A multi-tone gray mask continuous forming method

Abstract

This application provides a multi-grayscale mask continuous molding method, comprising: slicing a 3D model into equal-thickness slices, then dividing them according to a projection device splicing mode; eliminating seams at the splicing points using an edge energy homogenization method; exposing the segmented unit mask images onto the resin surface in a combined splicing projection manner according to the curing sequence; controlling the ultraviolet light during the projection process using an optimal grayscale modulation formula during the curing process of each slice to complete single-layer curing. For resin slurries with good fluidity, the multi-grayscale mask continuous molding scheme of this application can complete the continuous molding of large-format 3D models with high quality, resulting in good surface quality and high molding accuracy. For ceramic slurries with poor fluidity, compared with the traditional CLIP-prepared 3D models, the model size is increased while maintaining the advantages of high molding accuracy and fast printing speed of CLIP, indicating that this scheme has high practical application value.

Description

Technical Field

[0001] This application relates to the field of 3D printing technology, and in particular to a method for continuous forming using multiple grayscale masks. Background Technology

[0002] In traditional photopolymer 3D printing systems, when liquid resin completely reflows into the forming platform, an imaging system projects a cross-sectional mask image of the 3D model onto the surface of the liquid resin with a specific ultraviolet light intensity, thus forming a thin slice of the target curing thickness. In the CLIP 3D printing system, the liquid resin reflow and photopolymerization processes occur simultaneously. As the forming platform moves continuously upwards along the Z-axis, the already cured portions of the printed part are continuously extracted from the liquid tank, building the 3D model layer by layer. During the continuous dynamic forming of large-format 3D models, due to the varying time it takes for the liquid resin to reflow from the projection area to the target curing location, excessive or insufficient ultraviolet exposure energy can lead to failure in the continuous forming of large-format CLIP-based models.

[0003] When using a lower UV exposure to ensure the liquid photosensitive resin flows quickly, the liquid resin at the target location may not receive enough exposure energy to undergo a complete curing reaction and will remain in a liquid state. When the UV exposure energy is too high, the outer liquid resin may undergo a complete curing reaction before reaching the target location, which can easily lead to hollow or bubble structures in the model and printing failure. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a multi-grayscale mask continuous molding method, which can specifically solve the existing problems. A multi-grayscale mask continuous molding scheme is proposed, which uses grayscale mask images to achieve controllable light intensity on the projection surface during photocuring, balances the relationship between the photocuring speed of liquid resin and the reflow filling speed, and realizes continuous molding of large-format three-dimensional models based on CLIP.

[0005] To achieve the above objectives, this application proposes a method for continuous forming of multiple grayscale masks, comprising:

[0006] The 3D model is sliced ​​into equal-thickness slices, and then the slices are cut according to the splicing mode of the projection device.

[0007] Use edge energy homogenization to eliminate seams at the splicing points;

[0008] According to the curing sequence, the segmented unit mask images are exposed to the resin surface by combining and splicing projection. During the curing process of each slice, the ultraviolet light during the projection process is controlled by the optimal grayscale modulation formula to complete the single-layer curing.

[0009] Furthermore, the method of eliminating seams at the splicing points using edge energy homogenization includes:

[0010] The sliced ​​mask image is segmented and pixel-filled according to the splicing mode of the projection device to obtain the unit mask image of each projection device.

[0011] A set of symmetrical grayscale virtual mask images is generated by non-linear decay, wherein the non-linear decay method is to construct the generation curve of the grayscale virtual mask image by combining the exponential decay function and the triangular decay function.

[0012] The grayscale virtual mask image is fused with the unit mask image according to the projection order.

[0013] Furthermore, the optimal grayscale modulation formula is as follows:

[0014] g(δ,t)=mδ+g init ,t∈[0,T]

[0015] Where δ is the dimensionless position of a unit amount of resin liquid at time t; g init is the initial value for grayscale modulation, m is a parameter used to adjust the ratio of the current position of the unit liquid to the center position of the molding platform, and T is the curing time of a single layer when using a single ultraviolet light.

[0016] Furthermore, the formula for calculating δ is as follows:

[0017] δ(t)=-0.0624g×t+0.0316g+0.4t+0.25

[0018] Where g is the grayscale value and t is the time.

[0019] Furthermore, the optimal grayscale modulation formula dynamically adjusts the ultraviolet light intensity on the current projection surface in units of time increment Δt, controlling the unit liquid resin entering the projection area at different time periods to receive different intensities of ultraviolet light exposure energy.

[0020] Furthermore, g is obtained through a grayscale modulation iterative optimization method. init The optimal initial value, and the grayscale modulation iterative optimization process are as follows:

[0021] Minimize the UV exposure energy E accumulated per unit unit of resin liquid as it flows to the center of the projection. sum The energy iteration between the difference between the UV exposure energy E required to generate a solid-state thin layer of the target thickness at the projection center and the energy required to achieve this.

[0022] By comparing E sum The difference between E and E determines whether the liquid photosensitive resin around the projected region δ will cure, and is based on E. sumThe comparison result with E increases or decreases g init The value of E when the total energy accumulated at the projection center. sum When the difference between g and E is less than a set threshold, g will be... init Set it as the optimal initial value for the grayscale modulation formula.

[0023] Furthermore, the number of iterations for the grayscale modulation depends on the energy E during the energy iteration. sum The comparison results with E.

[0024] Furthermore, the energy iteration threshold σ of the grayscale modulation iteration d The calculation formula is as follows:

[0025]

[0026] In summary, the advantages of this application and the user experience it brings are as follows: Experimental results show that for resin slurries with good fluidity, the multi-grayscale mask continuous molding scheme of this application can complete the continuous molding of large-format 3D models with high quality, good surface quality of the model entity, and high molding accuracy; for ceramic slurries with poor fluidity, the molding size is not as good as that of resin slurries, but the model size is improved compared with the 3D models prepared by traditional CLIP, while maintaining the advantages of high molding accuracy and fast printing speed of CLIP, indicating that this scheme has high practical application value. Attached Figure Description

[0027] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0028] Figure 1 This diagram illustrates the large-format forming process of the multi-grayscale mask in this application.

[0029] Figure 2 A schematic diagram illustrating the principle of multi-grayscale mask forming according to an embodiment of this application is shown.

[0030] Figure 3 A flowchart illustrating the grayscale modulation iterative optimization process according to an embodiment of this application is shown.

[0031] Figure 4 A schematic diagram showing the model size evaluation results according to an embodiment of this application is provided.

[0032] Figure 5 A schematic diagram showing the model quality evaluation results according to an embodiment of this application is provided.

[0033] Figure 6A schematic diagram showing the results of a reusability evaluation experiment according to an embodiment of this application.

[0034] Figure 7 A schematic diagram of the structure of an electronic device provided in one embodiment of this application is shown.

[0035] Figure 8 A schematic diagram of a storage medium provided in one embodiment of this application is shown. Detailed Implementation

[0036] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0037] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] The key reason for the difficulty in continuous molding of large-format models based on CLIP is that it is difficult to balance the relationship between the photocuring speed and the flow rate of liquid resin. This application will explore and analyze the molding method of large-format models based on CLIP from two aspects: the photocuring characteristics of liquid resin and the reflow filling speed of liquid resin during CLIP printing.

[0039] On the large-format 3D printing equipment with fixed splicing and forming as designed in this application, the large-format forming process of multiple grayscale masks is as follows: Figure 1 As shown.

[0040] After slicing the large-format 3D model into equal-thickness slices, it is segmented according to the splicing mode of the projection equipment, and the seam problem at the splicing point is eliminated using the edge energy homogenization method. Then, the PC program exposes the segmented unit mask images onto the resin surface in a combined splicing projection according to the curing sequence. During the curing process of each slice, the ultraviolet light during the projection process is controlled by the optimal grayscale modulation formula designed in the large-format forming scheme of multi-grayscale mask, and the single-layer curing work is completed. The curing time of each slice can be calculated by formula (1), where This is the curing time T for a single layer.

[0041]

[0042] t i Multiple exposure time periods are divided into the curing time of a single layer.

[0043] The method of eliminating seams at splicing points using edge energy homogenization includes:

[0044] The sliced ​​mask image is segmented and pixel-filled according to the splicing mode of the projection device to obtain the unit mask image of each projection device.

[0045] A set of symmetrical grayscale virtual mask images is generated by non-linear decay, wherein the non-linear decay method is to construct the generation curve of the grayscale virtual mask image by combining the exponential decay function and the triangular decay function.

[0046] To predict the dimensionless position δ(t) of a unit liquid resin exposed under different grayscale mask images at time t, the experimental data were fitted, and the linear fitting function can be expressed by formula (2).

[0047] δ(t)=-0.0624g×t+0.0316g+0.4t+0.25 (2)

[0048] g represents the grayscale value, and t represents the time.

[0049] 1. Core Idea of ​​Large-Format Continuous Molding Solution

[0050] The key challenge in continuous molding of large-format 3D models based on CLIP is the imbalance between the curing speed and reflow filling speed of the liquid resin during photocuring. Combining the photocuring characteristics of liquid resin under different grayscale masks and the analysis of resin flow rate in continuous CLIP molding, this application proposes a multi-grayscale mask molding scheme to balance the relationship between the photocuring speed and flow rate of the liquid resin. Its core idea is to utilize grayscale masks to achieve controllable light intensity on the projection surface during photocuring, using variable light intensity instead of the traditional CLIP molding method that uses a single ultraviolet light intensity for photocuring, thus improving printing defects caused by differences in the time it takes for the liquid resin to reflow to the target curing position. The multi-grayscale mask molding scheme, combining the photocuring characteristics of liquid photosensitive resin and the flow rate under dynamic CLIP molding, designs a grayscale modulation formula (3).

[0051] g(δ,t)=mδ+g init ,t∈[0,T] (3)

[0052] Where δ is the dimensionless position of a unit resin liquid at time t, which can be calculated by formula (2). init The initial value for grayscale modulation is denoted by 'm', which is a parameter used to adjust the ratio between the current position of the unit liquid and the center position of the molding platform. 'T' represents the single-layer curing time when using a single ultraviolet light source. Without increasing the single-layer curing time 'T', the grayscale modulation formula dynamically adjusts the ultraviolet light intensity on the current projection surface in units of time increment 'Δt', controlling the amount of ultraviolet light exposure energy received by the unit liquid resin entering the projection area at different time periods. Taking a time increment 'Δt' = 'T / 3' as an example, the grayscale modulation process for each slice mask image is divided into three stages, such as... Figure 2 As shown in the figure, the initial grayscale value of grayscale modulation is relatively large. The purpose is to allow the liquid resin on the outermost edge of the projection area to flow to the projection center and undergo a curing reaction. Successful curing at the center ensures that the liquid resin in other locations can move to their target projection area. As time increases, grayscale modulation gradually reduces the grayscale value of the mask image, and the ultraviolet exposure energy of the projection surface also increases. The ultraviolet exposure energy accumulated by the liquid resin on the projection surface during the movement gradually increases until grayscale modulation ends at time t=T. At this point, the liquid resin in the projection area has received sufficient ultraviolet exposure energy at its target position and has completely cured, obtaining a large-format solid thin layer of the target thickness.

[0053] 2. Multi-grayscale mask forming algorithm steps

[0054] The grayscale modulation formula is the core of the multi-grayscale mask forming scheme. The g in the grayscale modulation formula... init The initial ultraviolet light intensity received during the photocuring process of each slice is determined, and this value is used as a benchmark to realize the continuous molding process of large-format three-dimensional models based on CLIP. Therefore, g init The selection of initial values ​​is crucial for balancing resin flow rate and curing speed. The following provides methods for obtaining the optimal g value. init The specific steps.

[0055] 2.1 Steps for Iterative Optimization of Gray-Scale Modulation

[0056] The multi-grayscale mask continuous molding scheme utilizes a grayscale modulation formula to control the ultraviolet light intensity on the projection surface, balancing the relationship between resin flow rate and photopolymerization speed. As the molding platform continuously rises, resin flows from the periphery of the platform towards the printing area. Each unit of liquid resin receives ultraviolet exposure energy from the moment it enters the printing area. Based on the optical properties of photosensitive resin, complete photopolymerization occurs only when the total ultraviolet exposure energy received by each unit of liquid resin upon reaching its target position is greater than or equal to the total exposure energy required to cure the target thickness. In the single-layer slice curing process, the multi-grayscale mask molding scheme controls the ultraviolet light intensity of the imaging system using different grayscale masks, causing the ultraviolet light intensity on the projection surface to gradually increase over time. As the liquid resin flows from the edge of the molding platform towards the center of the printing area, the received ultraviolet exposure energy also gradually increases over time. In the grayscale modulation formula, g... init The selection of the resin must ensure that the unit liquid resin can move from the edge of the molding platform to the target curing position, and also ensure that the resin accumulates enough ultraviolet exposure to generate a solid thin layer of the target thickness when it moves to the target curing position.

[0057] Based on the above objectives, a grayscale modulation iterative optimization method is proposed to obtain g for the multi-grayscale mask forming scheme. initThe optimal initial value. Gray-scale modulation iterative optimization mainly consists of two processes. The first process is to minimize the ultraviolet exposure energy E accumulated per unit resin liquid as it flows to the projection center. sum The energy iteration process involves comparing the difference between the UV exposure energy E required to generate a solid-state film of the target thickness at that location and the energy required to achieve the desired thickness. sum The difference between E and E determines whether the liquid photosensitive resin around the projected region δ will cure, and is based on E. sum The comparison result with E increases or decreases g init The value of E when the total energy accumulated at the projection center. sum When the difference between g and E is less than a set threshold, g will be... init Set the initial value as the optimal value for the grayscale modulation formula, and the grayscale modulation iterative optimization process ends.

[0058] The grayscale modulation iterative optimization process is as follows: Figure 3 As shown, the left side represents the iterative process of minimizing the energy difference, and the right side represents the optimal g. init Grayscale value iteration process.

[0059] In the experiments of this application, the liquid photosensitive resin used can completely solidify at or near its target location when exposed to a mask image with grayscale values ​​between 205 and 225. Therefore, the grayscale modulation iterative optimization method sets the median of this range as g. init The estimated initial value is used to enter the energy gap minimization iteration process. Before performing the first part of the energy gap minimization iteration process, it is first necessary to determine the projection center position δ of the forming platform. c The parameters include: single-layer slice exposure time T, single-layer slice curing thickness d, iterative optimization time interval increment Δt, grayscale increment Δg, and energy iteration threshold σ. d Initial values ​​are given. In continuous photopolymerization molding, in order to maintain good adhesion between printed layers, the target curing depth is set to 1.2 times the curing thickness d of a single layer. Substituting 1.2d into formula (4) yields δ. c The required ultraviolet exposure energy E is determined, and then the process of minimizing the energy difference iteratively begins.

[0060]

[0061] C d For the curing depth, the exposure energy E in equation (4) can be converted from the grayscale value of the mask image to the incident ultraviolet light by formula (5). The critical exposure energy E of the liquid photosensitive resin used in this application is... c It is 0.83 mJ / cm 2 .

[0062] E=(0.84g-0.14)t (5)

[0063] The iteration to minimize the energy difference is carried out in units of time increment Δt, accumulating the unit liquid resin at time t. n-1 To t n The time period exposed to a grayscale value of g(δ) n ,t n The ultraviolet exposure energy E(g) received under the grayscale mask n ,t n ), E(g n ,t n ) can be calculated by a variation of formula (5) (6).

[0064] E(g,t)=(0.026g-0.14)(t n -t n-1 (6)

[0065] Taking the first iteration as an example, let g init Substituting the estimated value into formula (2), the dimensionless position δ1 of the unit liquid resin at time t1 is calculated. Then, according to formula (3), the gray value g(δ1,t1) of the current gray-scale mask image of the projection system at time t1 can be calculated. Next, the ultraviolet light exposure energy received by the unit liquid resin from time t0 to time t1 is calculated using formula (6). The above process is repeated continuously until δ n The value is equal to δ c Finally, the time required for a unit volume of liquid to reach the target position δ is calculated using formula (7). c The total exposure energy E received at that time sum .

[0066]

[0067] To ensure that the printed part does not have a hollow structure, the goal of energy iterative optimization is to optimize the initial value of g. min During the grayscale modulation projection process, when a unit of liquid resin reaches the center of the molding platform at time T and undergoes a complete curing reaction, sufficient ultraviolet exposure energy is accumulated when the resin in other locations of the printing area also flows to its target curing position. Therefore, the grayscale distribution optimization iterative scheme sets an energy iteration threshold in the first part of the energy iteration process to minimize the total exposure energy E received by a unit of liquid resin at the projection center position. sum The error between the target energy E and the second part of the grayscale distribution optimization iteration is the optimal g. init The value of E is calculated through the iteration of formula (8). sum The difference between E and g, and consequently the difference between g and E. init The value is adjusted, and the result is related to the set threshold (the threshold is set to 3% in this application's experiment). When |σ d |Greater than the threshold and σ d A value greater than 0 indicates that the current g initIf the value is too small, the liquid resin will not receive sufficient exposure energy when it reaches the projection center, thus reducing the current g. init Increase Δg; when |σ d |Greater than the threshold and σ d A value less than 0 indicates that the current g init If the current size is too large, the liquid resin will completely solidify before reaching the projection center, resulting in voids in the middle of the model. Therefore, the current size needs to be adjusted. init Reduce Δg to improve resin flowability. Then, adjust the g... init Substituting the value into the energy iteration process in the first part, the energy iteration is performed continuously, and the above process is repeated until |σ d The value of | is less than the set threshold. The final optimal g is obtained. init Substituting these values ​​into the grayscale modulation formula yields a large-format molding scheme for multiple grayscale masks made from liquid photosensitive resins with the same physical properties.

[0068]

[0069] According to the above process, the grayscale distribution optimization process is divided into two stages. The result of the energy iteration in the first stage serves as the initial condition for the grayscale value iteration in the second stage, and this is accomplished using two nested loops. The energy iteration process calculates the UV exposure energy accumulated by the resin during its movement based on the time increment Δt; therefore, the time complexity of this loop is O(N). The number of iterations for grayscale value iteration depends on the total exposure energy E in the energy iteration. sum The comparison result with the target energy E shows that the time complexity of the gray value iteration is O(1), so the time complexity of the gray-scale modulation iterative optimization process is O(N). The pseudocode is shown in Algorithm 1.

[0070]

[0071]

[0072] 3. Experimental Verification and Evaluation

[0073] The large-format forming scheme of multi-grayscale masks was experimentally evaluated on the splicing CLIP 3D printing experimental machine designed in this application. The evaluation mainly included feasibility assessment and reusability assessment. The feasibility assessment included model size assessment and model quality assessment.

[0074] 3.1 Feasibility Assessment

[0075] The feasibility assessment experiment selected three three-dimensional mesh models with different cross-sectional dimensions as printing models. The length and width dimensions of the model cross-sections were 10.05cm×10.00cm, 13.50cm×10.00cm, and 16.35cm×10.00cm, respectively. These dimensions meet the requirement that the maximum projected size of the combined splicing projection 3D printing experimental machine designed in this application is 275mm×480mm. High-transparency polycarbonate liquid photosensitive resin was selected as the photocuring raw material to facilitate viewing the internal printing structure of the model. The model entity is as follows: Figure 4 As shown, the 3D mesh model slice mask image is spliced ​​and projected onto the bottom of the resin tank. The seam is treated with an edge energy homogenization scheme at the splicing point, and a multi-grayscale mask forming scheme is applied to the continuous forming process. As can be seen from the model, the surface quality of the model is good, the details are complete, and there are no obvious printing defects such as hollow structures inside the model.

[0076] Model quality assessment involved printing multiple sets of 3D models of different sizes for experimental evaluation, such as... Figure 5 (a) and 5(b), where the original dimensions of the wolf head model's 3D model file are 26.37mm (length), 20.49mm (width), and 9.50mm (height), and the original dimensions of the turbine model are 25.00mm (length), 25.00mm (width), and 10.44mm (height). Three large-format models, each magnified by a factor of 2, were printed using a multi-grayscale masking large-format printing scheme. A detailed comparison between the magnified models and the original models reveals that the multi-grayscale masking large-format printing scheme maintains the advantages of CLIP continuous surface exposure printing in terms of model detail, quality, and accuracy.

[0077] As demonstrated by the model size evaluation experiment and the model quality evaluation experiment, by combining the edge energy homogenization distortion elimination scheme and the multi-grayscale mask large-format continuous forming scheme, the CLIP-type 3D printing experimental machine with combined splicing projection designed in this application can complete the continuous forming of large-format three-dimensional models with high quality.

[0078] 3.2 Reusability Assessment

[0079] Feasibility assessment experiments show that the multi-mask continuous molding scheme achieves good printing results using liquid photosensitive resin with relatively good flowability as the photocuring raw material. To further verify the reusability of this scheme for photocuring slurries with different physical properties, the Shenyang Institute of Metal Research formulated four types of ceramic slurries with high viscosity and low flowability as photocuring materials. Alumina, yttrium oxide, zirconium oxide, and calcium phosphate were selected as the photocurable ceramic powders, and they were mixed with photosensitive resin in equal proportions to form four different ceramic slurry systems. Models printed using these four ceramic slurries are shown below. Figure 6 As shown.

[0080] Among them, alumina ceramic slurry and zirconia ceramic slurry have better fluidity than yttrium oxide ceramic slurry and calcium phosphate ceramic slurry. Turbine models printed with these two slurries have relatively large cross-sections and good model quality, and alumina ceramic slurry can print solid turbine models. In contrast, yttrium oxide ceramic slurry and calcium phosphate ceramic slurry, with their poorer fluidity, print honeycomb models with relatively smaller cross-sections, but still show some improvement compared to traditional CLIP molding dimensions. The above experiments demonstrate that the multi-grayscale mask molding scheme has a certain degree of reusability for photopolymerizable printing materials with different viscosities and fluidities.

[0081] 4. Summary

[0082] In a CLIP-type 3D printing system that combines splicing projections to enlarge the forming area, a multi-grayscale continuous forming scheme is proposed to address the difficulty of continuous forming of large-format 3D models. This scheme improves the traditional CLIP photopolymerization process by controlling the ultraviolet exposure energy on the projection surface through grayscale mask modulation imaging system light intensity. Furthermore, an iterative optimization method for grayscale modulation is designed to reduce the impact of increased viscosity during liquid resin polymerization on the flow rate, thereby balancing the relationship between the photopolymerization speed and the reflow speed during liquid resin surface exposure forming, thus achieving a CLIP-based continuous forming scheme for large-format 3D models.

[0083] The scheme evaluation included model size assessment, model quality assessment, and material comparison assessment experiments, verifying the feasibility and reusability of the CLIP-based multi-grayscale mask continuous molding scheme. Experimental results show that for resin slurries with good flowability, the multi-grayscale mask continuous molding scheme can complete the continuous molding of large-format 3D models with high quality, good surface quality, and high molding accuracy. For ceramic slurries with poor flowability, the molding size is not as good as that of resin slurries, but compared to 3D models prepared by traditional CLIP, the model size is improved, while maintaining the advantages of CLIP's high molding accuracy and fast printing speed. This indicates that the scheme has high practical application value.

[0084] This application also provides an electronic device corresponding to the multi-grayscale mask continuous molding method provided in the foregoing embodiments, for executing the multi-grayscale mask continuous molding method. The embodiments in this application are not limited to any particular type.

[0085] Please refer to Figure 7 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 7As shown, the electronic device 20 includes: a processor 200, a memory 201, a bus 202, and a communication interface 203. The processor 200, the communication interface 203, and the memory 201 are connected via the bus 202. The memory 201 stores a computer program that can run on the processor 200. When the processor 200 runs the computer program, it executes the multi-grayscale mask continuous forming method provided in any of the foregoing embodiments of this application.

[0086] The memory 201 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 203 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0087] Bus 202 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs. After receiving an execution instruction, the processor 200 executes the program. The multi-grayscale mask continuous forming method disclosed in any of the foregoing embodiments of this application can be applied to the processor 200, or implemented by the processor 200.

[0088] The processor 200 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 200 or by instructions in software form. The processor 200 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 201. The processor 200 reads the information in memory 201 and, in conjunction with its hardware, completes the steps of the above method.

[0089] The electronic device provided in this application embodiment and the multi-grayscale mask continuous forming method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0090] This application also provides a computer-readable storage medium corresponding to the multi-grayscale mask continuous forming method provided in the foregoing embodiments. Please refer to... Figure 8 The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the multi-grayscale mask continuous molding method provided in any of the foregoing embodiments.

[0091] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0092] The computer-readable storage medium provided in the above embodiments of this application and the multi-grayscale mask continuous forming method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0093] It should be noted that:

[0094] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0095] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0096] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0097] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0098] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0099] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the virtual machine creation system according to the embodiments of this application. This application can also be implemented as a device or system program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0100] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of multiple gray scale mask continuous patterning, comprising: The method comprises the following steps: Slicing a three-dimensional model into equal-thickness slices, and then cutting the slices according to a projection device splicing mode; Eliminating the seams at the splicing positions by using an edge energy homogenization method; Exposing the cut single-unit mask images to a resin surface in a combined splicing projection mode according to a solidification sequence, and controlling the ultraviolet light in the projection process by using an optimal gray modulation formula during the solidification of each slice, thereby completing the solidification of a single layer; The edge energy homogenization method comprises the following steps: Dividing the slice mask images according to the projection device splicing mode, and performing pixel filling to obtain single-unit mask images of each projection device; Generating a set of symmetrical gray virtual mask images in a non-linear attenuation manner, wherein the non-linear attenuation manner is to construct a generation curve of the gray virtual mask images by combining an exponential attenuation function and a triangular attenuation function; Fusing the gray virtual mask images with the single-unit mask images according to a projection sequence; The optimal gray modulation formula is as follows: g(δ, t) = mδ + g init t∈[0, T] wherein δ is the dimensionless position of the unit resin liquid at time t; g init is the initial value of the gray scale modulation, m is a parameter for adjusting the proportion of the current position of the unit liquid to the center position of the molding platform, and T is the single-layer curing time when a single ultraviolet light is used.

2. The method of claim 1, wherein, The calculation formula of δ is as follows: δ(t) = -0.0624g x t + 0.0316g + 0.4t + 0.25 wherein g is a gray value, and t is a time.

3. The method of claim 1 or 2, wherein, The optimal gray modulation formula is in units of time increment Δt, dynamically adjusts the ultraviolet light intensity on the current projection surface, and controls the unit liquid resin entering the projection area to receive different intensities of ultraviolet light exposure energy at different time periods.

4. The method of claim 1, wherein, g is obtained through a grayscale modulation iterative optimization method. init The optimal initial value, and the grayscale modulation iterative optimization process are as follows: Minimizing the ultraviolet exposure energy E that the unit resin liquid accumulates in flowing to the projection center sum Energy iteration that differs from both the ultraviolet exposure energy E needed to generate a target thickness solid thin layer at the projection center; By comparing the difference between E sum and E, it is determined whether the liquid photosensitive resin around the projection area δ will be cured, and based on the comparison result of E sum and E, the value of g init is increased or decreased, when the total energy E sum accumulated at the projection center is less than a set threshold value, g init at this time is set as the optimal initial value of the gray scale modulation formula.

5. The method of claim 4, wherein, The number of iterations of the gray scale modulation depends on E sum The comparison result with E.

6. The method of claim 4, wherein, The energy iteration threshold σ of the gray scale modulation iteration d The calculation formula is as follows:

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor runs the computer program to implement the method of any one of claims 1-6.

8. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-6.

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