Method and apparatus for controlling multi-mode shaped beams in powder bed additive manufacturing

Abstract

The present application provides a method and apparatus for controlling a multi-mode shaped beam in powder bed additive manufacturing. The method comprises: analyzing a geometric model of a component to obtain multiple characteristic regions corresponding to different structural types, including at least a portion of the component; assigning corresponding beam modes to the multiple characteristic regions based on the structural type and in combination with material properties and / or process objectives, wherein the beam modes are modulated to have different light intensity distributions and / or phases; applying the beam modes to the light beam, so that the light beam switches to the corresponding beam modes when scanning the multiple characteristic regions on the powder bed, so as to control the light spots matching the different characteristic regions during the additive manufacturing process, thereby simultaneously meeting the different forming requirements of multiple structural types in the same component.

Description

Technical Field

[0001] The present application relates to the field of additive manufacturing technology, and more specifically to a method for controlling a multi-mode shaped beam in powder bed additive manufacturing, an optical path system, an additive manufacturing device, an electronic device, and a computer-readable storage medium. Background Art

[0002] Additive Manufacturing (AM), also known as 3D printing, is a process that builds three-dimensional objects by stacking materials layer by layer. Known additive manufacturing technologies, such as powder bed-based ones, are particularly suitable for manufacturing metal components. For example, the powder bed-based Laser Powder Bed Fusion (LPBF) process has been widely used in aerospace, medical, mold manufacturing and other fields because it can manufacture metal components with high precision and high complexity. The LPBF process generally includes the following steps: evenly laying metal powder on a substrate, using a laser beam to scan the powder through a galvanometer to melt and solidify the powder according to the cross-sectional contour of the component, and after completing one layer of scanning, the substrate is lowered by one layer of thickness, and the process is repeated until a complete component is obtained.

[0003] During the LPBF process, the laser beam's spot shape, energy distribution, focus position, and scanning strategy have a decisive influence on the melt pool size, temperature gradient, cooling rate, and microstructural evolution. Existing LPBF processes typically use a Gaussian spot. This is due to its high central energy density and rapid edge decay, resulting in strong convection within the melt pool. This makes it difficult to maintain a stable temperature field, which can easily lead to large fluctuations in solidification conditions and uneven heat input across different geometric features. For example, in thin-walled or slender structural areas, the high energy concentration of a Gaussian spot can cause excessive melting, warping, or increased surface roughness. In large or thick-walled areas, insufficient energy at the edge of the spot can easily lead to poor fusion or porosity defects.

[0004] To overcome these shortcomings, the industry has experimented with beam shaping technologies, such as using diffractive optical elements (DOEs) to generate a non-Gaussian pattern of shaped beam spots. This can adjust the width and depth distribution of the melt pool to a certain extent, optimizing the local thermal field. Currently, it is possible to effectively control the geometric characteristics and solidification behavior of the LPBF melt pool using different beam field modes, thereby achieving the regulation of material structure.

[0005] However, existing beam shaping technologies typically only target a single geometric feature, making it difficult to simultaneously meet the diverse shaping requirements of multiple structural types within the same component. Therefore, a comprehensive solution is urgently needed that can adaptively allocate multiple spot patterns to multiple geometric feature areas within complex components. Summary of the Invention

[0006] The present invention provides a method, optical path system, additive manufacturing equipment, electronic device, and computer-readable storage medium for controlling a multi-mode shaped beam in powder bed additive manufacturing. The method dynamically allocates and switches between multiple beam modes by analyzing a component's geometric model and combining structural type, material properties, and / or process objectives. This allows for precise spot control for different feature areas in additive manufacturing, optimizing melt pool morphology, reducing residual stress, and improving build quality.

[0007] In a first aspect, an embodiment of the present application provides a method for regulating a multi-mode shaped beam in powder bed additive manufacturing, wherein the additive manufacturing manufactures a component by scanning a powder bed stacked layer by layer with a beam propagating along an optical path, the method comprising: parsing a geometric model of the component to obtain a plurality of feature regions corresponding to different structural types including at least a portion of the component; assigning corresponding beam modes to the plurality of feature regions according to the structural type and in combination with material properties and / or process goals, the beam modes being modulated to have different light intensity distributions and / or phases; and applying the beam mode to a beam so that the beam switches to a beam mode corresponding thereto when scanning the plurality of feature regions on the powder bed.

[0008] According to a preferred embodiment of the first aspect, the structure type includes at least two of overhang, curved surface, porous, lattice, thin wall, thick wall and connected structure, wherein the thickness of the thin wall is less than a first threshold and the thickness of the thick wall is greater than a second threshold; the beam mode includes at least two of Gaussian, annular, flat top, Bessel, vector and saddle.

[0009] According to a preferred embodiment of the first aspect, the beam pattern further includes a transition pattern between at least two of the beam patterns and / or a composite pattern formed by superposition or segmentation of at least two of the beam patterns.

[0010] According to a preferred embodiment of the first aspect, the method further includes: identifying the characteristic area to divide it into at least two grid units; and assigning different beam modes to the at least two grid units according to the structure type and in combination with material properties and / or process goals.

[0011] According to a preferred embodiment of the first aspect, the method further includes: assigning corresponding process parameters to the multiple feature areas based on the structure type and in combination with material properties and / or process goals, the process parameters including at least one of beam power, scanning speed, spot size and scanning trajectory; applying the process parameters to the light beam so that the light beam switches to the corresponding process parameters when scanning the multiple feature areas on the powder bed.

[0012] According to a preferred embodiment of the first aspect, the process objectives include reducing at least one of thermal stress, residual stress, surface roughness, structural warping, porosity and / or improving at least one of density, construction efficiency, energy efficiency ratio, isotropy of mechanical properties, dimensional accuracy, and interlayer bonding strength.

[0013] According to a preferred embodiment of the first aspect, the method further includes: performing test scans on the multiple feature areas respectively with different beam modes to obtain test data characterizing the process targets corresponding to the multiple feature areas; determining the beam mode and / or process parameters that enable the multiple feature areas to reach the process targets based on the test data, so as to determine them for allocation to the corresponding multiple feature areas.

[0014] According to a preferred embodiment of the first aspect, the method further includes: simulating the temperature field, stress field and molten pool morphology of the multiple characteristic areas under different beam modes using finite element simulation to obtain simulation data for characterizing the process target; determining the beam mode and / or process parameters that enable the multiple characteristic areas to achieve the process target based on the simulation data, so as to determine them for allocation to the corresponding multiple characteristic areas.

[0015] According to a preferred embodiment of the first aspect, the method further includes: constructing a machine learning model with the structural type, material properties, beam mode and / or process parameters of the multiple feature areas as input and the degree of achievement of the process goal as output; training the machine learning model with the test data and / or simulation data; and using the trained machine learning model to predict the degree of achievement of the process goal of the multiple feature areas under different beam modes and / or process parameters, and determining the beam mode and / or process parameters assigned to the multiple feature areas accordingly.

[0016] According to a preferred embodiment of the first aspect, the method further includes: capturing real-time data of the temperature field, stress field and molten pool morphology of the multiple characteristic areas during the scanning process; inputting the real-time data into the machine learning model after training for prediction, and adjusting the beam mode and / or process parameters corresponding to the multiple characteristic areas in real time according to the prediction results.

[0017] According to a preferred embodiment of the first aspect, a variable aperture assembly and a variable focus lens assembly are respectively arranged in the optical path. When a non-Gaussian beam mode is applied to the beam, the method further includes: driving the variable aperture assembly and the variable focus lens assembly to adjust the spot diameter and the focal length to the target values ​​in the beam mode, so that the beam always maintains the optimal focus state at different layer heights.

[0018] According to a preferred embodiment of the first aspect, the method for obtaining the target value includes: using a ranging sensor integrated in the optical path to obtain the real-time distance from the reference position to the top layer of the powder bed during the scanning process, and combining a pre-calibrated optical model to calculate the target value required for the current layer; or, before scanning, calculating and storing the required target values ​​for each layer in advance based on the geometric model and layer thickness of the component, generating a target value-layer number mapping table, and directly reading and applying the corresponding target value according to the layer number during the scanning process.

[0019] In a second aspect, an embodiment of the present application provides an optical path system for use in additive manufacturing equipment, comprising: a beam emitter for generating and emitting a light beam; a beam deflector for deflecting the light beam according to a preset scanning path onto a layer-by-layer powder bed to manufacture a component; a shaping unit disposed in the optical path between the beam emitter and the beam deflector, for modulating the light beam into a plurality of beam modes having different light intensity distributions and / or phases; and a control system for connecting and controlling the beam emitter, the beam deflector and the shaping unit to perform any one of the methods of the first aspect.

[0020] In a third aspect, an embodiment of the present application provides an optical path system for use in additive manufacturing equipment, comprising: a beam emitter for generating and emitting a light beam; a beam deflector for deflecting the light beam according to a preset scanning path onto a layer-by-layer powder bed to manufacture a component; a shaping unit, which is arranged in the optical path between the beam emitter and the beam deflector, and is used to modulate the light beam into a plurality of beam modes with different light intensity distributions and / or phases; a variable aperture assembly, which is arranged in the optical path between the shaping unit and the beam deflector, and is used to adjust the spot diameter of the light beam; a variable focus lens assembly, which is arranged in the optical path between the variable aperture assembly and the beam deflector, and is used to adjust the focal length of the light beam; a ranging sensor, which is arranged in the optical path, and is used to measure the distance from a reference position to the top layer of the powder bed in real time; a control system, which connects and controls the beam emitter, the shaping unit, the variable aperture assembly, the variable focus lens assembly, the beam deflector and the ranging sensor to perform any one of the methods of the first aspect.

[0021] In a fourth aspect, an embodiment of the present application provides an additive manufacturing device comprising the optical path system of the second aspect or the third aspect.

[0022] In a fifth aspect, an embodiment of the present application provides an electronic device comprising: 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, enables the electronic device to execute a method according to any one of the first aspects.

[0023] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements any one of the methods of the first aspect.

[0024] It should be understood that the foregoing general description and the following detailed description are merely illustrative and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which are incorporated herein and form a part of the 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 one of ordinary skill in the relevant art to make and use the present application.

[0026] Figure 1 Schematic diagram of scanning of a powder bed by an optical system according to an exemplary embodiment of the present application;

[0027] Figure 2 is a flow chart of a method for controlling a multi-mode shaped light beam according to an exemplary embodiment of the present application;

[0028] Figure 3A 1 is a diagram of light spot models of different beam modes according to an exemplary embodiment of the present application, wherein (a)-(d) are flat-top, Gaussian, annular and Bessel light spots, respectively;

[0029] Figure 3B The microstructure diagrams of In738 alloy samples under different beam modes, where (a)-(d) are the crystal orientation characterizations of In738 alloy samples under flat-top, Gaussian, annular and Bessel modes, respectively; (e)-(h) are the local average orientation difference (KAM) characterizations of In738 alloy samples under flat-top, Gaussian, annular and Bessel modes, respectively;

[0030] Figure 4A is a schematic diagram of a first distribution scheme of beam patterns according to an exemplary embodiment of the present application;

[0031] Figure 4B is a schematic diagram of a second distribution scheme of the beam pattern according to an exemplary embodiment of the present application;

[0032] Figure 5 1 is a flow chart of a method for controlling a multi-mode shaped beam by dividing a grid unit according to an exemplary embodiment of the present application;

[0033] Figure 6 is a flow chart of a method for regulating process parameters according to an exemplary embodiment of the present application;

[0034] Figure 71 is a flow chart of a method for controlling a multi-mode shaped beam by using a test scan according to an exemplary embodiment of the present application;

[0035] Figure 8 is a flow chart of a method for controlling a multi-mode shaped beam using finite element simulation according to an exemplary embodiment of the present application;

[0036] Figure 9 is a flow chart of a method for controlling a multi-mode shaped light beam using machine learning according to an exemplary embodiment of the present application;

[0037] Figure 10 is a schematic diagram of scanning a powder bed by another optical path system according to an exemplary embodiment of the present application;

[0038] Figure 11 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application;

[0039] Figure 12 It is a structural diagram of a computer-readable storage medium according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0040] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments may be implemented in a variety of 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 concepts of the example 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. In the following description, numerous specific details are provided to provide a deeper understanding of the embodiments of this application.

[0041] The embodiments of this application are not limited to a specific type of additive manufacturing and can be applied to various powder-bed-based additive manufacturing processes, including laser powder bed fusion (LPBF), electron beam melting (EBM), selective laser melting (SLM), selective laser sintering (SLS), and other additive manufacturing processes that use powder as the forming material and a light beam as the energy source. The term "powder" generally refers to the base material for component formation, which is in particulate form and allows for variations in morphology, particle size, and size distribution. Preferred embodiments utilize metal powder systems, encompassing typical engineering materials such as stainless steel, aluminum alloys, titanium alloys, nickel-based alloys, cobalt-based alloys, copper, and copper alloys. Other optional embodiments can also be expanded to ceramic-based, polymer-based, or composite powder systems. In the following description, "beam" can refer to either a laser beam or an electron beam. Unless otherwise specified, the following embodiments assume that "beam" refers to a laser beam.

[0042] Figure 1 An example of an optical path system 100 is shown. The optical path system 100 is used in additive manufacturing equipment and refers to an optical component arranged in the optical path along the propagation direction of the light beam, which can guide the light beam to the powder bed 110 stacked layer by layer for irradiation scanning, thereby accumulating the manufacturing component 111 layer by layer. The optical path system 100 has a light beam emitter 101 (such as a laser) and a light beam deflector 102 (such as a galvanometer). The light beam emitter 101 is used to generate and emit a light beam, and the light beam deflector 102 is used to deflect the light beam according to a preset scanning path to the top layer of the powder bed 110 to irradiate the powder, melt the powder, and form a solid tissue after solidification. In some embodiments, the optical path system 100 may also have elements for further processing the light beam, such as optical elements such as collimators, beam expanders, and focusing lenses. Since these elements are already known to exist in the optical path system 100, their structure and principles will not be described in detail.

[0043] The optical system 100 includes a shaping unit 104. The shaping unit 104 is positioned in the optical path between the beam emitter 101 and the beam deflector 102 and is used to modulate the optical beam into a variety of beam modes with varying intensity distributions and / or phases. The shaping unit 104 can be a switchable diffractive optical element (DOE) component. For example, multiple prefabricated DOEs can be installed in the optical path. Each DOE can be translated or rotated laterally along the optical beam to the center of the optical path via a micromotor or piezoelectric actuator to achieve rapid switching between various beam modes. The surface of each DOE can be micro-nanomachined and etched to form a predetermined phase distribution pattern, which is used to modulate the intensity distribution and / or phase characteristics of the incident beam. In an optional embodiment, the shaping unit 104 can also be a programmable spatial light modulator component. For example, by integrating a liquid crystal or MEMS micromirror array spatial light modulator in the optical path, the spatial light modulator can dynamically generate any custom beam mode within a single device by sending a digital phase mask via a control chip. The spatial light modulator also supports superposition or interpolation of phase distributions of two or more modes to achieve transition between different beam modes or composite spot output.

[0044] The optical system 100 has a control system 103. The control system 103 is connected to the beam emitter 101, the beam deflector 102 and the shaping unit 104 respectively, and is used to control each optical element according to the manufacturing task of the component 111. In addition, as the control center of the additive manufacturing equipment, the control system 103 can also be connected to other components in the equipment, such as Figure 1 A powder dispenser 112 is shown for applying the powder in layers onto the powder bed 110 and other components are not shown to enable full control of the entire manufacturing process.

[0045] In some embodiments, the shaping unit 104 may further include a calibration module. This calibration module can perform online detection of the output light spot (e.g., using a monitoring camera or a light spot sensor) and continuously fine-tune the phase pattern or component position through closed-loop feedback to correct for light spot deviations caused by temperature drift, mechanical errors, or changes in the incident angle, ensuring that the light spot shape is consistent with the target requirements.

[0046] Figure 2 An example of a method 200 for controlling a multi-mode shaped beam is shown, including steps 201-203 constituting the method 200. It should be understood that the steps of the method 200 may be configured as follows: Figure 1 The control system 103 is executed, and the following reference will be made to Figure 1 and Figure 2 Describe the implementation of these steps.

[0047] 201. Analyze the geometric model of the component 111 to obtain a plurality of characteristic regions corresponding to different structural types including at least a portion of the component.

[0048] The control system 103 can read the geometric model of the component to be manufactured 111 from a storage unit. This geometric model can be generated using common computer-aided design (CAD) software and stored in STL, STEP, or other formats. After reading, the control system 103 performs preprocessing on the geometric model, such as meshing and voxelization. Meshing discretizes the surface of the geometric model into a large number of triangular facets and extracts the vertex coordinates, normal vectors, and adjacency relationships of each facet. Voxelization divides the volume of the geometric model into regular cubic units to facilitate subsequent wall thickness measurement and internal structure analysis.

[0049] Based on this, the control system 103 performs structural feature analysis on the meshing results and voxel data. For thin-walled and thick-walled structures, the control system 103 samples multiple distance points inward and outward along the normal direction of the patch, and obtains the local wall thickness distribution through ray casting or distance field calculation. When the minimum wall thickness of a region is less than a preset first threshold, the region is identified as "thin-walled"; when the maximum wall thickness of a region is greater than a preset second threshold, the region is identified as "thick-walled." It should be understood that the first and second thresholds are used to distinguish between "thin-walled" and "thick-walled" structures, and their values ​​should be determined based on the actual manufacturing dimensions of the component 111 and the layer thickness of the powder bed 110 used. Typically, the first threshold can be set between 2 and 10 times the layer thickness (for example, when the layer thickness is 0.05 mm, the first threshold can be set between 0.1 mm and 0.5 mm) to ensure sufficient energy concentration in thin-walled areas during processing and prevent overburning. The second threshold can be set between 5% and 20% of the component's minimum cross-sectional dimension (for example, for a 10mm cross-sectional component, the second threshold can be set between 0.5mm and 2mm). This ensures sufficient energy input to thick-walled areas and prevents insufficient fusion. The above threshold ranges are examples only; in practice, they can be optimized based on material thermal properties, beam power, scanning speed, and desired build quality.

[0050] For overhanging structures, the control system 103 calculates the angle between the patch normal and the build layer direction (Z axis) and determines whether there is sufficient support beneath the patch. If the tilt angle exceeds a preset value and there is no support below, the area is classified as "overhanging." Identification of porous and lattice structures can rely on connectivity analysis of voxel data: the control system 103 detects internally closed or connected pores and classifies them as "porous"; regularly arranged microchannels or lattice-like units are labeled "lattice." Curved surface structures are determined based on patch curvature: when the average curvature radius of a patch group is less than a preset value, it is labeled "curved." Connected structures can include areas that do not fall into any of the above categories but are located at the transition between different geometric units, such as transition bodies between beams and slabs, or between columns and foundations, such as stiffeners.

[0051] After completing the structural type classification, control system 103 spatially clusters patches or voxel units of the same structural type, generating multiple non-overlapping feature regions. For areas with blurred boundaries or overlapping multiple types, refinement is performed based on priority rules or morphological segmentation algorithms to ensure that each feature region is unique and has clear boundaries. Control system 103 stores each feature region's identifier, structural type, spatial extent, and statistical characteristics (such as average curvature and wall thickness distribution) in a feature region database, providing input data for subsequent beam pattern assignment and process parameter optimization.

[0052] 202. Assign corresponding beam modes to multiple feature areas according to the structure type and in combination with material properties and / or process goals, wherein the beam modes are modulated to have different light intensity distributions and / or phases.

[0053] "Material properties" can refer to the powder material used for the entire component 111, such as stainless steel, aluminum alloy, titanium alloy, nickel-based alloy, cobalt-based alloy, copper and copper alloy, etc., or it can refer to the different powder materials corresponding to each characteristic area in a multi-material additive manufacturing scenario. Furthermore, "material properties" can be further refined into thermophysical parameters related to the component 111 or each characteristic area, including thermal conductivity, laser absorptivity, specific heat capacity, melting temperature and surface tension. Thermophysical parameters will affect the transfer and distribution of laser energy in the powder and molten pool, and are important factors in determining the morphology of the molten pool, temperature gradient and solidification rate. Therefore, when assigning beam modes to each characteristic area, its material properties can be given priority to ensure that the selected light intensity distribution and / or phase modulation scheme can match the thermal response characteristics of the material to achieve the best melting effect and tissue control.

[0054] "Process objectives" refer to the various performance or quality indicators that are expected to be achieved in the additive manufacturing process, and can be selected or combined according to specific application requirements. For example, the process objectives can be the reduction of at least one of thermal stress, residual stress, surface roughness, structural warping, porosity, and / or the improvement of at least one of density, construction efficiency, energy efficiency ratio, mechanical property isotropy, dimensional accuracy, and interlayer bonding strength. For example, local or overall thermal stress and residual stress are reduced to reduce the risk of cracks and deformation inside the component; surface roughness and structural warping are reduced to improve the surface quality and geometric accuracy of the component; porosity is reduced to improve forming density and mechanical properties; construction efficiency and energy efficiency ratio are improved to reduce manufacturing time and energy consumption; the isotropy of mechanical properties is improved to obtain a uniform mechanical response; dimensional accuracy and interlayer bonding strength are improved to achieve dimensional accuracy of the component and a strong bond between layers. In practical applications, one or more of the above process objectives can be selected for optimization.

[0055] "Beam mode" refers to different irradiation forms formed by modulating the intensity distribution and / or phase of the light beam to achieve control of the molten pool morphology and thermal field distribution. In some embodiments, the beam mode may include but is not limited to at least two types of Gaussian mode, annular mode, flat top mode, Bessel mode, vector mode and saddle mode. Figure 3A (a)-(d) in the figure respectively show the spot models of flat top, Gaussian, annular and Bessel modes. The Gaussian mode has the characteristics of the highest energy density in the center and fast attenuation at the edge, which is suitable for scenes requiring deep melting and rapid accumulation; the light intensity of the annular mode is concentrated in the annular zone area, which is beneficial to reduce central overburning and improve surface flatness; the flat top mode has a uniform light intensity distribution and is suitable for improving forming density and surface quality; the Bessel mode has a long non-diffraction transmission distance and a large depth of focus, which is suitable for complex geometric structures that are sensitive to depth of focus. In addition, the vector mode achieves precise control of the lateral diffusion of the molten pool through directional light intensity distribution; the saddle mode forms high-energy zones in the center and on both sides of the symmetry to optimize the multi-directional thermal field distribution of complex structures.

[0056] Figure 3B The crystal orientation and local average misorientation (KAM) characterization of In738 alloy specimens under different beam modes are shown. Figure 3B In (a) and (e), under the action of the flat-top beam, the molten pool obtains uniform energy distribution and maintains a stable heat flow field throughout the entire molten pool depth. The cooling rate remains consistent throughout the entire molten pool area. The columnar crystals have strong epitaxial growth characteristics, forming a uniformly oriented {001} <001> Nearly single crystal structure. This indicates that the material has good high temperature performance and creep performance; in the corresponding KAM characterization, the local KAM value only reaches below 3° in a few areas, and the average KAM is 0-2°, indicating that the residual stress and dislocation density are very low. Figure 3B In (b) and (f), the Gaussian beam forms fine equiaxed crystals at the top of the molten pool due to the high energy density at the center and rapid attenuation at the edge, while columnar crystals grow along the thermal gradient in the center of the molten pool. The texture is significantly weakened, and the number of fine equiaxed crystals increases significantly compared to the flat-top beam sample. The KAM value reaches 4-5° in the central area and gradually decreases to ≤1° outward, indicating residual stress concentration under the high thermal gradient in the center. Figure 3B In (c) and (g), the annular beam provides the highest energy in the annular zone, which enhances the stability of the molten pool. However, the center and outer rings have coarse grains due to lower energy or accumulated overheating, forming a ring-shaped microstructure. The KAM value in the annular zone is about 3-4°, while the center and outer rings are less than 2°, indicating that the shear stress or thermal gradient in the annular zone is more significant. Figure 3BIn (d) and (h), the Bessel beam, with its non-diffraction characteristics and long focal depth, produces nearly equiaxed crystals extending uniformly along the optical axis. The KAM value is generally high, reaching 4-5°, indicating large lattice distortion and residual stress.

[0057] Figure 4A A first allocation scheme of the beam modes is shown. In the first allocation scheme, the control system 103 may allocate the beam modes according to the structural type and material properties of each characteristic region.

[0058] For example, in overhanging structural areas, the lack of underlying support during the printing process can easily lead to a suspended melt pool and thermal overhang effects, resulting in molten pool sagging and unstable forming. Taking into account the absorptivity and thermal conductivity of typical metal powders such as stainless steel and titanium alloys, the control system 103 prioritizes assigning saddle beams or vector beams. Saddle beams have high-energy bands at the center and on both sides, creating three controlled melting points in the overhanging area. This not only enhances heating in the central area but also provides thermal "support" on both sides, minimizing melt pool sagging. Vector beams, by enhancing energy in one or two dimensions, can compensate for heat loss in the overhanging area. For curved structural areas, where the surface exhibits continuous curvature, localized focal depth shifts can easily lead to overburning or incomplete fusion. Given the high reflectivity of metal powders and the varying angles of incidence on curved surfaces, the control system 103 selects Bessel beams. Bessel beams, with their long focal depth and non-diffraction properties, can maintain a stable spot size and shape within a certain range, adapting to focal shifts caused by curved surfaces and ensuring uniform melting and consistent forming across the curved area. For porous structure areas, where the pores are interconnected and the channels are small, rapid filling and reduced pore residue are required. Considering the thermal diffusion characteristics of highly thermally conductive materials such as copper and copper alloys near the pores, the control system 103 allocates a Gaussian beam. The Gaussian mode has concentrated energy at the center and a high peak value, which can quickly generate a sufficient molten pool depth in the porous area, fill small channels, and promote pore closure. At the same time, the rapid attenuation of its edge energy helps to limit the diffusion range of the molten pool and reduce excessive heating of the surrounding microstructure. For lattice structure areas, whose regular lattice microstructures require both local heating and supporting mechanical properties, the control system 103 allocates a flat-top beam. The flat-top mode has a uniform light intensity distribution, which can achieve stable melting within each lattice unit, reduce the occurrence of hot spots and cold spots, and thus ensure the uniform density and mechanical consistency of the lattice structure. For thin-walled structure areas, due to the small wall thickness, overburning or warping due to excessive energy concentration is easy to occur, and the thermal diffusion of the material is limited. The control system 103 preferably allocates a flat-top beam. The flat-top mode provides uniform light intensity distribution, de-concentrating energy at the center and reducing the risk of localized overheating. Furthermore, its wider spot diameter helps reduce thermal gradients, thereby improving surface roughness and dimensional accuracy in thin-walled areas. For thick-walled structural areas, a deeper melt pool and higher build efficiency are required to reduce interlayer unfused defects. Given the high heat capacity of the material in thick-walled areas, control system 103 can also allocate Bessel beams. Their long focal depth ensures that the focus is not easily shifted during deep melting, and their non-diffraction properties help maintain melt pool depth, improving fusion quality and build speed. For connecting structural areas, which often serve as transitions between different geometric units, simultaneous control of heat input is required to prevent stress concentration and cracking. Control system 103 can allocate either annular or vector beams. The annular mode reduces direct energy by leaving a hollow center, thereby reducing thermal stress at the joint. The vector mode, through directional energy enhancement, strengthens the fusion strength of the joint, ensuring a strong bond in the transition area.

[0059] Figure 4B A second allocation scheme of the beam modes is shown. In the second allocation scheme, the control system 103 can allocate the beam modes according to the structure type and process target of each feature area.

[0060] For example, for process goals primarily focused on reducing thermal and residual stresses, the control system 103 prioritizes allocating beam modes with annular or vector energy distributions in overhang and connection structure regions, as these regions are prone to generating local temperature gradients and stress concentrations. The annular beam leaves a low-energy band at the center, which can reduce the temperature peak in the central region and suppress the generation of thermal stresses; the vector beam adjusts the molten pool shape and cooling rate through directional energy enhancement, thereby reducing regional stress concentration and improving residual stress distribution. In porous and lattice structure regions, to reduce residual pores and ensure microscopic density, the control system 103 may use reducing porosity as a process goal and allocate Gaussian or flat-top beams. The high energy characteristics of the Gaussian beam at the center are suitable for rapidly filling pores, and the uniform energy distribution of the flat-top beam is conducive to maintaining uniform formation of the microstructure. For thin-walled and curved structure regions, the main process goals are usually to improve surface quality and dimensional accuracy and suppress warping. The control system 103 allocates a flat-top or Bessel beam for this purpose: the flat-top beam reduces the temperature gradient by uniform heating and improves the surface roughness; the Bessel beam, due to its long focal depth, can maintain a stable focus and reduce local overburning or unfused defects caused by focal deviation in curved and thin-walled areas. In thick-walled areas, for example, where construction efficiency and mechanical property consistency are the process goals, the control system 103 allocates a Bessel beam or a Gaussian beam with a strong deep melting ability to accelerate the formation of the molten pool and achieve interlayer fusion strength. At the same time, appropriate energy distribution can reduce interlayer residual stress and improve the isotropy of the overall mechanical properties. In addition, for complex areas that need to optimize multiple process goals at the same time, such as functional gradients or local strengthening areas, the control system 103 can select a beam mode that can provide compromise performance in multiple dimensions for the feature area based on the target priority, such as alternating between a flat-top beam and an annular beam to take into account both surface quality and thermal stress control.

[0061] In the third allocation scheme, the control system 103 may allocate the beam mode according to the structural type, material properties, and process objectives of each characteristic region through a multi-dimensional comprehensive evaluation.

[0062] For example, the control system 103 first creates a vector for each feature region, containing a structural category identifier (e.g., overhang, thin-wall, thick-wall, etc.), material thermophysical properties (e.g., thermal conductivity, absorptivity, specific heat capacity, melting temperature, etc.), and process objective weights (e.g., reducing thermal stress, reducing residual stress, increasing density, improving surface quality, etc.). Subsequently, based on a pre-calibrated optical-melt pool response model, the control system 103 calculates the effects of different beam modes on the melt pool geometry, temperature distribution, and cooling rate under this vector. The control system 103 uses a multi-objective optimization algorithm (e.g., genetic algorithm, particle swarm optimization, or weighted sum method) to rank the comprehensive fitness of each mode. During the optimization process, the control system 103 can dynamically adjust the weights of each process objective to reflect manufacturing priorities or real-time user feedback. Finally, the control system 103 automatically selects the beam mode with the highest score under the current conditions for allocation.

[0063] 203. Applying a beam mode to the light beam so that the light beam switches to a corresponding beam mode when scanning multiple feature areas on the powder bed.

[0064] The control system 103 can generate a layer-by-layer scanning plan based on the mapping relationship between the characteristic area and the beam pattern obtained in step 202, wherein each scanning path segment corresponds to a characteristic area and its assigned beam pattern. During the actual scanning process of the powder bed 110, the control system 103 drives the light beam to move along a predetermined path through the beam deflector 102. When the light beam is about to enter a new characteristic area, the control system 103 sends a mode switching instruction to the shaping unit 104 and triggers the switching using the position feedback or encoder signal of the beam deflector 102. For the shaping unit 104 using a DOE array, the micromotor or piezoelectric actuator translates or rotates the corresponding DOE element to the center of the optical path after receiving the switching instruction; for the shaping unit 104 using a spatial light modulator component, the control system 103 loads the new digital phase mask into the control chip of the spatial light modulator so that the phase pattern is updated in the next refresh cycle. Through the above-mentioned continuous switching process, the light beam can always maintain the corresponding optimal mode when scanning each characteristic area, realizing targeted energy regulation of different structural areas.

[0065] In some embodiments, the beam pattern further includes a transition pattern between at least two beam patterns and / or a composite pattern formed by superposition or segmentation of at least two beam patterns.

[0066] The transition mode interpolates or weighted-superimposes the phase or amplitude distributions of two or more adjacent beam modes, creating a continuously varying intensity and phase gradient in space or time. This ensures that when the beam switches from one mode to another, the heat input to the molten pool does not abruptly change, thus avoiding stress concentration or geometric mismatch. Specifically, the control system 103 specifies the transition duration or transition interval length before and after the mode switch, and issues a multi-frame phase mask or multi-level DOE position sequence to the spatial light modulator or DOE array, sequentially changing the phase pattern or physical element position to achieve the predetermined transition curve.

[0067] The composite mode produces a composite light spot with multiple intensity distribution characteristics by simultaneously superimposing two or more beam modes or projecting them spatially in segments. For example, the control system 103 can simultaneously load two DOEs with different phase patterns into the optical path, using the superimposed composite phase mask to generate a light intensity distribution with a flat top in the center and a ring shape around the periphery. Alternatively, the spatial light modulator can be partitioned, with each region loaded with a different phase mask, to produce a partitioned Gaussian and Bessel hybrid characteristic within the same light spot. The composite mode can address the complex requirements of specific structural regions (such as functional gradients or localized enhancements), simultaneously meeting multiple process objectives such as deep melting, densification, and surface quality in a single scan.

[0068] For example, for a lattice structure region, a conventional Gaussian mode can quickly fill microcells, while an annular mode provides uniform heating at the periphery, facilitating improved lattice connectivity. In this case, the control system 103 can assign a "Gaussian-annular" composite mode to the lattice region. Specifically, during the same scan, the shaping unit 104 superimposes a central Gaussian distribution and a peripheral annular distribution, achieving the dual effects of internal microcell densification and boundary reinforcement. For another example, at the boundary between a thin-walled structure and a curved structure, a direct transition from a flat-top mode to a Bessel mode can easily lead to a sudden change in the melt pool's heat input. In this case, the control system 103 can generate a "flat-top-to-Bessel" transition mode. By loading an intermediate phase mask in stages, the light intensity distribution gradually transitions from a flat-top to a Bessel morphology, ensuring smooth changes in the melt pool shape and temperature gradient. For another example, at the interface between an overhang and a connecting structure, the control system 103 can employ a "vector-annular" composite mode, enhancing the vector direction while utilizing the annular peripheral energy band to reduce central thermal stress.

[0069] Figure 5 Another example of the method 200 for controlling a multi-mode shaped beam is shown, including steps 211 and 212 that further constitute the method 200 .

[0070] 211. Identify a feature region to divide it into at least two grid cells.

[0071] The control system 103 can subdivide each feature region into at least two functional grid cells, such as outline grid cells and fill grid cells, based on the geometric boundaries of the feature region and the scan layer thickness. The outline grid cells correspond to the boundary lines or near-boundary areas of the feature region and require high-precision melting and dimensional control. The fill grid cells correspond to the larger infill areas within the region and place greater emphasis on forming efficiency and density.

[0072] 212. Assign different beam modes to the at least two grid units according to the structure type and in combination with material properties and / or process goals.

[0073] After completing the grid division, the control system 103 allocates different beam modes to the contour grid cells and the filling grid cells respectively in step 212, combining the structural type, material properties and / or process goals of each grid cell. Specifically, the contour grid cell is preferentially allocated a flat-top or vector mode with a small spot diameter and uniform light intensity distribution to achieve control of boundary size accuracy and surface quality; the filling grid cell can be allocated a Gaussian or Bessel mode with a large spot diameter and strong deep melting ability to improve the scanning speed and filling density. In addition, for grid cells with special structures such as porous or lattice structures, the control system 103 can further subdivide between filling and contour and adopt an annular mode or a composite mode to optimize pore closure and connection strength. By implementing gridding and differentiated beam mode allocation within the feature area, the control system 103 can achieve dual optimization of boundary accuracy and internal performance in the same area.

[0074] Figure 6 Another example of the method 200 for controlling a multi-mode shaped beam is shown, including steps 221 and 222 that further constitute the method 200 .

[0075] 221. Assign corresponding process parameters to multiple feature areas based on structure type and in combination with material properties and / or process objectives, wherein the process parameters include at least one of beam power, scanning speed, spot size, and scanning trajectory.

[0076] In addition to the beam pattern, the control system 103 can also assign and apply corresponding process parameters to each feature area. For example, the control system 103 can determine the optimal combination of process parameters such as beam power, scanning speed, spot size, and scanning trajectory based on the structural type, material properties, and / or process objectives of each feature area. For example, for thin-walled structures, lower beam power and higher scanning speed can be assigned to reduce local heat input and prevent overheating; for thick-walled structures, higher beam power and lower scanning speed can be assigned to achieve sufficient penetration depth and interlayer fusion strength; for porous or lattice structures, the control system 103 may prefer a smaller spot size and overlapping scanning trajectories to achieve pore closure and uniform densification.

[0077] 222. Apply process parameters to the light beam so that the light beam switches to the corresponding process parameters when scanning multiple feature areas on the powder bed.

[0078] After completing the process parameter allocation, the control system 103 transmits the determined process parameters to the beam emitter 101 and beam deflector 102. Combined with the mode switching instructions from the shaping unit 104, this enables dynamic switching of multiple parameters. During the powder bed scanning process, the control system 103 monitors the scanning position and layer number information in real time, triggering process parameter switching at the beginning of each feature area. This synchronizes the beam power, scanning speed, spot size, and trajectory pattern to the predetermined values ​​for that area. This allows the control system 103 to apply differentiated energy input and scanning behavior to different structural areas during the same component manufacturing process, achieving a balance between overall efficiency and local quality.

[0079] By optimizing process parameters, the control system 103 can compensate for differences in melt pool behavior under different beam modes and reduce non-uniform shrinkage stress during cooling by dynamically adjusting the spot shape and scanning strategy. For example, after the control system 103 assigns a specific beam mode to a feature area, it can automatically optimize the following process parameters based on the intensity distribution and phase characteristics of that mode: The spot shape within the same area can be fine-tuned. For example, when using the Gaussian mode, the spot diameter can be slightly expanded to smooth edge energy decay. In the Bessel mode, the intensity ratio of the zero-order and higher-order Bessel components can be adjusted to optimize the ratio of melt pool depth and width. For example, rotational scanning can be used within the feature area. This means that within each layer or grid cell, the scanning direction is changed at a predetermined angle (e.g., 90°, 120°, or randomly). This allows the heat input direction to continuously vary between adjacent scan paths, resulting in a more uniform thermal gradient and a more balanced residual stress distribution. In areas with complex geometries or stress-sensitive regions, the control system 103 can further subdivide the feature area into multiple sub-areas, each with a different scanning starting point and scanning direction. For example, at the junction of thin and thick walls, the thin-walled subregion can be scanned linearly along the long axis of the thin wall, while the thick-walled subregion can be scanned along its short axis or diagonally. This ensures more uniform heat accumulation and cooling paths, reducing interfacial stress concentration. For thick-walled regions with large heat capacity, the control system 103 can use a higher power, lower speed "preheat" strategy at the beginning of the scan, then switch to a lower power, higher speed "fill" strategy to control temperature gradients and improve efficiency. Conversely, for thin-walled or overhanging regions, low power, slow scanning can be used initially to form a stable melt pool, and then the speed can be gradually increased to reduce thermal shock. The control system 103 can freely switch the scanning trajectory between contouring and filling of the feature area. For example, after completing the boundary contour scan, the interior can be immediately filled in the opposite direction or along a spiral trajectory to disperse heat concentration and balance stress. For porous or lattice structures, cross-fill or honeycomb scanning trajectories can also be used to further uniformize the thermal field.

[0080] Figure 7 Another example of the method 200 for controlling a multi-mode shaped beam is shown, including steps 231 and 232 that further constitute the method 200 .

[0081] 231. Test scans are performed on multiple feature areas using different beam modes to obtain test data representing process targets corresponding to the multiple feature areas.

[0082] The control system 103 can perform test scans using different beam modes on each characteristic region to obtain test data that characterizes the degree of achievement of process objectives in each region. Specifically, the control system 103 sequentially applies different beam modes to each characteristic region according to a predefined list of beam modes, and performs short-range test scans under the same scanning path and layer thickness conditions. During the test, key process indicator data is simultaneously collected, including the melt pool temperature field curve, melt pool geometry, cooling rate, surface roughness of the formed layer, microstructure orientation distribution, and local average misorientation (KAM). All test data is recorded in a database, with a one-to-one correspondence between region and mode.

[0083] 232. Determine, based on the test data, a beam pattern and / or process parameters that enable the plurality of feature areas to achieve the process target, so as to allocate them to the corresponding plurality of feature areas.

[0084] For example, the control system 103 can use only the beam mode as the optimization variable and compare the test results of each mode within the same area. The control system 103 sorts the test data according to process objectives (such as minimized thermal stress, maximized density, or minimized surface roughness) and selects the beam mode with the highest achievement within each feature area as the final allocation mode. For example, the control system 103 can also use both the beam mode and process parameters (including beam power, scanning speed, spot size, and scanning trajectory) as optimization variables. For each beam mode, the control system 103 fine-tunes the process parameter combination in adjacent test scans, collects test data from a wider range of dimensions, and comprehensively evaluates the process objective achievement of each mode-parameter combination using a multidimensional scoring function. Ultimately, the control system 103 selects the beam mode and process parameter combination that maximizes the objective function for each feature area as the actual scan configuration. Furthermore, the control system 103 can also independently test only the process parameters when necessary to further fine-tune the processing conditions for each area.

[0085] Figure 8 Another example of the method 200 for controlling a multi-mode shaped beam is shown, including steps 241 and 242 that further constitute the method 200 .

[0086] Finite element simulation is used to simulate the temperature field, stress field and molten pool morphology of multiple characteristic areas under different beam modes to obtain simulation data for characterizing process targets.

[0087] Based on the geometric models and material properties (such as thermophysical parameters) of multiple characteristic regions, the control system 103 can use finite element simulation to numerically simulate the thermo-mechanical behavior of each characteristic region under different beam modes. Specifically, the control system 103 can establish a heat conduction model that incorporates the beam mode characteristics. This heat conduction model converts the intensity distribution and phase information of the beam mode into surface heat flux boundary conditions, and combines material parameters such as thermal conductivity, specific heat capacity, density, and latent heat of fusion to solve the two-dimensional or three-dimensional temperature field evolution. Subsequently, based on the resulting temperature gradient field, the control system 103 calculates the stress field distribution of each characteristic region through thermal-stress coupling analysis, focusing on the peak thermal stress and uniformity of the residual stress distribution. Simultaneously, the control system 103 can also use a melt pool dynamics model or phase field method to simulate the geometric morphology of the melt pool and obtain indicators such as melt pool depth, width, and solidification rate. The above simulation process can be performed in parallel for each beam mode, including Gaussian, annular, flat-top, Bessel, vector, and saddle, and the temperature field, stress field, and melt pool morphology data for each beam mode are stored in a simulation database.

[0088] 242. Determine, based on the simulation data, a beam pattern and / or process parameters that enable the plurality of feature regions to achieve the process target, and determine them for allocation to the corresponding plurality of feature regions.

[0089] Based on various indicators in the simulation database, the control system 103 can compare the peak thermal stress, residual stress, and melt pool geometric uniformity of each beam mode within the same characteristic region and select the beam mode that optimizes the process objectives (such as minimizing thermal stress, stabilizing the melt pool, or optimizing surface smoothness) as the allocation mode for that region. For scenarios requiring simultaneous optimization of multiple objectives, the control system 103 can employ a multi-objective ranking or weighted evaluation method to score the comprehensive performance of each beam mode and select the mode with the highest score. Furthermore, the control system 103 can incorporate process parameters (including beam power, scanning speed, spot size, and scanning trajectory) along with the beam mode into the simulation optimization. By performing a parametric sweep of the key process parameter combinations for each beam mode, finite element simulation is used to obtain temperature-stress-melt pool response data. Global optimization is then performed based on multi-dimensional objective functions (such as the weighted sum of thermal and residual stresses, melt pool geometric consistency indicators, and solidification rate differences) to determine the optimal mode-parameter combination.

[0090] For example, in finite element simulation, a typical heat conduction model can be described by the following partial differential equation:

[0091]

[0092] Where x, y, and z represent the coordinate components in the three-dimensional space coordinate system, which are used to define the spatial position of the powder bed or component; t represents the time variable, which is used to describe the evolution of temperature over time; ρ represents the material density; c p represents specific heat capacity; k represents thermal conductivity, represents the spatial gradient operator; Indicates position in space and absolute temperature at time t; yes The gradient in space indicates the direction and magnitude of temperature change with spatial position; Represents the laser heat source term, which is used to describe the heat power absorbed per unit volume at the spatial position (x, y, z) and time t.

[0093] Laser heat source term It can be described as:

[0094]

[0095] Where η represents the absorption efficiency of the material to the laser; I0 represents the peak intensity of the incident laser; Represents the normalized distribution function of the light spot in the xy cross section (such as Gaussian or annular); Represents the exponential decay function of laser energy along the depth direction z, which is used to describe how the energy of the laser decreases rapidly with depth when penetrating the material; Represents a time window function, which is used to describe when the heat source arrives at the coordinate (x, y), where Related to scanning speed and scanning trajectory.

[0096] By solving the above partial differential equations, the three-dimensional temperature field T(x, y, z, t) of the entire powder bed during the scanning process can be obtained, and then the temperature gradient can be calculated. , melt pool size, and subsequent thermal stress field distribution.

[0097] Figure 9 Another example of the method 200 for steering a multi-mode shaped beam is shown, including steps 251 - 253 that further constitute the method 200 .

[0098] 251. Construct a machine learning model using the structural type, material properties, beam mode and / or process parameters of multiple feature areas as input and the degree of achievement of process goals as output.

[0099] The control system 103 can use the structural type identifier (e.g., overhang, thin wall), material properties (e.g., thermal conductivity, absorptivity), beam mode number, and / or process parameters (e.g., beam power, scanning speed, spot size, scanning trajectory) of each feature region as model inputs, and output process goal achievement (e.g., quantitative indicators such as residual stress, density, and surface roughness) to construct a trainable machine learning model. Optional machine learning models include, but are not limited to, support vector machines (SVMs), random forests, gradient boosted decision trees (GBDTs), multilayer perceptron neural networks (MLPs), and convolutional neural networks (CNNs).

[0100] 252. Train machine learning models using test data and / or simulation data.

[0101] The control system 103 can use the test data obtained in step 231 and / or the finite element simulation data obtained in step 241 to train and verify the selected machine learning model. Taking a multilayer perceptron neural network as an example, the control system 103 encodes the structure type, material properties, mode number, and parameter value into a numerical vector by normalizing the input features. It then designs a feedforward network containing several hidden layers and uses mean square error or cross entropy as the loss function. The network weights are iteratively updated through backpropagation and gradient descent algorithms. After training is completed, the control system 103 evaluates the prediction accuracy of the model through cross-validation and test sets, and adjusts the network structure or hyperparameters based on the evaluation results to obtain optimal generalization ability.

[0102] 253. Use the trained machine learning model to predict the degree of achievement of process targets for multiple feature areas under different beam modes and / or process parameters, and determine the beam modes and / or process parameters assigned to the multiple feature areas accordingly.

[0103] Exemplarily, the control system 103 uses the beam pattern as the optimization target, calls the trained model to input the structural type and material properties of each feature area, predicts the process goal achievement degree under each beam pattern, and automatically selects the pattern number with the highest predicted value as the beam pattern ultimately assigned to each feature area. Furthermore, the control system 103 can also use the beam pattern and process parameter combination as a joint optimization target, using a machine learning model to input the structural type, material properties, and multiple pattern-parameter combinations to predict the corresponding process goal achievement degree, and select the optimal pattern-parameter combination as the processing configuration assigned to each feature area. Through this machine learning-driven allocation method, the control system 103 can achieve precise matching of beam pattern and process parameters under complex and changing additive manufacturing conditions.

[0104] In some embodiments, the control system 103 can also capture real-time data on the temperature field, stress field, and molten pool morphology of multiple characteristic areas during the scanning process, and then input the real-time data into the trained machine learning model for prediction, and adjust the beam mode and / or process parameters corresponding to the multiple characteristic areas in real time based on the prediction results.

[0105] For example, during the actual scanning process, the control system 103 can capture temperature, stress, and melt pool morphology data for each characteristic region in real time using various online sensors deployed within the additive manufacturing equipment, such as high-speed cameras, infrared thermal imagers, optical coherence tomography (OCT), or strain gauges. Temperature field data can be obtained by using an infrared thermal imager to obtain surface temperature distribution, or by using OCT combined with optical ranging to indirectly infer melt pool depth. Stress field data can be measured using digital image correlation (DIC) or a strain gauge array. Melt pool morphology can be extracted in real time using a high-speed camera coupled with an image processing algorithm to extract melt pool width, depth, and edge curve features. The captured real-time data can be preprocessed (denoising, correction, and feature extraction) and then fed into the machine learning model trained in the previous step according to the input format used during training. Based on the real-time data, the model rapidly predicts the current process target achievement for each characteristic region, such as the predicted residual stress level, porosity, or surface roughness. Based on the deviation of the model output from a preset threshold or target value, the control system 103 dynamically adjusts the shaping unit 104's mode switching instructions and / or adjusts process parameters such as beam power, scanning speed, spot size, and scanning trajectory, ensuring timely correction of the melt pool's behavior and maintaining it within the optimal processing window. By combining closed-loop real-time feedback with machine learning predictions, the control system 103 can rapidly respond to material and environmental disturbances, achieving dynamic regulation of each characteristic region.

[0106] It should be understood that compared with traditional Gaussian beams, shaped beams have obvious advantages in controlling the melt pool size and temperature gradient. However, in actual operation, researchers have found that when using Gaussian beams for direct scanning, the surface quality remains basically stable as the component forming height increases, and there is no obvious defocus or forming degradation; when DOE is added to the optical path, the depth range of the shaped spot in the Z-axis direction, that is, the depth of focus, is greatly compressed, and the optimal shape and focus state is maintained only within a very narrow axial range. As the component layer height continues to increase, the powder bed surface is prone to deviation from the focal plane of the optical system, and the shaped spot is out of focus, resulting in a sudden change in the melt pool geometry and increased roughness of the formed surface, affecting the forming consistency of subsequent layers.

[0107] In order to solve this technical problem, Figure 10Another example of an optical system 100 is shown. Optical system 100 further includes a variable aperture assembly 105 and a variable focus lens assembly 106, each connected to a control system 103 via a high-speed bus. Variable aperture assembly 105 is disposed in the optical path between shaping unit 104 and beam deflector 102 to adjust the spot diameter of the light beam; variable focus lens assembly 106 is disposed in the optical path between variable aperture assembly 105 and beam deflector 102 to adjust the focal length of the light beam.

[0108] In a specific implementation, the variable aperture assembly 105 can consist of an adjustable aperture mechanism, an aperture position sensor, and a spot control unit. For example, the adjustable aperture mechanism comprises a set of concentrically mounted blades driven by a micro-stepping motor or piezoelectric actuator, capable of varying the aperture opening diameter with millisecond-level response, thereby controlling the beam diameter passing through the aperture. The aperture position sensor uses an optical encoder or an inductive displacement sensor to detect the blade opening in real time and provide feedback to the control system 103 on the current aperture diameter, achieving closed-loop control. The spot control unit integrates a motor driver and an aperture control algorithm. Based on the target spot diameter command issued by the control system 103, it generates a corresponding drive signal and performs closed-loop position correction. The variable focus lens assembly 106 can consist of a variable focus lens assembly, a focal length measurement device, and a lens control unit. For example, the variable focus lens assembly consists of two or more movable lens elements, with the lens assembly spacing adjusted by a micro-linear actuator (such as a piezoelectric push rod or stepper motor) to achieve continuously adjustable focal length range. The lens material is preferably low-dispersion, high-transmittance optical glass or silicon-based microlenses. The focal length measurement device integrates an optical encoder or laser interferometer into the lens assembly to measure the lens spacing or focal position in real time and feed the measurement results back to the control system 103. The lens control unit includes a motion controller and a focal length calculation module. It receives the target focal length command issued by the control system 103, combines the optical model to inversely solve the required lens spacing, and drives the actuator to adjust it, while also performing closed-loop error correction.

[0109] It should be understood that in some embodiments of the present application, an additive manufacturing device is also provided, which has an optical path system 100 of any structure described, such as Figure 1 or Figure 10 The optical path system 100 shown can be used as a part of the additive manufacturing equipment of the present application.

[0110] In some embodiments, the optical path system 100 may further include a distance measuring sensor connected to the control system 103. The distance measuring sensor is disposed in the optical path and is used to measure the distance from the reference position to the top layer of the powder bed in real time. During the scanning process, the control system 103 may utilize the distance measuring sensor integrated in the optical path to obtain the real-time distance from the reference position to the top layer of the powder bed, and calculate the target value required for the current layer in combination with a pre-calibrated optical model, and then drive the variable aperture assembly 105 and the variable focus lens assembly 106 to adjust the spot diameter and focal length to the target values ​​in the beam mode, so that the light beam always maintains the best focus state at different layer heights.

[0111] For example, a distance sensor can be placed along the centerline of the optical path to measure the distance from the optical path reference position (base position) to the top layer of the powder bed in real time, recorded as Z(t). Combined with a pre-calibrated optical model, the spot radius ω and focus offset Δf required for the current layer can be calculated:

[0112]

[0113] in, Indicates the spot radius at the designed focal plane; Indicates the design focal length; represents the Rayleigh length, Indicates the laser wavelength; represents the height of the top layer of the powder bed measured at time t.

[0114] The control system 103 can directly convert the calculated spot radius and focus offset into variable aperture opening diameter and variable focus lens group spacing instructions according to the above formula, and send them to the variable aperture assembly 105 and the variable focus lens assembly 106 respectively, so as to realize real-time adjustment of the spot diameter and focal length.

[0115] In some embodiments, the control system 103 can also calculate and store the required target values ​​for each layer in advance based on the geometric model and layer thickness of the component before scanning, generate a target value-layer number mapping table, and directly read and apply the corresponding target value according to the layer number during the scanning process, and then drive the variable aperture assembly 105 and the variable focus lens assembly 106 to adjust the spot diameter and focal length to the target value in the beam mode, so that the light beam always maintains the best focus state at different layer heights.

[0116] Exemplarily, the control system 103 calculates the absolute height of the top layer of each powder bed according to the geometric model of the component 111 and the predetermined layer thickness Δh before scanning:

[0117]

[0118] in represents the initial height of the top surface of the first layer of powder bed, and n is the current layer number. Subsequently, the control system 103 uses the same optical model to calculate the spot radius ω and focus offset Δf required for each layer offline:

[0119]

[0120] in 、 and The meaning of is the same as the above model. The layer number is used as the index to store the lookup table. During the additive manufacturing process, the control system 103 reads the corresponding spot radius from the lookup table according to the current layer number n. and focus shift These values ​​are converted into the aperture setting value for the variable aperture assembly 105 and the lens spacing setting value for the variable focus lens assembly 106, and are transmitted to both components in real time. By precalculating the "layer number-focus parameter" and directly looking up the table, fast and accurate focus and spot adjustment can be achieved for each layer without the need for online distance measurement, ensuring that the shaped beam is in optimal focus during each layer scan.

[0121] Figure 11 The following figure shows an example structure of an electronic device 300. The electronic device 300 includes a processor 301 (which may be one or more) and a memory 302 (which may be one or more). The memory 302 is coupled to the processor 301 and is used to store instructions executed by the processor 301. When executed by the processor 301, the instructions cause the electronic device 300 to perform the method of the embodiment of the present application. The memory 302 can be a separate device independent of the processor 301, or it can be integrated into the processor 301.

[0122] Processor 301 can be a general-purpose processor (e.g., a microprocessor), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device. It can also implement instruction decoding and execution through a combination of hardware logic circuits and software modules. Memory 302 can be volatile memory (e.g., SRAM, DRAM, SDRAM, DDR SDRAM, etc.) or non-volatile memory (e.g., ROM, PROM, EPROM, EEPROM, flash memory, etc.), or any combination thereof, and is used to store the operating system, application programs, data, and the execution code of the aforementioned method.

[0123] In some embodiments, electronic device 300 further includes an input interface 303 and an output interface 304, both of which are controlled by processor 301 and communicate with other external devices or chips. Input interface 303 can be used to receive instructions and data from external sensors, ranging modules, or a host computer; output interface 304 can send control signals or feedback information to optical system 100 and other actuators. Processor 301, memory 302, and various interfaces can be integrated on a single chip or distributed across multiple chips or modules. The specific form can be selected based on system integration requirements.

[0124] It should be understood that the above configuration of the electronic device 300 is for illustrative purposes only and should not be considered as limiting the embodiments of the present application. Any processor type and storage medium capable of implementing the aforementioned processing, storage, and communication functions may fall within the scope of protection of the present application.

[0125] Figure 12 The figure shows an example of the structure of the computer-readable storage medium 400. The computer-readable storage medium 400 stores a computer program, which, when executed by a processor, implements the method described in the embodiment of the present application.

[0126] The computer-readable storage medium includes a computer program for executing a computer process on a computing device. In some embodiments, the computer-readable storage medium is provided using a signal-bearing medium 401. The signal-bearing medium 401 may include one or more program instructions that, when executed by one or more processors, may provide the functions or portions of the functions described above for the method embodiments of the present application. Thus, for example, one or more features of the method may be performed by one or more instructions associated with the signal-bearing medium 401. In addition, Figure 12 The program instructions in the 401 also describe example instructions. In some examples, signal-bearing medium 401 may include computer-readable medium 402, including a non-volatile storage medium. In specific implementations, a hard drive, a compact disc (CD), a digital video disc (DVD), a digital tape, a read-only memory (ROM), or a flash memory chip (e.g., a NOR / NAND memory) may be selected. The data storage characteristics thereof meet the requirements for long-term retention of computer programs.

[0127] In some embodiments, the signal-carrying medium 401 may include a computer-recordable medium 403, including a rewritable carrier, which may include a random access memory (RAM), a rewritable optical disc (CD-RW / DVD-RW), a solid-state drive (SSD) and a phase-change memory (PCM) in specific implementations, and supports dynamic updating of program instructions through a read-write controller.

[0128] In some embodiments, signal bearing medium 401 may include communication medium 404 such as, but not limited to, digital and / or analog communication media (eg, fiber optic cables, waveguides, wired communication links, wireless communication links, etc.).

[0129] Signal bearing medium 401 may be communicated by a wireless form of communication medium 404 (eg, a wireless communication medium conforming to the IEEE 802.11 standard or other transmission protocols). The one or more program instructions may be, for example, computer executable instructions or logic implemented instructions.

[0130] It should be understood that the units and algorithm steps of each example described in the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0131] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in this application. The description and examples are to be considered merely as exemplary, and the present application is not limited to the precise structures described above and shown in the drawings, and various modifications and variations may be made without departing from the scope thereof.

Claims

1. A method for controlling a multi-mode shaped beam in powder bed additive manufacturing, wherein the additive manufacturing process involves scanning a layer-by-layer powder bed with a beam propagating along an optical path to produce a component, characterized in that: The method comprises: Analyzing the geometric model of the component to obtain a plurality of characteristic regions corresponding to different structural types including at least a portion of the component, the structural types comprising at least two of an overhang, a curved surface, a porous structure, a lattice structure, a thin wall, a thick wall, and a connected structure, wherein the thickness of the thin wall is less than a first threshold, and the thickness of the thick wall is greater than a second threshold; According to the structure type and in combination with material properties and / or process goals, corresponding beam modes are assigned to the plurality of characteristic regions, wherein the beam modes are modulated to have different light intensity distributions and / or phases, and the beam modes include at least two of Gaussian, annular, flat-top, Bessel, vector, and saddle shapes; Applying the beam mode to the light beam, so that the light beam switches to the corresponding beam mode when scanning the plurality of feature areas on the powder bed; A variable aperture assembly and a variable focus lens assembly are respectively arranged in the optical path. When a non-Gaussian beam pattern is applied to the light beam, the method further includes: Driving the variable aperture assembly and the variable focus lens assembly to adjust the spot diameter and focal length to target values ​​in the beam mode, so that the beam always maintains an optimal focus state at different layer heights; The target value is obtained by: During the scanning process, the distance measuring sensor integrated in the optical path is used to obtain the real-time distance from the reference position to the top layer of the powder bed, and the target value required for the current layer is calculated in combination with the pre-calibrated optical model; or before scanning, the required target value is calculated and stored for each layer in advance according to the geometric model and layer thickness of the component, and the target value-layer number mapping table is generated, and the corresponding target value is directly read and applied according to the layer number during the scanning process.

2. The method according to claim 1, characterized in that The beam pattern further includes a transition pattern between at least two beam patterns and / or a composite pattern formed by superposition or segmentation of at least two beam patterns.

3. The method according to claim 1 or 2, characterized in that The method further comprises: Identifying the characteristic region to divide it into at least two grid cells; Different beam modes are assigned to the at least two grid units according to the structure type and in combination with material properties and / or process targets.

4. The method according to claim 1, wherein The method further comprises: assigning corresponding process parameters to the plurality of characteristic regions according to the structure type and in combination with material properties and / or process goals, the process parameters comprising at least one of beam power, scanning speed, spot size, and scanning trajectory; The process parameters are applied to the light beam, so that the light beam is switched to the corresponding process parameters when scanning the plurality of feature areas on the powder bed.

5. The method according to claim 1 or 4, characterized in that The process objectives include reducing at least one of thermal stress, residual stress, surface roughness, structural warping, and porosity, and / or improving at least one of density, construction efficiency, energy efficiency ratio, mechanical property isotropy, dimensional accuracy, and interlayer bonding strength.

6. The method according to claim 5, characterized in that The method further comprises: Performing test scans on the plurality of characteristic regions respectively using different beam modes to obtain test data representing process targets corresponding to the plurality of characteristic regions; A beam pattern and / or process parameter that enables the plurality of feature areas to achieve the process target is determined according to the test data, so as to be allocated to the corresponding plurality of feature areas.

7. The method according to claim 6, characterized in that The method further comprises: Using finite element simulation to simulate the temperature field, stress field and molten pool morphology of the multiple characteristic areas under different beam modes to obtain simulation data for characterizing the process target; A beam pattern and / or process parameter that enables the plurality of feature regions to achieve the process target is determined according to the simulation data, so as to be allocated to the corresponding plurality of feature regions.

8. The method according to claim 7, characterized in that The method further comprises: Constructing a machine learning model using the structural types, material properties, beam modes, and / or process parameters of the plurality of characteristic regions as input and the degree of achievement of the process goal as output; Training the machine learning model using the test data and / or simulation data; The trained machine learning model is used to predict the degree of achievement of the process targets of the plurality of feature areas under different beam modes and / or process parameters, and the beam modes and / or process parameters assigned to the plurality of feature areas are determined accordingly.

9. The method according to claim 8, characterized in that The method further comprises: During the scanning process, real-time data of temperature field, stress field and molten pool morphology of the plurality of characteristic regions are captured; The real-time data is input into the trained machine learning model for prediction, and the beam modes and / or process parameters corresponding to the multiple feature areas are adjusted in real time according to the prediction results.

10. An optical path system for additive manufacturing equipment, characterized in that: The optical path system includes: a beam emitter, for generating and emitting a light beam; A beam deflector, configured to deflect the beam along a preset scanning path onto a layer-by-layer powder bed to manufacture a component; a shaping unit, disposed in an optical path between the light beam emitter and the light beam deflector, for modulating the light beam into a plurality of beam modes having different light intensity distributions and / or phases; A control system is provided for connecting and controlling the beam emitter, the beam deflector and the shaping unit to execute the method according to any one of claims 1 to 9.

11. An optical path system for additive manufacturing equipment, characterized in that: The optical path system includes: a beam emitter, for generating and emitting a light beam; A beam deflector, configured to deflect the beam along a preset scanning path onto a layer-by-layer powder bed to manufacture a component; a shaping unit, disposed in an optical path between the light beam emitter and the light beam deflector, for modulating the light beam into a plurality of beam modes having different light intensity distributions and / or phases; a variable aperture assembly, arranged in the optical path between the shaping unit and the beam deflector, for adjusting the spot diameter of the light beam; a variable focus lens assembly, disposed in the optical path between the variable aperture assembly and the beam deflector, for adjusting the focal length of the light beam; a distance measuring sensor, arranged in the optical path, for measuring the distance from the reference position to the top layer of the powder bed in real time; A control system is provided for connecting and controlling the light beam emitter, the shaping unit, the variable aperture assembly, the variable focus lens assembly, the light beam deflector and the ranging sensor to execute the method according to any one of claims 1 to 9.

12. An additive manufacturing device, characterized in that: Comprising the optical path system according to claim 10 or 11.

13. 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 configured to store instructions to be executed by the at least one processor, wherein the instructions, when executed by the at least one processor, cause the electronic device to perform the method according to any one of claims 1 to 9.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which implements the method according to any one of claims 1 to 9 when executed by a processor.

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