3. This constructs the trigger-to-control delay time difference (i.e., the time difference between T1 and T2), the control-to-actual launch delay time difference (i.e., the time difference between T2 and T3), and the total response delay time difference (i.e., the time difference between T1 and T3) during beam launch. The specific trigger-to-control delay time difference... Controlled to the actual launch delay time difference Total response delay time difference .

[0079] 2023: Compare the time difference parameter with the time tolerance threshold and, based on the comparison result, determine the time difference parameter that exceeds the time tolerance threshold as a beam emission anomaly.

[0080] Specifically, in step 2023, the time tolerance threshold can be set to a single threshold, that is... , and Respectively with threshold Perform a comparison; if any condition is met ( , , If the signal is abnormal, then the beam emission is determined to be abnormal.

[0081] Different thresholds can also be set for each stage, that is... With threshold Compare and put With threshold Compare and put With threshold Perform a comparison; if any condition is met ( , , If the signal is abnormal, then the beam emission is determined to be abnormal.

[0082] Therefore, when any time difference is detected to exceed the time tolerance threshold, the current scanning path, abnormal time point, and beam parameter data are immediately recorded, and the abnormal position registration mechanism is triggered.

[0083] In this application, to ensure the accuracy of time difference calculation, a unified high-precision clock source (such as a clock module synchronized with the galvanometer control unit 101 and the beam generator 100) is used, and a high-speed communication protocol is adopted to reduce data sampling and transmission errors.

[0084] By adopting the above technical solution, time difference parameters for the entire launch process can be constructed, which can accurately identify minute delay deviations in the process from triggering to control, control to launch, and overall response. Then, by comparing each item with the preset time tolerance threshold, it is possible to effectively determine abnormal beam launch events caused by abnormal signal response, control delay, or launch failure.

[0085] In practical implementation, a monitoring timestamp can be added when the energy action zone of the beam is activated, identified by the off-axis monitoring. The recorded trigger timestamp is set to T1, the beam control timestamp to T2, the actual launch timestamp to T3, and the monitoring timestamp to T4. This constructs the time difference between trigger and control delays during beam launch (i.e., the time difference between T1 and T2), the time difference between control delays and actual launch delays (i.e., the time difference between T2 and T3), and the time difference between actual launch delays and energy action zone activation delays (i.e., the time difference between T3 and T4). Specifically, the trigger-to-control delay time difference... Controlled to the actual launch delay time difference Actual launch to the energy action zone activation delay time difference Total response delay time difference If any of the following conditions are met ( , , , If the signal is abnormal, then the beam emission is determined to be abnormal.

[0086] By adopting the above technical solution and introducing a side-axis monitoring system to obtain the monitoring timestamp of the activated beam action area, various anomalies in the signal response link can be comprehensively quantified. By comparing and judging with the corresponding time tolerance threshold, a high-sensitivity identification of micro-hour time offset can be achieved, thereby improving the ability of additive manufacturing equipment to perceive problems such as energy transmission failure, optical path misalignment, and abnormal controller response.

[0087] refer to Figure 6 As shown, Figure 6 A flowchart illustrating an exemplary characteristic deviation determination method according to some embodiments of this application is shown. In some embodiments of this application, when determining a beam emission anomaly, the method (specifically step 202) includes the following steps 2024-2026.

[0088] 2024: Extract the beam trigger data of the trigger signal, the beam control data of the beam control parameters, and the actual beam transmission data of the actual transmission parameters, respectively.

[0089] Specifically, in step 2024, the beam trigger data typically includes preset beam power values, voltage, current, band, and other information. The beam control data consists of control parameters generated by the beam generator 100 in response to the trigger signal, which includes beam control power values, voltage, current, band, and other information. The actual beam transmission data is the sampling result of the operating signal, reflecting the actual power values, voltage, current, band, and other information when the beam is actually transmitted.

[0090] 2025: Construct beam characteristic difference parameters for the beam emission process based on the beam triggering data, beam control data, and actual beam emission data.

[0091] Specifically, in step 2025, characteristic difference parameters of the beam transmission process are constructed. If the difference is in intensity, parameters including but not limited to the trigger-to-control intensity difference (i.e., the difference between trigger power and control power), the control-to-actual transmission intensity difference (i.e., the difference between control power and actual transmission power), and the total response intensity difference (i.e., the difference between actual transmission power and trigger power) are constructed. If the difference is in band, parameters including but not limited to the trigger-to-control band difference (i.e., the difference between trigger band and control band), the control-to-actual transmission band difference (i.e., the difference between control band and actual transmission band), and the total response band difference (i.e., the difference between actual transmission band and trigger band) are constructed. Alternatively, the difference can be in the combination of intensity and band, and parameters including but not limited to the trigger-to-control intensity difference, the control-to-actual transmission intensity difference, the total response intensity difference, the trigger-to-control band difference, and the control-to-actual transmission band difference are constructed.

[0092] 2026: Compare the beam characteristic difference parameter with the characteristic tolerance threshold and, based on the comparison result, determine the beam characteristic difference parameter that deviates from the characteristic tolerance threshold as an emission anomaly.

[0093] Specifically, in step 2026, the constructed intensity difference is compared with a set intensity tolerance threshold, or the constructed band difference is compared with a set band tolerance threshold, or the constructed intensity difference is compared with a set intensity tolerance threshold and the constructed band difference is compared with a band tolerance threshold. If any difference exceeds its corresponding tolerance range, it is determined that there is an abnormality in the beam emission; otherwise, the characteristics of the beam emission process are considered normal. For example, the intensity deviation exceeds ±5%, and the band offset exceeds the set range.

[0094] By adopting the above technical solutions, it is possible to achieve full-process tracking and anomaly identification of beam energy output characteristics, and to promptly identify problems such as abnormal emission intensity or band drift caused by equipment fluctuations, control delays, and unstable energy beams.

[0095] refer to Figure 7 As shown, Figure 7 The diagram illustrates an exemplary multi-beam parameter deviation determination method according to some embodiments of this application. In some embodiments of this application, when determining a beam emission anomaly, the method (specifically step 202) further includes steps 2001-2003.

[0096] 2001: Synchronously acquire the corresponding trigger parameters, beam control parameters and actual beam emission parameters of multiple beams under the scanning task, and construct independent time difference parameters and / or beam characteristic difference parameters for each beam, and align each time difference parameter and / or beam characteristic difference parameter on the time axis in sequence.

[0097] Specifically, in step 2001, during the execution of the additive manufacturing scanning task, trigger parameters, beam control parameters, and actual beam emission parameters corresponding to multiple beams (such as laser arrays, electron beam groups, etc.) are simultaneously acquired. Then, based on each beam, at least one of the corresponding time difference parameters and beam characteristic difference parameters is constructed. The time difference parameters and / or beam characteristic difference parameters are then aligned on a unified time axis (for example, using the scanning task start time as a reference timestamp). The aforementioned characteristic difference parameters include at least one of intensity difference and band difference.

[0098] 2002: Compare each time difference parameter and / or beam characteristic difference parameter with the tolerance threshold, and based on the comparison results, determine the time difference parameter and / or beam characteristic difference parameter that deviates from the tolerance threshold as beam emission anomaly.

[0099] Specifically, in step 2002, the constructed time difference is compared with the set time tolerance threshold, or the constructed characteristic difference is compared with the set characteristic tolerance threshold, or the constructed time difference is compared with the set time tolerance threshold and the constructed characteristic difference is compared with the set characteristic tolerance threshold. If any difference exceeds its corresponding tolerance range, it is determined that there is an abnormality in the beam emission; otherwise, the beam emission process is considered normal.

[0100] 2003: When at least two beams are detected to emit beams abnormally within a preset time interval and / or in the same scanning path, the beam emission abnormality is marked as a regional beam abnormality, and a preset control strategy is triggered based on the regional beam abnormality.

[0101] Specifically, in step 2003, the regional beam anomaly judgment is divided into time clustering judgment and path clustering judgment. For example, if at least two different beams are detected to have emission anomalies within the same preset time window (e.g., Δt≤50 ms), the beam emission anomaly is upgraded to a regional beam anomaly. For example, if multiple beams have emission anomalies in different time periods within the same or adjacent scanning path segments, the beam emission anomaly is upgraded to a regional beam anomaly. The preset control strategy includes at least one of pausing the scanning task of the current construction layer, issuing a warning to the target terminal, identifying the registration anomaly point location, and performing repeated scanning or power compensation at the anomaly point location.

[0102] By adopting the above technical solution, compared with the anomaly monitoring method using a single beam feedback, the multi-beam regional beam anomaly identification method can promptly identify potential regional anomalies when multiple beams emit anomalies continuously within the same time period or the same scanning path. This prevents multiple local anomalies from accumulating and evolving into serious construction defects, such as incomplete sintering, forming voids, or discontinuous structures, without being detected.

[0103] refer to Figure 8 As shown, Figure 8 The diagram illustrates an exemplary multi-beam coordination parameter deviation determination method according to some embodiments of this application. In some embodiments of this application, when determining a beam emission anomaly, the method (specifically step 202) further includes steps 2004-2006.

[0104] 2004: Synchronously acquire the corresponding trigger parameters, beam control parameters and actual beam emission parameters of multiple beams under any scanning path, and construct independent time difference parameters and / or beam characteristic difference parameters for each beam.

[0105] Specifically, in step 2004, during the execution of the additive manufacturing scanning task, trigger parameters, beam control parameters, and actual beam emission parameters corresponding to multiple beams (such as laser arrays, electron beam groups, etc.) are acquired simultaneously. Then, based on each beam, at least one of the corresponding time difference parameters and beam characteristic difference parameters is constructed; wherein, the aforementioned characteristic difference parameters include at least one of intensity difference and band difference.

[0106] 2005: Align each time difference parameter and / or beam characteristic difference parameter on the time axis in sequence and construct the corresponding beam parameter matrix.

[0107] Specifically, in step 2005, the constructed time difference parameters and / or beam characteristic difference parameters are aligned on a unified time axis (for example, using the scan task start time as a reference timestamp), and then a beam parameter matrix is ​​constructed based on the constructed time difference parameters and / or beam characteristic difference parameters. The beam parameter matrix includes at least time difference, intensity difference, and band difference.

[0108] 2006: When any set of diagonal elements in the beam parameter matrix deviates from the cooperative tolerance threshold, it is determined to be a synchronous beam emission anomaly, and a preset control strategy is triggered based on the synchronous beam emission anomaly.

[0109] Specifically, in step 2006, synchronous beam emission anomaly usually indicates a synchronization deviation problem when multiple beams are performing a scanning task along a unified path. This may be caused by a common control link failure, scanning path interference, or equipment malfunction. By identifying synchronous beam emission anomalies, potential cooperative offset characteristics can be captured in a timely manner when there are small but consistent abnormal fluctuations among multiple beams. The preset control strategy includes at least one of pausing the scanning task of the current construction layer, issuing a warning to the target terminal, identifying the registration anomaly point location, and performing repeated scanning or power compensation at the anomaly point location.

[0110] By adopting the above technical solution, the collaborative working status of multiple beams under the same scanning path can be fully reflected, avoiding the blind spot of abnormal identification caused by dependence on single beam parameters. At the same time, by introducing the diagonal element deviation identification method, that is, after aligning multiple beams on the time axis, extracting their time difference parameters or characteristic difference parameter matrices, and analyzing the consistency and deviation between their diagonal elements, the collaborative offset characteristics in the synchronous beam transmission process can be identified, thereby realizing early warning of synchronization misalignment or local mismatch, and further enhancing the monitoring capability of multi-beam transmission consistency under complex working conditions.

[0111] refer to Figure 9 As shown, Figure 9 The diagram illustrates an exemplary beam type threshold allocation method according to some embodiments of this application. In some embodiments of this application, during the multi-beam comparison and anomaly determination process of any of the above embodiments, the method (specifically step 202) further includes the following step 2008.

[0112] 2008: Set corresponding characteristic tolerance thresholds according to different beam types and compare the independent time difference parameters and / or beam characteristic difference parameters of each constructed beam with the characteristic tolerance thresholds that match the beam type.

[0113] Specifically, in step 2008, the different beam types include, but are not limited to, laser beams and electron beams with different powers. During the scanning task configuration stage, the beam type information corresponding to each beam channel is read or identified, and a characteristic tolerance threshold matching the beam type is set according to the different types of beams. Thus, after constructing the time difference parameter and / or characteristic difference parameter for each beam, the corresponding beam type information is found according to the beam type number, and the characteristic tolerance threshold under the beam type is loaded according to the type information. Then, the constructed independent time difference parameter and / or characteristic difference parameter for each beam is compared with the characteristic tolerance threshold matching the beam type.

[0114] By adopting the above technical solution, the beam anomaly determination results will be more sensitive to type, increasing the accuracy of anomaly identification. Compared with identification methods that use uniform tolerance standards (such as uniform intensity tolerance threshold, band tolerance threshold, and time tolerance threshold), this embodiment can set a determination boundary that is more in line with the physical characteristics of beams with different functions, power and control methods, avoiding false alarms due to too narrow a range or anomaly omissions due to too wide a range.

[0115] refer to Figure 10 As shown, Figure 10 A schematic diagram of an exemplary calibration substrate 106, a light source 107, and a paraxial monitoring unit according to some embodiments of this application is shown. In some embodiments of this application, the anomaly monitoring system 1 of this application further includes at least one calibration substrate 106 and a light source 107. The at least one calibration substrate 106 is disposed in the forming area and is provided with a plurality of calibration points 1061 with unique spatial codes for providing data calibration; the light source 107 is disposed in the forming chamber for generating visible light of a preset wavelength.

[0116] Specifically, each correction point 1061 of the at least one correction substrate 106 is provided with unique spatial identification information, including point number and physical coordinates; the correction points 1061 are arranged on the correction substrate 106 according to a preset rule, for example, in a grid, cross, or spiral pattern, and the three-dimensional spatial coordinates of each correction point 1061 are recorded in the anomaly monitoring system 1; wherein, each correction point 1061 of the at least one correction substrate 106 can be set to reflect different visible light bands; and the at least one correction substrate 106 can also be divided into regions, each region containing multiple correction points 1061, thereby setting the correction points 1061 of each region to reflect different visible light bands, thereby improving the correction effect.

[0117] Specifically, the light source 107 is placed at any position within the forming chamber and does not interfere with the energy beam. It is preferably placed on a bracket at a certain angle to the industrial camera to ensure that the irradiation range of visible light can uniformly cover the area of ​​the correction substrate 106. The light source 107 is an LED array or a linear laser that generates visible light in a preset wavelength band (such as 500–1100nm) to balance imaging contrast and environmental adaptability. If there are multiple light sources 107, each light source 107 is dispersed and arranged with a certain spacing and incident angle. Each light source 107 can use the same or different power and wavelength.

[0118] refer to Figure 11 As shown, Figure 11A schematic flowchart of an exemplary light source 107 illumination data calibration method according to some embodiments of this application is shown. In some embodiments of this application, before parameter acquisition, the method (specifically step 201) includes the following steps 2111-2113.

[0119] 2111: At least one visible light is used to illuminate at least a portion of a region of the calibration substrate 106, and the visible light illumination signal of each calibration point 1061 is collected.

[0120] Specifically, in step 2111, at least one visible light source 107 is activated, which emits visible light of a set wavelength to illuminate multiple correction points 1061 in at least a portion of the correction substrate 106. Multiple correction points 1061 with unique spatial codes on the surface of the correction substrate 106 reflect characteristic visible light signals under illumination. Then, the image acquisition device (such as an industrial camera) in the parietal monitoring captures image frames under this illumination state, identifies and records the visible light reflection image features of each correction point 1061 and its pixel coordinates in the image plane.

[0121] 2112: Register the visible light reflection signals of each correction point 1061 with the correction substrate 106 to construct a visible light correction matrix.

[0122] Specifically, in step 2112, the position coordinates of each correction point 1061, i.e., the pixel coordinates in the image data, are extracted from the image data using an image processing algorithm. The image processing algorithm includes, but is not limited to, image thresholding, encoding recognition, and subpixel fitting methods. Then, the extracted image pixel coordinates are registered one by one with the pre-calibrated three-dimensional spatial coordinates of each correction point 1061 to form a mapping pair of correction points 1061. Based on at least four mapping pairs, the projection transformation relationship between the image domain and the actual spatial coordinates is solved using a polynomial method based on least squares fitting, thereby establishing a visible light correction matrix, i.e., realizing the mapping relationship between two-dimensional image pixel coordinates and three-dimensional physical coordinates.

[0123] 2113: Apply the visible light correction matrix to the spatial mapping and deviation correction of the beam monitoring parameters and anomaly point locations collected during subsequent beam monitoring to complete data calibration.

[0124] Specifically, in step 2113, the constructed visible light correction matrix serves as a transformation reference and is applied to the spatial mapping and deviation correction of beam parameters and anomaly locations in the subsequent monitoring process. That is, after identifying a potential anomaly, the image pixel coordinates that match the anomaly are extracted from the beam image data acquired by the off-axis monitoring. Then, the constructed visible light correction matrix is ​​used to map these pixel coordinates to the corresponding three-dimensional spatial coordinates to restore the actual physical location. Based on the spatial mapping results, the spatial error of the anomaly is corrected and the true location is confirmed.

[0125] By adopting the above technical solution, namely by introducing a correction substrate 106 with a unique spatial code and a visible light source 107, and combining image processing and spatial registration algorithms, a visible light correction matrix is ​​constructed, realizing the accurate mapping of pixel coordinates to constructed spatial coordinates in paraxial image monitoring. This application can effectively correct spatial positioning errors caused by image distortion, imaging angle deviation and system assembly errors, and improve the spatial positioning accuracy of abnormal points.

[0126] refer to Figure 12 and Figure 13 As shown, Figure 12 The diagram shows a flowchart illustrating an exemplary multi-source 107 periodic on / off data calibration method according to some embodiments of this application. Figure 13 This paper illustrates a flowchart of an exemplary multi-source 107 periodic on / off data calibration method according to some embodiments of this application; wherein, in Figure 12 In this method, the light source is set at multiple positions. The light source at the first position is designated as the first light source 107-1, the light source at the second position is designated as the second light source 107-2, the light source at the third position is designated as the third light source, and the light source at the fourth position is designated as the fourth light source. The number of the first light source 107-1, the second light source 107-2, the third light source, and the fourth light source can be single or multiple, as set by the debugging personnel according to actual needs. In some embodiments of this application, when multiple correction points 1061 of at least a portion of the correction substrate 106 are irradiated with multiple beams of visible light, the method (specifically step 201) includes the following steps 2121-2122.

[0127] 2121: Multiple beams of visible light with the same parameters and different positions and / or angles are simultaneously or alternately irradiated onto multiple correction points 1061 in at least a portion of the area of ​​the correction substrate 106, and the multiple beams of visible light are periodically switched on and off according to a preset switching frequency to form a time-segmented controllable irradiation frame sequence.

[0128] Specifically, in step 2121, multiple visible light sources (i.e., the first light source 107-1, the second light source 107-2, the third light source, and the fourth light source) are arranged with the same parameters but different positions and / or angles, for example, distributed on a spherical, ring, or planar array above the correction substrate 106 with different polar angles and azimuth angles; the multiple visible light sources 107 are periodically switched on and off according to a set switching frequency, which can be in synchronous mode or alternating mode. In synchronous mode, all light sources are switched on and off synchronously, forming a full-frame illumination and full-frame extinguishing sequence; in alternating mode... The mode is that the first light source 107-1, the second light source 107-2, the third light source and the fourth light source are turned on and off in sequence or in groups according to their numbers. In each frame, only one beam or group of light sources 107 is in the on state. In this way, during the periodic illumination process, some correction points 1061 are illuminated by different beams within the period to form overlapping bright areas, while the unilluminated areas form regular shadow distribution areas. That is, multiple alternating bright and dark frame image sequences that change with time are formed on the surface of the correction substrate 106, where each frame corresponds to the shadow projection boundary and bright area boundary image under illumination from different directions.

[0129] 2122: Utilizing the boundary differences between multiple shadow areas and bright areas formed during the illumination process of the multiple visible light beams, a mapping matrix between the multiple visible light beams and image coordinates is constructed, and the mapping matrix is ​​used to assist in data calibration during subsequent beam monitoring.

[0130] Specifically, in step 2122, in each frame of the image sequence, Sobel edge detection or the Canny algorithm is used to extract the gray-level transition boundaries from light to dark or from dark to light within the region of each correction point 1061, and to mark the boundary direction, position, and rate of change of each correction point 1061 from light to dark when illuminated by different light sources in each frame. Then, based on the known spatial geometric layout of the correction points 1061, the shadow direction vector and spot distribution area generated by each beam of visible light, the gray-level transition area of ​​the correction point 1061 from the bright area to the shadow area boundary on the image plane, and the illumination response of the same correction point 1061 under different light sources in the time series are calculated as boundary response data. Then, the mapping relationship between the coordinates of the correction point 1061 in the image plane and the direction vector or position coordinates of each beam of visible light is constructed through the above boundary response data, and the mapping matrix between the image coordinate system and the physical illumination space is established by the least squares fitting method.

[0131] Therefore, this mapping matrix can be used to correct image distortion, improve the accuracy of subsequent spatial registration of anomalies, and provide auxiliary reference in the process of decoupling and spatial positioning of multi-beam anomaly monitoring images.

[0132] By adopting the above technical solution, and by using multiple visible light sources to periodically illuminate the calibration substrate at a preset frequency to form a shadow area, the separability of different light spot responses in the image frame can be enhanced and the registration accuracy between image coordinates and three-dimensional spatial coordinates can be improved.

[0133] refer to Figure 14 As shown, Figure 14 This document illustrates an exemplary calibration substrate 106, heating unit 108, and parietal monitoring structure according to some embodiments of this application. In some embodiments of this application, the anomaly monitoring system 1 further includes a heating unit 108 disposed at a calibration point 1061 on the calibration substrate 106, for providing independent heating to the calibration point 1061.

[0134] Specifically, the heating unit 108 provides controllable independent heating excitation for the calibration point 1061, thereby enabling the calibration point 1061 to generate an infrared signal after heating, so as to present a clear thermal response image.

[0135] For example, miniature resistance heating elements or MEMS heating elements can be pre-embedded or attached at each calibration point 1061, and heating and cooling can be achieved by controlling the current on / off and the on / off time.

[0136] refer to Figure 15 As shown, Figure 15 A schematic flowchart of an exemplary calibration point 1061 heating data calibration method according to some embodiments of this application is shown. In some embodiments of this application, before parameter acquisition, the method (specifically step 201) further includes the following steps 2131-2133.

[0137] 2131: Provide heating for multiple correction points 1061 in at least a portion of the correction substrate 106 and acquire infrared light signals generated by each correction point 1061.

[0138] Specifically, in step 2131, the heating unit 108 is controlled to heat the correction point 1061 one by one or in groups according to the set power and time curves. Then, the thermal imaging device in the off-axis monitoring unit 103 is used to collect infrared images in real time during the heating process. At the same time, the brightness center, edge contour and gray scale distribution of the infrared hot spot area are extracted in each frame of infrared image, which are used as the infrared response characteristics of the correction point 1061.

[0139] 2132: Register the infrared light signals of each correction point 1061 with the correction substrate 106 to construct an infrared light correction matrix.

[0140] Specifically, in step 2132, based on the identification of hotspot areas in the infrared image corresponding to the correction point 1061 according to the infrared response characteristics, the actual three-dimensional spatial coordinates are matched according to the image coordinates of the hotspot center or the centroid of the high grayscale area. Then, the mapping transformation relationship between the infrared image coordinates and the position of the correction point 1061 is solved by applying the least squares method, affine transformation and other methods to generate an infrared light correction matrix.

[0141] 2133: Apply the infrared light correction matrix to the spatial mapping and deviation correction of the beam monitoring parameters and anomaly point locations collected during subsequent beam monitoring to complete data calibration.

[0142] Specifically, in step 2133, the constructed infrared light correction matrix can be used to compare the actual forming path, equipment calibration trajectory, or desired beam energy distribution center to accurately extract the spatial deviation; or, the constructed infrared light correction matrix can be used to correct the beam pointing and adjust the energy distribution using the deviation data; or, in performing a multi-beam scanning task, the constructed infrared light correction matrix can be used to distinguish the response characteristics of different beam channels under infrared imaging to achieve independent beam calibration.

[0143] By adopting the above technical solution, measurement errors caused by environmental reflection, multipath beam interference and optical obstruction during visible light calibration can be avoided, and spatial registration and calibration operations can be completed stably and with high precision under complex lighting conditions and obstruction environments.

[0144] In some embodiments, this application also relates to an additive manufacturing apparatus that includes an anomaly monitoring system 1 of any of the above embodiments.

[0145] In some embodiments, reference Figure 16 As shown, Figure 16 A connection diagram of an exemplary electronic device according to some embodiments of this application is shown. The electronic device 3 includes a memory 301 and a processor 302. The memory 301 stores a computer program that can run on the processor 302. When the processor 302 executes the computer program, it implements the methods described in the above embodiments. The number of memories 301 and processors 302 can be one or more.

[0146] The electronic device 3 also includes a communication interface 303 for communicating with external devices and transmitting data.

[0147] If the memory 301, processor 302, and communication interface 303 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 16 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0148] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0149] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor 302, implements the method provided in this application.

[0150] This application also provides a chip, which includes a processor 302 for calling and running instructions stored in a memory 301, so that a communication device equipped with the chip executes the method provided in this application.

[0151] This application also provides a chip, including: an input interface, an output interface, a processor 302 and a memory 301. The input interface, the output interface, the processor 302 and the memory 301 are connected through an internal connection path. The processor 302 is used to execute code in the memory 301. When the code is executed, the processor 302 is used to execute the method provided in the application embodiment.

[0152] It should be understood that the processor 302 mentioned above can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that processor 302 can be a processor 302 that supports Advanced Reduced Instruction Set Machines (ARM) architecture.

[0153] Furthermore, the aforementioned memory 301 may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile or non-volatile, or may include both. The non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0154] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0155] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for anomaly monitoring of a beam used in additive manufacturing, characterized in that, The method includes: The system collects trigger parameters of the trigger signal sent to at least one beam generator to perform the scanning task, and simultaneously collects beam control parameters of the at least one beam generator when it receives the trigger signal. The system also collects actual beam emission parameters based on the operating signals of the at least one beam generator, and uses a parietal axis to monitor the beam monitoring parameters used to perform the scanning task. The trigger parameters, beam control parameters, and actual beam emission parameters are compared, and parameters that deviate from the preset tolerance threshold are judged as beam emission abnormalities based on the comparison results. Based on the trigger parameters, beam control parameters, and actual beam emission parameters that indicate a transmission anomaly, abnormal beam parameters are constructed and registered with beam monitoring parameters to generate and feed back the corresponding anomaly point locations.

2. The method according to claim 1, characterized in that, When determining beam emission anomalies, the method includes: Extract the trigger timestamp of the trigger signal, the beam control timestamp of the beam control parameters, and the actual transmission timestamp of the actual transmission parameters, respectively. The time difference parameters of the beam emission process are constructed based on the trigger timestamp, beam control timestamp, and actual emission timestamp. The time difference parameter is compared with the time tolerance threshold, and based on the comparison result, the time difference parameter exceeding the time tolerance threshold is determined to be an abnormal beam emission.

3. The method according to claim 2, characterized in that, When constructing the time difference parameters of the beam emission process based on the trigger timestamp, beam control timestamp, and actual emission timestamp, the method includes: Construct the time difference between triggering and control, control and actual launch, actual launch and energy activation zone activation, and total response delay during beam launch.

4. The method according to claim 3, characterized in that, When determining beam emission anomalies, the method further includes: The time difference between triggering and control delay, the time difference between control and actual launch delay, the time difference between actual launch and beam operation area activation delay, and the total response delay are compared with the time tolerance threshold in sequence. When any time difference exceeds the time tolerance threshold, it is determined that the beam launch is abnormal.

5. The method according to claim 1 or 2, characterized in that, When determining beam emission anomalies, the method further includes: The beam triggering data of the trigger signal, the beam control data of the beam control parameters, and the actual beam emission data of the actual emission parameters are extracted respectively. The beam characteristic difference parameters of the beam emission process are constructed based on the beam triggering data, beam control data, and actual beam emission data. The beam characteristic difference parameter is compared with the characteristic tolerance threshold, and based on the comparison result, the beam characteristic difference parameter that deviates from the characteristic tolerance threshold is determined to be an emission anomaly.

6. The method according to claim 5, characterized in that, The method for extracting the beam control data and the actual beam emission data includes: The beam triggering intensity and / or band of the trigger signal, the beam control intensity and / or band of the beam control parameters, and the actual beam transmission intensity and / or band of the actual transmission parameters are extracted respectively. Constructing the beam transmission process triggering to control strength and / or band difference, control to actual transmission strength and / or band difference, and triggering to actual transmission strength and / or band difference.

7. The method according to claim 1, 4, or 6, characterized in that, When generating and reporting the corresponding anomaly locations, the method includes: Associate the abnormal beam parameters with the corresponding scanning path of the scanning task and match the spatial coordinates of the actual target component according to the scanning path index; By matching the beam monitoring parameters with the time period of beam emission anomaly occurrence and combining them with the spatial coordinates of the actual target component, the location of the anomaly point is generated. The beam anomaly information, including at least a timestamp, anomaly control parameters, error amplitude, and anomaly location, will be transmitted to the target terminal.

8. The method according to claim 7, characterized in that, When generating outlier locations, the method includes: Extract the abnormal timestamp of the abnormal beam occurrence and obtain the scan path segment number corresponding to the abnormality in the scan task based on the abnormal timestamp; Based on the path index information, query the geometric mapping in the 3D model of the target component and obtain the spatial coordinates of the scanned path segment in the 3D model of the target component. Then, combine the spatial coordinates with the construction layer thickness and layer sequence number of the scanned path segment to perform Z-axis interpolation or calibration. Based on the abnormal timestamp, a matching frame is found in the image data of the beam monitoring parameters, and the intersection area between the matching frame image and the scanning path segment is extracted. In this way, the intersection area is converted into a physical coordinate system through image calibration. The spatial coordinates of the intersecting regions are registered to generate the locations of outliers.

9. The method according to claim 6, characterized in that, When determining beam emission anomalies, the method further includes: Simultaneously acquire the corresponding trigger parameters, beam control parameters and actual beam emission parameters of multiple beams under the scanning task, and construct independent time difference parameters and / or beam characteristic difference parameters for each beam, and align each time difference parameter and / or beam characteristic difference parameter on the time axis in sequence; Each time difference parameter and / or beam characteristic difference parameter is compared with the tolerance threshold, and based on the comparison results, the time difference parameters and / or beam characteristic difference parameters that deviate from the tolerance threshold are judged as beam emission anomalies. When at least two beams are detected to emit beams abnormally within a preset time interval and / or in the same scanning path, the beam emission abnormality is marked as a regional beam abnormality, and a preset control strategy is triggered based on the regional beam abnormality.

10. The method according to claim 6, characterized in that, When determining beam emission anomalies, the method further includes: Simultaneously acquire the corresponding trigger parameters, beam control parameters and actual beam emission parameters of multiple beams under any scanning path, and construct independent time difference parameters and / or beam characteristic difference parameters for each beam; Align each time difference parameter and / or beam characteristic difference parameter sequentially on the time axis and construct the corresponding beam parameter matrix; When any set of diagonal elements in the beam parameter matrix deviates from the cooperative tolerance threshold, it is determined to be a synchronous beam emission anomaly, and a preset control strategy is triggered based on the synchronous beam emission anomaly.

11. The method according to claim 9 or 10, characterized in that, In the process of multi-beam comparison and anomaly detection, the method includes: Set corresponding characteristic tolerance thresholds for different beam types and compare the independent time difference parameters and / or beam characteristic difference parameters of each constructed beam with the characteristic tolerance thresholds that match the beam type.

12. The method according to claim 1, characterized in that, Before collecting parameters, the method includes: At least one visible light is used to illuminate at least a portion of a calibration substrate at multiple calibration points, and the visible light illumination signal of each calibration point is collected. A visible light correction matrix is ​​constructed by registering the visible light reflection signals of each correction point with the correction substrate. The visible light correction matrix is ​​applied to the spatial mapping and deviation correction of the beam monitoring parameters and anomaly locations collected during subsequent beam monitoring, thereby completing data calibration.

13. The method according to claim 12, characterized in that, When illuminating at least a portion of a correction point on a correction substrate with multiple beams of visible light, the method includes: Multiple beams of visible light with the same parameters and different positions and / or angles are simultaneously or alternately irradiated onto multiple correction points in at least a portion of the correction substrate, and the multiple beams of visible light are periodically switched on and off according to a preset switching frequency to form a time-segmented controllable irradiation frame sequence. By utilizing the boundary differences between multiple shadow areas and bright areas formed during the illumination of multiple visible light beams, a mapping matrix between the multiple visible light beams and image coordinates is constructed, and the mapping matrix is ​​used to assist in data calibration during subsequent beam monitoring.

14. The method according to claim 12 or 13, characterized in that, Before collecting parameters, the method further includes: Heating is provided to multiple correction points in at least a portion of the correction substrate, and infrared light signals generated by each correction point are acquired. An infrared light correction matrix is ​​constructed by registering the infrared light signals of each correction point with the correction substrate. The infrared light correction matrix is ​​applied to the spatial mapping and deviation correction of the beam monitoring parameters and anomaly point locations collected during subsequent beam monitoring, thereby completing the data calibration.

15. An anomaly monitoring system for an additive manufacturing beam, characterized in that, The anomaly monitoring system includes: A beam generator is used to generate and emit beams; The galvanometer control unit is used to connect to the galvanometer and the beam generator respectively, and is used to trigger and adjust the beam parameters generated and emitted by the beam generator; The coaxial monitoring unit is used to acquire signal parameters during the scanning process of the galvanometer. The off-axis monitoring unit is used to acquire timestamp parameters, image data, spectral data, and thermal imaging data of the beam acting on the surface of the target component during at least one scanning task; A control device is connected to and controls the beam generator, galvanometer control unit, coaxial monitoring unit, and paraxial monitoring unit to perform the method according to any one of claims 1 to 11.

16. An anomaly monitoring system for an additive manufacturing beam, characterized in that, The anomaly monitoring system includes: A beam generator is used to generate and emit beams; The galvanometer control unit is connected to both the galvanometer and the beam generator, and is used to trigger and adjust the beam parameters generated and emitted by the beam generator. The coaxial monitoring unit is used to acquire signal parameters during the scanning process of the galvanometer. The off-axis monitoring unit is used to acquire timestamp parameters, image data, spectral data, and thermal imaging data of the beam acting on the surface of the target component during at least one scanning task; At least one calibration substrate is disposed in the forming area and has multiple calibration points with unique spatial codes for providing data calibration; The light source, located inside the forming chamber, is used to generate visible light of a preset wavelength. A control device, connected to and controlling the beam generator, galvanometer control unit, coaxial monitoring unit, paraxial monitoring unit, and light source, to perform the method according to any one of claims 12 to 13.

17. An anomaly monitoring system for an additive manufacturing beam, characterized in that, The anomaly monitoring system includes: A beam generator is used to generate and emit beams; The galvanometer control unit is connected to both the galvanometer and the beam generator, and is used to trigger and adjust the beam parameters generated and emitted by the beam generator. The coaxial monitoring unit is used to acquire signal parameters during the scanning process of the galvanometer. The off-axis monitoring unit is used to acquire timestamp parameters, image data, spectral data, and thermal imaging data of the beam acting on the surface of the target component during at least one scanning task; At least one calibration substrate is disposed in the forming area and has multiple calibration points with unique spatial codes for providing data calibration; A heating unit, which is disposed at the correction point of the correction substrate, is used to provide independent heating for the correction point; A control device is connected to and controls the beam generator, galvanometer control unit, coaxial monitoring unit, off-axis monitoring unit, and at least one heating unit to perform the method of claim 14.

18. An additive manufacturing apparatus, characterized in that, Includes the anomaly monitoring system as described in any one of claims 15 to 17.

19. An electronic device, characterized in that, include: At least one processor; At least one memory; The at least one memory is coupled to the at least one processor and is used to store instructions executed by the at least one processor, which, when executed by the at least one processor, cause the electronic device to perform the method according to any one of claims 1 to 14.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 14.

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