Intelligent manufacturing method for heat-insulation and sound-insulation material with complex structure based on 3D printing technology

Abstract

The invention discloses an intelligent manufacturing method of a heat-insulation and sound-insulation material with a complex structure based on a 3D printing technology, and relates to the technical field of material manufacturing, and the method comprises the steps of interacting equipment precision and material characteristics, and determining a printing scale and parameters. Compounding, spray head configuration and spray material distribution are determined based on material coupling and manufacturing structures. And obtaining a building manufacturing target topology, and training a dynamic model. And with the scale and the parameter as constraints, simulation analysis and optimization are performed to determine a control strategy. And the strategy PLC is programmed, and full-cycle automation of the control equipment is realized. The technical problems that in the prior art, when a heat insulation and sound insulation material of a complex structure is manufactured, due to the fact that factors such as equipment precision, material characteristics and material coupling cannot be effectively integrated, the material performance is poor, the manufacturing process is inaccurate, and the requirement for the complex structure is difficult to meet are solved. And the technical effect of high performance, high precision, high efficiency and high stability manufacturing of the heat insulation and sound insulation material with the complex structure in the building field is achieved.

Description

Technical Field

[0001] This application relates to the field of materials manufacturing technology, and in particular to a smart manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology. Background Technology

[0002] In the field of thermal and sound insulation material manufacturing, the production of complex structure thermal and sound insulation materials faces numerous challenges, with increasingly urgent demand but still insufficient related technologies. The application of complex structure thermal and sound insulation materials in fields such as construction is constantly expanding, leading to increasingly higher requirements for their performance and manufacturing precision; however, existing technologies are insufficient to effectively address these challenges. Traditional manufacturing methods for thermal and sound insulation materials reveal many shortcomings when facing complex structures and high-performance requirements. For example, insufficient precision in matching the accuracy of 3D printing equipment with material properties results in suboptimal product microstructure and performance; inadequate understanding and application of the coupling between materials affects the overall thermal and sound insulation effect; a lack of effective control strategies and optimization methods in manufacturing complex structural components makes it difficult to achieve high-precision, high-performance production; and low automation limits production efficiency and product quality stability.

[0003] At present, there are technical problems in the manufacturing of complex thermal and sound insulation materials. Due to the inability to effectively integrate factors such as equipment precision, material properties, material coupling, manufacturing structure, and printing control strategies, the material performance is poor, the manufacturing process is inaccurate, the degree of automation is low, and it is difficult to meet the technical requirements of complex structures. Summary of the Invention

[0004] This application provides an intelligent manufacturing method for complex structural thermal and sound insulation materials based on 3D printing technology. It utilizes the precision and material properties of interactive 3D printing equipment to determine the voxel scale and equipment parameters. Based on material coupling and the manufacturing structural layers, it determines the composite material, multi-nozzle configuration, and material distribution. The 3D topology of the building-type manufacturing target is obtained, and a dynamic model containing simulation and optimization units is trained under supervised training combined with the material distribution. Using the voxel scale and equipment parameters as constraints, hierarchical simulation analysis and optimization are performed using the dynamic model to determine the control strategy. The control strategy is programmed into a PLC, enabling automated control of the 3D printing equipment throughout the entire lifecycle of the manufacturing target. This achieves the technical effect of high-performance, high-precision, high-efficiency, and high-stability manufacturing of complex structural thermal and sound insulation materials in the construction field.

[0005] This application provides a smart manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology, including: The interactive 3D printing equipment's precision and material properties are used to determine the printing voxel scale and configure equipment parameters. Based on material coupling and manufacturing structure layers, composite materials are determined and multi-nozzle configurations are performed to determine the material distribution. The material coupling includes material self-coupling and coupling based on thermal and sound insulation performance. The 3D topology of the manufacturing target is obtained and combined with the material distribution to supervise the training of a dynamic model. The dynamic model includes simulation units and optimization units, with the optimization unit including microstructure optimization branches and selective deposition optimization branches. The manufacturing target is a building type. Using the printing voxel scale and equipment parameter configuration as constraints, combined with the dynamic model, hierarchical simulation analysis and compensation optimization are performed to determine the control strategy for the entire manufacturing cycle of the target. The hierarchical structure includes the manufacturing structure layer and the printing layers below each manufacturing structure layer. The control strategy is programmed into a PLC to control the 3D printing equipment for automated control throughout the entire lifecycle of the manufacturing target.

[0006] This application proposes a smart manufacturing method for complex structural thermal and sound insulation materials based on 3D printing technology. First, by interacting with the 3D printing equipment's precision and material properties, the voxel scale and equipment parameters are determined. Based on material coupling and the manufacturing structure layers, the composite material, multi-nozzle configuration, and material distribution are determined. The 3D topology of the building-type manufacturing target is obtained, and a dynamic model containing simulation and optimization units is trained under supervised training combined with the material distribution. Using the voxel scale and equipment parameters as constraints, hierarchical simulation analysis and optimization are performed using the dynamic model to determine the control strategy. The control strategy is then programmed into a PLC, enabling automated control of the 3D printing equipment throughout the entire lifecycle of the manufacturing target. This achieves the technical effect of high-performance, high-precision, high-efficiency, and high-stability manufacturing of complex structural thermal and sound insulation materials in the construction field. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0008] Figure 1 A flowchart illustrating the intelligent manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology provided in this application embodiment; Figure 2 This is a schematic diagram of the feedback manufacturing management process for the intelligent manufacturing method of complex structure thermal and sound insulation materials based on 3D printing technology provided in the embodiments of this application. Detailed Implementation

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0010] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0012] This application provides an intelligent manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology, such as... Figure 1 As shown, the method includes: Step S100 involves analyzing the equipment precision and material properties of the interactive 3D printing equipment to determine the voxel scale and configure the equipment parameters. Specifically, this involves clarifying the precision indicators of the 3D printing equipment, including the minimum dimensional precision, positional precision, and precise shape control capabilities achievable by the equipment. It also involves analyzing the characteristics of various thermal and sound insulation materials, such as hardness, melting point, coefficient of thermal expansion, and flowability. Based on the understanding of equipment precision and the research on material properties, the voxel scale is determined. The voxel scale is similar to a three-dimensional pixel and is the smallest achievable printing unit. For example, if the equipment precision is high and the material has good flowability and curing properties, a smaller voxel scale can be selected to print more intricate structures. However, if the equipment precision is limited, or the material properties are not conducive to small-scale printing, a larger voxel scale needs to be selected to ensure printing quality and success rate. After determining the voxel scale, the equipment parameters are then configured. Configuration parameters include, but are not limited to, printing power, nozzle travel speed, nozzle temperature, and extrusion speed. Taking printing power as an example, if the power is set too high, the material may over-melt or overheat, affecting print quality and voxel scale control. If the power is set too low, the material may not melt sufficiently, resulting in discontinuous printing or insufficient structural strength. The nozzle travel speed also affects voxel deposition and arrangement. If the speed is too fast, it may lead to uneven material distribution; if the speed is too slow, it will reduce printing efficiency. By continuously testing and adjusting these equipment parameters to match the determined printing voxel scale, the best printing effect can be achieved. At the same time, it is also necessary to dynamically optimize and adjust the equipment parameters according to the actual printing situation to deal with various possible problems and ensure that the manufactured thermal and sound insulation materials meet the performance requirements.

[0013] Step S200: Based on material coupling and manufacturing structure layers, composite materials are determined and multi-nozzle configuration is performed to determine the spray distribution. The material coupling includes both intrinsic material coupling and coupling based on thermal and sound insulation performance. Specifically, the characteristics of various available materials are analyzed to obtain their intrinsic coupling, identifying which materials will react chemically upon contact, producing new substances or altering the properties of existing materials; which materials are physically incompatible and difficult to use together; and which materials can be directly mixed without negative effects. Considering coupling based on thermal and sound insulation performance, the comprehensive impact of different material combinations on thermal and sound insulation effects needs to be evaluated. For example, some materials may have good thermal insulation performance individually, while others excel in sound insulation. By studying the combination effect, material combinations that achieve optimal thermal and sound insulation performance are found. Based on a comprehensive analysis of material coupling, composite materials are determined. For materials that react with each other or produce adverse effects, isolation measures need to be taken, such as using an intermediate layer or special treatment methods. For materials that can be directly mixed and... For materials that enhance performance, they are rationally proportioned and configured with multiple nozzles according to the needs of the manufacturing structural layer. The manufacturing structural layer may include different layers and parts, and the requirements and amounts of materials for each part may be different. Based on these differences, specific materials are allocated to each nozzle. After the multi-nozzle configuration is completed, the spray distribution is further determined. It is necessary to consider the shape, size, and complexity of the manufacturing structure. For critical parts of the structure or areas that require enhanced thermal and sound insulation performance, it is necessary to increase the spray volume of specific materials or adopt a denser spraying method to ensure that the spraying of materials between different nozzles can be coordinated to form a uniform and continuous material distribution, avoiding gaps, overlaps, or unevenness. Continuous testing, analysis, and adjustment are carried out to ensure that the final determined composite material, multi-nozzle configuration, and spray distribution can meet the high-performance requirements of thermal and sound insulation materials and the manufacturing needs of complex structures.

[0014] Step S300: Obtain the 3D topology of the manufacturing target and combine it with the spray distribution to supervise the training of the dynamic model. The dynamic model includes simulation units and optimization units. The optimization units include microstructure optimization branches and selective deposition optimization branches. The manufacturing target is a building type. Specifically, using professional 3D modeling software, scanning equipment, or other data acquisition methods, the 3D topological information of the building-type manufacturing target is accurately obtained. This includes the shape, size, internal structure, and various detailed features of the building components. After obtaining the 3D topology, it is combined with the previously determined material distribution scheme. That is, the material distribution information is matched with the geometry and structural characteristics of the manufacturing target to ensure that the material can be distributed and deposited in the expected way during the subsequent printing process. The combined information is used to supervise the training of the dynamic model. The dynamic model is a tool used to simulate and predict various physical and chemical changes during the printing process. In the early stage of dynamic model training, based on existing material property data, printing process parameters, and the initial model architecture, preliminary simulations and predictions are performed. The model's output results are compared with the actual expected results to calculate the error. Based on the calculated error, optimization algorithms (such as gradient descent) are used to adjust the parameters in the model, such as the material's physical parameters, heat transfer coefficient, and chemical reaction rate. In the training of the simulation unit, different printing conditions and 3D topological data are continuously input, allowing the model to learn the material's properties under different conditions. The model studies the flow, solidification, and heat transfer patterns under the same environment to improve the accuracy of simulating physical phenomena during the printing process. For the microstructure optimization branch of the optimization unit, the model is trained with a large amount of sample data to learn to identify and optimize the arrangement and combination of materials at the micro level to achieve the best thermal and sound insulation performance. The selective deposition optimization branch learns during training to accurately determine the deposition location and quantity of materials according to different manufacturing goals and process conditions. During the training process, simulation, error calculation, and parameter adjustment are continuously performed in cycles until the error between the model's prediction results and the actual expected results reaches an acceptable range or no longer decreases significantly. Techniques such as cross-validation are used to evaluate the model's generalization ability to ensure that the model can also provide accurate predictions and optimization suggestions when facing new manufacturing goals. During the training of the dynamic model, the model's prediction results are continuously compared and adjusted with the actual expected manufacturing goals to improve the model's accuracy and reliability. Through repeated training and optimization, the dynamic model can accurately simulate and predict the printing process of building-related manufacturing goals, providing strong support for the manufacturing of high-quality, high-performance thermal and sound insulation materials.

[0015] Step S400: Using the printed voxel scale and the device parameter configuration as constraints, and combining the dynamic model, perform hierarchical simulation analysis and compensation optimization to determine the control strategy for the entire manufacturing cycle of the target. The hierarchical structure includes the manufacturing structure layer and the printing layer below each manufacturing structure layer. Specifically, the previously determined printing voxel scale and equipment parameter configuration are clarified. The printing voxel scale determines the smallest unit size of the print, while equipment parameters such as power and speed affect printing efficiency and quality. These factors constitute important constraints for subsequent analysis and optimization. A previously trained dynamic model is introduced, which can simulate various physical and chemical changes during the printing process, providing a theoretical basis and predictive capability for analysis and optimization. Hierarchical simulation analysis begins, including the manufacturing structure layer and the printing layers below each manufacturing structure layer. For the manufacturing structure layer, the main considerations are the overall structural integrity of the component, the macroscopic performance of thermal and sound insulation, and its compatibility with other components. For the printing layers below each manufacturing structure layer, the focus is on the microscopic details such as the printing accuracy of each layer, the uniformity of material distribution, and the bonding strength between layers. During the simulation analysis, based on the constraints of the printing voxel scale and equipment parameter configuration, and combined with the predictions of the dynamic model, different printing processes and parameter combinations are simulated. The impact on manufacturing results is analyzed, for example, by examining the effects of different nozzle movement paths, spray speeds, and printing temperatures on the quality and performance of structural and printed layers. Based on the simulation analysis results, compensation and optimization are performed. If the printing quality in certain areas does not meet requirements, or the thermal and sound insulation performance does not meet expectations, the corresponding process parameters need to be adjusted and optimized. For example, if a structural layer has performance defects caused by uneven material distribution, it may be necessary to adjust the nozzle's spray angle or speed; if a printed layer has a problem with weak interlayer bonding, it may be necessary to optimize the printing temperature or add an intermediate transition layer. Through repeated simulation analysis and compensation optimization, the control strategy for the entire manufacturing cycle is finally determined. The strategy covers the entire process from raw material preparation and printing start to finish, including process parameters, equipment control commands, and quality inspection standards at each stage, ensuring that the quality and performance of the product are always guaranteed during the manufacturing process, meeting the stringent requirements of building manufacturing targets for thermal and sound insulation materials.

[0016] In one possible implementation, the printing voxel scale and the device parameter configuration are used as constraints. Combined with the dynamic model, hierarchical simulation analysis and compensation optimization are performed to determine the control strategy for the entire manufacturing cycle of the target. The hierarchical approach includes the manufacturing structure layer and the printing layers below each manufacturing structure layer. Step S400 further includes step S410, which, based on the isomorphism of printing control, divides the structure layers and determines the manufacturing structure layers. Specifically, a comprehensive analysis of the entire manufacturing target is performed, considering various isomorphic factors in printing control. Isomorphism can include the type of printing material (e.g., metal, plastic, composite materials) and the printing method (e.g., fused deposition modeling, laser sintering). For architectural manufacturing targets, different parts require different printing materials and methods. For example, load-bearing structural parts may require high-strength metal materials and high-precision printing methods, while non-load-bearing decorative parts may use lighter plastic materials and relatively faster printing methods. Based on these differences, the manufacturing target is divided into different structural layers, clarifying the characteristics and requirements of each structural layer, thereby determining the manufacturing structure layers.

[0017] Step S420: Traverse the manufacturing structure layers and determine the printing layers based on the single-layer printing scale. The printing layers correspond to the manufacturing structure layers, and each manufacturing structure layer contains at least one printing layer. Specifically, each of the pre-defined manufacturing structure layers is examined individually. The single-layer printing scale refers to parameters such as the thickness or precision of each print. Based on the scale, each manufacturing structure layer is further subdivided to determine its printing layers. There is a clear correspondence between the printing layers and the manufacturing structure layers; that is, each printing layer belongs to a specific manufacturing structure layer. Due to the complexity and precision requirements of the manufacturing structure layers, each manufacturing structure layer typically contains at least one printing layer. For example, a thicker structure layer may require multiple printing layers to ensure printing quality and precision.

[0018] Step S430: Integrate the manufacturing structure layer and the printing layer to perform hierarchical simulation analysis. Specifically, the determined manufacturing structure layer and its corresponding printing layer are organically integrated. In the integrated model, the overall performance requirements of the structure layer and the microscopic characteristics of the printing layer are fully considered. Professional simulation software or algorithms are used to perform simulation analysis on the integrated hierarchical model. During the analysis, attention is paid to both the macroscopic performance of the entire manufacturing structure layer, such as strength, stability, and heat and sound insulation effects, and the microscopic performance of each printing layer, such as interlayer bonding strength and material distribution uniformity. Through hierarchical simulation analysis, potential problems in the manufacturing process can be comprehensively evaluated, and corresponding solutions can be developed in advance to ensure that the final manufactured product meets the design requirements.

[0019] In one possible implementation, the printing voxel scale and the device parameter configuration are used as constraints. Combined with the dynamic model, hierarchical simulation analysis and compensation optimization are performed to determine the control strategy for the entire manufacturing cycle. The hierarchical structure includes the manufacturing structure layer and the printing layer below each manufacturing structure layer. Step S400 further includes step S440, which combines the simulation unit to perform simulation derivation analysis, determine the first control strategy based on the manufacturing structure layer, and determine the second control strategy based on the printing layer. Specifically, the pre-trained simulation unit is invoked. The simulation unit can simulate various physical and chemical changes and material behavior during the printing process. The simulation unit is used to perform detailed derivation and analysis of the manufacturing process. For the manufacturing structural layer, factors such as its position in the overall manufacturing goal, its functions (such as load-bearing, heat insulation, sound insulation, etc.), and its connection relationship with other structural layers are considered to formulate a first control strategy suitable for the structural layer. For example, for a structural layer that bears important load-bearing functions, the control strategy may include using high-strength materials and increasing printing density. For the printing layer, the focus is on its printing accuracy, the bonding quality between layers, and the uniformity of material distribution, thereby determining the second control strategy. For example, for printing layers that require high precision, the control strategy may involve reducing the printing voxel size and optimizing the nozzle movement speed.

[0020] Step S450: Based on the layer correspondence, the first control strategy and the second control strategy are integrated to determine the initialization strategy. Specifically, the correspondence between the manufacturing structure layer and the printing layer is clarified, and it is understood how each printing layer constitutes the corresponding manufacturing structure layer. The first control strategy for the manufacturing structure layer and the second control strategy for the printing layer are comprehensively considered and integrated. During the integration process, the structural performance requirements at the macro level and the printing accuracy requirements at the micro level are coordinated. Through integration, a preliminary and comprehensive initialization strategy is formed. The initialization strategy includes both the control points of the overall manufacturing structure and the specific control measures for each printing layer, providing a basic framework for subsequent optimization and adjustment.

[0021] In one possible implementation, the printing voxel scale and the device parameter configuration are used as constraints. Combined with the dynamic model, hierarchical simulation analysis and compensation optimization are performed to determine the control strategy for the entire manufacturing cycle. The hierarchical approach includes the manufacturing structure layer and the printing layers below each manufacturing structure layer. Step S400 further includes step S460, introducing local electrostatic attraction and determining the first constraint relationship between electrostatic parameters and deposition characteristics. Specifically, the physical phenomenon of local electrostatic attraction is introduced during the manufacturing process. Local electrostatic attraction can affect the material deposition process. Through experiments, simulations, or theoretical analysis, the quantitative relationship between electrostatic parameters (such as electric field strength and charge distribution) and material deposition characteristics (such as deposition rate and accuracy of deposition location) is determined, establishing the first constraint relationship. For example, it determines how the material deposition rate changes under a specific electric field strength, or how the charge distribution affects the deposition location accuracy.

[0022] Step S470: Combining the selective deposition optimization branch, the initialization strategy is traversed to perform deposition misalignment assessment and identify the first adjustment misalignment point. Specifically, the function of the selective deposition optimization branch is invoked to evaluate the accuracy of material deposition and optimize the deposition process. Based on the initialization strategy, each deposition step in the manufacturing process is checked and analyzed in detail. Through traversal evaluation, points that deviate from the expected deposition location or method are identified, i.e., the first adjustment misalignment points. The first adjustment misalignment points are caused by reasons such as equipment accuracy, changes in material properties, or unstable process parameters.

[0023] Step S480: Based on the first adjustment misalignment point, a local electrostatic attraction triggering decision is made, and material deposition is adjusted in conjunction with the first constraint relationship. Specifically, for the identified first adjustment misalignment point, a decision is made on whether to trigger local electrostatic attraction based on the current manufacturing situation and requirements. If triggering is decided, the electrostatic parameters are adjusted according to the previously determined first constraint relationship to change the intensity and range of local electrostatic attraction. Through adjustment, precise control of the material deposition process is achieved, the deposition misalignment is corrected, and the material can be accurately deposited at the desired location, thereby improving manufacturing accuracy and quality.

[0024] In one possible implementation, the printed voxel scale and the device parameter configuration are used as constraints. Combined with the dynamic model, hierarchical simulation analysis and compensation optimization are performed to determine the control strategy for the entire manufacturing cycle. The hierarchical approach includes the manufacturing structure layer and the printing layers below each manufacturing structure layer. Step S400 further includes step S490, performing a dynamic analysis based on melting and solidification to determine a second constraint relationship for the microscopic state. Specifically, using principles of physics and materials science, the thermodynamic and dynamic behavior of materials during melting and solidification is analyzed in depth. This includes studying heat transfer, phase transitions, molecular diffusion, and other processes, focusing on material changes in the microscopic state, such as crystal structure formation, grain size distribution, and defect generation. Through analysis, the quantitative relationship between relevant parameters (such as temperature, cooling rate, and pressure) and microscopic characteristics during melting and solidification is determined, thereby establishing a second constraint relationship. For example, it clarifies what grain size a specific cooling rate will result in, or what type of defects will occur under certain pressure conditions.

[0025] Step S4100: Combining the microstructure optimization branch, the initialization strategy is traversed to locate micro-state anomalies and determine the second adjustment anomaly point. Specifically, the function of the microstructure optimization branch is invoked. This branch focuses on optimizing the microstructure of the printed product to achieve optimal performance. Based on the printing parameters and processes set by the initialization strategy, the impact of each printing step on the microstructure is comprehensively checked and evaluated. During the traversal, the expected microstructure state and the actual formed microstructure are compared to identify the locations or areas with differences, which are determined as the second adjustment anomaly point. The anomaly point is caused by reasons such as uneven material, uneven temperature distribution, or inappropriate printing speed.

[0026] Step S4110: For the second adjustment anomaly point, and in conjunction with the second constraint relationship, perform printing control adjustment based on the micro-state. Specifically, for the identified second adjustment anomaly point, a targeted analysis is conducted based on the current printing conditions and requirements. Referring to the second constraint relationship, a specific control parameter adjustment scheme that can improve the micro-state is determined. Based on the scheme, relevant parameters (such as temperature, pressure, printing speed, etc.) during the printing process are precisely adjusted to correct the micro-structure anomaly, achieve effective control of the micro-state of the printed product, and improve the product's performance and quality.

[0027] In one possible implementation, using the printed voxel scale and the device parameter configuration as constraints, and combining the dynamic model, hierarchical simulation analysis and compensation optimization are performed to determine the control strategy for the entire manufacturing cycle of the target. The hierarchical approach includes the manufacturing structure layer and the printing layers below each manufacturing structure layer. Step S400 further includes step S4120, acquiring dynamic structural changes, whereby the dynamic structural changes refer to the dynamic relationship between the state of the manufacturing target and external environmental stimuli. Specifically, through monitoring methods and technologies, the state information of the manufacturing target at different time points is continuously collected. This state information includes, but is not limited to, changes in shape, size, physical properties (such as strength, hardness, heat and sound insulation performance), and chemical composition. The stimulating effect of external environmental factors on the manufacturing target is analyzed, such as temperature changes, humidity fluctuations, pressure changes, and chemical corrosion. The dynamic structural change model and data records are established to determine how the state of the manufacturing target dynamically changes with these external environmental stimuli.

[0028] Step S4130: Based on the dynamic structural change, a mediating element is introduced. Specifically, based on the acquired dynamic structural change data and analysis results, the mediating element to be introduced is determined. The mediating element may be a reactant that can promote or inhibit a certain reaction, a catalyst that can accelerate or change the material properties, etc. When introducing the mediating element, its potential impact on existing materials and manufacturing processes is fully considered. The compatibility and interaction between the mediating element and other raw materials are assessed to determine whether it will lead to raw material denaturation, performance degradation, or other adverse chemical reactions. Experiments and simulations are conducted to verify the introduction effect and safety of the mediating element, ensuring that it can effectively improve the dynamic structural change of the manufacturing target without causing negative impacts.

[0029] Step S4140: Based on the mediating elements, and taking structural steady-state changes as a condition, the control strategy is compensated. Specifically, based on the successfully introduced mediating elements, the target and conditions for structural steady-state changes are set. Structural steady-state changes refer to the stable state that the manufacturing target reaches after experiencing external stimuli and the action of mediating elements, meeting the expected performance and functional requirements. The current control strategy is checked against the set structural steady-state change conditions to see if it can achieve this target. If there are deviations or deficiencies, the control strategy is adjusted and compensated accordingly. Compensation methods include modifying printing parameters (such as temperature, speed, pressure, etc.), adjusting material ratios, changing the amount or timing of mediating element addition, etc., to ensure that the manufacturing process can maintain the structural stability and performance reliability of the manufacturing target in a dynamically changing environment.

[0030] Step S500: The control strategy is programmed into a PLC program to control the 3D printing equipment for full lifecycle automation control of the manufacturing target. Specifically, the previously determined complex and precise control strategy is transformed into program code that the PLC (Programmable Logic Controller) can understand and execute. This requires precise programming expression of various parameters, conditional judgments, logical operations, and action instructions in the control strategy. During programming, each link and step in the control strategy must be mapped in detail to the PLC program. For example, for the control of the printing voxel scale, corresponding variables and calculation logic need to be set to ensure that the PLC can accurately output control signals and adjust the nozzle accuracy of the 3D printing equipment. For configuring equipment parameters such as power and speed, corresponding input and output ports must be defined, and control programs must be written to achieve real-time adjustment and monitoring. After completing the PLC program, it is downloaded to the PLC controller. The PLC controller establishes a communication connection with the 3D printing equipment, obtains real-time operating status information of the equipment, and precisely controls the equipment according to the instructions in the program. In the full life cycle automation control of the manufacturing target, from loading raw materials and initializing the equipment to every action in the printing process, including the movement trajectory of the nozzle, control of the material spraying, and temperature adjustment, all are automatically executed by the PLC according to the pre-set program. During the printing process, the PLC continuously monitors the equipment's operating parameters and printing effect. If any deviation or abnormality occurs, such as insufficient material supply, excessively high temperature, or printing quality that does not meet requirements, the PLC will judge and handle it according to the preset logic, adjust the control parameters in a timely manner, issue an alarm, or even suspend the printing process to ensure printing quality and production safety. The entire life cycle of automated control is only completed after the manufacturing target is printed and after subsequent processing and inspection. Through PLC programmable control, the 3D printing equipment can operate efficiently, accurately, and stably, ensuring that the manufacturing target can be manufactured with high quality according to the design requirements.

[0031] In one possible implementation, the control strategy is programmed into a PLC to control the 3D printing equipment for full lifecycle automation control of the manufacturing target. Step S500 further includes step S510, identifying the control strategy, performing switching response analysis based on multi-nozzle configuration, and determining nozzle switching characteristics, which include switching time intervals. Specifically, the pre-defined control strategy is read and understood, clarifying the requirements and rules regarding multi-nozzle configuration and switching. For the multi-nozzle configuration, the working state and switching conditions of each nozzle are analyzed in detail. For example, it is determined whether nozzle switching is necessary based on the solidification state, semi-solidification state, or molten state of the material. During the analysis, the focus is on the timing and time interval of nozzle switching. Through experiments, simulations, or analysis based on historical data, the optimal time interval from when one nozzle stops working to when another starts working is determined under different material states and printing conditions to ensure the continuity and stability of the printing process, while avoiding material mixing or defects.

[0032] Step S520: Based on the nozzle switching characteristics, multi-nozzle control constraints are applied to the 3D printing equipment. Specifically, based on the previously determined nozzle switching characteristics, including switching time intervals, strict control constraints are set for the multi-nozzle system of the 3D printing equipment. These constraints are implemented in the equipment's control system through programming, for example, by setting timers or triggering mechanisms to ensure that nozzle switching strictly follows the determined time intervals. Parameters such as the nozzle switching sequence, switching speed, and acceleration are constrained to avoid affecting printing quality due to overly rapid or unstable switching actions. Through control constraints, the multi-nozzle system can work in coordination, performing efficient and precise material deposition in a predetermined manner, thereby achieving high-quality 3D printing.

[0033] In one possible implementation, such as Figure 2 As shown, the control strategy is programmed into a PLC to control the 3D printing equipment for full lifecycle automation of the manufacturing target. Step S500 further includes step S530, which involves manufacturing monitoring to determine the monitoring sensor data. Specifically, during the manufacturing process, various sensors and monitoring devices are deployed, such as temperature sensors, pressure sensors, displacement sensors, and speed sensors, to monitor the operating status of the 3D printing equipment and key parameters in the manufacturing process in real time. The sensors continuously collect data, including temperature changes during the printing process, pressure applied by the nozzle, and the movement displacement and speed of the print head, thereby determining rich monitoring sensor data.

[0034] Step S540: Based on the assembly precision standards of the manufacturing target, precision allocation is performed to determine the precision of the structural layers. Specifically, the assembly precision standards required by the manufacturing target are clearly defined, including strict requirements for overall dimensional accuracy, shape accuracy, surface roughness, etc. According to the structural characteristics and functional requirements of the manufacturing target, the assembly precision standards are reasonably allocated to each structural layer. For example, structural layers that bear the main load-bearing function need to be assigned higher precision requirements; while for some non-critical decorative structural layers, the precision requirements can be relatively lower. Through precision allocation, the specific precision level that each structural layer needs to achieve is determined.

[0035] Step S550: Based on the accuracy of the structural layers, a control deviation assessment is performed to determine the control degrees of freedom for each structural layer. Specifically, the actual manufacturing conditions of each structural layer are compared with the previously determined accuracy requirements to assess whether control deviation exists, and to analyze the degree and cause of the control deviation, such as improper equipment parameter settings, material performance fluctuations, or inaccurate process execution. Based on the assessment results, the control degrees of freedom for each structural layer in the subsequent manufacturing process are determined. Control degrees of freedom may include adjusting the range of printing parameters (such as temperature, speed, pressure, etc.), changing the method and amount of material supply, and correcting the movement path of the nozzle.

[0036] Step S560: Using the control degrees of freedom as constraints, the monitored sensor data is judged for exceeding limits, and feedback manufacturing management is performed to achieve the manufacturing target. Specifically, the determined control degrees of freedom of each structural layer are used as constraints to analyze and judge the collected monitored sensor data in real time, checking whether the data exceeds the preset normal range limit. If the monitored data exceeds the limit, it indicates that there is an abnormality or deviation from the expected manufacturing process. According to the control degrees of freedom, corresponding adjustment measures are taken in a timely manner, such as automatically adjusting equipment parameters, pausing printing for troubleshooting, etc., to achieve timely feedback and effective management of the manufacturing target and ensure that the final product meets the accuracy requirements.

[0037] This application utilizes the precision and material properties of interactive 3D printing equipment to determine the voxel scale and equipment parameters. Based on material coupling and manufacturing structural layers, it determines the composite material, multi-nozzle configuration, and material distribution. The 3D topology of the architectural manufacturing target is obtained, and a dynamic model containing simulation and optimization units is trained under supervised training based on the material distribution. Using the voxel scale and equipment parameters as constraints, hierarchical simulation analysis and optimization are performed using the dynamic model to determine the control strategy. The control strategy is programmed into a PLC, enabling automated control of the 3D printing equipment throughout the entire lifecycle of the manufacturing target. This achieves the technical effect of high-performance, high-precision, high-efficiency, and high-stability manufacturing of complex structural thermal and sound insulation materials in the construction field.

[0038] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for intelligent manufacturing of complex structure thermal and sound insulation materials based on 3D printing technology, characterized in that, The method includes: The equipment precision and material properties of the interactive 3D printing equipment are used to determine the voxel scale and configure the equipment parameters. Based on material coupling and manufacturing structure layers, composite materials are combined and multi-nozzle configurations are performed to determine the spray distribution. The material coupling includes material self-coupling and coupling based on thermal and sound insulation performance. The 3D topology of the manufacturing target is obtained and combined with the spray distribution to supervise the training of a dynamic model. The dynamic model includes simulation units and optimization units. The optimization units include microstructure optimization branches and selective deposition optimization branches. The manufacturing target is a building type. Using the printed voxel scale and the device parameter configuration as constraints, and combined with the dynamic model, hierarchical simulation analysis and compensation optimization are performed to determine the control strategy for the entire manufacturing cycle of the target. The hierarchical structure includes the manufacturing structure layer and the printing layer below each manufacturing structure layer. The control strategy is programmed into a PLC to control the 3D printing equipment to perform full lifecycle automation control of the manufacturing target.

2. The intelligent manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology as described in claim 1, characterized in that, The hierarchical simulation analysis includes: Based on the isomorphism of printing control, structural layers are divided to determine the manufacturing structural layer; Traverse the manufacturing structure layers and determine the printing layers based on the single-layer printing scale, wherein the printing layers correspond to the manufacturing structure layers, and each manufacturing structure layer contains at least one printing layer; The manufacturing structure layer and the printing layer are integrated for hierarchical simulation analysis.

3. The intelligent manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology as described in claim 2, characterized in that, The hierarchical simulation analysis includes: Based on the simulation unit, a simulation derivation analysis is performed to determine a first control strategy based on the manufacturing structure layer and a second control strategy based on the printing layer. Based on the layer correspondence, the first control strategy and the second control strategy are integrated to determine the initialization strategy.

4. The intelligent manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology as described in claim 3, characterized in that, Compensation optimization includes: Local electrostatic attraction is introduced, and the first constraint relationship between electrostatic parameters and deposition characteristics is determined; Combining the selective deposition optimization branch, the initialization strategy is traversed to evaluate deposition misalignment and identify the first adjustment misalignment site; Based on the first adjustment misalignment point, a local electrostatic attraction triggering decision is made, and the material deposition is adjusted in combination with the first constraint relationship.

5. The intelligent manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology as described in claim 3, characterized in that, Compensation optimization includes: A kinetic analysis based on melting and solidification is conducted to determine the second constraint relationship for the microscopic state; Combining the microstructure optimization branch, the initialization strategy is traversed to locate microstate anomalies and determine the second adjustment anomaly point; For the second adjustment anomaly point, and in conjunction with the second constraint relationship, print control adjustment based on microstate is performed.

6. The intelligent manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology as described in claim 1, characterized in that, After determining the control strategy for the entire manufacturing cycle, it includes: The dynamic structural changes are obtained, wherein the dynamic structural changes are the dynamic relationship between the state of the manufacturing target and external environmental stimuli. Based on the aforementioned dynamic structural changes, media elements are introduced; Based on the aforementioned media elements, and taking structural steady-state changes as a condition, the control strategy is compensated.

7. The intelligent manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology as described in claim 1, characterized in that, Controlling the 3D printing equipment to perform full lifecycle automated control of the manufacturing target includes: The control strategy is identified, and a switching response analysis based on multi-nozzle configuration is performed to determine the nozzle switching characteristics, which include the switching time interval. Based on the nozzle switching characteristics, multi-nozzle control constraints are applied to the 3D printing equipment.

8. The intelligent manufacturing method for complex structure thermal and sound insulation materials based on 3D printing technology as described in claim 1, characterized in that, After implementing full lifecycle automation control for the manufacturing target, the following is included: Conduct manufacturing monitoring and determine the monitoring sensor data; Based on the assembly precision standard of the manufacturing target, the precision of the structural layer is determined by precision allocation; Based on the accuracy of the structural layers, a control deviation assessment is performed to determine the control degrees of freedom for each structural layer. Using the aforementioned degrees of freedom as constraints, the monitoring and sensing data are used to determine if they exceed limits, and feedback manufacturing management is performed to achieve the manufacturing target.

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