Laser-assisted direct writing printing sensing monitoring and intelligent prediction method and system

Abstract

The invention discloses a laser-assisted direct writing printing perception monitoring and intelligent prediction method and system, and the system comprises an optical dynamic focusing module, a liquid supply module, a temperature / modulus feedback module, a real-time morphology observation module, a printing platform motion module, a performance prediction and parameter reverse optimization module, and a programming controller cooperating with the modules. Further, through the steps of data acquisition, prediction model construction, modeling partition, parameter optimization and automatic process and intelligent printing, a multi-parameter coupling prediction model among temperature field parameters, printing size parameters and structural modulus parameters is constructed through a performance prediction and parameter reverse optimization module; according to the in-situ monitoring system, high-resolution in-situ multi-parameter detection and printing performance prediction feedback control in the printing process can be met at the same time, key performance indexes can be dynamically perceived and predicted in the printing process, and parameter self-adaptive regulation and control are achieved.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing technology, and in particular to a method and system for sensing, monitoring and intelligent prediction in laser-assisted direct-write printing. Background Technology

[0002] 3D printing (additive manufacturing) technology is developing rapidly and has many varieties, including selective laser sintering (SLS), stereolithography (SLA), digital light processing (DLP), and direct ink writing (DIW). Among them, direct ink writing (DIW) uses precisely controlled nozzles to extrude "ink" with specific rheological properties, stacking it layer by layer to form a structure. It has been widely used in fields such as biomedicine, flexible electronics, and functional materials due to its advantages such as strong material compatibility, flexible structural design, low cost, and ease of operation. However, existing additive manufacturing technologies have problems such as long process time, difficulty in simultaneously controlling modulus and size, and poor forming accuracy, which seriously restrict the industrial application of stiffness gradient structures.

[0003] Laser-assisted direct-write printing, as a highly efficient additive manufacturing technology, is widely used in flexible electronics, soft robotics, and biomedicine. This process combines the advantages of controllable material deposition with precise laser energy curing, enabling the free construction of complex three-dimensional structures, multi-material integration, and the fabrication of stiffness gradient structures. However, current laser-assisted direct-write printing processes primarily rely on preset process parameters (such as laser power, nozzle movement speed, and extrusion rate) for control, lacking real-time monitoring methods for key performance indicators (such as structural dimensions, stiffness, and temperature distribution). The performance of printed structures is often influenced by multiple factors, including material properties, ambient temperature, and curing rate. Existing performance evaluations largely depend on subsequent offline testing, which suffers from low efficiency, delayed feedback, and difficulty in real-time control of the printing process, severely impacting printing accuracy, performance consistency, and process optimization.

[0004] Currently, there is no solution that can simultaneously meet the requirements of high-resolution in-situ multi-parameter detection and printing performance prediction feedback control. There is an urgent need to develop an in-situ monitoring method and system that can dynamically sense and predict key performance indicators during the printing process and achieve adaptive parameter control. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the technical problem to be solved by the present invention is to propose a method and system for sensing, monitoring and intelligent prediction of laser-assisted direct writing printing, which can realize real-time prediction and feedback control of structural performance parameters (such as Young's modulus, temperature field, structural dimensions, etc.) during the printing process.

[0006] To achieve this objective, the present invention adopts the following technical solution: This invention provides a laser-assisted direct-write printing sensing monitoring and intelligent prediction method, S00: experimental acquisition and multimodal data acquisition. During the laser-assisted printing process, multidimensional dynamic parameter data of the printed structure during ink curing are acquired in real time. The multidimensional dynamic parameter data includes: printing process parameters, structural morphology parameters, surface temperature field distribution parameters, and offline measurement of structural stiffness / Young's modulus. S10: Construct a multiphysics prediction model, which includes a temperature field prediction model, a Young's modulus prediction model, and a printed structure size prediction model. S20: 3D modeling slicing and parameter partitioning. The 3D model of the object to be printed is analyzed and sliced ​​into layers. The printing process characteristics of each layer are calculated by combining the size and modulus requirements of each part through the multiphysics prediction model. S30: Target-driven parameter reverse optimization and automatic process setting. The user inputs the modulus gradient of each region of the target structure or the size and modulus of the functional partitions and other performance parameters. The system calls the multiphysics model in step S10 and reverse calculates to generate the optimal combination of printing parameters that meet the target. S40: Intelligent printing and online verification. Prepolymer printing ink is configured according to the physical characteristics of the object to be printed and the printing ink is extruded for printing. During the printing process, the parameters of the printed structure model are continuously collected and compared with the results of the multiphysics prediction model. S50: Adaptive parameter correction and closed-loop feedback. If the actual printing parameters are consistent with the target, printing continues. Otherwise, if the actual printing parameters are detected to deviate from the target, return to step S40 to automatically adjust the relevant printing parameters. S60: Dynamic model update and self-learning. After correcting the printing parameters, the printed structure is measured again. The actual printing parameters are compared with the model prediction values. Based on the data-driven strategy, the prediction model parameters are dynamically corrected and optimized to achieve adaptive model update.

[0007] Additionally, it includes a system for performing the laser-assisted direct-write printing sensing monitoring and intelligent prediction method described above, comprising an optical dynamic focusing module, a liquid supply module, a temperature / modulus feedback module, a real-time morphology observation module, a printing platform motion module, and a performance prediction and parameter reverse optimization module, preferably also including a programmable controller that works in conjunction with it.

[0008] The beneficial effects of this invention are as follows: The laser-assisted direct-write printing sensing monitoring and intelligent prediction system of this invention is based on the coupling mechanism of in-situ monitoring, intelligent prediction, and dynamic control. It mainly includes an optical dynamic focusing module, a liquid supply module, a temperature / modulus feedback module, a real-time morphology observation module, a printing platform motion module, and a performance prediction and parameter reverse optimization module, as well as a programmable controller that works in conjunction with these modules. By constructing a multi-parameter coupled prediction model between temperature field parameters, printing size parameters, and structural modulus parameters in the performance prediction and parameter reverse optimization module, it achieves synchronous monitoring of in-situ temperature, stiffness, and size, forming a closed-loop feedback adjustment and control of the three parameters of modulus-temperature-morphology. It can simultaneously meet the requirements of high-resolution in-situ multi-parameter detection and printing performance prediction feedback control during the printing process. It can realize an in-situ monitoring system that dynamically senses and predicts key performance indicators and achieves adaptive parameter control during the printing process. Attached Figure Description

[0009] Figure 1 This is a flowchart of a laser-assisted direct-write printing sensing monitoring and intelligent prediction method provided in a specific embodiment of the present invention; Figure 2 This is a schematic diagram of the composition of a laser-assisted direct-write printing sensing monitoring and intelligent prediction system provided in a specific embodiment of the present invention; Figure 3 This is a schematic diagram of a laser-assisted direct-write printing sensing monitoring and intelligent prediction system device provided in Embodiment 2 of the present invention.

[0010] In the picture: 1. Optical dynamic focusing module; 11. Laser generator; 12. Laser shaper; 13. Laser beam; 2. Liquid supply module; 21. Extrusion needle; 22. Syringe holder; 23. Ink syringe; 24. Pressure regulator; 25. Pressure source; 3. Temperature / modulus feedback module; 31. Laser Doppler vibration meter; 32. Infrared thermal imager; 33. Surface temperature sensor; 4. Real-time morphology observation module; 41. High-resolution liquid supply camera; 5. Printing platform motion module; 51. Printing platform; 52. Printed part; 53. X, Y, Z three-axis linear motion platform; 6. Programmable controller panel. Detailed Implementation

[0011] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0012] This invention provides a solution for laser-assisted direct-write printing that can simultaneously meet the requirements of high-resolution in-situ multi-parameter detection and printing performance prediction feedback control. Specifically, it is an in-situ monitoring method and system that can dynamically sense and predict key performance indicators and achieve adaptive parameter control during the printing process.

[0013] Example 1: This invention provides a laser-assisted direct-write printing sensing monitoring and intelligent prediction method, which mainly includes the following steps: S00: Experimental Data Acquisition and Multimodal Data Generation. During laser-assisted printing, multidimensional dynamic parameter data of the printed structure are acquired in real time during ink curing. These multidimensional dynamic parameter data include: printing process parameters, structural morphology parameters, and surface temperature field distribution parameters. The structural stiffness / Young's modulus is measured offline. The printing process parameters include laser power, laser frequency, spot diameter, focal length, printing scanning speed, and liquid supply flow rate (controlled by using air pressure, mechanical pressure, etc. to eject the printing ink). The structural morphology parameters include the evolution of the fluid-solid interface, layer thickness, and linewidth of the product to be printed. The surface temperature field distribution parameters include the spatial / temporal thermal characteristics of the jet and the curing zone. The acquired data is then spatiotemporally synchronized and feature-annotated to serve as the basis dataset for subsequent modeling.

[0014] S20: Construct a multiphysics prediction model, which includes a temperature field prediction model, a Young's modulus prediction model, and a printed structure size prediction model; The temperature field prediction model mainly considers the spatiotemporal evolution of the material temperature field during direct-write printing from three perspectives, based on the data collected in step S00 and physical modeling. These include standard heat conduction (i.e., natural diffusion of heat within the material), heating by a moving laser heat source (simulating localized heating of the material by the laser, influenced by the distribution of the moving laser heat source, with the heat source moving with the printing platform and energy concentrated within the laser spot area), and environmental heat exchange (i.e., heat exchange between the material and the environment). Using the one-dimensional Fourier heat conduction equation, combined with the laser heat source term and Newton's law of cooling, the model describes the spatiotemporal temperature evolution of the printed jet under laser action. Specifically: Considering standard heat conduction, in the laser-assisted in-situ 3D direct-write printing process, a high-energy laser continuously irradiates the surface of the printed PDMS jet. Due to the photothermal effect, its temperature rises rapidly and it quickly changes from a liquid state to a solid state. The printing process follows the one-dimensional Fourier law of heat conduction:

[0015] in, It's temperature. It is the material density. It is the specific heat capacity of the material. The thermal conductivity of the dielectric material.

[0016] When considering heating with a moving Gaussian heat source, a Gaussian laser model is established based on the experimental conditions. The laser spot is constructed with a Gaussian beam profile, and its irradiance and flux are highest on the optical axis and decrease with increasing deviation from the optical axis. Simultaneously, the effect of the laser in the jet depth direction is considered, and the laser power density (…) The spatiotemporal distribution pattern is as follows:

[0017] in, For radiation absorptivity, The power density of the laser ( ), The depth of laser action, This refers to the ink extrusion rate for direct-write printing, which is also the laser scanning rate. The laser spot diameter is denoted as .

[0018] When considering environmental heat exchange, during laser-assisted 3D direct-write printing, laser energy induces the thermal curing of the PDMS jet. Simultaneously, the PMDS printed structure also undergoes radiative and convective heat exchange with the surrounding medium, and this heat exchange conforms to Newton's law of cooling.

[0019] in, The coefficient of performance is the cooling factor. The ambient temperature.

[0020] Based on the above three physical assumptions, the spatiotemporal temperature field u(x,t) during the direct-write printing process should satisfy a one-dimensional nonhomogeneous heat conduction equation containing a Gaussian distributed heat source term and an environmental heat exchange term:

[0021] The other parameters are as described above. and These are the parameters to be fitted; At the same time, the equation is closed using the third type of boundary conditions (Robin boundary conditions):

[0022] Where A is the cross-sectional area of ​​the printed structure, L is the length of the printed area, and m is the mass of the printed structure.

[0023] By numerical discretization and fitting the equation with experimental data using the least squares method, the optimal model parameters can be obtained, enabling quantitative prediction of the temperature field during the laser printing process and providing a thermal field basis for subsequent modulus prediction.

[0024] In the Young's modulus prediction model, based on the Arrhenius kinetics and autocatalytic curing model, and combined with the temperature field output, a correlation between curing degree and Young's modulus is established. To address the defect phenomenon induced by high power, a sigmoid function correction is introduced to achieve accurate prediction of temperature / crosslinking degree-modulus. Specifically: The curing reaction kinetic model was constructed using the Arrhenius formula:

[0025] Where K(T) represents the reaction rate constant, A represents the pre-exponential factor, Ea represents the activation energy, R represents the universal gas constant, and T represents the temperature.

[0026] For the autocatalytic reaction model, the degree of curing function The format can be selected as:

[0027] in, The degree of curing is represented by m and n, which are empirical indices. The curing reaction kinetic equation is:

[0028] Substituting the temperature-time curve obtained in the previous step into the above equation, and combining it with the experimentally collected parameters, the equation is fitted and solved to obtain the curing reaction process under different laser power densities and scanning speeds.

[0029] In practice, the crosslinking reaction of materials such as PDMS essentially stops when cooled to room temperature (e.g., 25°C). Therefore, the degree of crosslinking at the structural endpoint temperature of 25°C is taken as the final degree of crosslinking and used to characterize the final material properties, i.e.:

[0030] The moment when the right end of the PDMS structure cools to 25 degrees Celsius, that is:

[0031] After obtaining the final degree of crosslinking, an empirical power-law model is used to describe the relationship between the degree of crosslinking and Young's modulus:

[0032] in, The reference modulus of the structure, and These are the power term coefficient and the power term exponent, respectively. Indicates the degree of crosslinking.

[0033] Experimental results show that the modulus and crosslinking degree of the printed structure do not exhibit a simple positive correlation, but rather a negative correlation at higher structural temperatures. Therefore, the traditional empirical power-law model is modified as follows: the sigmoid function is used to simulate the defect accumulation process (negative correlation) inside the structure as the temperature gradually reaches the critical temperature.

[0034] Therefore, we get:

[0035] in, The reference modulus of the structure; This refers to the degree of curing. This indicates the degree to which crosslinking contributes to Young's modulus; This indicates the degree of crosslinking influence on Young's modulus; k is the shape parameter of the Sigmoid function. Indicates the maximum temperature the material has experienced; This indicates the maximum critical temperature at which internal defects in a material begin to accumulate significantly. It is a temperature-dependent material constant, representing the contribution of temperature to Young's modulus; It is an index of the degree to which temperature affects Young's modulus.

[0036] In the aforementioned printed structure size prediction model, based on experimental data and physical mechanisms, an influence model is established on the structural dimensions (linewidth) of printing parameters (such as printing platform movement speed, printing extrusion nozzle inner diameter, liquid supply flow rate, etc.), providing a theoretical basis for variable size printing. Specifically: Printing ink is an incompressible fluid. The cross-section of the lines extruded from the printhead can be approximated as circular. The ink undergoes a certain degree of mold expansion after leaving the printhead. According to the ink volume conservation formula:

[0037] in, This is the coefficient for the expansion effect of the mold. For the inner diameter of the printhead, Q represents the velocity of the printing ink as it leaves the printhead, and Q is the fluid flow rate at the printhead outlet. During extrusion, flow rate Q and velocity It is only related to the rheological properties of the printing ink and the ink supply flow rate. Keeping the extrusion flow rate Q constant, it only depends on the printing platform speed. satisfy: and the height of the printhead and the print platform substrate At that time, the printing ink is extruded and can be evenly deposited on the substrate of the printing platform. Assuming that the parameters such as ink material, nozzle inner diameter, and liquid supply flow rate are fixed, that is... and Without changing the variables, define the dimensionless velocity and dimensionless height variables as follows: , Depending on the different parameter values, when At that time, the printing platform moving speed For deposition linewidth It has a decisive influence.

[0038] When determined In this case, the printing platform movement speed Adjusting the print line width Analyzing the changes, according to the principle of volume conservation, the volume of extruded ink is equal to the volume of deposited linear ink, therefore:

[0039] Further simplification yields:

[0040] V represents the printed deposition linewidth; V is the dimensionless velocity. , This is the print platform travel speed that works in conjunction with the print head; thus, the print platform travel speed can be set. Achieve print line width Precise adjustment.

[0041] After constructing the multiphysics prediction model, the structure to be printed can be modeled and printed, which involves the following steps: S20: 3D modeling slicing and parameter partitioning. The 3D model of the object to be printed is analyzed and sliced ​​into layers. Combining the size and modulus requirements of each part, the printing process characteristics of each layer are calculated through the multiphysics prediction model. Specifically, after the printing process characteristics of each layer are calculated by the multiphysics prediction model, the movement / rotation speed of the printing platform motion system and the pressure parameters of the liquid supply system are determined based on the size of each part of the object to be printed and the established printing structure size prediction model, so as to realize variable size structure printing. Furthermore, based on the required modulus of each part of the object to be printed and the established temperature field prediction model and Young's modulus prediction model, the spot diameter, the relative position of the printing needle and the laser spot, and the laser curing power are determined during the printing process, so as to realize modulus-adjustable structure printing. S30: Target-driven parameter reverse optimization and automatic process setting. The user inputs the modulus gradient of each region of the target structure or the size and modulus of the functional partitions and other performance parameters. The system calls the multiphysics model in step S10 to reverse calculate and generate the optimal combination of printing parameters that meet the target (i.e., laser power, printing scanning speed, liquid supply flow rate, printing platform motion trajectory, etc.). Preferably, multi-objective optimization algorithms such as genetic algorithms and Bayesian optimization can also be used to ensure that the recommended parameters take into account both equipment capabilities and process window.

[0042] S40: Intelligent printing and online verification. Prepolymer printing ink is configured according to the physical characteristics of the object to be printed, and the ink is extruded for printing. During the printing process, the parameters of the printed structure model are continuously collected and compared with the results of the multiphysics prediction model. Specifically, it consists of the following two steps: S41: Configure printing ink: Based on the physical properties of the object to be printed, determine the printing substrate material, weigh out the thermosetting flexible material in proportion (functional materials can be added if necessary), stir at a specific temperature to obtain a uniform prepolymer printing ink; load the uniform prepolymer printing ink into the printing syringe, perform high-speed centrifugation to remove air bubbles, then fix the ink syringe 23 on the syringe holder 22, connect the pressure supply device, and realize the extrusion of printing ink; S42: The programmable controller automatically issues instructions to control optical focusing, printing platform movement, and extrusion liquid supply to complete the printing task in a coordinated manner. During the printing process, it continuously collects key parameters such as temperature field, modulus, and structural dimensions, and compares them with the results of the prediction model to determine whether there are risks such as over-curing or heat concentration. S50: Adaptive parameter correction and closed-loop feedback. When the actual printing parameters are consistent with the target, printing continues. Otherwise, if the actual printing parameters are detected to deviate from the target (such as over-curing or defect accumulation), return to step S30 to automatically adjust the relevant printing parameters, i.e., automatically readjust the relevant printing parameters (such as reducing laser power or adjusting the speed of the printing platform). S60: Dynamic model update and self-learning. The printed structure is remeasured after the printing parameters are corrected. The actual printing parameters are compared with the model prediction values. That is, the actual temperature field, modulus and morphology data are compared with the model prediction values. According to the data-driven strategy, the prediction model parameters are dynamically corrected and optimized to achieve adaptive model update. This process is repeated in a loop. The system model and process control strategy will gradually learn and improve the prediction accuracy and printing consistency.

[0043] Example 2: The present invention also provides a laser-assisted direct-write printing sensing monitoring and intelligent prediction system for performing a laser-assisted direct-write printing sensing monitoring and intelligent prediction method as described in Example 1, comprising the following modules, specifically the following modules: The optical dynamic focusing module 1 includes a laser generator 11 and a laser shaper 12 (collimating lens, shaping lens, focusing lens, etc.) to generate a laser beam 13 of a specific wavelength (such as near-infrared or visible light), modulate a laser spot with different energy distribution shapes, and obtain a high-energy-density laser spot that is focused on the ink jet at the printhead exit. Specifically, after collimation, shaping, and focusing by the above-mentioned optical elements, a concentrated laser spot is formed. The shaping lens combination is automatically adjusted by the program (implemented by a programmable controller), and the laser spot can be dynamically modulated into different energy distribution shapes, such as Gaussian, flat-top, or square, according to process requirements, and programmable spots of different sizes can be achieved. Finally, the laser spot is focused by the focusing lens to obtain a high-energy-density laser spot that is precisely applied to the ink jet at the printhead exit.

[0044] The liquid supply module 2 (i.e., the liquid supply system) includes a pressure source 25, a pressure regulator 24, a gas supply head, an ink syringe 23 (i.e., a printing syringe), and an extrusion needle 21 (i.e., a printing needle). It precisely sets the liquid supply flow rate to push the printing ink out of the printing needle to form a fine and continuous jet. Specifically, the printing ink is pre-loaded inside the sealed ink syringe 23. The pressure source 25 is connected to the top of the ink syringe 23 through a gas supply line. The output pressure is precisely set by the pressure regulator 24. During operation, under the automatic control of the program (achieved through a programmable controller), the stable pressure pushes the ink through the extrusion needle to form a fine and continuous jet, ensuring the flow rate, speed, and consistency of material delivery. Therefore, this system can achieve accurate printing of materials of different types and viscosities and is suitable for additive manufacturing applications of various functional inks.

[0045] The temperature / modulus feedback module 3 consists of intelligent sensors and detection devices, including an infrared thermal imager 32, a surface temperature sensor 33, a laser Doppler vibrometer 31, or a nano-indentation device. Specifically, the intelligent sensors and detection devices are installed on the printing platform and can collect key parameters such as the temperature field distribution, local modulus, and curing state of the surface and near-surface of the printing area in real time without damage. Furthermore, through comprehensive analysis of the reflected light field, heat distribution, and morphological characteristics of the printed surface, this system can dynamically calibrate the current printing temperature, curing rate, and mechanical properties, realizing closed-loop feedback of the three parameters of modulus, temperature, and morphology. The acquired data will be transmitted to the central programmable controller in real time to automatically correct and update the process database, providing a basis for adaptive adjustment of subsequent parameters such as printing path, laser power, and extrusion speed, greatly improving printing accuracy and structural consistency.

[0046] The real-time morphology observation module 4 mainly consists of a high-resolution industrial camera 41, a microscopic imaging device, and an auxiliary lighting unit. It dynamically and in real-time collects and analyzes parameters such as layer thickness, linewidth, surface roughness, and boundary integrity during the printing process. Specifically, this module can dynamically and in real-time collect and analyze images of layer thickness, linewidth, surface roughness, and boundary integrity during the printing process. Combined with image processing algorithms, it can achieve intelligent judgment of printing quality, defect identification, and dimensional measurement, ensuring the geometric accuracy and surface quality of each layer. The observation results are synchronously uploaded to the programming controller and participate in the real-time optimization of printing parameters together with temperature / modulus feedback data.

[0047] Printing platform motion module 5 (i.e., attached) Figure 2 The worktable motion module in the middle mainly adopts a high-precision X, Y, Z three-axis linear motion platform 53 (i.e., the attached...). Figure 2 The high-precision three-axis linear motion platform enables the printing platform 51 to move precisely along a predetermined path, driving the substrate on the printing platform 51 to achieve layer-by-layer deposition of the structure in three-dimensional space to obtain the printed part 52. Each axis is equipped with a high-precision encoder and servo drive to ensure high repeatability of the relative position of the printing nozzle and the substrate and smooth movement. Preferably, this module, in conjunction with the aforementioned optical dynamic focusing module, can achieve fast and high-quality printing of complex three-dimensional structures.

[0048] The performance prediction and parameter reverse optimization module is used to clean, extract features, and standardize the data collected by the temperature / modulus feedback module and the real-time morphology observation module (mainly including multi-source process and performance data such as laser power, printhead movement speed, liquid supply pressure, real-time temperature distribution, printed structure dimensions, and modulus feedback) to establish a training dataset. The system then constructs a multi-parameter coupled prediction model between the temperature field, printed dimensions (such as layer thickness and linewidth), and structural modulus, and reverse-calculates the printing process parameters required to achieve the target printing performance. In other words, after sufficient training and cross-validation, this model can accurately predict the printing area and... The system monitors the temperature distribution and mechanical modulus changes of the formed structure. After the user inputs the desired target structural modulus or functional gradient distribution, the system automatically invokes the aforementioned prediction model. Combining this with parameter optimization algorithms such as genetic algorithms, Bayesian optimization, or gradient descent, it reverse-engineers the process parameters required to achieve the target performance, including laser power, printing platform movement speed, and liquid supply flow rate. It then provides suggestions for the optimal parameter range. Simultaneously, during the actual printing process, the system continuously collects real-time monitoring data and compares it with the prediction results. If the actual parameters deviate from the target range, the controller automatically corrects and optimizes the printing parameters to ensure that the printing always remains within the set target range, thus guaranteeing print quality.

[0049] Preferably, it also includes a programmable controller that works in conjunction with the optical dynamic focusing module, the liquid supply module, the temperature / modulus feedback module, the real-time morphology observation module, the printing platform motion module, and the performance prediction and parameter inverse optimization module.

[0050] This invention has been described through preferred embodiments. Those skilled in the art will understand that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. This invention is not limited to the specific embodiments disclosed herein; other embodiments falling within the scope of the claims are also within the protection scope of this invention.

Claims

1. A laser-assisted direct-write printing sensing monitoring and intelligent prediction method, characterized in that, Includes the following steps: S00: Experimental data acquisition and multimodal data acquisition. During the laser-assisted printing process, multidimensional dynamic parameter data of the printed structure are acquired in real time during the ink curing process. The multidimensional dynamic parameter data includes: printing process parameters, structural morphology parameters, surface temperature field distribution parameters, and offline measurement of structural stiffness / Young's modulus. S10: Construct a multiphysics prediction model, which includes a temperature field prediction model, a Young's modulus prediction model, and a printed structure size prediction model. S20: 3D modeling slicing and parameter partitioning. The 3D model of the object to be printed is analyzed and sliced ​​into layers. The printing process characteristics of each layer are calculated by combining the size and modulus requirements of each part through the multiphysics prediction model. S30: Target-driven parameter reverse optimization and automatic process setting. The user inputs the modulus gradient of each region of the target structure or the size and modulus of the functional partitions and other performance parameters. The system calls the multiphysics model in step S10 and reverse calculates to generate the optimal combination of printing parameters that meet the target. S40: Intelligent printing and online verification. Prepolymer printing ink is configured according to the physical characteristics of the object to be printed and the printing ink is extruded for printing. During the printing process, the parameters of the printed structure model are continuously collected and compared with the results of the multiphysics prediction model. S50: Adaptive parameter correction and closed-loop feedback. When the actual printing parameters are consistent with the target, printing continues. Otherwise, if the actual printing parameters are detected to deviate from the target, return to step S30 to automatically adjust the relevant printing parameters. S60: Dynamic model update and self-learning. After correcting the printing parameters, the printed structure is measured again. The actual printing parameters are compared with the model prediction values. Based on the data-driven strategy, the prediction model parameters are dynamically corrected and optimized to achieve adaptive model update.

2. The laser-assisted direct-write printing sensing monitoring and intelligent prediction method according to claim 1, characterized in that: In step S00, the printing process parameters include laser power, laser frequency, spot diameter, focal length, printing scanning speed, and liquid supply flow rate; the structural morphology parameters include the evolution of the fluid-solid interface of the product to be printed, layer thickness, and linewidth; and the surface temperature field distribution parameters include the spatial / temporal thermal characteristics of the jet and the curing zone.

3. The laser-assisted direct-write printing sensing monitoring and intelligent prediction method according to claim 2, characterized in that, In step S10, the spatiotemporal temperature field u(x,t) during the direct-write printing process in the temperature field prediction model should satisfy a one-dimensional non-homogeneous heat conduction equation containing a Gaussian distributed heat source term and an environmental heat exchange term: in, It's temperature. It is the density of the material. It is the specific heat capacity of the material. The thermal conductivity of the dielectric material, For radiation absorptivity, The power density of the laser ( ), The depth of laser action, This refers to the ink extrusion rate for direct-write printing, which is also the laser scanning rate. The diameter of the laser spot. The coefficient of performance is the cooling factor. For ambient temperature, and These are the parameters to be fitted; The equation is closed using third-type boundary conditions: Where A is the cross-sectional area of ​​the printed structure, L is the length of the printed area, and m is the mass of the printed structure.

4. The laser-assisted direct-write printing sensing monitoring and intelligent prediction method according to claim 3, characterized in that, In step S10, the Young's modulus prediction model, combined with the temperature field output, establishes a correlation between curing degree and Young's modulus, and the prediction model for temperature / degree of crosslinking and modulus is expressed as follows: in, The reference modulus of the structure; This refers to the degree of curing. This indicates the degree to which crosslinking contributes to Young's modulus; This indicates the degree of crosslinking influence on Young's modulus; k is the shape parameter of the Sigmoid function. Indicates the maximum temperature the material has experienced; This indicates the maximum critical temperature at which internal defects in a material begin to accumulate significantly. It is a temperature-dependent material constant, representing the contribution of temperature to Young's modulus; It is an index of the degree to which temperature affects Young's modulus.

5. The laser-assisted direct-write printing sensing monitoring and intelligent prediction method according to claim 4, characterized in that, In step S10, the printed structure size prediction model is: in, This refers to the linewidth of the deposited material after printing. This is the coefficient for the expansion effect of the mold. V is the inner diameter of the printhead, and V is the dimensionless velocity. , The printing platform movement speed is for coordination with the print head. The speed at which printing ink leaves the printhead.

6. The laser-assisted direct-write printing sensing monitoring and intelligent prediction method according to claim 5, characterized in that, In step S20, after the printing process characteristics of each layer are calculated by the multiphysics prediction model, the moving / rotating speed of the printing platform motion system and the pressure parameters of the liquid supply system are determined according to the size of each part of the object to be printed and combined with the established printing structure size prediction model, so as to realize variable size structure printing. Based on the required modulus of each part of the object to be printed, and combined with the established temperature field prediction model and Young's modulus prediction model, the spot diameter, the relative position of the printing needle and the laser spot, and the laser curing power are determined during the printing process, so as to realize the printing of structures with adjustable modulus.

7. The laser-assisted direct-write printing sensing monitoring and intelligent prediction method according to claim 5, characterized in that, Step S40 includes the following steps: S41: Configure printing ink: Based on the physical properties of the object to be printed, determine the printing substrate material, weigh the thermosetting flexible material according to the proportion, and stir it at a specific temperature to obtain a uniform prepolymer printing ink; load the uniform prepolymer printing ink into the ink syringe (23), perform high-speed centrifugation to remove air bubbles, then fix the ink syringe (23) on the syringe holder (22), connect the pressure supply device, and realize the printing ink extrusion; S42: The programmable controller automatically issues instructions to coordinate optical focusing, printing platform movement, and extrusion liquid supply to complete the printing task. During the printing process, it continuously collects key parameters such as temperature field, modulus, and structural dimensions, and compares them with the results of the prediction model to determine whether there are risks such as over-curing or heat concentration.

8. A laser-assisted direct-write printing sensing monitoring and intelligent prediction system, used to execute a laser-assisted direct-write printing sensing monitoring and intelligent prediction method as described in any one of claims 1 to 7, characterized in that, Includes the following modules: The optical dynamic focusing module (1) is used to generate lasers of a specific wavelength, modulate light spots of different energy distribution shapes, and obtain high energy density light spots that are focused on the ink jet at the nozzle outlet. The liquid supply module (2) precisely sets the liquid supply flow rate, pushing the printing ink to be extruded from the printing needle to form a fine and continuous jet; Temperature / modulus feedback module (3) collects key parameters such as temperature field distribution, local modulus and curing state of the surface and near-surface of the printing area in real time without damage; The real-time morphology observation module (4) dynamically collects and analyzes parameters such as structural layer thickness, line width, surface roughness, and boundary integrity during the printing process. The printing platform motion module (5) enables the printing platform to move precisely along a predetermined path, ensuring high repeatability and smooth movement of the relative position of the printing nozzle and the substrate; The performance prediction and parameter reverse optimization module is used to clean, extract features and standardize the data collected by the temperature / modulus feedback module (3) and the morphology real-time observation module (4) to establish a training dataset. The system constructs a multi-parameter coupled prediction model between temperature field, printing size and structural modulus, and reverse calculates the printing process parameters required to achieve the target printing performance.

9. The laser-assisted direct-write printing sensing monitoring and intelligent prediction system according to claim 8, characterized in that, It also includes a programmable controller that works in conjunction with the optical dynamic focusing module (1), the liquid supply module (2), the temperature / modulus feedback module (3), the real-time morphology observation module (4), the printing platform motion module (5), and the performance prediction and parameter inverse optimization module.

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