A multi-syringe forming parameter online control system and method in additive manufacturing

Abstract

The application provides a kind of multi-slurry forming parameter online control system and method in additive manufacturing, by slurry supply and regulation module according to current printing control parameter dynamic adjustment multi-component slurry supply ratio, by microstructure printing execution module according to the parameter layer-by-layer deposition solidification;Through online monitoring and feedback module, the measured deposition state characteristic quantity in printing process is collected in real time and is synchronized to motion control module, and the expected deposition state characteristic quantity is determined based on process-deposition state mapping model by motion control module, the deposition state characteristic quantity is compared with the expected deposition state characteristic quantity, and the deposition state deviation is obtained, and the subsequent printing control parameter is dynamically corrected according to the deposition state deviation, so that the deposition state converges to the preset tolerance range in real time.The above-mentioned modules cooperatively constitute a complete online closed-loop compensation mechanism, effectively overcome the cumulative problem of forming deviation caused by lack of online compensation, and significantly improve the construction precision and batch consistency of multi-slurry microstructure.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing process control technology for ceramic capacitor dielectrics, and more specifically, to an online control system and method for multiple slurry molding parameters in additive manufacturing. Background Technology

[0002] With the rapid development of new power systems, high-voltage transmission and distribution equipment, and power electronic devices, the objects of voltage sensing and measurement are gradually expanding from traditional power frequency steady-state signals to more complex voltage forms, including harmonics, transient overvoltages, fast pulses, and broadband electromagnetic disturbances. In capacitive voltage transformers (CVTs), GIL / GIS combined electrical appliances, medium and low voltage distribution network capacitor voltage divider measurement devices, AC / DC circuit breaker voltage equalization capacitors, and related high-voltage capacitor voltage divider, equalization, and coupling sensing equipment, the capacitor dielectric not only undertakes basic voltage dividing, equalization, or coupling functions, but also needs to consider high insulation strength, low loss, excellent temperature stability, broadband response consistency, and long-term service reliability. Therefore, developing high-performance capacitor materials and their precise preparation technologies for these scenarios is of significant engineering importance. Traditional thin-film capacitors have relatively low dielectric constants, and the capacitance values ​​required to achieve high-voltage voltage division are often large in size, and their reliability under high field strength and long-term operating conditions faces challenges. High dielectric constant ceramic capacitors, represented by barium titanate-based, barium strontium titanate-based, and magnesium titanate-based capacitors, have advantages such as small size, high stability, and long lifespan, making them an ideal alternative to thin film capacitors. However, their macroscopic performance is essentially determined by microstructures such as grain size, grain boundary characteristics, porosity, and domain structure.

[0003] Traditional ceramic capacitor fabrication primarily relies on processes such as tape casting and dry pressing. While these processes are mature for fabricating uniform thin films or simple shapes, they have certain limitations: 1) It is difficult to achieve gradient design of properties such as dielectric constant and coefficient of thermal expansion in three-dimensional space, making it impossible to effectively optimize electric field distribution and suppress edge effects; 2) It is impossible to actively construct anisotropic microstructures (such as oriented grains or grain boundaries), resulting in a lack of directional control over heat conduction and mechanical strengthening; 3) It is difficult to integrally fabricate complex biomimetic strengthening structures or three-dimensional electrode / heat dissipation channels within the dielectric layer. This leads to many optimal microstructure schemes derived through advanced methods such as cross-scale simulation and reverse engineering being unable to be verified due to a lack of matching manufacturing processes.

[0004] Additive manufacturing (3D printing) technology has brought new ideas to ceramic forming, but current research mainly focuses on forming macroscopically complex shapes. Existing systems for multi-slurry systems primarily employ a technical approach of setting fixed parameters before printing, without sensing the deposition state or making dynamic adjustments during the process. Due to differences in the characteristics of multi-slurry components and environmental fluctuations, fixed parameters easily lead to deviations between the microstructure and the target design, resulting in performance consistency that fails to meet engineering requirements. Therefore, developing an online control system and method for multi-slurry forming parameters is crucial for achieving precise construction of functional microstructures. Summary of the Invention

[0005] In view of this, the present invention proposes an online control system and method for multi-slurry molding parameters in additive manufacturing, which aims to solve the problem that the existing multi-slurry additive manufacturing process uses preset fixed process parameters and lacks online perception and closed-loop compensation of slurry supply state and deposition molding state, resulting in the multi-slurry supply ratio, deposition geometry and functional microstructure deviating from the design target, thus causing insufficient microstructure construction accuracy and finished product consistency.

[0006] In a first aspect, the present invention proposes an online control system for multiple slurry molding parameters in additive manufacturing, comprising: The slurry supply and control module is used to store and transport ceramic slurries with two or more different components, and dynamically adjust the supply flow rate and supply ratio of each ceramic slurry according to the received current printing control parameters. A microstructure printing execution module is used to deposit and solidify the ceramic slurry layer by layer according to the current printing control parameters; The online monitoring and feedback module includes an in-situ sensor assembly and a signal processing unit that are electrically connected to each other. The in-situ sensor assembly is used to acquire raw sensing signals in real time during the printing process. The signal processing unit is used to convert the raw sensing signals into measured deposition state characteristics of the same type as the expected deposition state characteristics, and synchronously send the measured deposition state characteristics to the motion control module. The motion control module is connected to the slurry supply and control module, the microstructure printing execution module, and the online monitoring and feedback module, respectively. It receives process input data containing target microstructure characteristic parameters, determines baseline printing control parameters based on a preset process-deposition state mapping model, and determines the corresponding expected deposition state characterization based on the baseline printing control parameters. It compares the measured deposition state characterization with the expected deposition state characterization to obtain the deposition state deviation, and dynamically corrects the printing control parameters for subsequent deposition processes based on the deposition state deviation, ensuring that the deviation between the measured deposition state characterization and the expected deposition state characterization is within a preset tolerance range. Finally, it sends the corrected printing control parameters as the updated current printing control parameters to the slurry supply and control module and the microstructure printing execution module.

[0007] Furthermore, in the above-mentioned online control system for multiple slurry molding parameters in additive manufacturing, the process-deposition state mapping model includes a target microstructure inversion sub-model and a deposition state prediction sub-model. The target microstructure inversion sub-model is used to determine the corresponding baseline printing control parameters based on the target microstructure characteristic parameters. The deposition state prediction sub-model is used to determine the expected deposition state characterization quantity of the same type as the detectable parameters of the in-situ sensor component based on the baseline printing control parameters. The target microstructure characteristic parameters include at least one of the following: target linewidth, target layer thickness, target porosity, target dielectric constant gradient, target grain orientation, and target grain boundary strengthening mesh characteristics.

[0008] Furthermore, in the above-mentioned online control system for multiple slurry molding parameters in additive manufacturing, the motion control module dynamically corrects the printing control parameters of the subsequent deposition process based on the deposition state deviation, including: When the deposition state deviation exceeds a preset threshold, a process compensation amount is generated based on a preset compensation rule, and the process compensation amount is used to correct the printing control parameters of the subsequent deposition process.

[0009] Furthermore, in the above-mentioned online control system for multiple slurry molding parameters in additive manufacturing, the motion control module is equipped with a process data storage unit and a path processing unit; wherein, The process data storage unit is used to store the process-deposition state mapping model and preset compensation rules; The path processing unit is used to receive the target microstructure feature parameters, slurry basic parameters, and equipment basic data, perform differentiated path planning for different functional areas, determine the corresponding baseline printing control parameters based on the process-deposition state mapping model, determine the corresponding expected deposition state characterization quantity based on the baseline printing control parameters, and generate a numerical control instruction sequence to drive the microstructure printing execution module.

[0010] Furthermore, in the above-mentioned online control system for multiple slurry molding parameters in additive manufacturing, the slurry supply and control module includes at least two independent slurry storage pipes, a fluid conveying device, and a dynamic mixer; wherein, Each of the slurry storage tubes is used to store one channel of ceramic slurry, and the outlet of each of the slurry storage tubes is connected to the inlet of the fluid conveying equipment. The outlet of the fluid conveying device is connected to the inlet of the dynamic mixer; The outlet of the dynamic mixer is connected to the feed end of the multi-nozzle module of the microstructure printing execution module.

[0011] Furthermore, in the above-mentioned online control system for multiple slurry molding parameters in additive manufacturing, the in-situ sensor assembly includes: The visual monitoring unit is used to acquire parameters of the surface morphology of the deposition layer and the distribution state of the slurry, so that the motion control module can correct the path spacing, scanning direction and scanning angle. A laser displacement sensor is used to acquire the geometric dimensions and flatness parameters of the deposition layer, so that the motion control module can correct the Z-axis lift of the subsequent deposition layer and / or the path planning of the next layer to compensate for the layer thickness deviation. An infrared thermal imaging sensor is used to acquire thermal state parameters of the deposition area, so that the motion control module can adjust the platform temperature and interlayer waiting time.

[0012] The additive manufacturing multi-slurry molding parameter online control system of this invention dynamically adjusts the supply ratio of multi-component slurry according to the baseline printing control parameters through the slurry supply and control module. This, combined with the microstructure printing execution module, deposits and solidifies layer by layer according to the baseline printing control parameters, providing a precise execution basis for multi-slurry molding. Simultaneously, the online monitoring and feedback module acquires the measured deposition state characterization in real time and synchronizes it to the motion control module. The motion control module determines the expected deposition state characterization based on the process-deposition state mapping model, compares it with the measured value to obtain the deposition state deviation, and dynamically corrects subsequent printing control parameters accordingly, ensuring that the deposition state converges to the preset tolerance range in real time. These modules work together to form a complete online closed-loop compensation mechanism, effectively overcoming the problem of accumulated molding deviations caused by the lack of online compensation, and significantly improving the construction accuracy and batch consistency of multi-slurry microstructures.

[0013] Secondly, this invention also proposes an online control method for multiple slurry molding parameters in additive manufacturing, comprising: The system receives process input data containing target microstructure feature parameters, and obtains baseline printing control parameters through inverse calculation based on a preset process-deposition state mapping model. The baseline printing control parameters are then substituted into the forward model of the process-deposition state mapping model to determine the corresponding expected deposition state characterization quantity. The multi-component ceramic slurry is driven to be deposited layer by layer according to the reference printing control parameters. The original sensor signals during the printing process are collected in real time and converted into measured deposition state characterizations of the same type as the expected deposition state characterizations. The sedimentation state deviation is obtained by comparing the measured sedimentation state characterization with the expected sedimentation state characterization. When the deposition state deviation exceeds a preset threshold, the printing control parameters of the subsequent deposition process are dynamically corrected based on the deposition state deviation, so that the deviation between the measured deposition state characterization and the expected deposition state characterization is within a preset tolerance range, and the corrected printing control parameters are used as the updated current printing control parameters for the control of the subsequent deposition process.

[0014] Furthermore, in the above-mentioned online control method for multiple slurry molding parameters in additive manufacturing, the dynamic correction of printing control parameters for subsequent deposition processes based on deposition state deviation includes: adjusting at least one of the following: nozzle moving speed, extrusion pressure, volumetric flow rate, feed ratio, single-layer thickness, platform temperature, ambient humidity, interlayer waiting time, light intensity, and exposure time.

[0015] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the program is executed by a processor, it implements the online control method for multiple slurry molding parameters in additive manufacturing as described in any of the preceding claims.

[0016] Fourthly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the online control method for multiple slurry molding parameters in additive manufacturing as described in any of the preceding claims.

[0017] The present invention provides an online control method for multi-slurry forming parameters in additive manufacturing. This method constructs a coupling mechanism of inverse inversion and forward prediction based on a process-deposition state mapping model. At the initial printing stage, it calculates the baseline printing control parameters adapted to the current working conditions and their corresponding expected deposition state characterizations. During layer-by-layer deposition, it uses in-situ sensor components to acquire and compare the measured state characterizations of the same type in real time. When the deviation exceeds a preset threshold, it dynamically corrects the subsequent printing control parameters based on this deviation, thereby achieving closed-loop compensation between the multi-slurry supply state and the deposition forming state during the deposition process. This method significantly improves the construction accuracy, manufacturing stability, and batch consistency of complex multi-slurry microstructures. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a structural block diagram of an online control system for multiple slurry molding parameters in additive manufacturing provided in an embodiment of the present invention. Figure 2 This is a flowchart of an online control method for multiple slurry molding parameters in additive manufacturing provided by an embodiment of the present invention. Detailed Implementation

[0019] Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this disclosure and to fully convey the scope of this disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] System Implementation Example: See Figure 1The present invention provides an online control system for multi-slurry molding parameters in additive manufacturing, comprising: a slurry supply and control module 1, a microstructure printing execution module 2, an online monitoring and feedback module 3, and a motion control module 4; wherein, the slurry supply and control module 1 is used to store and transport ceramic slurries of two or more different components, and dynamically adjust the supply flow rate and supply ratio of each ceramic slurry according to the current printing control parameters issued by the motion control module 4; the microstructure printing execution module 2 is used to deposit and solidify the ceramic slurry layer by layer according to the current printing control parameters; the online monitoring and feedback module 3 includes an in-situ sensor assembly (not shown in the figure) and a signal processing unit (not shown in the figure) electrically connected to each other, the in-situ sensor assembly is used to collect the original sensing signals during the printing process in real time; the signal processing unit is used to convert the original sensing signals into a measured deposition state characterization quantity of the same type as the expected deposition state characterization quantity, and to characterize the measured deposition state. The quantity data is synchronously sent to the motion control module 4. The motion control module 4 is connected to the slurry supply and control module 1, the microstructure printing execution module 2, and the online monitoring and feedback module 3, respectively. It is used to receive process input data containing target microstructure feature parameters, determine the baseline printing control parameters based on the preset process-deposition state mapping model, and determine the corresponding expected deposition state characterization quantity based on the baseline printing control parameters. The measured deposition state characterization quantity is compared with the expected deposition state characterization quantity to obtain the deposition state deviation. When the deposition state deviation exceeds the preset threshold, the printing control parameters of the subsequent deposition process are dynamically corrected according to the deviation, so that the deviation between the measured deposition state characterization quantity and the expected deposition state characterization quantity of the subsequent deposition is within the preset tolerance range. The corrected printing control parameters are sent as the updated current printing control parameters to the slurry supply and control module 1 and the microstructure printing execution module 2.

[0021] Specifically, the online monitoring and feedback module 3 is responsible for collecting, processing and sending measured deposition state data; the motion control module 4 is responsible for deviation comparison, compensation decision and control parameter update; the slurry supply and control module 1 and the microstructure printing execution module 2 are responsible for executing the corrected current printing control parameters issued by the motion control module 4.

[0022] In this embodiment, the process input data includes target microstructure feature parameters, slurry basic parameters, and equipment basic parameters. After receiving the above process input data, the motion control module 4 uses the target microstructure feature parameters as constraints and combines the slurry basic parameters and equipment basic parameters to perform inverse inversion through the process-deposition state mapping model to determine the baseline printing control parameters.

[0023] The target microstructure characteristic parameters include at least one of the following: target linewidth, target layer thickness, target porosity, target dielectric constant gradient, target grain orientation, and target grain boundary strengthening mesh characteristics.

[0024] The basic parameters of the slurry include the solid content, viscosity, yield stress, shear thinning index, particle size, sintering shrinkage, dielectric constant after sintering, flow-pressure relationship, drying window and curing window of each component slurry.

[0025] The basic parameters of the equipment include nozzle inner diameter, nozzle length, nozzle-platform gap, dynamic mixer dead volume, pipeline volume, pump flow accuracy, pressure control range, platform positioning accuracy, sensor sampling frequency, and nozzle switching deviation.

[0026] After receiving the above-mentioned various process input data, the motion control module 4 calls the process-deposition state mapping model to determine the corresponding baseline printing control parameters, and further determines the expected deposition state characterization quantity corresponding to the baseline printing control parameters.

[0027] The baseline printing control parameters are selected from at least one of the following: nozzle travel speed, extrusion pressure, volumetric flow rate, feed ratio, single layer thickness, path spacing, scanning direction, scanning angle, platform temperature, ambient humidity, interlayer waiting time, light intensity, or exposure time.

[0028] Since the target microstructure characteristic parameters such as dielectric constant, porosity, and grain orientation cannot be directly measured in real time by in-situ sensor components during the printing process, this embodiment of the invention achieves indirect closed-loop control of the target microstructure characteristic parameters by comparing the real-time obtainable deposition state characterization quantity with the expected deposition state characterization quantity.

[0029] In this embodiment, the measured deposition state characterization quantities are physical quantities directly obtained by the in-situ sensor components, including actual linewidth, actual layer thickness, actual height, pressure, flow rate, temperature, humidity, trajectory deviation, etc.

[0030] The expected deposition state characterization parameters are the same type of parameters obtained by substituting the baseline printing control parameters back into the physical relationship in the process-deposition state mapping model and calculating them in a forward direction. These parameters include expected linewidth, expected layer thickness, expected pressure, expected flow rate, expected temperature, expected humidity, expected trajectory position, expected feed ratio, expected grid spacing, expected channel inner diameter, and expected channel wall thickness.

[0031] It should be noted that the expected deposition state characterization quantity is a reference value or reference sequence determined before printing begins. It serves as a fixed reference benchmark for online monitoring and comparison and does not dynamically change due to feedback compensation. Based on the microstructural distribution characteristics of the target printed part, the expected deposition state characterization quantity can be a single fixed value, a reference sequence that varies with the printed layer, a reference field that varies with spatial coordinates, or a state reference value bound to each path segment. In this embodiment, the target printed part can be a ceramic capacitor dielectric.

[0032] Different target microstructural characteristic parameters lead to different methods for determining the expected depositional state characterization parameters: For parameters such as target linewidth and target layer thickness that can be measured directly or indirectly by industrial cameras and laser displacement sensors, the expected linewidth characterization can be equal to the target linewidth after correction for equipment error, slurry expansion and drying shrinkage, and the expected layer thickness characterization can be equal to the target layer thickness after correction for substrate height and deposition shrinkage. At this time, the expected deposition state characterization is basically consistent with the target microstructure parameters.

[0033] For target microstructure parameters that cannot be directly measured online by in-situ sensor components, such as target porosity, target dielectric constant gradient, and target grain orientation, the target microstructure parameters cannot be directly used as expected online deposition state quantities. Instead, alternative state quantities that can be monitored online should be derived from the target microstructure parameters. For example, target porosity corresponds to expected linewidth, line spacing, layer thickness, overlap, flow rate, and evaporation state; target dielectric constant gradient corresponds to expected flow rate and expected mixing ratio of each slurry; and target grain orientation corresponds to expected shear rate, pressure, scanning direction, and deposition line state.

[0034] The raw sensing signals acquired by the in-situ sensor assembly include one or more of the following: image signals, displacement signals, pressure signals, flow signals, and temperature signals. Since these raw sensing signals cannot be directly compared with the expected depositional state characterization quantities, the signal processing unit performs filtering, feature extraction, and parameter conversion on the raw sensing signals. This converts the image signals into measured linewidths, the displacement signals into measured layer thicknesses, the pressure signals into measured extrusion pressures, the flow signals into measured flow rates and measured proportions, and the temperature signals into measured temperature values, thereby obtaining measured depositional state characterization quantities of the same type and dimension as the expected depositional state characterization quantities.

[0035] During the printing process, the online monitoring and feedback module 3 collects the measured sedimentation state characterization quantity, and the motion control module 4 dynamically updates the current printing control parameters based on the deviation between the measured sedimentation state characterization quantity and the expected sedimentation state characterization quantity, thus forming a closed-loop control.

[0036] In this embodiment, the printing control parameters for the subsequent deposition process are the current printing control parameters issued by the motion control module 4 after the update. Both refer to the latest printing control parameters that are actually executed after correction.

[0037] To achieve precise spatial control of the parameters of each target microstructure, the process data storage unit in motion control module 4 digitally stores each parameter in the form of voxel field, point cloud field, or layered vector field. Each voxel in the voxel field, each point in the point cloud field, and each vector segment in the layered vector field serves as the smallest spatial unit for storing the target microstructure feature parameter value. Each spatial unit stores the corresponding target microstructure feature parameter value, thereby establishing the correspondence between the three-dimensional spatial position and the target microstructure feature parameter value.

[0038] In this embodiment, the target microstructure feature parameters can be generated by material design methods well known in the art, that is: based on the target macroscopic performance index, the optimal target microstructure feature parameters corresponding to the target macroscopic performance are obtained by cross-scale physical modeling, numerical simulation, data-driven model or a combination thereof, and then input into the process data storage unit in the motion control module 4 of this embodiment.

[0039] The microstructure printing execution module 2 includes: an air-floating precision three-axis motion platform and a multi-nozzle module 21 mounted on it; the air-floating precision three-axis motion platform is designed based on a marble base, with an X-axis and Y-axis travel of 200mm×200mm and a positioning accuracy of ±0.5μm; the Z-axis travel is 100mm and the positioning accuracy is ±1μm; the multi-nozzle module 21 can simultaneously install 4 high-precision conical nozzles, each with an inner diameter selectable of 50μm, 100μm, or 200μm to adapt to the construction accuracy requirements of different microstructures; the multi-nozzle module 21 is also equipped with an automatic nozzle calibration unit, using... To achieve automatic calibration of positional deviations between multiple nozzles with a calibration accuracy of ±2μm, ensuring the overlay accuracy of multi-material printing; the motion control module 4 sends drive commands to the microstructure printing execution module 2 by parsing the printing path G code. After the drive commands are decoded by the air-float precision three-axis motion platform, they drive the X, Y, and Z axes to move along preset trajectories respectively, and synchronously control the start, stop, and switching of each nozzle in the multi-nozzle module 21, so that the air-float precision three-axis motion platform and the multi-nozzle module 21 move in coordination, and complete the layer-by-layer deposition of slurry according to the preset path and the current printing control parameters issued by the motion control module 4.

[0040] In this embodiment, the motion control module 4 includes a multi-axis synchronous motion controller based on the EtherCAT bus, capable of synchronous control of more than 16 axes with a control cycle of no more than 1ms. This controller incorporates path parsing and process adaptation algorithms, enabling it to automatically generate multi-axis motion control commands by parsing 3D printing path files, and synchronously coordinate the slurry supply of the slurry supply and control module 1, the nozzle action of the microstructure printing execution module 2, and the monitoring feedback of the online monitoring and feedback module 3. Furthermore, the motion control module 4 is equipped with a human-machine interface to display the printing status, monitoring data, and printing control parameters in real time, and supports online adjustment of printing control and emergency shutdown to meet the requirements of engineering applications for ease of operation and safety.

[0041] Preferably, to adapt to the printing requirements of different target microstructures, the motion control module 4 is equipped with a process data storage unit and a path processing unit, which work together to convert target microstructure parameters into printing execution instructions. The process data storage unit stores the process-deposition state mapping model and preset compensation rules; the path processing unit receives target microstructure feature parameters, slurry basic parameters, and equipment basic data, performs differentiated path planning based on the process-deposition state mapping model, generates a 3D printing path file containing the expected deposition state characterization, and generates a sequence of numerical control instructions to drive the microstructure printing execution module 2 based on the 3D printing path file.

[0042] Specifically, the process-deposition state mapping model includes a target microstructure inversion sub-model and a deposition state prediction sub-model. The target microstructure inversion sub-model is used to determine the corresponding baseline printing control parameters by inversely solving the physical mechanism model or retrieving from the calibration database, constrained by the target microstructure characteristic parameters. The deposition state prediction sub-model is used to calculate, based on the baseline printing control parameters and combined with the slurry basic parameters, equipment basic data, and path space information, the expected deposition state characterization quantity of the same type as the detectable parameters of the in-situ sensor component. The target microstructure characteristic parameters include at least one of the following: target linewidth, target layer thickness, target porosity, target dielectric constant gradient, target grain orientation, and target grain boundary strengthening mesh characteristics.

[0043] In this embodiment, the process-deposition state mapping model characterizes the mapping relationship between printing control parameters (such as nozzle movement speed, extrusion pressure, volumetric flow rate, feed ratio, etc.) and target microstructure characteristic parameters (such as porosity, grain orientation direction, grain orientation degree, etc.). When the target microstructure characteristic parameters are used as input, the model determines the baseline printing control parameters and the expected deposition state characterization quantity through the following two steps: The first step is inverse inversion, using the target microstructure characteristic parameters as constraints to invert the corresponding baseline printing control parameters. The second step is forward prediction, where the inverted baseline printing control parameters are substituted into the forward model to predict the expected depositional state characterization that should occur under this parameter combination and can be monitored online. In the above forward prediction process, the forward model is established based on physical mechanism relationships, mapping relationships obtained from previous process calibration, or a combination of both. This model takes the baseline printing control parameters obtained from inverse inversion as input, and uses this mapping relationship to calculate the depositional state that should occur under the current parameter combination, thereby outputting the corresponding expected depositional state characterization.

[0044] In this embodiment, the process-deposition state mapping model includes two collaborative construction methods: One approach is a physical mechanism model based on slurry rheology and multiphase mixing theory: this model employs the Herschel-Bulkley constitutive equation (τ = τ). y + Kγ n Where τ is the shear stress, τ y The shear thinning and yielding behavior of the slurry is characterized by yield stress (K = γ, consistency coefficient, shear rate, and n = shear thinning exponent). An extrusion pressure-flow rate relationship is established based on the pipe pressure loss relationship (ΔP = 4Lτ / D, where ΔP represents the theoretical pressure drop of the slurry flowing within the nozzle, L is the nozzle length, τ is the shear stress, and D is the nozzle inner diameter). Furthermore, based on the principle of mass conservation (Q = βwhv, where Q is the volumetric flow rate, β is a correction coefficient considering slurry expansion, contraction, and cross-sectional morphology, h is the layer thickness, w is the linewidth, and v is the nozzle moving speed), the volumetric flow rate, nozzle moving speed, and deposition geometry are correlated. For multi-slurry mixtures, a linear mapping relationship ε between components and dielectric constant is further introduced. eff =(1-r B )ε A +r B ε B (ε) eff (Equivalent dielectric constant of the mixture of low dielectric constant base slurry A and high dielectric constant slurry B), and based on the dielectric constant at the target location. Inversely determine the target volume fraction of high dielectric constant slurry B. .

[0045] Another approach involves establishing a process parameter calibration database based on prior calibration experiments. The motion control module 4 pre-establishes a lookup table of target microstructure characteristic parameters and baseline printing control parameters through offline calibration experiments. The calibration process includes: under fixed slurry batches and equipment conditions, applying multiple sets of process parameter combinations for target linewidth, target layer thickness, target porosity, target dielectric constant gradient, target grain orientation, and target grain boundary strengthening mesh characteristics. After printing, the deposition state characterization is measured, and the parameter combination with the smallest deviation is selected and stored in the database. During runtime, after receiving the target microstructure characteristic parameters, the motion control module 4 first matches the nearest neighbor baseline value in the calibration database, then performs linear interpolation or offset correction based on the current slurry baseline parameters and equipment baseline data, outputs the baseline printing control parameters, and forward verifies the expected deposition state characterization based on the physical mechanism model. For example, for a working condition with a target linewidth of 120μm, a layer thickness of 30μm, and an orientation factor S≥0.80, the preliminary calibration experiment used formulas to back-calculate the baseline values ​​Q≈2.94μL / min and v≈13.6mm / s. These parameters were stored in the calibration database as lookup table entries. If the current slurry viscosity deviates from the calibration batch during operation, Q is linearly corrected according to the viscosity deviation (the correction amount is determined through small-scale experiments, for example, ±3%).

[0046] Based on the above physical mechanism, the mapping model supports using target microstructure feature parameters as input to inversely deduce the corresponding printing control parameters. The expected deposition state characterization is determined according to the benchmark printing control parameters. After inversely deduce the benchmark printing control parameters, the motion control module 4 substitutes the benchmark printing control parameters into the corresponding physical relational expression and calculates the expected deposition state characterization under the current parameters. For example: With target line width When inputting the nozzle speed, first determine the initial value v0 of the nozzle movement speed based on the nozzle inner diameter, the set layer thickness h0, and the allowable nozzle movement speed range of the equipment. Then, based on the law of conservation of mass... Calculate the initial volumetric flow rate Q0; calculate the shear rate within the nozzle based on the initial volumetric flow rate (for a circular nozzle, the wall shear rate can be calculated using the formula γ=32Q / πD). 3An estimation is performed, where Q is the volumetric flow rate and D is the nozzle inner diameter. The initial extrusion pressure is calculated using the Herschel-Bulkley constitutive relation and the pressure loss relationship of the circular tube. Simultaneously, the nozzle height or the gap between the nozzle and the deposition surface is determined based on the calibration relationship between the target linewidth and the nozzle inner diameter. At least one of the following—volumetric flow rate, nozzle movement speed, extrusion pressure, and nozzle height—is obtained as a baseline printing control parameter. The motion control module 4 then calculates the expected linewidth, expected flow rate, and expected pressure based on the baseline printing control parameters. The expected linewidth is the target linewidth after corrections for slurry expansion, drying shrinkage, and equipment deviation, and is used for comparison with the measured linewidth obtained by the industrial camera.

[0047] With target layer thickness When inputting, the initial values ​​of the deposition linewidth w0 and nozzle moving speed v0 are determined based on the nozzle inner diameter and process window, according to the mass conservation law. Calculate the initial value of the volumetric flow rate.

[0048] Based on the target layer thickness The calibration relationship between the nozzle gap and Z-axis lift is used to determine the initial gap between the nozzle and the deposition surface, as well as the Z-axis increment of adjacent layers. Based on the target density or inter-channel bonding state, the target path overlap rate is determined through the pre-established calibration relationship between the inter-channel bonding state and the path overlap rate. Then, based on the expected line width w0, use the formula... Calculate the path spacing p0 (the center-to-center distance between adjacent printed paths), where the overlap rate is... Increasing the porosity reduces the path spacing p0 and results in denser interlayer bonding; conversely, allowing for higher porosity reduces the overlap rate and increases the path spacing. Thus, at least one of the following—volume flow rate, nozzle movement speed, Z-axis height, nozzle gap, and path overlap rate—is obtained as a baseline printing control parameter. The motion control module 4 forward-determines the expected layer thickness, expected local height, and expected flow rate based on the determined baseline printing control parameters, including volume flow rate, nozzle movement speed, Z-axis height, and path overlap rate. The expected layer thickness is calculated based on the mass conservation principle using the baseline printing control parameters; the expected local height is determined by summing the expected layer thicknesses of all layers up to the current layer, using the substrate reference plane as a reference; and the expected flow rate is determined based on the volume flow rate setting in the baseline printing control parameters, or calculated using a pre-established flow rate mapping relationship based on pump speed and extrusion pressure.

[0049] With target porosity When inputting, a pre-calibrated correlation model between porosity and solid content, deposition linewidth, line spacing, path overlap rate, curing energy, drying rate, and interlayer waiting time is invoked. Using the target porosity as a constraint, a reverse solution or database matching is performed to determine at least one of the following as a baseline printing control parameter: solid content, line spacing, path overlap rate, extrusion flow rate, curing energy, and interlayer waiting time. Since porosity cannot be directly measured online by existing in-situ sensor components, the motion control module 4 does not directly use the target porosity as an online comparison quantity. Instead, it determines the expected linewidth, expected layer thickness, expected line spacing, expected flow rate, expected pressure, expected temperature, and expected solvent evaporation characterization quantity based on the baseline printing control parameters. This is used for indirect closed-loop control of the deposition state affecting porosity formation. The solvent evaporation characterization quantity is used to quantitatively characterize the degree of solvent evaporation in the deposition slurry. The expected linewidth, expected layer thickness, expected line spacing, expected flow rate, expected pressure, and expected temperature, or the solvent evaporation characterization quantity E, are determined based on the baseline printing control parameters. solvent Solvent evaporation characterization quantity E solvent It can be defined as dimensionless solvent volatility, for example: E solvent =(m s,0 -m s,t ) / m s,0 , where m s,0 m is the initial solvent mass. s,t Given the remaining solvent mass at the corresponding time, therefore: 0 ≤ E solvent ≤1, 0 indicates that there is almost no volatilization, and 1 indicates that the set state of complete volatilization has been reached.

[0050] With the gradient of the target dielectric constant When inputting, the target volume fraction of slurry B is calculated based on the target dielectric constant at the target spatial location and the dielectric constants of each base slurry, according to the component-dielectric constant mapping relationship. ;in, Let ε be the dielectric constant at the target location. A The dielectric constant of the base slurry A, ε B The dielectric constant of high dielectric constant slurry B; at the total feed flow rate Q total After determining the target line width and target layer thickness, the feed flow rates of slurry A and slurry B are further calculated: Q B =r B Q total , Q A =(1-r B )Q total .

[0051] This provides the feed flow rate, feed ratio, pump speed, and slurry switching sequence for each slurry channel as baseline printing control parameters. The motion control module 4 uses the expected flow rate of each channel, the expected feed ratio, and the expected total flow rate as characterizations of the expected deposition state, comparing them with the measured flow rate and mixing ratio obtained from the flow sensor. The target dielectric constant gradient, as the final microstructure or performance target, can be verified through offline testing after sintering.

[0052] Target grain orientation When the target orientation direction is the input, the shear-induced orientation function is used. S = S max [1-exp(-aγ m t res Inverse calculation to find the shear rate required to achieve the target grain orientation : Where S is the grain orientation factor, S max γ is the maximum orientation factor achievable by the slurry system under the set process conditions, t is the shear rate of the slurry within the nozzle, and t is the maximum orientation factor achievable by the slurry system under the set process conditions. res S is the residence time of the slurry in the nozzle, a is the orientation kinetics fitting coefficient, and m is the shear rate influence index; max a and m were determined through prior process calibration experiments; then, based on the relationship between shear rate and flow rate γ=32Q / πD 3 The initial volumetric flow rate is calculated, and the scanning path direction is determined based on the target orientation direction. The nozzle moving speed is further determined based on the target linewidth and layer thickness. At least one of the following parameters—scanning direction, nozzle inner diameter, extrusion flow rate, nozzle moving speed, and extrusion pressure—is obtained as a reference printing control parameter. Since grain orientation cannot be directly measured by the in-situ sensor assembly, the motion control module 4 determines the expected flow rate, expected pressure, expected scanning direction, and expected deposition linewidth based on the reference printing control parameters as an online control reference. The actual grain orientation after sintering is obtained through offline microstructure testing and used to correct the calibration coefficients of the orientation model.

[0053] When the target grain boundary strengthening mesh features are used as input, the spatial position, path shape, mesh line width, mesh spacing, and strengthening slurry distribution range of the target mesh are analyzed. Based on the spatial position and strengthening slurry distribution range of the target mesh, the nozzle number adapted to the corresponding space is determined. Based on the path shape and printing sequence of the target mesh, the start and stop sequence of each nozzle is determined to selectively deposit grain boundary strengthening slurry according to predetermined layers and positions during the slurry deposition process. Based on the target mesh line width, layer thickness, and nozzle moving speed, the volumetric flow rate of the grain boundary strengthening slurry is calculated according to the mass conservation relationship, and cross meshes or other corresponding scanning paths are generated based on the target mesh spacing. Thus, at least one of the nozzle number, scanning path, mesh spacing, nozzle moving speed, and grain boundary strengthening slurry supply flow rate is obtained as a reference printing control parameter. The motion control module 4 uses the expected mesh line width, expected mesh spacing, expected trajectory position, and expected grain boundary strengthening slurry flow rate as the expected deposition state characterization quantities, which are used to compare with the measured mesh geometry and supply state obtained by the industrial camera and flow sensor. The grid line width, grid spacing, and trajectory position can be measured by an industrial camera, thus directly forming an online closed loop.

[0054] This embodiment only uses the above six mapping relationships as examples. In practical applications, other mapping relationships of microstructure feature parameters can be constructed based on the same physical mechanism according to specific needs, which will not be elaborated here.

[0055] Furthermore, in this embodiment, the process-deposition state mapping model, based on the baseline printing control parameters output by the target microstructure feature parameters, is differentiated and adapted by the path processing unit according to different functional regions, forming differentiated printing control parameters specific to each region. The path processing unit further calculates the corresponding expected deposition state characterization based on the differentiated printing control parameters, and uses the differentiated printing control parameters and the corresponding expected deposition state characterization as path processing results, writing them respectively into the printing control parameter field and the deposition state characterization reference field in the printing path file to generate a numerical control instruction sequence driving the microstructure printing execution module. In this embodiment, a functional region refers to a spatial region in the target printed part that has specific target microstructure and material composition requirements. For example, it may include the following functional regions: gradient dielectric layer region, oriented grain region, grain boundary strengthening mesh region, and heat dissipation microchannel region. Different functional regions correspond to different slurry combinations, path planning rules, and printing control parameters.

[0056] When performing differentiated path planning, the path processing unit first analyzes the target microstructure feature parameters to extract the types of each functional area. Then, for each functional area, using the target microstructure feature parameters as constraints, it obtains the corresponding initial values ​​of printing control parameters through reverse engineering based on the process-deposition state mapping model. Next, it integrates slurry base parameters, equipment base data, and measured state data of the deposited layer for regional adaptation and correction, ultimately forming the specific baseline printing control parameters for each functional area and their associated expected deposition state characterization. Based on this, the path processing unit encapsulates the above results into a printing path file, which is used to generate CNC instruction sequences. Its data structure includes spatial coordinate fields, geometric trajectory fields, and deposition state characterization reference fields. The deposition state characterization reference field is bound one-to-one with the spatial coordinate field, ensuring that each trajectory segment in the path file carries the expected microstructure feature information at that location. The initial values ​​of the printing control parameters are initial reference values ​​obtained through reverse engineering, which are then used to generate the final baseline printing control parameters after regional adaptation and correction.

[0057] Specifically, the path processing unit retrieves the target microstructure feature parameters at the corresponding spatial location based on the spatial coordinates of the current path, obtains the corresponding baseline printing control parameters based on the process-deposition state mapping model, and further determines the expected deposition state characterization quantity of the same type as the detectable parameters of the in-situ sensor component; the baseline printing control parameters and the expected deposition state characterization quantity are written into the corresponding printing control parameter field and deposition state characterization quantity reference field in the printing path file, respectively.

[0058] In practice, users first determine the corresponding target microstructure characteristic parameters (including porosity, channel diameter, etc.) based on the macroscopic performance requirements of the target ceramic capacitor dielectric (such as dielectric constant, breakdown field strength, thermal conductivity, etc.) using a material design model well-known in the field.

[0059] Using the aforementioned target microstructure characteristic parameters as input, the motion control module 4 calls the process-deposition state mapping model for inverse inversion, outputting the corresponding baseline printing control parameters (including extrusion flow rate, nozzle moving speed, feed ratio, etc.), and generating a numerical control command sequence through the path processing unit. During the printing process, the online monitoring and feedback module 3 collects deposition state data in real time and feeds it back to the motion control module 4 to achieve dynamic correction of subsequent deposition processes; at the same time, the system synchronously records equipment status and printing process data for continuous updating of the calibration database and iterative optimization of printing control parameters.

[0060] It should be noted that the measured sedimentation state characterization of the deposited area can be used as feedback input for adjusting the parameters of subsequent path segments or subsequent sedimentary layers; the motion control module 4 corrects the printing control parameters of the subsequent deposition process based on the feedback information, so that the deviation between the measured sedimentation state characterization of the subsequent deposition and the expected sedimentation state characterization is within the preset tolerance range.

[0061] The motion control module 4 receives the measured deposition state characterization from the online monitoring and feedback module 3, compares it with the expected deposition state characterization determined by itself based on the process-deposition state mapping model, and obtains the deposition state deviation. Based on this deviation, the motion control module 4 calls the preset compensation rules in the process data storage unit to dynamically correct the printing control parameters of the subsequent deposition process, so that the deviation between the measured value and the expected value of the subsequent deposition is kept within the preset tolerance range.

[0062] In this embodiment, the preset tolerance range refers to a pre-defined allowable deviation interval. When the deviation between the measured depositional state characterization and the expected depositional state characterization is within this range, the system determines that the current depositional quality is qualified and no compensation is required; when the deviation exceeds this range, the system initiates dynamic correction according to preset compensation rules. For example, when the target layer thickness is 30 μm, the preset tolerance range can be set to ±2 μm, meaning that a measured layer thickness between 28 μm and 32 μm is considered qualified.

[0063] In this embodiment, the preset compensation rule includes three levels of deviation processing logic: Level 1: When the deviation between the measured sedimentary state characterization and the expected sedimentary state characterization is within the preset tolerance range, continue printing and recording the current deviation; Level 2: When the absolute value of the deviation exceeds the upper limit of the preset tolerance range but does not exceed the failure threshold, the motion control module 4 performs local compensation and adjusts at least one of the following in the subsequent path segment: extrusion pressure, nozzle moving speed, or path spacing. Level 3: When the absolute value of the deviation exceeds the failure threshold, printing is paused, and local repairs, scraping and reprinting, or printing is terminated.

[0064] In this embodiment, the motion control module 4 dynamically corrects the printing control parameters of the subsequent deposition process based on the deposition state deviation, including: generating a process compensation amount based on a preset compensation rule, and using the process compensation amount to correct the printing control parameters of the subsequent deposition process.

[0065] The preset threshold is used to determine whether process compensation is triggered, and it is determined based on the preset tolerance range.

[0066] Specifically, the preset threshold is set to be equal to the upper limit of the preset tolerance range of the corresponding functional area, and less than the failure threshold, to ensure that compensation correction is initiated in a timely manner after the deposition state deviation exceeds the allowable range. For example, if the target layer thickness is 30μm, the preset tolerance range can be set to ±2μm, the preset threshold to ±2μm, and the failure threshold to ±5μm. When the measured layer thickness is 33μm (deviation of +3μm), the system determines that the deviation exceeds the preset threshold but does not exceed the failure threshold, triggering the second level of compensation: reducing the extrusion pressure by 5% and increasing the nozzle speed by 3%; if the subsequent deviation continues to exceed the preset threshold and still cannot converge to the tolerance range after multiple compensations, the third level is triggered, printing is paused and a layer height rescan is performed, and the Z-axis path of the next layer is corrected according to the local height map.

[0067] The preset compensation rules are stored in the process data storage unit and can be called by the motion control module 4.

[0068] The process compensation amount includes flow compensation, speed compensation, Z-axis compensation, or temperature compensation generated based on at least one of linewidth deviation, layer thickness deviation, or temperature deviation.

[0069] As one specific implementation method, the process parameter compensation amount can be calculated according to the following formula: Line width closed loop can be adopted as follows: 1 2 Speed ​​compensation can be achieved by: 3 4 Z-axis compensation can be achieved using: 5 Temperature compensation can be achieved by: 6 7 in, , , and These represent the target linewidth, target layer thickness, target temperature, and expected solvent evaporation characterization values, respectively. w, h, and T are measured values, and E... solvent The current solvent evaporation characterization is estimated by the calibrated model, and K1 to K7 are compensation coefficients tuned experimentally. The motion control module 4 sends the corrected parameters (Q0+ΔQ, v0+Δv, Z0+ΔZ, T0+ΔT) as the current printing control parameters for the subsequent deposition process.

[0070] In this embodiment, the local compensation adopts a finite memory and zoned compensation strategy. For example, for geometric deviations, the motion control module 4 can perform corresponding compensation in the subsequent deposition process according to the deviation type and amplitude (for example, if the layer thickness in a certain area is 3μm higher, the extrusion flow rate can be appropriately reduced, the nozzle speed increased, or the Z-axis lift reduced in the next segment or layer). The finite memory means that the compensation amount is determined based on the deviation data of the current path segment and the preset number of path segments or deposition layers before it.

[0071] Furthermore, during the layer-by-layer deposition process, the target path for each deposition layer is still generated based on the original 3D design model that produces the 3D printing path file. Before printing the next layer, the motion control module 4 controls the online monitoring and feedback module 3 to scan the already deposited layers, obtaining their height map, linewidth map, and defect locations. The path processing unit makes local corrections based on the scanning results on the original path (for example, if the previous layer is locally too high, the extrusion flow rate is reduced or the Z-axis height is adjusted at the corresponding position in the next layer; if the previous layer is locally too low, the flow rate is appropriately increased or the nozzle speed is reduced in the next layer). At the same time, the path processing unit dynamically adjusts the interlayer scanning direction according to the type of functional area: for interlayer bonding functional areas, the scanning direction of the next layer is deflected by a preset angle relative to the previous layer; for oriented grain areas, the scanning direction of each layer remains consistent; for heat dissipation channel areas, the scanning direction of each layer is aligned according to the geometric continuity of the 3D channel.

[0072] In a preferred embodiment of the present invention, a multi-slurry system is configured to meet the microstructure construction requirements such as gradient dielectric distribution, grain boundary strengthening, and thermal management. The multi-slurry system includes at least two basic dielectric slurries and at least one functional slurry; wherein the first and second basic dielectric slurries have different post-sintering dielectric constants and form a dielectric gradient through dynamic mixing; the functional slurry includes one or more of grain boundary strengthening slurries, insulating and thermally conductive slurries, or sacrificial support slurries. The solid content, viscosity, sintering shrinkage rate, and drying window of each slurry are pre-calibrated to meet the requirements of co-printing in the same layer and interlayer bonding. Those skilled in the art can obtain a functional slurry system that meets the requirements of printing accuracy, interlayer bonding, and sintering densification by adjusting the powder type, particle size distribution, solid content, dispersant, rheology modifier, photosensitive resin system, and dopant components.

[0073] Taking the additive manufacturing of ceramic capacitor dielectric as an example, this embodiment configures four different functional ceramic pastes: low dielectric constant basic ceramic paste A, high dielectric constant ceramic paste B, grain boundary strengthening paste C, and insulating and thermally conductive paste D, as detailed below: Slurry A (low dielectric constant base ceramic slurry): As the first base dielectric slurry, it is used to construct the low dielectric constant region, for example, the dielectric constant after sintering is ε. AThe powder is a MgTiO3–CaTiO3 composite powder (Ca / Ti molar ratio 0.95:1.05), doped with 0.2 mol% MnO2 and 0.15 mol% SiO2 (for optimizing sintering density and temperature frequency stability), with an average particle size of 150 nm; the organic carrier consists of acrylate prepolymer, dibutyl phthalate plasticizer, and BYK-111 dispersant, with a solid content of 58 vol% and a room temperature dielectric constant ε. r ≈85@1kHz, dielectric loss tanδ≤0.03%.

[0074] Slurry B (high dielectric constant ceramic slurry): As the second basic dielectric slurry, it is used to dynamically mix with slurry A in microstructure printing execution module 2 to form a dielectric constant gradient. After sintering, the dielectric constant is ε. B The powder is a ZnNb2O6 modified MgTiO3-CaTiO3 composite powder (doped with 2 mol% ZnNb2O6 to improve the dielectric constant), doped with 0.15 mol% MnO2 and 0.1 mol% Al2O3, with an average particle size of 150 nm; the organic carrier is consistent with the slurry, with a solid content of 58 vol% and a room temperature dielectric constant ε. r ≈130@1kHz, dielectric loss tanδ≤0.04%.

[0075] Slurry C (Grain Boundary Strengthening Slurry): As a functional slurry, it is used to print grain boundary strengthening meshes, crack-resistant networks, or localized reinforcement regions. The powder is a MgO-Al2O3-SiO2 glass phase, doped with 0.5 mol% Y2O3, with an average particle size of 150 nm. The organic carrier is the same as the base slurry, and the solid content is 58 vol%.

[0076] Slurry D (Insulating and Thermally Conductive Slurry): As a functional slurry, it is used for insulating and thermally conductive coating of the inner wall of heat dissipation channels, heat conduction paths, or thermal management areas. The powder is hexagonal boron nitride (h-BN) nanosheets, combined with 5wt% powder as a sintering aid. The organic carrier is the same as the base slurry, and the solid content is 55 vol%.

[0077] Optionally, the multi-slurry system may further include slurry E (sacrificial support slurry) for forming suspended structures such as hollow heat dissipation channels, which is removed during subsequent degreasing or sintering processes without affecting the functional performance of the final ceramic component.

[0078] All slurries are compatible with high-precision direct-write 3D printing equipment, with solid content controlled between 55 vol% and 62 vol%, rheological properties meeting shear-thinning characteristics, and a static viscosity of 10. 5 Pa•s, shear rate 100s -1 viscosity ≤10 3 Pa•s ensures continuous extrusion during printing and shape retention after molding.

[0079] In this embodiment, the path processing unit pre-stores path planning rules for four functional areas. Each rule is used to generate differentiated printing control parameters adapted to that functional area, and simultaneously calculates the expected deposition state characterization based on the corresponding mapping relationship. The specific implementation is as follows: Differential path planning for gradient dielectric layer regions: When the target dielectric constant exhibits a gradient change trend along the dielectric thickness direction based on the target microstructure characteristic parameters, it is determined to be a gradient dielectric layer region. The path processing unit calls the reciprocating parallel scanning path rule and adjusts the mixing ratio of slurry A and slurry B layer by layer according to a preset proportional function; the preset proportional function is a linear function, a piecewise linear function, or other continuously changing function.

[0080] In a preferred example, a linear scaling function is used. Every five layers, the mixing ratio of slurry A and slurry B is adjusted according to this linear function to achieve a controllable gradient distribution of the dielectric constant in space. Simultaneously, the scanning path direction is deflected by 90° for each layer to ensure interlayer bonding and structural uniformity. The aforementioned mixing ratio adjustment sequence, scanning path parameters, and interlayer deflection angle serve as the differentiated printing control parameters for this region. The path processing unit calculates the expected flow rate and expected feed ratio for each layer based on these differentiated printing control parameters, serving as a characterization of the expected deposition state for this region.

[0081] Differential path planning for oriented grain regions: When a target orientation factor is identified as greater than or equal to a preset threshold, it is determined to be an oriented grain region. The path processing unit calls the longitudinal heat flow alignment scanning path rule to determine that the scanning path is uniformly arranged along the longitudinal heat flow direction of the medium. The inner diameter of the extrusion nozzle is set to 100 μm, and the extrusion shear rate is controlled to induce the oriented alignment of plate-like seed crystals along the path direction, laying the foundation for the growth of oriented grains in the subsequent sintering process. The above-mentioned nozzle inner diameter, scanning path arrangement direction, and extrusion shear rate control parameters serve as the reference printing control parameters for this region. The path processing unit calls the shear-induced orientation function stored in the process data storage unit to forward calculate the model to predict the grain orientation degree, which is used to determine the matching relationship between the reference printing control parameters and the target grain orientation degree. At the same time, based on the reference printing control parameters, the expected linewidth, expected flow rate, and expected pressure are forward calculated as the expected deposition state characterization quantities for this region.

[0082] Differential path planning for grain boundary strengthening mesh regions: When a periodic mesh-like distribution of the target grain boundary strengthening phase is identified, it is determined to be a grain boundary strengthening region. The path processing unit calls the cross-grid scanning path rule to determine the grid linewidth as 50 μm and the grid spacing as 50 μm. Simultaneously, it determines the printing timing of the grain boundary strengthening slurry C at the corresponding layer, so that the grain boundary strengthening slurry C forms an interpenetrating network structure with the basic dielectric slurry A and / or the high dielectric constant slurry B, prefabricating the grain boundary strengthening framework. The above-mentioned grid linewidth, grid spacing, grain boundary strengthening slurry C switching timing, and collaborative printing parameters serve as the baseline printing control parameters for this region. The path processing unit also calculates the expected grid linewidth, expected grid spacing, and expected trajectory position based on the baseline printing control parameters, as characterizations of the expected deposition state of this region.

[0083] Differentiated path planning for the heat dissipation microchannel region: When the geometric parameters of the target heat dissipation channel or internal functional channel are analyzed, it is determined to be a heat dissipation microchannel region. The path processing unit calls the hollow ring scanning path rule, determines the slurry switching sequence according to the order of printing the inner wall BN coating layer of the channel first, and then printing the outer dielectric substrate, and reserves a hollow channel with a diameter of 200μm; the channel array is arranged at equal intervals in the horizontal direction, and the interlayer channels are staggered to form a three-dimensional interconnected heat dissipation network; at the same time, the curing rate control parameters, interlayer support structure parameters, and sintering shrinkage matching compensation parameters are determined to ensure that the channel structure remains stable and continuously connected during degreasing and sintering. The switching sequence of the BN coating layer and dielectric substrate slurry, the extrusion flow rate, the interlayer support parameters, the curing rate control parameters, and the shrinkage compensation parameters are used as the reference printing control parameters for this region; the path processing unit also calculates the expected channel inner diameter and expected channel wall thickness based on the reference printing control parameters as the expected deposition state characterization of this region.

[0084] Furthermore, when generating differentiated printing control parameters, the path processing unit uses the global process constraints stored in the process data storage unit (e.g., nozzle movement speed set to 30mm / s; extrusion pressure set to 0.2~0.5MPa and adjusted in real time according to slurry viscosity; printing environment temperature controlled at 25℃; relative humidity controlled below 30% to avoid excessive evaporation of slurry solvent leading to interlayer cracking) as the value boundary, and automatically generates transition segment path parameters in the multi-slurry switching area; finally, the differentiated printing control parameters of each functional area are integrated into a numerical control instruction sequence that drives the microstructure printing execution module 2.

[0085] The present invention also includes: a printing platform 5; wherein the printing platform 5 is disposed below the microstructure printing execution module 2 and serves as a support base for forming the workpiece.

[0086] It is evident from the above that the online control system for multi-slurry molding parameters in additive manufacturing of this invention, based on the slurry supply and control module 1 and the microstructure printing execution module 2, dynamically supplies and deposits multi-component slurries layer by layer according to the current printing control parameters issued by the motion control module 4. Simultaneously, the online monitoring and feedback module 3 acquires the measured deposition state characterization in real time and synchronizes it to the motion control module 4. The motion control module 4 determines the expected deposition state characterization based on the process-deposition state mapping model, compares it with the measured deposition state characterization to obtain the deposition state deviation, and dynamically corrects the printing control parameters of subsequent deposition processes accordingly, ensuring that the deviation between the subsequent deposition state characterization and the expected deposition state characterization is within a preset tolerance range. These modules work together to form a complete online closed-loop compensation mechanism, effectively overcoming the problem of accumulated molding deviations caused by the lack of online compensation, and significantly improving the construction accuracy and batch consistency of multi-slurry microstructures.

[0087] Continue reading Figure 1 In the above embodiments, the slurry supply and control module 1 includes at least two independent slurry storage pipes 11, a fluid conveying device (not shown in the figure), and a dynamic mixer 12; wherein, each of the slurry storage pipes 11 is used to store one channel of ceramic slurry, and the outlet of each of the slurry storage pipes 11 is connected to the inlet of the fluid conveying device; the outlet of the fluid conveying device is connected to the inlet of the dynamic mixer 12; and the outlet of the dynamic mixer is connected to the nozzle inlet of the microstructure printing execution module.

[0088] Specifically, each of the slurry storage pipes 11 is used to store a ceramic slurry (such as slurry A, slurry B, slurry C, and slurry D), and is connected to the fluid conveying equipment through an independent slurry conveying pipeline to form multiple independent slurry channels.

[0089] The fluid delivery device can be a high-precision fluid pump, used to extract slurry from each slurry storage tube 11 in proportion to the mixing ratio indicated by the current printing control parameters issued by the motion control module 4, adjust the supply flow rate of each slurry channel, and deliver it to the dynamic mixer, and then output it to the nozzle of the microstructure printing execution module 2 through the slurry delivery path.

[0090] Furthermore, each slurry conveying path is also equipped with a pressure sensor and a flow sensor to monitor the pressure and flow ratio in the pipeline in real time, and to feed the monitoring data back to the motion control module 4 so that it can correct the pump speed of the fluid conveying equipment and thereby adjust the extrusion pressure.

[0091] The dynamic mixer 12 is preferably a static spiral mixer or an active stirring mixer, with a built-in micro stirring rotor and an adjustable speed of 0~3000rpm. It can adjust the feed ratio of different slurries in real time according to control commands to achieve online continuous mixing and uniform dispersion of multi-component slurries. The dead volume of the mixer is pre-calibrated to compensate for the delay effect when switching between multiple slurries. In addition, the slurry supply and control module 1 is also equipped with a slurry temperature control unit to control the temperature of all slurries at 25±0.5℃, ensuring the stability of the slurry rheological properties and avoiding the impact of ambient temperature fluctuations on printing accuracy.

[0092] The outlet of the dynamic mixer is connected to the feed end of the multi-nozzle module 21 of the microstructure printing execution module 2 via a pipeline.

[0093] In the above embodiments, the in-situ sensor assembly includes: a visual monitoring unit, a laser displacement sensor, and an infrared thermal imaging sensor; wherein: The visual monitoring unit is used to acquire the surface morphology of the deposition layer and the distribution parameters of the slurry, so that the motion control module 4 can correct the path spacing, scanning direction and scanning angle.

[0094] Specifically, the visual monitoring unit is located near the multi-nozzle module 21 of the microstructure printing execution module 2. It is used to acquire parameters of the surface morphology and slurry distribution of the deposited layer, including deposition trajectory, linewidth, edge contour, broken lines, collapses, material accumulation, voids, and material boundary positions. The motion control module 4 corrects the path spacing, scanning direction, and scanning angle of subsequent paths based on the deviation between the measured linewidth and the expected linewidth obtained by the visual monitoring unit, and determines whether the slurry switching is in place.

[0095] In this embodiment, the visual monitoring unit can be a three-axis linkage visual monitoring unit, which is equipped with a 20-megapixel high-resolution industrial camera and a telecentric lens, with an imaging resolution of 2μm / pixel.

[0096] The laser displacement sensor is used to obtain the geometric dimensions and flatness parameters of the deposited layer, so that the motion control module 4 can correct the single-layer thickness.

[0097] Specifically, a laser displacement sensor is positioned above the printing platform to acquire the geometric dimensions and flatness parameters of the deposited layer, including single-layer thickness, local height, interlayer height difference, surface flatness, and substrate flatness. The motion control module 4 corrects the Z-axis lifting amount of the subsequent deposited layer and / or the path planning of the next layer based on the deviation between the measured layer thickness and the expected layer thickness obtained by the laser displacement sensor, in order to compensate for the layer thickness deviation.

[0098] In this embodiment, the laser displacement sensor has a sampling frequency of 10kHz and a measurement accuracy of ±0.2μm.

[0099] Infrared thermal imaging sensors are used to acquire thermal state parameters of the deposition area, which are then used by the motion control module to adjust the platform temperature and interlayer waiting time.

[0100] Specifically, the infrared thermal imaging sensor can be an infrared thermal imager, which is positioned above the printing platform to acquire thermal state parameters of the deposition area, including surface temperature and local temperature field. The motion control module 4, based on the surface temperature and local temperature field, and combined with a pre-established calibration relationship between temperature and solvent evaporation, estimates the current solvent evaporation characterization E of the deposition area using temperature, ambient humidity, and time. solvent Based on this, the platform temperature and inter-layer waiting time are adjusted.

[0101] In this embodiment, the infrared thermal imaging sensor is an infrared thermal imager with a temperature measurement range of 0~150℃ and a temperature measurement accuracy of ±0.5℃.

[0102] In this embodiment, the in-situ sensor assembly further includes a pressure sensor for acquiring pressure status parameters of the slurry conveying path, so that the motion control module 4 can correct the volumetric flow rate and extrusion pressure.

[0103] Specifically, a pressure sensor is installed in the slurry conveying path to obtain pressure status parameters of the slurry conveying path, including the pressure of each slurry channel, the nozzle back pressure, and pressure fluctuations. The motion control module 4 corrects the volume flow rate and extrusion pressure based on the deviation between the measured pressure and the expected pressure obtained by the pressure sensor, and triggers an alarm or shutdown protection when nozzle blockage or insufficient material output is detected.

[0104] In this embodiment, the in-situ sensor assembly further includes a flow sensor, used to acquire the flow rate and proportion status parameters of the slurry conveying path, so that the motion control module 4 can correct the feeding ratio and pump speed.

[0105] Specifically, flow sensors are installed in each slurry conveying path to acquire flow rate and mixing ratio parameters, including the actual flow rate and mixing ratio deviation of each slurry channel. The motion control module 4 adjusts the feeding ratio and pump speed based on the deviation between the measured flow rate and the expected flow rate acquired by the flow sensors.

[0106] In this embodiment, the in-situ sensor assembly may also include a temperature and humidity sensor for acquiring temperature and humidity parameters of the printing environment; the motion control module 4, based on the environmental parameters fed back by the temperature and humidity sensor, coordinates and regulates the constant temperature control unit and the environmental conditioning equipment to ensure the stability of the slurry rheological properties and the controllability of the drying process.

[0107] Method Implementation Examples: See Figure 2The present invention also provides a method for online control of multiple slurry molding parameters in additive manufacturing, comprising: Step S1: Receive process input data containing target microstructure feature parameters; based on a preset process-deposition state mapping model, obtain baseline printing control parameters through inverse calculation; substitute the baseline printing control parameters into the forward prediction process of the process-deposition state mapping model to determine the corresponding expected deposition state characterization quantity. Step S2: Drive the multi-component ceramic slurry to be deposited layer by layer according to the benchmark printing control parameters, and collect the original sensor signals during the printing process in real time, and convert them into measured deposition state characterization quantities of the same type as the expected deposition state characterization quantities. Step S3: Compare the measured sedimentation state characterization quantity with the expected sedimentation state characterization quantity using dimensional comparison to obtain the sedimentation state deviation; Step S4: When the deposition state deviation exceeds a preset threshold, the printing control parameters of the subsequent deposition process are dynamically corrected based on the deposition state deviation, so that the deviation between the measured deposition state characterization quantity and the expected deposition state characterization quantity of the subsequent deposition is within a preset tolerance range, and the corrected printing control parameters are used as the updated current printing control parameters for the control of the subsequent deposition process.

[0108] The dynamic correction of printing control parameters for subsequent deposition processes based on deposition state deviation includes: adjusting at least one of the following: nozzle moving speed, extrusion pressure, volumetric flow rate, feed ratio, single-layer thickness, platform temperature, ambient humidity, interlayer waiting time, light intensity, and exposure time.

[0109] The specific implementation of each step in the above method is consistent with the relevant description in the aforementioned system embodiment, and can be referred to each other. It will not be repeated here.

[0110] In this embodiment, the above four types of ceramic slurries are used for additive printing. After printing, the green body is degreased and sintered to obtain a ceramic capacitor dielectric with controllable microstructure.

[0111] The specific process is as follows: 1. Degreasing process: A two-step degreasing method is adopted. First, the green body is placed in a vacuum drying oven and heated to 180°C at a heating rate of 1°C / min, and held for 4 hours to remove low-boiling-point solvents. Then, it is transferred to an atmosphere degreasing furnace and heated to 600°C at a heating rate of 0.5°C / min under an air atmosphere, and held for 8 hours to fully oxidize and decompose the organic carrier and avoid carbon residue. Finally, it is cooled to room temperature at a rate of 2°C / min to obtain the degreased green body. 2. Sintering process: The degreased green blank is placed in an oxygen atmosphere sintering furnace. First, the temperature is increased to 1000℃ at a rate of 5℃ / min and held for 2 hours to initially densify the green blank. Then, the temperature is increased to the peak sintering temperature of 1380℃ at a rate of 3℃ / min and held for 4 hours. During the holding process, an air atmosphere is maintained to promote the directional growth and densification of grains, while also allowing the grain boundary strengthening phase to precipitate uniformly, forming a continuous strengthening network at the grain boundaries. Subsequently, the temperature is decreased to 800℃ at a rate of 2℃ / min and held for 2 hours to eliminate internal stress. Finally, the furnace is cooled to room temperature to obtain a sintered and dense ceramic capacitor dielectric.

[0112] After sintering, the dielectric is precision ground and polished to obtain a ceramic capacitor dielectric sample with dimensions of 40mm×40mm×2mm, which is used for subsequent performance testing and verification.

[0113] The gradient-structure ceramic capacitor dielectric prepared in this embodiment was characterized by its microstructure and tested by its macroscopic performance. The microstructure characterization results show that the prepared ceramic capacitor dielectric can form a dielectric gradient structure that varies along the thickness direction, a grain orientation structure that is arranged along a predetermined direction, a grain boundary strengthening structure, and a through-type heat dissipation microchannel structure; after sintering, the dielectric has high density, a continuous distribution of the grid-like grain boundary strengthening phase, and the heat dissipation microchannel remains intact and interconnected, with a continuous BN coating layer on the inner wall of the channel.

[0114] Macroscopic performance test results show that the prepared ceramic capacitor dielectric possesses excellent dielectric, insulating, thermal conductivity, and mechanical properties. At a test frequency of 1 kHz, its room-temperature relative permittivity can vary within a preset gradient range, and it exhibits low dielectric loss. Simultaneously, it demonstrates good breakdown performance, thermal conductivity, and bending resistance. These test results indicate that the online control method described in this embodiment can achieve the collaborative construction of multiple target microstructures.

[0115] The measured data printed in this embodiment includes measured deposition state characterization quantities, microstructure characterization results, and macroscopic performance test data. As complete sample data, these are fed back to the pre-design stage and the calibration database of this invention. On the one hand, this is used to optimize the constitutive parameters of the process-deposition state mapping model and improve the accuracy of reverse design; on the other hand, it is used to iteratively optimize the matching relationship between 3D printing path planning and printing control parameters, further improving the accuracy and yield of microstructure construction, and forming a closed-loop R&D system covering the entire process of design, printing, sintering, verification, and optimization.

[0116] Traditional methods employ a trial-and-error process, relying on manual experience to repeatedly adjust process parameters and verify performance. This makes precise control of microstructures difficult, resulting in a lengthy process development cycle. This invention utilizes a process-deposition state mapping model for reverse design, combined with online monitoring and closed-loop feedback to dynamically correct the printing process. This achieves precise microstructure construction and significantly shortens the process development cycle. Measured data is fed back to a calibration database, further improving the accuracy of printing control parameter matching and yield in subsequent deposition processes.

[0117] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the online control method for multiple slurry molding parameters in additive manufacturing as described in any of the preceding claims.

[0118] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the online control method for multiple slurry molding parameters in additive manufacturing as described in any of the preceding claims.

[0119] The present invention provides an online control method for multi-slurry forming parameters in additive manufacturing. By constructing a coupling mechanism of inverse inversion and forward prediction based on a process-deposition state mapping model, the method calculates the baseline printing control parameters and their corresponding expected deposition state characterizations to match the target microstructure feature parameters at the initial printing stage. During the layer-by-layer deposition process, it acquires and compares the measured state characterizations of the same type in real time. When the deviation exceeds a preset threshold, the printing control parameters for subsequent deposition processes are dynamically adjusted based on this deviation, thereby achieving closed-loop compensation between the multi-slurry supply state and the deposition forming state during the deposition process. This method significantly improves the construction accuracy, manufacturing stability, and batch consistency of complex multi-slurry microstructures.

[0120] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An online control system for multiple slurry molding parameters in additive manufacturing, characterized in that, include: The slurry supply and control module is used to store and transport ceramic slurries with two or more different components, and dynamically adjust the supply flow rate and supply ratio of each ceramic slurry according to the received current printing control parameters. A microstructure printing execution module is used to deposit and solidify the ceramic slurry layer by layer according to the current printing control parameters; The online monitoring and feedback module includes an in-situ sensor assembly and a signal processing unit that are electrically connected to each other. The in-situ sensor assembly is used to acquire raw sensing signals in real time during the printing process. The signal processing unit is used to convert the raw sensing signals into measured deposition state characteristics of the same type as the expected deposition state characteristics, and synchronously send the measured deposition state characteristics to the motion control module. The motion control module is connected to the slurry supply and control module, the microstructure printing execution module and the online monitoring and feedback module, respectively. It is used to receive process input data containing target microstructure feature parameters, determine the baseline printing control parameters based on the preset process-deposition state mapping model, and determine the corresponding expected deposition state characterization quantity based on the baseline printing control parameters. The measured deposition state characterization quantity is compared with the expected deposition state characterization quantity to obtain the deposition state deviation. When the deposition state deviation exceeds a preset threshold, the printing control parameters of the subsequent deposition process are dynamically corrected based on the deviation, so that the deviation between the measured deposition state characterization quantity and the expected deposition state characterization quantity of the subsequent deposition is within a preset tolerance range. The corrected printing control parameters are then sent as the updated current printing control parameters to the slurry supply and control module and the microstructure printing execution module.

2. The online control system for multiple slurry molding parameters in additive manufacturing according to claim 1, characterized in that, The process-deposition state mapping model includes: a target microstructure inversion sub-model and a deposition state prediction sub-model; wherein... The target microstructure inversion sub-model is used to determine the corresponding baseline printing control parameters by using the target microstructure feature parameters as constraints. The deposition state prediction sub-model is used to determine the expected deposition state characterization quantity of the same type as the detectable parameter of the in-situ sensor component based on the benchmark printing control parameters; wherein, the target microstructure characteristic parameters include at least one of target linewidth, target layer thickness, target porosity, target dielectric constant gradient, target grain orientation, and target grain boundary strengthening mesh characteristics.

3. The online control system for multiple slurry molding parameters in additive manufacturing according to claim 1, characterized in that, The motion control module includes a process data storage unit and a path processing unit; wherein... The process data storage unit is used to store the process-deposition state mapping model and preset compensation rules; The path processing unit is used to receive target microstructure feature parameters, slurry basic parameters, and equipment basic data, perform differentiated path planning for different functional areas, determine the corresponding baseline printing control parameters based on the process-deposition state mapping model, determine the corresponding expected deposition state characterization quantity based on the baseline printing control parameters, and generate a numerical control instruction sequence to drive the microstructure printing execution module.

4. The online control system for multiple slurry molding parameters in additive manufacturing according to claim 3, characterized in that, The motion control module dynamically corrects the printing control parameters of the subsequent deposition process based on the deposition state deviation, including: The process compensation amount is generated based on the preset compensation rule, and the process compensation amount is used to correct the printing control parameters of the subsequent deposition process.

5. The online control system for multiple slurry molding parameters in additive manufacturing according to claim 1, characterized in that, The slurry supply and control module includes at least two independent slurry storage pipes, fluid delivery equipment, and a dynamic mixer; wherein... Each of the slurry storage tubes is used to store one channel of ceramic slurry, and the outlet of each of the slurry storage tubes is connected to the inlet of the fluid conveying equipment. The outlet of the fluid conveying device is connected to the inlet of the dynamic mixer; The outlet of the dynamic mixer is connected to the feed end of the multi-nozzle module of the microstructure printing execution module.

6. The online control system for multiple slurry molding parameters in additive manufacturing according to claim 1, characterized in that, The in-situ sensor assembly includes: The visual monitoring unit is used to acquire parameters of the surface morphology of the deposition layer and the distribution state of the slurry, so that the motion control module can correct the path spacing, scanning direction and scanning angle. A laser displacement sensor is used to acquire the geometric dimensions and flatness parameters of the deposition layer, so that the motion control module can correct the Z-axis lift of the subsequent deposition layer and / or the path planning of the next layer to compensate for the layer thickness deviation. An infrared thermal imaging sensor is used to acquire thermal state parameters of the deposition area, so that the motion control module can adjust the platform temperature and interlayer waiting time.

7. A method for online control of multiple slurry molding parameters in additive manufacturing, characterized in that, include: Receive process input data containing target microstructure feature parameters, and obtain baseline printing control parameters through inverse calculation based on a preset process-deposition state mapping model; Substitute the baseline printing control parameters into the forward model of the process-deposition state mapping model to determine the corresponding expected deposition state characterization quantity. The multi-component ceramic slurry is driven to be deposited layer by layer according to the reference printing control parameters, and the original sensor signals during the printing process are collected in real time and converted into measured deposition state characterizations of the same type as the expected deposition state characterizations. The sedimentation state deviation is obtained by comparing the measured sedimentation state characterization with the expected sedimentation state characterization. When the deposition state deviation exceeds a preset threshold, the printing control parameters of the subsequent deposition process are dynamically corrected based on the deviation, so that the deviation between the measured deposition state characterization and the expected deposition state characterization is within a preset tolerance range, and the corrected printing control parameters are used as the updated current printing control parameters for the control of the subsequent deposition process.

8. The online control method for multiple slurry molding parameters in additive manufacturing according to claim 7, characterized in that, The dynamic correction of printing control parameters for subsequent deposition processes based on deposition state deviation includes: adjusting at least one of the following: nozzle moving speed, extrusion pressure, volumetric flow rate, feed ratio, single-layer thickness, platform temperature, ambient humidity, interlayer waiting time, light intensity, and exposure time.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the online control method for multiple slurry molding parameters in additive manufacturing as described in any one of claims 7 to 8.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the online control method for multiple slurry molding parameters in additive manufacturing as described in any one of claims 7 to 8.

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