Multi-model driven powder bed additive manufacturing method and device

Abstract

The present application provides a multi-model driven powder bed additive manufacturing method and device. By establishing at least one first 3D model and applying a first process parameter to at least one first 3D model; establishing at least one second 3D model that fully or partially overlaps with at least one first 3D model and applying a second process parameter different from the first process parameter to at least one second 3D model; and using at least one first 3D model and at least one second 3D model to drive at least one high-energy beam, respectively, so that it performs a first scanning process corresponding to the first process parameter and a second scanning process corresponding to the second process parameter on the powder bed, so as to manufacture components layer by layer, thereby realizing accurate scanning of different process parameters at the same position, improving manufacturing accuracy and microstructure quality of components.

Description

Technical Field

[0001] The present application relates to the field of additive manufacturing, and more specifically to a multi-model driven powder bed additive manufacturing method and apparatus. Background Art

[0002] In the traditional LPBF (Laser Powder Bed Fusion) 3D printing process, the scanning path of the high-energy beam and the molten pool formation process are usually controlled by a single 3D model and its corresponding process parameters. Although this method can realize the layer-by-layer construction of parts, it has obvious limitations. Especially when different process parameters need to be processed at the same position, the existing technology relies on a single 3D model, has low scanning efficiency, and is difficult to achieve accurate process parameter adjustment. The accuracy of this method is low, resulting in difficulty in effectively controlling the stability of the molten pool and the microstructural quality of the final component in the manufacture of some parts with complex structures or high performance requirements. In addition, since different process parameters are difficult to apply accurately at the same position, the existing technology often faces problems such as uneven material properties and difficult to eliminate defects during the manufacturing process, which limits its application in the manufacture of high-performance parts. Summary of the invention

[0003] The present application proposes a multi-model driven powder bed additive manufacturing method and device, which realizes accurate scanning of different process parameters at the same position by adopting at least two fully or partially overlapping 3D models and different process parameters, thereby improving the manufacturing accuracy and microstructure quality of the component and the forming efficiency, and solving the problems of low accuracy, uneven material properties, and difficulty in eliminating defects caused by relying on a single 3D model in the prior art.

[0004] In a first aspect, the present application provides a multi-model driven powder bed additive manufacturing method, comprising: establishing at least one first 3D model, applying first process parameters to the at least one first 3D model; establishing at least one second 3D model that fully or partially overlaps with the at least one first 3D model, applying second process parameters different from the first process parameters to the at least one second 3D model; and using the at least one first 3D model and at least one second 3D model to drive at least one high-energy beam, respectively, so that it performs a first scanning process corresponding to the first process parameter and a second scanning process corresponding to the second process parameter on the powder bed, so as to manufacture components layer by layer.

[0005] Preferably, the at least one second 3D model is established on the entire or partial position of the at least one first 3D model, and overlaps with the entire or partial area of ​​the at least one first 3D model.

[0006] Preferably, the process of establishing the at least one first 3D model and the at least one second 3D model includes: providing geometric data of a component; using the geometric data to generate at least one first 3D model consistent with the size and shape of the component, and applying the first process parameters indicating the manufacturing of the entire structure to the at least one first 3D model; based on the geometric data of the at least one first 3D model, generating at least one second 3D model with the same size and shape at a corresponding position overlapping with it, and applying the second process parameters indicating the manufacturing of the overlapping structure to the at least one second 3D model.

[0007] Preferably, the first process parameter and the second process parameter include at least one of a scanning rate, a scanning path, a scanning power, a spot diameter, a focal position, a track spacing and a layer thickness.

[0008] Preferably, the first scanning process and the second scanning process are performed simultaneously or at intervals at the same position of the powder bed.

[0009] Preferably, when the first scanning process and the second scanning process are performed at intervals at the same position of the powder bed, the powder is sintered or melted by the first scanning process to form a crystal structure, and the second scanning process performs a secondary melting of the crystal structure; or, the powder is broken up by the first scanning process, and the second scanning process sinters or melts the broken up powder to form a crystal structure.

[0010] Preferably, the at least one first 3D model and the at least one second 3D model correspond to different material components respectively, and when the first scanning process and the second scanning process are performed at intervals at the same position of the powder bed, the method further includes: applying a powder layer of the first material on the powder bed, and using the at least one first 3D model to drive the at least one high-energy beam to perform a first scanning process corresponding to the first process parameter on the powder layer; supplementing a group of nanoparticles of the second material at at least a portion of the powder layer, and using the at least one second 3D model to drive the at least one high-energy beam to perform a second scanning process corresponding to the second process parameter on the nanoparticle group.

[0011] Preferably, the second scanning process has a higher scanning power and / or scanning rate than the first scanning process.

[0012] Preferably, the method also includes: obtaining molten pool parameters and / or structural parameters in the manufacturing process, wherein the molten pool parameters include one or more of the size, shape, temperature gradient and solidification rate of the molten pool, and the structural parameters include one or more of the density, strength, porosity, roughness and stress distribution of the component; automatically adjusting the structure of the at least one second 3D model and / or the second process parameters according to the molten pool parameters and / or structural parameters to achieve structural and / or performance compensation.

[0013] Preferably, the molten pool parameters and / or structural parameters are obtained by real-time monitoring or process simulation during the manufacturing process.

[0014] Preferably, the structure of the at least one second 3D model and / or the second process parameters are generated using a DQN model, specifically comprising: inputting the molten pool parameters and / or structural parameters into the DQN model; defining the action space of the DQN model as operations for adjusting the structure of the at least one second 3D model and / or the second process parameters; setting a reward function according to the influence of the adjusted structure of the at least one second 3D model and / or the second process parameters on component performance, wherein the reward function is evaluated according to the structural parameters; using a deep neural network to approximate a Q-value function Q(S t , a t ; θ) represents the specific state S t Perform an action a t The expected cumulative reward that can be obtained after the adjustment; and using the DQN model to repeatedly train the adjusted structure of the at least one second 3D model and / or the second process parameters, and continuously updating the model parameters using the feedback reward value until the optimal at least one second 3D model structure and / or second process parameters are obtained.

[0015] Preferably, the structure of the at least one second 3D model and / or the second process parameters are generated using finite element simulation, specifically including: establishing a finite element model based on the at least one first 3D model to simulate the thermodynamic behavior and structural response of the component during the additive manufacturing process; inputting the molten pool parameters and / or structural parameters into the finite element model to simulate and analyze the structural response of the component; identifying the areas causing structural and / or performance defects based on the simulation analysis results; and adjusting the structure of the at least one second 3D model and / or the second process parameters based on the identified defective areas to generate optimized structures of the at least one second 3D model and / or the second process parameters.

[0016] The second aspect of the present application provides a multi-model driven powder bed additive manufacturing device, comprising: a first model building module, configured to establish at least one first 3D model and apply first process parameters to the at least one first 3D model; a second model building module, configured to establish at least one second 3D model overlapping with the at least one first 3D model, and apply second process parameters different from the first process parameters to the at least one second 3D model; and an additive manufacturing module, configured to drive at least one high-energy beam using the at least one first 3D model and at least one second 3D model respectively, so that it performs a first scanning process corresponding to the first process parameter and a second scanning process corresponding to the second process parameter on the powder bed, so as to manufacture components layer by layer.

[0017] A third aspect of the present application provides an electronic device, comprising: at least one processor; at least one memory, wherein 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, and when the instructions are executed by the at least one processor, the electronic device performs the additive manufacturing method according to any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] Figure 1 A schematic diagram showing a construction process of multiple models in some embodiments of the present application is shown;

[0020] Figure 2 and Figure 3 Another schematic diagram of the multi-model construction process in some embodiments of the present application is respectively shown;

[0021] Figure 4 A schematic diagram of the application of multi-model driven additive manufacturing in some embodiments of the present application is shown;

[0022] Figure 5 A schematic diagram showing the application of dual high-energy beams in some embodiments of the present application is shown;

[0023] Figure 6 A schematic diagram of a multi-model driven multi-material additive manufacturing application in some embodiments of the present application is shown;

[0024] Figure 7 A schematic diagram showing the structure of the second 3D model and / or the adjustment process of the second process parameters in some embodiments of the present application is shown;

[0025] Figure 8A schematic diagram of a process of using a DQN model to generate a structure of a second 3D model and / or a second process parameter in some embodiments of the present application is shown;

[0026] Fig. 9 A schematic diagram of a process for generating a structure of a second 3D model and / or a second process parameter by using finite element simulation in some embodiments of the present application is shown;

[0027] Fig.10 A schematic diagram of a framework of a multi-model driven powder bed additive manufacturing device in some embodiments of the present application is shown;

[0028] Fig.11 A schematic diagram of the framework of an electronic device in some embodiments of the present application is shown. DETAILED DESCRIPTION

[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present application will be more comprehensive and complete and 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, many specific details are provided to provide a full understanding of the embodiments of the present application.

[0030] The "additive manufacturing" involved in this application refers to the 3D printing technology that manufactures three-dimensional objects by adding powder materials layer by layer and consolidating the powder materials using high-energy beams (such as lasers or electron beams) (this technology uses a digital model generated by computer-aided design (CAD) software to precisely control the melting and consolidation process of powder materials to build complex three-dimensional objects layer by layer, that is, the "components" involved in this application. In practical applications, components can be functional parts, structural parts, tools or any other form of physical objects, and their design and manufacture can be customized according to the specific needs of their final use. The "powder materials" involved are the raw materials used for additive manufacturing components, including but not limited to metal powders, ceramic powders, polymer powders and composite material powders. The "consolidation" involved refers to the process in which the powder material undergoes a transformation from a discrete particle state to a continuous solid structure under the action of an energy beam. For example, this transformation involves the partial melting of powder particles after absorbing energy to form a liquid molten pool, and then as the energy is withdrawn and the material cools, the liquid molten pool gradually solidifies and finally consolidates into a tightly connected solid structure with a stable form.

[0031] <Overview of this Application>

[0032] The present application generally relates to a method for additively manufacturing components in a powder bed, by constructing multiple 3D models and applying different process parameters in each 3D model, and using the constructed multiple 3D models to drive one or more high-energy beams to scan on the powder bed with their respective corresponding process parameters to manufacture the components layer by layer.

[0033] <Construction of multiple models>

[0034] Figure 1 A schematic diagram of a construction process of multiple models of the present application is shown. Figure 1 First, according to the geometric data of the component to be printed, an A model 140 (i.e., the first 3D model) corresponding to the geometric data is established. During the construction of the A model 140, certain specific manufacturing requirements of the component are taken into consideration, and the first process parameters 143 corresponding to these requirements are set and applied to directly act on the A model 140 to drive the first stage of the additive manufacturing process. After the A model 140 is established and associated with the first process parameters 143, a B model 141 (i.e., the second 3D model) is then established at the location of the A model 140 to completely overlap with its entire area. The B model 141 is completely consistent with the A model 140 in terms of geometric size and shape, but in response to different manufacturing requirements of the component, the B model 141 will set a second process parameter 144 different from the first process parameter 143 to meet the requirements of different stages in the component manufacturing process. By establishing the B model 141 at the same location and assigning it different process parameters, a complete fusion is achieved between the A model 140 and the B model 141, forming a Figure 1 AB model 142 shown in .

[0035] It should be understood that although in theory the AB model 142 is composed of two independent models "A model 140" and "B model 141", in actual application, they are combined into a unified solid model by nesting. In other words, during the additive manufacturing process, the AB model 142 is not two separate models, but an overall three-dimensional model is formed by nesting (i.e., completely overlapping or partially overlapping) the B model 141 in the corresponding area of ​​the A model 140. In this model, the A model 140 and the B model 141 are assigned different process parameters: the A model 140 drives the high-energy beam to scan on the powder bed with the first process parameter 143, while the B model 141 drives the high-energy beam to scan with the second process parameter 144 different from the first process parameter 143.

[0036] Figure 2 Another schematic diagram of the multi-model construction process of the present application is shown. Figure 1 Compared to the full overlap construction process shown in Figure 2The situation of establishing the B model 141 (second 3D model) on the local area is shown. After completing the construction of the A model 140 and applying the first process parameter 143, the local areas that need special processing or optimization are identified according to the manufacturing requirements of the component. In these local areas, the B model 141 is established. The geometric size and shape of the B model 141 are consistent with those of the A model 140 in the local area, but because the B model 141 is only for the local area, it only covers part of the position of the A model 140. In this way, the B model 141 partially overlaps with the A model 140 in these specific areas, forming Figure 2 AB model 142 shown in FIG. Applying a second process parameter 144 different from that of A model 140 to B model 141 can further optimize the microstructure or performance of the local area, so as to more finely control and process the key area of ​​the component, thereby taking into account the special needs of local features in the overall manufacturing process.

[0037] In a more specific example, an example of the establishment process of the A model 140 and the B model 141 is as follows: First, the geometric data of the component (for example, including the overall shape, size and detailed topological information of the component) is obtained and provided. Geometric data is the basis for model construction and is usually generated by computer-aided design (CAD) software. Topological information provides a detailed description of the surface and internal structure of the component, including features such as boundaries, connectivity and holes, which is of great significance for subsequent manufacturing processes. Then, the A model 140 is generated using the acquired geometric data. The A model 140 is completely consistent with the overall size and shape of the component and covers all the geometric features of the component. After the A model 140 is generated, it is necessary to set the corresponding first process parameters 143 according to the manufacturing requirements of the component and apply them to the A model 140 to ensure that the A model 140 can correctly guide the high-energy beam for scanning processing during the additive manufacturing process. After the A model 140 is established and the first process parameters 143 are applied, all or local areas that need further optimization or special treatment are identified according to the specific manufacturing requirements of the component. In these regions, based on the geometric data of the A model 140, a B model 141 is generated that completely or partially overlaps the A model 140 at the corresponding position. Subsequently, the second process parameters 144 are applied to the B model 141. The second process parameters 144 are generally different from the process parameters of the A model 140.

[0038] Figure 3Another schematic diagram of the multi-model construction process of the present application is shown. In the example of 3, for example, when the B model 141 is set to partially overlap with the A model 140, the B model 141 does not need to have the same overall shape as the A model 140. The B model 141 can be composed of multiple different sub-models, and these sub-models can be scattered in different areas of the A model 140, without having to completely overlap with a single area of ​​the A model 140. Specifically, the sub-models of the B model 141 can correspond to specific functional areas or material property areas of the component, respectively, and these sub-models apply different process parameters at different positions in the A model 140, thereby achieving precise control of the local performance of the component.

[0039] For example, in some complex components, different areas may require different mechanical strength, thermal conductivity or surface roughness. By decomposing the B model 141 into multiple sub-models and distributing these sub-models at different positions of the A model 140, specific process parameters can be applied to these areas during the manufacturing process, such as adjusting the scanning rate, power or focus position. In some special additive manufacturing applications, the B model 141 can even be simplified to one or more vector line models. These vector line models can, for example, represent specific scanning paths or energy concentration areas, which are used to form specific microstructures or functional areas inside or on the surface of the component. For example, the B model 141 can define the precise scanning path of the high-energy beam through vector lines to form a reinforced area or microstructure in the component.

[0040] The first process parameters 143 and the second process parameters 144 involved in this article include, for example, scanning rate, scanning path, scanning power, spot diameter, focal position, track spacing, layer thickness, etc. The second process parameters 144 are different from the first process parameters 143, which means that any one or more of these parameters are different, and the examples are as follows:

[0041] The first process parameter 143 adopts a conventional and mature strategy to ensure good melting of the powder layer and the metallurgical quality of the powder layer; and after the melting of the powder layer is completed, the second process parameter 144 (strategies such as increasing the scanning rate, beam power, and reducing the scanning spacing) is applied. On the basis of further adjusting and optimizing the microstructure, the forming efficiency can be effectively improved.

[0042] The first process parameter 143 selects a lower scanning rate to ensure sufficient melting of the powder material and a better microstructure; while the second process parameter 144 selects a higher scanning rate to improve production efficiency or control the cooling rate in certain areas.

[0043] The first process parameter 143 uses a parallel line scanning path, which is suitable for uniform processing of a large area; while the second process parameter 144 uses a spiral or chessboard path to optimize stress distribution or detail processing in a local area.

[0044] The first process parameter 143 sets a higher laser power to melt thicker powder layers, while the second process parameter 144 uses a lower power to process thinner layers or fine features to prevent over-melting.

[0045] The first process parameter 143 uses a larger spot diameter to cover a larger area and increase processing speed, while the second process parameter 144 uses a smaller spot diameter to achieve higher processing accuracy and surface details.

[0046] The focus position of the first process parameter 143 is set on the surface of the powder bed to ensure the stability of the molten pool; while the second process parameter 144 adjusts the focus position to optimize the melting depth or shape of a specific area.

[0047] The first process parameter 143 uses a larger track pitch to speed up manufacturing, while the second process parameter 144 reduces the track pitch to ensure high quality fusion and a denser structure.

[0048] The first process parameter 143 selects a thicker layer thickness to speed up the formation of the component, while the second process parameter 144 selects a thinner layer thickness to improve detail resolution and surface finish.

[0049] It should be understood that the models with different process parameters involved in the present application are not limited to the combination of model A 140 and model B 141, and the number of models can be increased according to the types of process parameters required in actual applications. Specifically, each different process parameter can correspond to an independent 3D model. Therefore, in some complex additive manufacturing applications, multiple different models may be constructed, each representing a combination of process parameters. This multi-model system can flexibly respond to different manufacturing needs and optimize the material properties and structural characteristics of each area by applying different process parameters in the same powder bed.

[0050] In addition, in order to meet larger-scale or more complex manufacturing tasks, multiple fused AB models 142 can also be constructed. That is, not only can a multi-model driven strategy be adopted within a single component, but multiple components can also be manufactured simultaneously in the same powder bed, each component being driven by multiple different models. For example, in the manufacturing process of a large component or multiple components, different process parameters can be set and corresponding models can be constructed for different areas, different functional requirements or different material properties. These models can be scanned by applying corresponding process parameters at different positions of the powder bed, so that each component can obtain the best forming quality and structural performance.

[0051] <Multi-model driven additive manufacturing>

[0052] After the overlapping construction of multiple 3D models is completed, each model is assigned different process parameters. Subsequently, at least one high-energy beam is driven by these 3D models to perform precise scanning on the powder bed. Figure 4 A schematic diagram of the application of multi-model driven additive manufacturing in the present application is shown, and the process is implemented by an additive manufacturing system 10.

[0053] Reference Figure 4 , the additive manufacturing system 10 of the present application is configured to include the following components: a beam emitting device 15 (such as a laser) for emitting a high-energy beam 11; a beam deflecting device 16 (such as a galvanometer) for receiving the high-energy beam 11 and deflecting it so that it can be accurately irradiated on a specified position of the powder bed 12; and a control system 14 for coordinating and controlling the entire manufacturing process. For example, the control system 14 can accurately control the beam emitting device 15 and the beam deflecting device 16 according to the instructions of the AB model 142, so that the high-energy beam 11 performs a scanning process corresponding to the first process parameter 143 and the second process parameter 144 on the powder bed 12, thereby constructing the component 13 layer by layer.

[0054] In the additive manufacturing process, the powder bed 12 is formed layer by layer on the substrate 19. Specifically, the process of forming the powder bed 12 generally includes the following steps: the powder cylinder (not shown) gradually drives the powder material stored inside to the construction plane, and the powder spreading device 17 (such as a roller or a scraper) applies the powder material evenly on the substrate 19 in a flat form to form each layer of the powder bed 12. After each layer of the powder bed 12 is selectively consolidated (sintered or melted) by the high-energy beam 11, a new powder layer is applied again, layer by layer, until the entire component 13 is manufactured.

[0055] The AB model 142 used by the control system 14 is embodied in the form of layer sections during the actual construction process. Usually, in the preprocessing stage of additive manufacturing, the A model 140 and the B model 141 are segmented using slicing tools, and the entire 3D model is decomposed into multiple layer sections by the slicing tools. These layer sections correspond to each layer of the powder bed in the manufacturing process, ensuring that the high-energy beam 11 can accurately perform scanning processing layer by layer according to the instructions of the model. In this way, the control system 14 can achieve high-precision control of each layer according to the process parameters of the AB model 142, ensuring that the final manufactured component has excellent structure and performance.

[0056] In some embodiments, the number of high-energy beams 11 can be set to be more than one, and the specific number can be the same as the number of 3D models. That is, each 3D model with different process parameters can correspond to a high-energy beam 11, so that each high-energy beam in the present application can be driven by the corresponding 3D model, thereby performing different scanning processes in the powder bed 12.

[0057] Figure 5 The schematic diagram of the application of dual high energy beams in this application is shown. Figure 5 , the additive manufacturing system 10 is configured to have two independent optical path systems to provide and control two different high-energy beams. The two high-energy beams are generated and guided by the first optical path and the second optical path, respectively, to ensure that each high-energy beam can accurately perform a scanning process that matches its corresponding 3D model and process parameters. Specifically, the first optical path includes a first beam emitting device 15A and a first beam deflecting device 16A. The first beam emitting device 15A is used to emit the first high-energy beam 11A, and the first beam deflecting device 16A is used to receive and guide the first high-energy beam 11A so that it can scan at a specified position of the powder bed 12 according to the instructions of the A model 140. The second optical path includes a second beam emitting device 15B and a second beam deflecting device 16B. Similarly, the second beam emitting device 15B is used to emit the second high-energy beam 11B, and the second beam deflecting device 16B receives and guides the second high-energy beam 11B so that it can perform different scanning processes at a specified position of the powder bed 12 according to the instructions of the B model 141.

[0058] Through this dual high-energy beam configuration, the additive manufacturing system 10 can simultaneously perform two different process parameter scanning processes, thereby greatly improving manufacturing efficiency and component quality, and is particularly suitable for the manufacture of complex structural parts that require different process parameter processing in different areas or layers. Each high-energy beam performs independent and precise operations in the powder bed 12 according to its corresponding 3D model and process parameters, so that the final manufactured component not only has a high degree of complexity and precision, but also can achieve different functional characteristics in each local area.

[0059] In some embodiments, the first scanning process and the second scanning process can be performed simultaneously at the same location on the powder bed.

[0060] Specifically, when the first high-energy beam 11A and the second high-energy beam 11B are synchronously scanned at the same position of the powder bed, the two high-energy beams are operated according to their respective corresponding 3D models and process parameters. The first high-energy beam 11A is used to perform a preliminary scan of the position according to the first process parameter 143, while at the same time the second high-energy beam 11B performs further scanning processing on the same position according to the second process parameter 144. This synchronous operation can not only speed up the manufacturing process, but also optimize the microstructure and physical properties of the final component through the synergistic effect of different process parameters. This dual processing method can effectively reduce the defects that may occur during single high-energy beam processing, such as incomplete fusion or loose material, thereby improving the density and mechanical properties of the component.

[0061] In addition, the simultaneous dual high-energy beam scanning process can also achieve fine control of the material microstructure by adjusting their respective energies and paths. For example, the first high-energy beam 11A can be used to adjust the grain size or phase structure of the material, while the second high-energy beam 11B can further optimize these structural features to ensure that the material has a uniform structure and good mechanical properties in the final state.

[0062] In some embodiments, the first scanning process and the second scanning process may be performed at intervals at the same location on the powder bed.

[0063] (Single high energy beam case)

[0064] In a single high-energy beam configuration, the first scanning process and the second scanning process are performed sequentially at the same position in a time interval. First, the high-energy beam performs the first scanning process to perform preliminary melting or sintering of the powder material at the position according to the first process parameter 143. Subsequently, the high-energy beam will move to other positions to continue the first scanning process. After completing the first scanning process of a certain area, the high-energy beam will return to the same position that has been processed before to perform the second scanning process. At this time, the second scanning process will reheat, melt or fine-process the material at the position according to the second process parameter 144 to further adjust the microstructure and properties of the material. This interval scanning method can control the heat input and avoid excessive melting during the cooling of the material after the first treatment, thereby improving the density of the material and the uniformity of the microstructure.

[0065] (Case of double high energy beams)

[0066] In the dual high-energy beam configuration, the first high-energy beam 11A and the second high-energy beam 11B respectively undertake the first scanning process and the second scanning process, and act on the same position at intervals. Specifically, the first high-energy beam 11A first processes the position according to the first process parameter 143 to form a preliminary molten pool or consolidation layer. The second high-energy beam 11B then performs a secondary scanning process according to the second process parameter 144 after the molten pool at the position is slightly cooled or solidified. In this way, the role of the second high-energy beam 11B can be to correct the deep melting of the material, refine the grains, change the phase composition, or repair the micro defects that may be left by the first scanning process. The length of the interval time can be adjusted according to the characteristics of the material and the process requirements to ensure that each scanning process can be carried out under the best thermal state.

[0067] One of the main advantages of this interval scanning method is that it can more finely control the thermal treatment process of the material. Because there is an interval between each scanning process, the material has a chance to cool or solidify, reducing the heat accumulation effect caused by continuous melting, thereby reducing thermal deformation, shrinkage stress and the formation of micro cracks.

[0068] In some embodiments, when the first scanning process and the second scanning process are performed at intervals at the same position of the powder bed, the entire process can accurately control the microstructure of the material through the two-step scanning operation, thereby optimizing the microstructure and macroscopic performance of the additively manufactured component. Specifically, the first scanning process and the second scanning process act on the powder material at different time points, and each step has a specific impact on the structure and performance of the material.

[0069] (The first operating mode - secondary melting crystal structure)

[0070] In this mode, the first scanning process is carried out with relatively low power and slow scanning rate, with the purpose of selectively sintering or melting the powder material on the powder bed. This process will produce a preliminary molten pool and form a preliminary crystal structure of the powder material at a local location. Due to the low power and slow scanning rate, the melting process is relatively gentle, which is conducive to controlling the formation of grains and the preliminary microstructure.

[0071] After the first scanning process is completed and the material has partially solidified or formed a preliminary crystal structure, the second scanning process is then performed at the same location. The second scanning process can use a higher scanning power and / or a faster scanning rate to rapidly increase the temperature of the molten pool and perform a secondary melting of the initially formed crystal structure. This process can further refine the grains, recrystallize, or in some cases promote grain growth, so as to achieve fine control of the material microstructure. This secondary melting treatment method can not only improve the uniformity and density of the material, but also improve the comprehensive mechanical properties of the component, including strength, toughness and fatigue performance.

[0072] It should be understood that the second scanning process uses a higher scanning power and / or a faster scanning rate than the first scanning process, which not only enables the material to melt in a very short time, but also enables fine control of the material microstructure through a fast cooling rate. The strong heat input brought by the high scanning power is conducive to the thoroughness of the secondary melting and eliminates any residual micropores or defects. The faster scanning rate can significantly increase the cooling rate and promote the refinement of grains.

[0073] (The second operation mode - sintering or melting after powder dispersion)

[0074] In this mode, the first scanning process does not directly melt or sinter, but breaks up the powder material by controlling the power and scanning mode of the high-energy beam. This operation is similar to a pretreatment of the powder material to eliminate agglomeration or break up large particles of powder to form a more uniform powder distribution. This process facilitates subsequent melting or sintering, greatly improving the density and uniformity of the material.

[0075] After the powder material is broken up and evenly distributed, the second scanning process is performed at the same location with a higher power (or faster scanning rate). This process melts or sinters the powder material to form a highly uniform and dense crystal structure, which can effectively improve the isotropic properties of the material and reduce the fluctuation of material properties caused by powder agglomeration or uneven distribution, thereby improving the forming quality of the component.

[0076] <Multi-model driven multi-material additive manufacturing>

[0077] In some embodiments, the first 3D model and the second 3D model correspond to different material components.

[0078] When the first scanning process and the second scanning process are performed at intervals at the same position on the powder bed, the additive manufacturing process further involves: applying a powder layer of the first material on the powder bed, and using a first 3D model to drive at least one high-energy beam to perform a first scanning process corresponding to the first process parameter 143 on the powder layer; then supplementing a group of nanoparticles of the second material (immersed in the gaps in the powder layer) at least at a portion of the powder layer, and using a second 3D model to drive the at least one high-energy beam to perform a second scanning process corresponding to the second process parameter 144 on the nanoparticle group.

[0079] Figure 6 FIG. 1 shows a schematic diagram of a multi-model driven multi-material additive manufacturing application in this application. Figure 6, the A model 140 corresponds to material A (i.e., the material used by the entire powder bed), and the B model 141 corresponds to material B (i.e., the material used by the nanoparticle group). In the additive manufacturing process, the powder material of material A is first evenly laid on the powder bed 12 to form a powder layer (e.g., metal powder). According to the instructions of the A model 140, the control system 14 uses the high-energy beam 11 to scan the powder layer and performs the first scanning process corresponding to the first process parameter 143. In this process, the high-energy beam 11 selectively sintered or melted the powder material to form a crystal structure according to the set path of the A model 140. After the first scanning process is completed, the process flow of additive manufacturing is not terminated, but a nanoparticle group with material B is applied at a specific position of the powder layer, namely, the first nanoparticle group 18A, the second nanoparticle group 18B, the third nanoparticle group 18C and the fourth nanoparticle group 18D (e.g., carbide, oxide, or other functional nanomaterials). By controlling the injection position of the nanoparticle group, it can be filled into the gap in the powder layer. The process of injecting the nanoparticle group can be performed in a variety of ways, such as spraying the nanoparticles onto the surface of the powder bed 12 through a liquid or gas carrier.

[0080] After the injection of the first nanoparticle group 18A, the second nanoparticle group 18B, the third nanoparticle group 18C, and the fourth nanoparticle group 18D is completed, the control system 14 drives the high-energy beam 11 using the B model 141 to perform a second scanning process on the area where the nanoparticle group is located. The second scanning process uses different process parameters from the first scanning process. For example, the high-energy beam 11 can melt the nanoparticle group or combine it with the surrounding powder material through a higher power or a faster scanning speed.

[0081] It should be understood that the present application forms a region with a multi-material structure in the component by making the nanoparticle group closely combine with the powder layer (material A) through secondary scanning. The application and combination of nanoparticles can not only improve the mechanical properties of the material, but also give the material special functionality, such as improving wear resistance, conductivity, or enhancing the thermal stability of the component, so as to effectively manufacture functional gradient materials and achieve continuous transition of different materials or significant differences in local performance in the same component.

[0082] <Structure of Second 3D Model and / or Adjustment of Second Process Parameters>

[0083] Figure 7 The structure of the second 3D model and / or the adjustment flow diagram of the second process parameters in this application are shown. Figure 7 In some embodiments, the additive manufacturing method of the present application further involves the following steps 21 and 22.

[0084] 21. Obtain the molten pool parameters and / or structural parameters during the manufacturing process, wherein the molten pool parameters include one or more of the size, shape, temperature gradient and solidification rate of the molten pool, and the structural parameters include one or more of the density, strength, porosity, roughness and stress distribution of the component.

[0085] 22. Automatically adjust the structure of the second 3D model and / or the second process parameters according to the molten pool parameters and / or structural parameters to achieve structural and / or performance compensation.

[0086] During the additive manufacturing process, the control system acquires multiple parameters related to the molten pool and components in real time. The molten pool parameters refer to the relevant characteristics of the molten area formed under the irradiation of the high-energy beam, including but not limited to the size of the molten pool (such as length, width, height), shape (such as elliptical, circular, etc.), temperature gradient (temperature change from the center to the edge of the molten pool), and solidification rate (the rate at which the molten material is converted into a solid). These parameters will affect the fluidity, cooling behavior and final crystal structure of the molten material. Structural parameters include performance indicators directly related to the final formed component, such as the density, strength, porosity, surface roughness and stress distribution of the component.

[0087] (Adjustment of the second 3D model structure)

[0088] Depending on the size and shape of the molten pool, the control system can dynamically modify the geometry or structure of the second 3D model. For example, if the size of the molten pool increases or decreases abnormally, the control system can compensate for possible manufacturing deviations by scaling or deforming certain parts of the model. In addition, the local structure of the model can also be adjusted based on structural parameters such as the strength or porosity of the component. For example, when it is detected that the porosity in a certain area is too high, the system can encrypt the model mesh in that area to increase the density of the material and thus reduce the porosity.

[0089] (Adjustment of the Second Process Parameters)

[0090] In addition to adjusting the model structure, the control system can also automatically adjust the second process parameters (including scanning rate, scanning path, high-energy beam power, spot diameter, focal position, and track spacing, etc.) according to the molten pool parameters and structural parameters. For example, when it is detected that the molten pool temperature is too high and may cause material burning, the system can reduce the power of the high-energy beam or increase the scanning rate to reduce energy input and avoid excessive melting of the material. In addition, if it is found that the shape of the molten pool does not meet expectations, the system can optimize the shape of the molten pool by adjusting the scanning path or focal position, thereby improving the structural integrity of the final component.

[0091] By combining the above two adjustment methods, the additive manufacturing method of the present application can achieve structural and performance compensation in the manufacturing process. Even under complex manufacturing conditions, the system can ensure that the final manufactured components meet the target requirements in terms of structure and performance through real-time monitoring and feedback adjustment. For example, for certain key parts, the system can adjust the scanning path and power distribution based on real-time data to achieve uniform distribution of stress and reduce cracks or deformation caused by local stress concentration. At the same time, by adjusting the focus position and track spacing, the cooling rate and solidification process of the material can be further optimized, thereby improving the microstructure of the material and improving the mechanical properties of the component.

[0092] <Acquisition of molten pool parameters and / or structural parameters & Generation of the structure of the second 3D model and / or second process parameters>

[0093] In some embodiments, the molten pool parameters and / or structural parameters are obtained by real-time monitoring or process simulation during the manufacturing process. On the one hand, real-time monitoring can include high-resolution thermal imaging, laser scattering analysis, X-ray detection and other technologies to capture the dynamic behavior of the molten pool under the action of a high-energy beam, such as the size, shape, temperature distribution and solidification rate of the molten pool. On the other hand, structural parameters can be obtained by simulation prediction or direct detection. Process simulation tools (such as finite element analysis) can predict structural characteristics such as density, strength, porosity and stress distribution of components based on information such as the temperature gradient and solidification rate of the molten pool. Real-time detection technology (such as micro-CT scanning and strain sensors) can directly measure the structural parameters of components.

[0094] (Using the DQN model to generate the structure and / or second process parameters of the second 3D model)

[0095] Figure 8 The schematic diagram of the process of using the DQN model to generate the structure of the second 3D model and / or the second process parameters in this application is shown. Figure 8 In some embodiments, generating the structure and / or the second process parameters of the second 3D model using the DQN model may be achieved by following steps 31-35:

[0096] 31. Input the melt pool parameters and / or structure parameters into the DQN model.

[0097] 32. Define the action space of the DQN model as operations for adjusting the structure of the second 3D model and / or the second process parameters.

[0098] Specifically, the action space can include modifications to the geometry of the 3D model, replanning of the scanning path, adjustment of the high-energy beam power, changes in the scanning rate, adjustment of the focus position, etc. These actions represent different adjustment strategies, each of which may have a different impact on the performance of the final component. By defining the action space, the DQN model is able to select and execute the optimal operation to achieve optimization of the manufacturing process.

[0099] 33. A reward function is set according to the influence of the adjusted structure of the second 3D model and / or the second process parameters on the component performance, wherein the reward function is evaluated according to the structural parameters.

[0100] In order to evaluate the effects of different adjustment strategies, a reward function is set. The reward function evaluates the impact of the structure of the adjusted second 3D model and / or the second process parameters on the performance of the component. Specifically, the reward function will give different reward values ​​according to the changes in structural parameters (such as the strength, density, porosity, etc. of the component). If a certain adjustment strategy improves the performance of the component (such as increasing the strength or reducing the porosity), then the strategy will receive a higher reward value. Conversely, if the adjustment strategy leads to a decrease in performance, the reward value will be reduced. Through this mechanism, the DQN model can identify which adjustment strategies are beneficial, thereby guiding the subsequent optimization direction.

[0101] 34. Using deep neural network to approximate Q value function Q(S t , a t ; θ) represents the specific state S t Perform an action a t The expected cumulative reward that can be obtained in the future.

[0102] Specifically, the DQN model (Deep Q Network) uses a deep neural network (DNN) to approximate the Q-value function Q(S t , a t ; θ). The Q-value function is used to evaluate the t Perform an action a t After that, the expected cumulative reward can be obtained. Through the training of deep neural networks, the model can estimate the cumulative reward value brought by performing different actions in different states. The approximation of the Q-value function can help the DQN model find the optimal adjustment strategy in a complex state space, thereby achieving higher performance optimization in the manufacturing process.

[0103] 35. Use the DQN model to repeatedly train the adjusted second 3D model structure and / or second process parameters, and use the feedback reward value to continuously update the model parameters until the optimal second 3D model structure and / or second process parameters 144 are obtained.

[0104] Finally, the DQN model continuously optimizes the structure of the second 3D model and / or the second process parameters through repeated training. In each training, the model updates its own parameters based on the feedback (i.e., reward value) after the previous adjustment. Through the continuous iterative process, the DQN model can gradually approach the optimal adjustment strategy. Finally, after a large number of training and feedback cycles, the DQN model can generate the optimal second 3D model structure and / or second process parameters.

[0105] Through the above steps 31-35, the DQN model can effectively realize the dynamic optimization of key parameters in the additive manufacturing process, thereby improving the comprehensive performance of the component.

[0106] (Using finite element simulation to generate the structure of the second 3D model and / or the second process parameters)

[0107] Fig. 9 The schematic diagram of the process of generating the structure and / or the second process parameters of the second 3D model by finite element simulation in the present application is shown. Fig. 9 In some embodiments, generating the structure and / or the second process parameters of the second 3D model using the DQN model may be achieved by following steps 41-44:

[0108] 41. Establish a finite element model based on the first 3D model to simulate the thermodynamic behavior and structural response of the component during the additive manufacturing process.

[0109] First, based on the first 3D model, a finite element model (FEM) is established to simulate the thermodynamic behavior and structural response of the component during the additive manufacturing process. The establishment of this finite element model requires a detailed description of the component's geometry, material properties, and process parameters (such as laser power, scanning speed, etc.) during the manufacturing process. Through the finite element model, the temperature distribution, stress distribution, and deformation that may occur in the component during the manufacturing process can be accurately simulated.

[0110] 42. Input the melt pool parameters and / or structural parameters into the finite element model to simulate the structural response of the component.

[0111] After the finite element model is established, the melt pool parameters and / or structural parameters obtained through real-time monitoring or process simulation are input into the finite element model. The melt pool parameters include the size, shape, temperature gradient, solidification rate, etc. of the melt pool, while the structural parameters may include the density, strength, porosity, roughness and stress distribution of the component. Through the input of these parameters, the finite element model can simulate and analyze the thermodynamic behavior and structural response of the component during the additive manufacturing process. This simulation analysis can predict the thermal deformation, residual stress, cold cracks and other problems that may occur in the component during the manufacturing process.

[0112] 43. Based on the simulation analysis results, identify areas that cause structural and / or performance defects.

[0113] Through simulation analysis of the finite element model, it is possible to identify areas in the manufacturing process that may lead to structural and / or performance defects. These defective areas, such as local stress concentration, excessive temperature gradients, non-uniform solidification of materials, etc., will have an adverse effect on the final quality of the component.

[0114] 44. Adjust the structure of the second 3D model and / or the second process parameters according to the identified defective area to generate an optimized second 3D model structure and / or second process parameters.

[0115] Finally, the structure of the second 3D model and / or the second process parameters are adjusted according to the defective area identified in step 43. The specific adjustment methods may include optimizing the scanning path, adjusting the laser power, changing the scanning speed, redefining the focus position, etc., to improve the thermodynamic and mechanical response of the defective area. Through these adjustments, an optimized second 3D model structure and / or second process parameters can be generated. The optimization process is intended to reduce or eliminate defects in the manufacturing process and improve the overall performance of the component.

[0116] Through the above steps 41-44, the finite element simulation technology can be used to accurately predict and optimize key parameters in the additive manufacturing process, so as to identify problems that may occur in the manufacturing process in advance, and by adjusting the 3D model and process parameters, ensure that the final manufactured component has excellent structural integrity and mechanical properties.

[0117] <Additive manufacturing equipment>

[0118] It should be understood that the various processes and steps described above can be implemented by configuring the functional modules of the additive manufacturing device. Although the specific content of the method has been described in detail, in order to more clearly understand the core part of the additive manufacturing device involved in this application, the following is combined with Fig.10 A brief introduction to its main components is given. Fig.10 A schematic framework diagram of a multi-model driven powder bed additive manufacturing device 50 of the present application is shown.

[0119] In some embodiments, the additive manufacturing device 50 involved in the present application includes a first model building module 51 , a second model building module 52 and an additive manufacturing module 53 .

[0120] The first model building module 51 is configured to build at least one first 3D model and apply the first process parameter 143 to the at least one first 3D model.

[0121] The second model building module 52 is configured to build at least one second 3D model overlapping the at least one first 3D model, and apply second process parameters different from the first process parameters to the at least one second 3D model.

[0122] Among them, the additive manufacturing module 53 is configured to use the at least one first 3D model and the at least one second 3D model to drive at least one high-energy beam, so that it performs a first scanning process corresponding to the first process parameters and a second scanning process corresponding to the second process parameters on the powder bed to manufacture components layer by layer.

[0123] <Electronic equipment>

[0124] Fig.11 The schematic diagram of the electronic device of the present application is shown. Fig.11 Some embodiments of the present application also relate to an electronic device 60, which is used to execute the various processes or steps of the additive manufacturing method described above. The core components of the electronic device 60 include at least one processor 61 and at least one memory 62. The processor 61 is the computing core of the device, which is used to execute various operations and instructions, while the memory 62 is used to store these instructions and related data, and is directly coupled to the processor 61 for efficient data transmission and processing. The memory 62 may include different types of storage media, such as random access memory (RAM), read-only memory (ROM), flash memory or other non-volatile memory. When the electronic device 60 is in working state, the instructions stored in the memory 62 will be read and executed by the processor 61. Specifically, the processor 61 can be programmed to perform a series of complex operations according to the process described in any embodiment of the additive manufacturing method, such as establishing multiple 3D models, setting different process parameters, controlling the scanning path and power of the high-energy beam, etc. These operations will ultimately guide the device to perform all steps of powder bed additive manufacturing and achieve precise hierarchical component manufacturing.

[0125] In addition, the electronic device 60 may include multiple processors 61, which can work in parallel to improve the speed and efficiency of task execution. At the same time, the capacity and type of the memory 62 can be configured according to actual needs to store a large amount of process parameters, 3D model data and real-time monitoring information. These data can be quickly accessed and processed to optimize the additive manufacturing process. Through the organic combination of hardware and software, the electronic device 60 can efficiently perform various complex operations of additive manufacturing and support the entire process from model building to final component forming. Its design not only improves the degree of automation of the manufacturing process, but also provides a strong technical guarantee for realizing additive manufacturing applications with higher precision and complexity.

[0126] It should be understood that the control process of the above method / process in the present application can be implemented in whole or in part through software, hardware, firmware or any other combination.

[0127] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application. The specification and embodiments are to be regarded as exemplary only, and the present application is not limited to the precise structures described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof.

Claims

1. A multi-model driven powder bed additive manufacturing method, characterized in that: include: Creating at least one first 3D model, applying first process parameters to the at least one first 3D model; Establishing at least one second 3D model partially overlapping with the at least one first 3D model, applying second process parameters different from the first process parameters to the at least one second 3D model; the at least one second 3D model is composed of a plurality of different sub-models dispersed in different areas of the at least one first 3D model, so as to apply different second process parameters at different positions of the at least one second 3D model; the second 3D model includes one or more vector line models representing a scanning path or an energy concentration area; and The at least one first 3D model and the at least one second 3D model are respectively used to drive at least one high-energy beam to perform a first scanning process corresponding to the first process parameters and a second scanning process corresponding to the second process parameters on a powder bed to manufacture components layer by layer.

2. The additive manufacturing method according to claim 1, characterized in that: The process of establishing the at least one first 3D model and the at least one second 3D model includes: Provide geometric data of components; generating at least one first 3D model consistent with the size and shape of the component using the geometric data, and applying the first process parameters indicative of overall structure fabrication to the at least one first 3D model; At least one second 3D model having the same size and shape is generated at a corresponding position partially overlapping with the at least one first 3D model according to the geometric data of the at least one first 3D model, and the second process parameters indicating the manufacturing of the overlapping structure are applied to the at least one second 3D model.

3. The additive manufacturing method according to claim 1, characterized in that: The first process parameter and the second process parameter include at least one of a scanning rate, a scanning path, a scanning power, a spot diameter, a focal position, a track spacing and a layer thickness.

4. The additive manufacturing method according to claim 1, characterized in that: The first scanning process and the second scanning process are performed simultaneously or at intervals at the same position of the powder bed.

5. The additive manufacturing method according to claim 4, characterized in that: When the first scanning process and the second scanning process are performed at intervals at the same position of the powder bed, The first scanning process is used to sinter or melt the powder to form a crystal structure, and the second scanning process is used to melt the crystal structure for a second time; or, the first scanning process is used to break up the powder, and the second scanning process is used to sinter or melt the broken up powder to form a crystal structure.

6. The additive manufacturing method according to claim 4, characterized in that: The at least one first 3D model and the at least one second 3D model correspond to different material components respectively, and when the first scanning process and the second scanning process are performed at intervals at the same position of the powder bed, the method further includes: applying a powder layer of a first material on the powder bed, and using the at least one first 3D model to drive the at least one high-energy beam to perform a first scanning process corresponding to the first process parameter on the powder layer; A group of nanoparticles of a second material is added to at least a portion of the powder layer, and the at least one second 3D model is used to drive the at least one high-energy beam to perform a second scanning process corresponding to the second process parameters on the group of nanoparticles.

7. The additive manufacturing method according to claim 1, 3 or 5, characterized in that: The second scanning process has a higher scanning power and / or scanning rate relative to the first scanning process.

8. The additive manufacturing method according to claim 1, characterized in that: The method further comprises: Acquire molten pool parameters and / or structural parameters during the manufacturing process, wherein the molten pool parameters include one or more of the size, shape, temperature gradient and solidification rate of the molten pool, and the structural parameters include one or more of the density, strength, porosity, roughness and stress distribution of the component; The structure of the at least one second 3D model and / or the second process parameter are automatically adjusted according to the molten pool parameters and / or structural parameters to achieve compensation of the structure and / or performance.

9. The additive manufacturing method according to claim 8, characterized in that: The molten pool parameters and / or structural parameters are obtained by real-time monitoring or process simulation during the manufacturing process.

10. The additive manufacturing method according to claim 8 or 9, characterized in that: The structure of the at least one second 3D model and / or the second process parameter are generated using the DQN model, specifically including: Inputting the melt pool parameters and / or structure parameters into the DQN model; defining an action space of the DQN model as operations for adjusting a structure and / or a second process parameter of the at least one second 3D model; Setting a reward function according to the adjusted structure of the at least one second 3D model and / or the influence of the second process parameter on the component performance, wherein the reward function is evaluated according to the structural parameters; Using deep neural network to approximate the Q value function Q(S t , a t ; θ) represents the specific state S t Perform an action a t The expected cumulative rewards that can be obtained after The DQN model is used to repeatedly train the adjusted structure of the at least one second 3D model and / or the second process parameters, and the model parameters are continuously updated using the feedback reward value until the optimal at least one second 3D model structure and / or second process parameters are obtained.

11. The additive manufacturing method according to claim 8 or 9, characterized in that: The structure of the at least one second 3D model and / or the second process parameter are generated by finite element simulation, specifically including: Establishing a finite element model based on the at least one first 3D model to simulate the thermodynamic behavior and structural response of the component during the additive manufacturing process; Inputting the molten pool parameters and / or structural parameters into the finite element model to simulate and analyze the structural response of the component; Identifying areas causing structural and / or performance defects based on the simulation analysis results; and According to the identified defective area, the structure of the at least one second 3D model and / or the second process parameters are adjusted to generate an optimized structure of the at least one second 3D model and / or the second process parameters.

12. A multi-model driven powder bed additive manufacturing device for executing the method according to any one of claims 1 to 11, characterized in that: include: A first model building module configured to build at least one first 3D model and apply a first process parameter to the at least one first 3D model; A second model building module is configured to build at least one second 3D model partially overlapping the at least one first 3D model, and apply a second process parameter different from the first process parameter to the at least one second 3D model; as well as The additive manufacturing module is configured to use the at least one first 3D model and the at least one second 3D model to drive at least one high-energy beam, so that it performs a first scanning process corresponding to the first process parameter and a second scanning process corresponding to the second process parameter on the powder bed to manufacture the component layer by layer.

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 used to store instructions executed by the at least one processor, and when the instructions are executed by the at least one processor, the electronic device performs the additive manufacturing method according to any one of claims 1 to 11.

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