3D printing filler state prediction method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a 3D printing filler state prediction method and device, equipment and a storage medium, and the method comprises the steps: building a CFD model for 3D printing, and carrying out the grid division of a fluid domain in the CFD model; calculating the fluid domain after grid division based on a fluid volume method to obtain a continuous phase flow field, and simulating the motion trail of filler particles in the continuous phase flow field based on a discrete phase model; extracting a velocity gradient of the continuous phase flow field at a current position corresponding to the motion trail, and determining a vorticity tensor and a deformation tensor according to the velocity gradient; inputting the current orientation vector, the vorticity tensor and the deformation tensor of the filler particles into an orientation trajectory function for operation, and determining a target orientation change rate of the filler particles; and adjusting the current orientation vector according to a product of the target orientation change rate and a preset unit time length to obtain a target orientation vector of the filler particles. According to the embodiment of the invention, the orientation behavior of the filler particles in the fused deposition modeling process can be accurately predicted.

Description

Technical Field

[0001] The present application relates to, but is not limited to, the field of 3D printing technology, and particularly relates to a method, device, equipment and storage medium for predicting the state of 3D printing fillers. Background Art

[0002] With the development of electronic devices towards high power density, the heat dissipation problem has become a key bottleneck restricting the performance and reliability of devices. Traditional polymer-based composite materials are limited by the randomly distributed filler structure and are difficult to form a continuous heat conduction path, which restricts the improvement of heat conduction performance. Through the shear orientation effect of fused deposition modeling, 3D printing technology can achieve the oriented arrangement of fillers in the matrix, which helps to construct a continuous heat conduction path, thereby improving the heat conduction performance of composite materials.

[0003] Currently, through the simulation of the 3D printing process, technical support can be provided for process parameter optimization and equipment design. However, traditional simulation methods cannot accurately predict the orientation behavior of filler particles, resulting in low simulation accuracy. Summary of the Invention

[0004] The following is an overview of the subject matter described in detail in this article. This overview is not intended to limit the scope of protection of the claims.

[0005] Embodiments of the present application provide a method, device, equipment and storage medium for predicting the state of 3D printing fillers, which can accurately predict the orientation behavior of filler particles during the fused deposition modeling process, thereby improving the simulation accuracy.

[0006] To achieve the above object, a first aspect of the embodiments of the present application proposes a method for predicting the state of 3D printing fillers, including: establishing a CFD model for 3D printing, and performing mesh division on the fluid domain in the CFD model; performing operations on the meshed fluid domain based on the volume of fluid method to obtain a continuous phase flow field, and simulating the movement trajectory of filler particles in the continuous phase flow field based on the discrete phase model; extracting the velocity gradient of the continuous phase flow field at the current position corresponding to the movement trajectory, and determining the vorticity tensor and the deformation tensor according to the velocity gradient; obtaining the current orientation vector of the filler particle, inputting the current orientation vector, the vorticity tensor and the deformation tensor into an orientation trajectory function for operation, and determining the target orientation change rate of the filler particle; adjusting the current orientation vector according to the product of the target orientation change rate and a preset unit time length to obtain the target orientation vector of the filler particle.

[0007] In some embodiments, the orientation trajectory function includes a rotation term, a deformation term, and a correction term. The operation of inputting the current orientation vector, the vorticity tensor, and the deformation tensor into the orientation trajectory function to determine the target orientation change rate of the filler particles includes: determining the rotation term according to the product of the vorticity tensor and the current orientation vector; determining the deformation term according to the product of the deformation tensor and the current orientation vector; determining the correction term according to the deformation tensor and the current orientation vector; obtaining the shape weight associated with the filler particles, and weighting the subtraction result of the deformation term and the correction term according to the shape weight; determining the target orientation change rate of the filler particles according to the addition result of the weighted result and the rotation term.

[0008] In some embodiments, the determining the correction term according to the deformation tensor and the current orientation vector includes: performing an outer product of the current orientation vector and the current orientation vector to obtain a direction tensor; multiplying the deformation tensor and the direction tensor element by element and then summing to obtain an orientation stretch ratio; performing a dot product of the orientation stretch ratio and the current orientation vector to determine the correction term.

[0009] In some embodiments, the shape of the filler particles is sheet-like. The obtaining the shape weight associated with the filler particles includes: obtaining the particle thickness-to-diameter ratio of the filler particles; determining a first parameter term according to the square of the particle thickness-to-diameter ratio; determining a second parameter term according to the subtraction result of the first parameter term and a first preset value; determining a third parameter term according to the addition result of the first parameter term and a second preset value; determining the shape weight according to the ratio of the second parameter term to the third parameter term.

[0010] In some embodiments, the shape of the filler particles is sheet-like. The obtaining the current orientation vector of the filler particles includes: constructing a Cartesian coordinate system, where the Cartesian coordinate system includes an X-axis, a Y-axis, and a Z-axis; in the Cartesian coordinate system, determining a first angle between the through-plane axis direction of the filler particles and the Y-axis, and determining a second angle between the projection of the through-plane axis direction on a reference plane and the Z-axis, where the reference plane is the plane determined by the X-axis and the Z-axis; determining a first direction component along the X-axis according to the product of the sine value of the first angle and the sine value of the second angle; determining a second direction component along the Y-axis according to the cosine value of the first angle; determining a third direction component along the Z-axis according to the product of the sine value of the first angle and the cosine value of the second angle; determining the current orientation vector of the filler particles according to the first direction component, the second direction component, and the third direction component.

[0011] In some embodiments, the 3D printing filler state prediction method further includes: obtaining a target direction component of the target orientation vector along the Y-axis direction; determining a target orientation degree of the filler particles according to the absolute value of the target direction component; generating an orientation degree cloud map according to the target orientation degree, and displaying the orientation degree cloud map.

[0012] In some embodiments, the 3D printing nozzle diameter parameter of the CFD model is a first reference diameter, and the target orientation degree is used as a first reference orientation degree. The 3D printing filler state prediction method further includes: obtaining a second reference diameter, where the second reference diameter is different from the first reference diameter; adjusting the 3D printing nozzle diameter parameter in the CFD model according to the second reference diameter; based on the adjusted CFD model, determining the target orientation degree again, and using the determined target orientation degree again as a second reference orientation degree; determining a first difference between the first reference orientation degree and a preset orientation degree threshold, determining a second difference between the second reference orientation degree and the orientation degree threshold, inputting the first difference and the second difference into a weight prediction model for prediction, and determining an adjustment weight; performing a weighted sum of the first reference diameter and the second reference diameter according to the adjustment weight to obtain a target diameter; and adjusting the 3D printing nozzle diameter parameter in the CFD model according to the target diameter.

[0013] To achieve the above object, a second aspect of the embodiments of the present application provides a 3D printing filler state prediction device, including: a building module, configured to build a CFD model for 3D printing and perform mesh division on a fluid domain in the CFD model; a first processing module, configured to perform operations on the meshed fluid domain based on the volume of fluid method to obtain a continuous phase flow field, and simulate the movement trajectory of filler particles in the continuous phase flow field based on the discrete phase model; a second processing module, configured to extract a velocity gradient of the continuous phase flow field at a current position corresponding to the movement trajectory, and determine a vorticity tensor and a deformation tensor according to the velocity gradient; a third processing module, configured to obtain a current orientation vector of the filler particles, input the current orientation vector, the vorticity tensor, and the deformation tensor into an orientation trajectory function for operation, and determine a target orientation change rate of the filler particles; and a fourth processing module, configured to adjust the current orientation vector according to a product of the target orientation change rate and a preset unit time length to obtain a target orientation vector of the filler particles.

[0014] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, where the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the 3D printing filler state prediction method described in the first aspect above is implemented.

[0015] To achieve the above object, a fourth aspect of the embodiments of the present application proposes a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the 3D printing filler state prediction method described in the first aspect above.

[0016] The embodiments of the present application at least include the following beneficial effects: By establishing a CFD model for 3D printing, meshing the fluid domain in the CFD model, then performing operations on the meshed fluid domain based on the volume of fluid method to obtain a continuous phase flow field, and then simulating the movement trajectory of filler particles in the continuous phase flow field based on the discrete phase model; then extracting the velocity gradient of the continuous phase flow field at the current position corresponding to the movement trajectory, and determining the vorticity tensor and the deformation tensor according to the velocity gradient; then obtaining the current orientation vector of the filler particle, inputting the current orientation vector, the vorticity tensor, and the deformation tensor into the orientation trajectory function for operation to determine the target orientation change rate of the filler particle, and adjusting the current orientation vector according to the product of the target orientation change rate and the preset unit time length to obtain the target orientation vector of the filler particle, which can not only accurately simulate the process of 3D printing composite material extrusion and deposition, but also accurately predict the orientation behavior of filler particles during the fused deposition modeling process, thereby improving the simulation accuracy.

[0017] Other features and advantages of the present application will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the present application. The objectives and other advantages of the present application can be achieved and obtained by the structures specifically pointed out in the specification, claims, and drawings. Description of the Drawings

[0018] The drawings are used to provide a further understanding of the technical solutions of the present application, and constitute a part of the specification. They are used together with the embodiments of the present application to explain the technical solutions of the present application, and do not constitute a limitation to the technical solutions of the present application.

[0019] Figure 1 It is an optional flow schematic diagram of the 3D printing filler state prediction method provided by the embodiments of the present application;

[0020] Figure 2 It is an optional structural schematic diagram of the CFD model provided by the embodiments of the present application;

[0021] Figure 3 is Figure 2 a partial enlarged view of position A in

[0022] Figure 4It is a schematic diagram of an optional specific process for determining the target orientation change rate provided by an embodiment of the present application;

[0023] Figure 5 It is a schematic diagram of an optional process for determining a correction term provided by an embodiment of the present application;

[0024] Figure 6 It is a schematic diagram of an optional process for determining the shape weight provided by an embodiment of the present application;

[0025] Figure 7 It is a schematic diagram of an optional process for determining the current orientation vector provided by an embodiment of the present application;

[0026] Figure 8 It is a schematic diagram of an optional orientation of filler particles provided by an embodiment of the present application;

[0027] Figure 9 It is a schematic diagram of an optional process for displaying an orientation degree cloud map provided by an embodiment of the present application;

[0028] Figure 10 It is a schematic diagram of the first orientation degree cloud map provided by an embodiment of the present application;

[0029] Figure 11 It is a schematic diagram of the second orientation degree cloud map provided by an embodiment of the present application;

[0030] Figure 12 It is a schematic diagram of the third orientation degree cloud map provided by an embodiment of the present application;

[0031] Figure 13 It is a schematic diagram of an optional process for adjusting the 3D printing nozzle diameter parameter provided by an embodiment of the present application;

[0032] Figure 14 It is a schematic diagram of an optional structure of a 3D printing filler state prediction device provided by an embodiment of the present application;

[0033] Figure 15 It is a schematic diagram of an optional hardware structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0034] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0035] It should be noted that in each specific embodiment of the present application, when it comes to performing relevant processing based on data related to the characteristics of the target object, such as target object attribute information or a set of attribute information, the permission or consent of the target object will be obtained first. Moreover, the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. Among them, the target object can be a user. In addition, when the embodiments of the present application need to obtain the target object attribute information, the separate permission or separate consent of the target object will be obtained by means of a pop-up window or jumping to a confirmation page. After clearly obtaining the separate permission or separate consent of the target object, the necessary target object-related data for the normal operation of the embodiments of the present application will be obtained.

[0036] In the description of the present application, the meaning of "several" is one or more, the meaning of "multiple" is more than two, and understandings such as "greater than", "less than", "exceeding", etc. do not include the present number, and understandings such as "above", "below", "within", etc. include the present number.

[0037] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the flowchart. Terms such as "first", "second", etc. in the specification, claims, or the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0038] Currently, by simulating the 3D printing process, it is possible to provide technical support for process parameter optimization and equipment design. However, traditional simulation methods cannot accurately predict the orientation behavior of filler particles, resulting in low simulation accuracy.

[0039] To address the problem of being unable to accurately predict the orientation of filler particles, the present application provides a 3D printing filler state prediction method, device, equipment, and storage medium, which can accurately predict the orientation behavior of filler particles during the fused deposition modeling process, thereby improving the simulation accuracy.

[0040] The 3D printing filler state prediction method, device, equipment, and storage medium provided by the embodiments of the present application are specifically described through the following embodiments. First, the 3D printing filler state prediction method in the embodiments of the present application is described.

[0041] The 3D printing filler state prediction method provided by the embodiments of the present application relates to the field of 3D printing technology. The 3D printing filler state prediction method provided by the embodiments of the present application can be applied to a terminal, or to a server side, or can also be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the 3D printing filler state prediction method, etc., but is not limited to the above forms.

[0042] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0043] The following further elaborates on the embodiments of the present application with reference to the accompanying drawings.

[0044] As Figure 1 shown, Figure 1 is an optional flowchart of the 3D printing filler state prediction method provided by the embodiments of the present application. The 3D printing filler state prediction method can be executed by a server, or can also be executed by a terminal, or can also be executed by a server in cooperation with a terminal. The 3D printing filler state prediction method includes but is not limited to the following steps S110 to step S150:

[0045] Step S110, establish a CFD model for 3D printing, and perform mesh division on the fluid domain in the CFD model.

[0046] Among them, the full name of CFD is Computational Fluid Dynamics. Therefore, the CFD model refers to the computational fluid dynamics model, and the CFD model is used to simulate the actual fluid flow situation.

[0047] Specifically, referring to Figure 2 and Figure 3 , Figure 2 is an optional structural schematic diagram of the CFD model provided by the embodiment of the present application. Figure 3 is Figure 2 the partial enlarged view of position A in

[0048] Among them, the CFD model includes four parts: the nozzle flow channel 201, the simplified nozzle outer wall 202, the air part 203, and the printing base plate 204. The length of the printing base plate 204 is L1, and L1 can be set to 12 mm. The length of the air part 203 is L2, and L2 can be set to 24 mm. The 3D printing nozzle is provided with a nozzle inlet 205 and a nozzle outlet 206. The diameter of the nozzle inlet 205 is D, and D can be set to 2 mm. The 3D printing nozzle includes a nozzle long pipe section, a nozzle converging section, and a nozzle short pipe section connected in sequence. The converging angle of the nozzle converging section is α, and the converging angle can be set to 135°. The length of the nozzle long pipe section is H1, and H1 can be set to 5 mm. The actual length of the nozzle long pipe section can be greater than 5 mm. For example, the actual length of the nozzle long pipe section is 11 mm. Since the flow in the nozzle long pipe section is a fully developed laminar flow, the flow state will not change due to the distance after the fluid enters the inlet for a certain distance. After obtaining the grid independence verification, the length of the nozzle long pipe section in the model is shortened to 5 mm to save computing resources. The length of the nozzle converging section is H2, and the length of the nozzle short pipe section is H3. Both H2 and H3 can be set to 0.8 mm. The distance between the nozzle outlet 206 and the printing base plate 204 is h, that is, the printing layer height is h. The diameter of the nozzle outlet 206 is d. h and d can be changed according to the actual layer height and nozzle diameter size.

[0049] Among them, the CFD model includes three calculation regions, which are the Fluid fluid domain where the nozzle flow channel 201 is located, the Air fluid domain where the air part 203 is located, and the Base solid domain where the printing base plate 204 is located. The Base solid domain is responsible for handling the movement of the dynamic mesh to simulate the process of the printing deposition line. The calculation of the flow field and the calculation of the filler particle orientation degree are concentrated in the Fluid fluid domain and the Air fluid domain.

[0050] It should be noted that hexahedral structured meshes can be used for the outer wall 202 of the nozzle and the printing base plate 204, tetrahedral meshes can be used for other regions, the global accuracy is 0.1 mm, 5 layers of boundary layers are set for the nozzle flow channel 201, the maximum thickness is 0.1 mm, and the total number of meshes is about 380,000. The dynamic mesh is enabled for the printing base plate 204, and the moving speed is synchronized with the printing speed to realize the dynamic simulation of the deposition process.

[0051] Step S120: Based on the volume of fluid method, perform operations on the fluid domain after mesh division to obtain the continuous phase flow field, and simulate the movement trajectory of the filler particles in the continuous phase flow field based on the discrete phase model.

[0052] Among them, the filler particles can be non-spherical particles. For example, the filler particles can specifically be flake particles.

[0053] It should be noted that performing operations on the fluid domain after mesh division based on the volume of fluid method can specifically refer to using Ansys Fluent for finite element calculation. The multiphase flow is calculated using the volume of fluid (VOF) method to obtain the continuous phase flow field. The first phase is set as air, and the second phase is the polymer matrix composite material. The viscosity of the composite material is set by establishing a power-law model based on experimental data.

[0054] It can be understood that the discrete phase model (DPM) is used to describe the movement of the filler particles. When the filler particles are non-spherical particles, the Haider&Levenspiel drag force model is used to calculate the drag force coefficient of the non-spherical particles to ensure correct force analysis of the non-spherical particles in the flow field. For two or more fillers, two or more injection sources can be established for processing.

[0055] Specifically, the following boundary conditions can be specified: the direction of gravitational acceleration is along the negative Y-axis, with a magnitude of 9.81 m / s 2 ; the melt material is incompressible, and the melt material is the manifestation of the 3D printed polymer matrix composite material in the molten state; the inlet is a velocity inlet, and the velocity magnitude can be calculated according to the continuity equation; the outlet is a pressure outlet, and backflow is not considered; the wall surface is set with a no-slip condition; the density and viscosity of the composite material are set in the parameter settings of the fluid material, and the material parameters of the filler particles are set in the parameter settings of the inert solid; in the DPM model, the filler particles are defaulted to rigid particles, the rotation of the particles themselves is considered, the interaction between the particles and the fluid is considered, the interaction between the particles is not considered, and the mass flow rate of the filler particles is calculated based on the nozzle diameter, the filler content, and the inlet velocity; under the initial conditions of multiphase flow, the second-phase polymer is at the nozzle inlet surface, with a volume fraction of 1, and the initial conditions of the remaining fluid domain are set as the first-phase air. The θ angle of the orientation of the flake particles is initialized in the UDF.

[0056] Step S130, extract the velocity gradient of the continuous phase flow field at the current position corresponding to the movement trajectory, and determine the vorticity tensor and the deformation tensor according to the velocity gradient.

[0057] Among them, the current position corresponding to the movement trajectory refers to the position where the filler particles are located at the current moment. When using the discrete phase model to simulate the movement trajectory of the filler particles in the continuous phase flow field, the filler particles have a definite position coordinate at each moment. Extracting the velocity gradient of the continuous phase flow field at the current position corresponding to the movement trajectory means determining the velocity gradient at the current position in the solved continuous phase flow field. This velocity gradient is a tensor composed of the partial derivatives of the three-dimensional velocity components with respect to the spatial coordinates. Since the velocity gradient can be solved and stored on each grid in the CFD solution, which is equivalent to storing the velocity gradient at each position, therefore, the stored velocity gradient can be retrieved through the current position to extract the velocity gradient, which can improve the processing speed.

[0058] It can be understood that the velocity gradient is used to characterize the spatial variation characteristics of the fluid velocity at the current position. The velocity gradient can be decomposed into a vorticity tensor used to characterize the rotation characteristics of the fluid and a strain rate tensor used to characterize the local shear and deformation behavior. Therefore, the vorticity tensor and the deformation tensor can be determined according to the velocity gradient.

[0059] Step S140, obtain the current orientation vector of the filler particles, input the current orientation vector, the vorticity tensor, and the deformation tensor into the orientation trajectory function for calculation, and determine the target orientation change rate of the filler particles.

[0060] It should be noted that the current orientation vector can also be called the direction vector. Each filler particle needs to be associated with an orientation vector. When predicting the state of the filler particles for the first time, the orientation vector needs to be set as the initialization vector, that is, the initialization vector is used as the current orientation vector of the filler particles at time step t0. Then, the target orientation vector of the filler particles at time step t1 can be determined through the current orientation vector of the filler particles at time step t0. Then, the target orientation vector of the filler particles at time step t1 can be used as the current orientation vector of the filler particles at time step t1, and the target orientation vector of the filler particles at time step t2 can be determined through the current orientation vector of the filler particles at time step t1. t2 is later than t1, and t1 is later than t0, so as to obtain the current orientation vectors of the filler particles at different time steps.

[0061] It can be understood that since the rotation, shear, and deformation behaviors in the flow field will cause changes in the orientation of the filler particles, inputting the current orientation vector, the vorticity tensor, and the deformation tensor into the orientation trajectory function for calculation can accurately determine the target orientation change rate of the filler particles.

[0062] Specifically, the orientation trajectory function can be constructed by writing a User Defined Function (UDF), which can improve the reliability and processing efficiency of the orientation trajectory function.

[0063] Step S150: Adjust the current orientation vector according to the product of the target orientation change rate and the preset unit time length to obtain the target orientation vector of the filler particles.

[0064] Among them, the unit time length can also be called the time step length. The unit time length is used to determine the time scale of the orientation change in each calculation period, usually denoted as Δt. The unit time length is preset. For example, the unit time length can be set to 0.01 seconds.

[0065] It can be understood that since the change amount of the orientation vector can be determined according to the product of the target orientation change rate and the preset unit time length, adjusting the current orientation vector according to the product of the target orientation change rate and the preset unit time length is equivalent to adjusting the current orientation vector according to the change amount. For example, summing the change amount and the current orientation vector can accurately obtain the target orientation vector of the filler particles.

[0066] Based on this, by establishing a CFD model for 3D printing, meshing the fluid domain in the CFD model, then performing operations on the meshed fluid domain based on the volume of fluid method to obtain a continuous phase flow field, and then simulating the movement trajectory of the filler particles in the continuous phase flow field based on the discrete phase model; then extracting the velocity gradient of the continuous phase flow field at the current position corresponding to the movement trajectory, determining the vorticity tensor and the deformation tensor according to the velocity gradient; then obtaining the current orientation vector of the filler particles, inputting the current orientation vector, the vorticity tensor, and the deformation tensor into the orientation trajectory function for operation to determine the target orientation change rate of the filler particles, and adjusting the current orientation vector according to the product of the target orientation change rate and the preset unit time length to obtain the target orientation vector of the filler particles, not only can accurately simulate the process of 3D printing composite material extrusion and deposition, but also can accurately predict the orientation behavior of the filler particles in the fused deposition modeling process, thereby improving the simulation accuracy.

[0067] In addition, referring to Figure 4 , in an embodiment, the orientation trajectory function includes a rotation term, a deformation term, and a correction term. Inputting the current orientation vector, the vorticity tensor, and the deformation tensor into the orientation trajectory function for operation to determine the target orientation change rate of the filler particles includes but is not limited to the following steps:

[0068] Step S410: Determine the rotation term according to the product of the vorticity tensor and the current orientation vector;

[0069] Step S420: Determine the deformation term based on the product of the deformation tensor and the current orientation vector.

[0070] Step S430: Determine the correction term based on the deformation tensor and the current orientation vector.

[0071] Step S440: Obtain the shape weight associated with the filler particles, and weight the subtraction result of the deformation term and the correction term according to the shape weight.

[0072] Step S450: Determine the target orientation change rate of the filler particles based on the addition result of the weighted result and the rotation term.

[0073] Among them, the rotation term is determined by the product of the vorticity tensor and the current orientation vector, so that the rotation term can represent the influence of the vorticity tensor on the orientation change of the filler particles. The deformation term is determined by the product of the deformation tensor and the current orientation vector, so that the deformation term can represent the influence of the deformation tensor on the orientation change of the filler particles. The correction term is determined based on the deformation tensor and the current orientation vector, so that the correction term can correct the influence of the deformation tensor on the orientation change of the filler particles. Then, by subtracting the deformation term from the correction term, the non-linear influence of the deformation tensor on the orientation change of the filler particles can be dynamically corrected, which is equivalent to correcting the deformation term. Weighting the subtraction result of the deformation term and the correction term according to the shape weight can dynamically adjust the corrected deformation term, so that the corrected deformation term can match the shape of the filler particles. Then, based on the addition result of the weighted result and the rotation term, the target orientation change rate of the filler particles is determined, which can improve the accuracy and reliability of the target orientation change rate.

[0074] In addition, referring to Figure 5 , in an embodiment, determining the correction term based on the deformation tensor and the current orientation vector includes, but is not limited to, the following steps:

[0075] Step S510: Perform an outer product of the current orientation vector with the current orientation vector to obtain a direction tensor.

[0076] Step S520: Multiply the deformation tensor and the direction tensor element by element and then sum to obtain the orientation stretch rate.

[0077] Step S530: Perform a dot product of the orientation stretch rate and the current orientation vector to determine the correction term.

[0078] It can be understood that the cross product of the current orientation vector and the current orientation vector is taken to obtain the orientation tensor, enabling the orientation tensor to characterize the distribution of the orientation strength in different directions. Then, the deformation tensor and the orientation tensor are multiplied element by element and summed to obtain the orientation stretch rate, enabling the orientation stretch rate to characterize the stretching degree along the current filler particle direction. Then, the orientation stretch rate and the current orientation vector are dotted to determine the correction term, enabling the correction term to represent the stretching projection of the deformation rate tensor in the current filler particle direction. By subtracting the deformation term from the correction term, the non-linear influence of the deformation tensor on the orientation change of the filler particles can be dynamically corrected, preventing orientation deviation and achieving self-regulation of orientation update.

[0079] Specifically, the calculation formula for the target orientation change rate is as follows:

[0080]

[0081] where P is the current orientation vector, t is time, represents the material derivative of P, specifically the target orientation change rate, ω is the vorticity tensor, ω·P is the rotation term, D is the deformation tensor, D·P is the deformation term, D:PPP is the correction term, and λ is the shape weight.

[0082] Then, the calculation formula for the correction term is as follows:

[0083]

[0084] where, refers to taking the cross product of the current orientation vector and the current orientation vector, refers to calculating the cross product, is the orientation tensor, refers to multiplying the deformation tensor and the orientation tensor element by element and then summing, is the orientation stretch rate, is the correction term;

[0085] For example, assume then:

[0086]

[0087] Therefore, the orientation tensor obtained by taking the cross product of the current orientation vector and the current orientation vector is a tensor, the orientation stretch rate obtained by multiplying the deformation tensor and the orientation tensor element by element and then summing is a scalar, and the correction term obtained by dotting the orientation stretch rate and the current orientation vector is a tensor.

[0088] Then, the calculation formulas for the vorticity tensor and the deformation rate tensor are as follows:

[0089]

[0090] wherein, is the velocity gradient, ω is the vorticity tensor, and D is the deformation tensor;

[0091] For example, assume Then the vorticity tensor and the deformation rate tensor can be determined respectively as:

[0092]

[0093] wherein, u, v, and w are the components of the velocity vector V in the three directions of the X-axis, Y-axis, and Z-axis respectively.

[0094] In addition, referring to Figure 6 , in an embodiment, the shape of the filler particles is sheet-like, and obtaining the shape weight associated with the filler particles includes, but is not limited to, the following steps:

[0095] Step S610, obtaining the particle thickness-diameter ratio of the filler particles;

[0096] Step S620, determining the first parameter term according to the square of the particle thickness-diameter ratio;

[0097] Step S630, determining the second parameter term according to the subtraction result of the first parameter term and the first preset value;

[0098] Step S640, determining the third parameter term according to the addition result of the first parameter term and the second preset value;

[0099] Step S650, determining the shape weight according to the ratio of the second parameter term to the third parameter term.

[0100] wherein, the shape of the filler particles is sheet-like, that is, the filler particles are sheet-like particles, the particle thickness-diameter ratio is the ratio between the particle diameter and the thickness of the filler particles, the particle thickness-diameter ratio is used to measure the flatness of the particle shape, and since the particle size of the filler particles can be pre-configured, the particle thickness-diameter ratio can be determined through the pre-configured particle size. For example, assume the particle diameter of the sheet-like filler particles is 40um and the thickness of the filler particles is 4um, then the thickness-diameter ratio can be determined to be 0.1.

[0101] It can be understood that by obtaining the particle thickness-diameter ratio of the filler particles, and then determining the first parameter term according to the square of the particle thickness-diameter ratio, the square operation can amplify the influence of the medium particle thickness-diameter ratio while suppressing the weight fluctuation of extreme values. Then, according to the subtraction result of the first parameter term and the first preset value, the second parameter term is determined, and according to the addition result of the first parameter term and the second preset value, the third parameter term is determined. Then, according to the ratio of the second parameter term to the third parameter term, the shape weight is determined. Both the first preset value and the second preset value can be 1. Since the particle thickness-diameter ratio is usually greater than 0 and less than 1, the value range of the shape weight is (-1, 0), which is equivalent to mapping the particle thickness-diameter ratio to the interval of (-1, 0), which can improve the smooth controllability of the shape weight, and further improve the accuracy and reliability of the target orientation change rate.

[0102] Specifically, the calculation formula of the shape weight is as follows:

[0103]

[0104] Where λ is the shape weight, a r is the particle thickness-diameter ratio, a r 2 is the first parameter term, a r 2 -1 is the second parameter term, a r 2 +1 is the third parameter term.

[0105] In addition, referring to Figure 7 , in an embodiment, the shape of the filler particles is sheet-like. Obtaining the current orientation vector of the filler particles includes, but is not limited to, the following steps:

[0106] Step S710: Construct a Cartesian coordinate system, where the Cartesian coordinate system includes the X-axis, Y-axis, and Z-axis;

[0107] Step S720: In the Cartesian coordinate system, determine the first angle between the through-plane axis direction of the filler particle and the Y-axis, and determine the second angle between the projection of the through-plane axis direction on the reference plane and the Z-axis;

[0108] Step S730: Determine the first direction component along the X-axis according to the product of the sine value of the first angle and the sine value of the second angle;

[0109] Step S740: Determine the second direction component along the Y-axis according to the cosine value of the first angle;

[0110] Step S750: Determine the third direction component along the Z-axis according to the product of the sine value of the first angle and the cosine value of the second angle;

[0111] Step S760: Determine the current orientation vector of the filler particles based on the first direction component, the second direction component, and the third direction component.

[0112] Among them, the shape of the filler particles is sheet-like, that is, the filler particles are sheet particles. The main plane of the filler particles is the plane with the largest surface area, and the through-plane axis direction of the filler particles is the direction perpendicular to the main plane.

[0113] Among them, the X-axis direction, the Y-axis direction, and the Z-axis direction are perpendicular to each other pairwise. The reference plane refers to the plane jointly determined by the X-axis and the Z-axis, that is, the reference plane is the XZ plane.

[0114] It can be understood that by constructing a three-dimensional Cartesian coordinate system, then in the Cartesian coordinate system, determine the first angle between the through-plane axis direction of the filler particles and the Y-axis, and determine the second angle between the projection of the through-plane axis direction on the reference plane and the Z-axis. Then, according to the product of the sine value of the first angle and the sine value of the second angle, determine the first direction component along the X-axis direction. According to the cosine value of the first angle, determine the second direction component along the Y-axis direction. According to the product of the sine value of the first angle and the cosine value of the second angle, determine the third direction component along the Z-axis direction. According to the first direction component, the second direction component, and the third direction component, determine the current orientation vector of the filler particles, so as to accurately represent the current orientation vector of the filler particles in the Cartesian coordinate system.

[0115] Specifically, referring to Figure 8 , Figure 8 which is an optional orientation schematic diagram of the filler particles provided by the embodiment of the present application, the calculation formula of the current orientation vector is as follows:

[0116]

[0117] Among them, P is the current orientation vector, p1 is the first direction component, p2 is the second direction component, p3 is the third direction component, θ is the first angle, Φ is the second angle, sinθ is the sine value of the first angle, sinΦ is the sine value of the second angle, cosθ is the cosine value of the first angle, and cosΦ is the cosine value of the second angle.

[0118] In addition, referring to Figure 9 , in an embodiment, the 3D printing filler state prediction method further includes but is not limited to the following steps:

[0119] Step S910: Obtain the target direction component of the target orientation vector along the Y-axis direction;

[0120] Step S920: Determine the target orientation degree of the filler particles according to the absolute value of the target direction component;

[0121] Step S930: Generate an orientation degree cloud map based on the target orientation degree and display the orientation degree cloud map.

[0122] It can be understood that taking the Y-axis direction as the reference direction, by obtaining the target direction component of the target orientation vector along the Y-axis direction, and then according to the absolute value of the target direction component, the target orientation degree of the filler particles can be determined. The target orientation degree can indicate the deviation degree of the orientation of the filler particles from the Y-axis direction. The lower the target orientation degree, the more deviated the orientation of the filler particles from the Y-axis direction, while the higher the target orientation degree, the more the orientation of the filler particles tends to align with the Y-axis direction. When the number of filler particles is multiple, the target orientation degrees of each filler particle can indicate the degree of alignment of the multiple filler particles in the Y-axis direction. Generate an orientation degree cloud map based on the target orientation degree, and then display the orientation degree cloud map, enabling relevant personnel to intuitively and quickly know the target orientation degree, which helps to improve the optimization efficiency and optimization effect of the parameters of the CFD model.

[0123] Specifically, assuming the target direction component is cosθ, then the target orientation degree a = |cosθ|. When a = 1, it means that the orientation of the filler particles is parallel to the Y-axis direction and perpendicular to the XZ plane, that is, the filler particles are arranged horizontally; when a = 0, it means that the orientation of the filler particles is perpendicular to the Y-axis direction, that is, the filler particles are arranged vertically.

[0124] Exemplarily, refer to Figures 10 to 12 , Figure 10 which is a schematic diagram of the first orientation degree cloud map provided by the embodiment of the present application. Figure 11 which is a schematic diagram of the second orientation degree cloud map provided by the embodiment of the present application. Figure 12 which is a schematic diagram of the third orientation degree cloud map provided by the embodiment of the present application.

[0125] Among them, the flaky filler particles are a composite material prepared by filling hexagonal boron nitride into thermoplastic polyurethane. The three orientation degree cloud maps are respectively the orientation degree cloud maps of the filler at the deposition line cross-section under different printing nozzle diameters. The first orientation degree cloud map is the orientation degree cloud map determined when the printing nozzle diameter is 0.4 mm, the second orientation degree cloud map is the orientation degree cloud map determined when the printing nozzle diameter is 0.6 mm, and the third orientation degree cloud map is the orientation degree cloud map determined when the printing nozzle diameter is 0.8 mm. The printing layer height is 0.2 mm for all. It can be seen that the larger the nozzle diameter, the lower the orientation degree. The left diagram in the figure is used to indicate the gray levels corresponding to multiple orientation degrees. The maximum orientation degrees of the filler at the deposition line cross-section printed by the 0.4 mm, 0.6 mm, and 0.8 mm nozzles are 0.756, 0.749, and 0.658 respectively. This is because the smaller the nozzle outlet size, the stronger the shear-induced alignment effect generated by the internal flow field of the nozzle. Therefore, it can effectively predict the orientation of flaky filler particles in the polymer matrix during the 3D printing process.

[0126] In addition, referring to Figure 13 , in one embodiment, the 3D printing nozzle diameter parameter of the CFD model is the first reference diameter, and the target orientation degree is used as the first reference orientation degree. The 3D printing filler state prediction method further includes but is not limited to the following steps:

[0127] Step S1310: Obtain a second reference diameter, where the second reference diameter is different from the first reference diameter;

[0128] Step S1320: Adjust the 3D printing nozzle diameter parameter in the CFD model according to the second reference diameter;

[0129] Step S1330: Based on the adjusted CFD model, determine the target orientation degree again, and use the determined target orientation degree again as the second reference orientation degree;

[0130] Step S1340: Determine a first difference between the first reference orientation degree and a preset orientation degree threshold, determine a second difference between the second reference orientation degree and the orientation degree threshold, input the first difference and the second difference into a weight prediction model for prediction, and determine an adjustment weight;

[0131] Step S1350: Perform a weighted sum of the first reference diameter and the second reference diameter according to the adjustment weight to obtain a target diameter;

[0132] Step S1360: Adjust the 3D printing nozzle diameter parameter in the CFD model according to the target diameter.

[0133] It can be understood that the first reference diameter and the second reference diameter are different 3D printing nozzle diameter parameters. During the 3D printing filler state prediction process, the CFD model can be configured with different 3D printing nozzle diameter parameters, and then the target orientation degrees corresponding to each reference diameter can be predicted respectively through different CFD models. For example, the target orientation degree corresponding to the first reference diameter is used as the first reference orientation degree, the target orientation degree corresponding to the second reference diameter is used as the second reference orientation degree, the first difference between the first reference orientation degree and the preset orientation degree threshold is determined, the second difference between the second reference orientation degree and the orientation degree threshold is determined, the first difference and the second difference are input into the weight prediction model for prediction to determine the adjustment weight, and then the first reference diameter and the second reference diameter are weighted and summed according to the adjustment weight to obtain the target diameter. Adjusting the 3D printing nozzle diameter parameter in the CFD model according to the target diameter can smoothly optimize the 3D printing nozzle diameter parameter and effectively reduce the difference between the target orientation degree and the orientation degree threshold.

[0134] In addition, referring to Figure 14, this application also provides a 3D printing filler state prediction device 1400, including:

[0135] A building module 1410, configured to build a CFD model for 3D printing and perform mesh division on the fluid domain in the CFD model;

[0136] A first processing module 1420, configured to perform operations on the meshed fluid domain based on the volume of fluid method to obtain a continuous phase flow field, and simulate the movement trajectory of filler particles in the continuous phase flow field based on the discrete phase model;

[0137] A second processing module 1430, configured to extract the velocity gradient of the continuous phase flow field at the current position corresponding to the movement trajectory, and determine the vorticity tensor and the deformation tensor according to the velocity gradient;

[0138] A third processing module 1440, configured to obtain the current orientation vector of the filler particles, input the current orientation vector, the vorticity tensor, and the deformation tensor into the orientation trajectory function for operations, and determine the target orientation change rate of the filler particles;

[0139] A fourth processing module 1450, configured to adjust the current orientation vector according to the product of the target orientation change rate and the preset unit time duration to obtain the target orientation vector of the filler particles.

[0140] It can be understood that the specific implementation manner of the 3D printing filler state prediction device 1400 is basically the same as the specific embodiments of the above 3D printing filler state prediction method, and will not be elaborated herein.

[0141] In addition, referring to Figure 15 , Figure 15 schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0142] A processor 1501, which can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of this application;

[0143] The memory 1502 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1502 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1502 and are called by the processor 1501 to execute the 3D printing filler state prediction method of the embodiments of this application;

[0144] The input / output interface 1503 is used to implement information input and output;

[0145] The communication interface 1504 is used to implement communication and interaction between this device and other devices. It can achieve communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0146] The bus 1505 transmits information between the various components of the device (such as the processor 1501, the memory 1502, the input / output interface 1503, and the communication interface 1504);

[0147] Among them, the processor 1501, the memory 1502, the input / output interface 1503, and the communication interface 1504 are communicatively connected to each other inside the device through the bus 1505.

[0148] The embodiments of this application also provide a storage medium. The storage medium is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned 3D printing filler state prediction method.

[0149] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely located relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0150] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. As can be known to those skilled in the art, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0151] Those skilled in the art can understand that Figures 1 to 13 the technical solutions shown in do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown, or combine certain steps, or different steps.

[0152] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations.

[0154] The terms "first", "second", "third", "fourth", etc. (if any) in the description of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0155] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (one) of the following" or its similar expressions refer to any combination of these items, including any combination of single items (one) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0156] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0157] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0159] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0160] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, and thus do not limit the scope of rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of rights of the embodiments of this application.

Claims

1. A method for predicting the state of 3D printing filler, characterized in that, Including: Establish a CFD model for 3D printing and perform mesh division on the fluid domain in the CFD model; Perform operations on the meshed fluid domain based on the volume of fluid method to obtain a continuous phase flow field, and simulate the movement trajectory of filler particles in the continuous phase flow field based on the discrete phase model; Extract the velocity gradient of the continuous phase flow field at the current position corresponding to the movement trajectory, and determine the vorticity tensor and the deformation tensor according to the velocity gradient; Obtain the current orientation vector of the filler particle, input the current orientation vector, the vorticity tensor, and the deformation tensor into the orientation trajectory function for operation, and determine the target orientation change rate of the filler particle; Adjust the current orientation vector according to the product of the target orientation change rate and the preset unit time length to obtain the target orientation vector of the filler particle.

2. The 3D printing filler state prediction method according to claim 1, characterized in that: The orientation trajectory function includes a rotation term, a deformation term, and a correction term. The step of inputting the current orientation vector, the vorticity tensor, and the deformation tensor into the orientation trajectory function for operation to determine the target orientation change rate of the filler particle includes: Determine the rotation term according to the product of the vorticity tensor and the current orientation vector; Determine the deformation term according to the product of the deformation tensor and the current orientation vector; Determine the correction term according to the deformation tensor and the current orientation vector; Obtain the shape weight associated with the filler particle, and weight the subtraction result of the deformation term and the correction term according to the shape weight; Determine the target orientation change rate of the filler particle according to the addition result of the weighted result and the rotation term.

3. The 3D printing filler state prediction method according to claim 2, characterized in that: The step of determining the correction term according to the deformation tensor and the current orientation vector includes: Perform an outer product of the current orientation vector and the current orientation vector to obtain a direction tensor; Multiply the deformation tensor and the direction tensor element by element and then sum to obtain an orientation stretch rate; Perform a dot product of the orientation stretch rate and the current orientation vector to determine the correction term.

4. The 3D printing filler state prediction method according to claim 2, characterized in that, The shape of the filler particle is sheet-like. The step of obtaining the shape weight associated with the filler particle includes: Obtain the particle thickness-to-diameter ratio of the filler particle; Determine a first parameter term according to the square of the particle thickness-to-diameter ratio; Determine a second parameter term according to the subtraction result of the first parameter term and a first preset value; Determine a third parameter term according to the addition result of the first parameter term and a second preset value; Determine the shape weight according to the ratio of the second parameter term to the third parameter term.

5. The 3D printing filler state prediction method according to claim 1, characterized in that The shape of the filler particle is sheet-like. The step of obtaining the current orientation vector of the filler particle includes: Construct a Cartesian coordinate system, where the Cartesian coordinate system includes an X-axis, a Y-axis, and a Z-axis; In the Cartesian coordinate system, determine a first angle between the through-plane axis direction of the filler particle and the Y-axis, and determine a second angle between the projection of the through-plane axis direction on a reference plane and the Z-axis, where the reference plane is the plane jointly determined by the X-axis and the Z-axis; determining a first direction component along the X-axis direction according to a product of the sine value of the first angle and the sine value of the second angle; Determining a second direction component along the Y-axis direction according to the cosine value of the first angle; determining a third direction component along the Z-axis direction according to a product of the sine value of the first angle and the cosine value of the second angle; A current orientation vector of the filler particle is determined based on the first direction component, the second direction component, and the third direction component.

6. The 3D printing filler state prediction method according to claim 5, characterized in that: The 3D printing filler state prediction method further includes: Obtaining a target direction component of the target orientation vector along the Y-axis direction; determining a target orientation degree of the filler particles according to the absolute value of the target direction component; An orientation cloud map is generated according to the target orientation, and the orientation cloud map is displayed.

7. The 3D printing filler state prediction method according to claim 6, characterized in that The 3D printing nozzle diameter parameter of the CFD model is a first reference diameter, the target orientation is used as the first reference orientation, and the 3D printing filler state prediction method further includes: obtaining a second reference diameter, wherein the second reference diameter is different from the first reference diameter; Adjusting a 3D printing nozzle diameter parameter in the CFD model according to the second reference diameter; re-determining a target orientation based on the adjusted CFD model, and using the re-determined target orientation as a second reference orientation; Determining a first difference between the first reference orientation degree and a preset orientation degree threshold, determining a second difference between the second reference orientation degree and the orientation degree threshold, inputting the first difference and the second difference into a weight prediction model for prediction, and determining an adjustment weight; performing a weighted summation of the first reference diameter and the second reference diameter according to the adjustment weight to obtain a target diameter; According to the target diameter, the 3D printing nozzle diameter parameters in the CFD model are adjusted.

8. A 3D printing filler state prediction device, characterized in that, include: An establishment module is used to establish a CFD model for 3D printing and to perform meshing on the fluid domain in the CFD model; A first processing module is configured to perform calculations on the meshed fluid domain based on a volume of fluid method to obtain a continuous phase flow field, and simulate the motion trajectory of filler particles in the continuous phase flow field based on a discrete phase model; a second processing module, configured to extract a velocity gradient of the continuous phase flow field at a current position corresponding to the motion trajectory, and determine a vorticity tensor and a deformation tensor according to the velocity gradient; a third processing module, configured to obtain a current orientation vector of the filler particle, input the current orientation vector, the vorticity tensor, and the deformation tensor into an orientation trajectory function for calculation, and determine a target orientation change rate of the filler particle; The fourth processing module is configured to adjust the current orientation vector according to the product of the target orientation change rate and a preset unit time length to obtain a target orientation vector of the filler particle.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the 3D printing filler state prediction method according to any one of claims 1 to 7 when executing the computer program.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the 3D printing filler state prediction method according to any one of claims 1 to 7 is implemented.

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