3D printing melt extrusion control method and system

Abstract

The invention relates to the technical field of melt extrusion control, in particular to a 3D printing melt extrusion control method and system. The method comprises the following steps that a 3D printing product drawing is obtained, printing forming process marking is carried out, the product structural form trend is analyzed, then, structural segment printing melt temperature matching simulation is carried out according to product structural form trend data, matching data is obtained, local printing stress matching is carried out on the basis, and a 3D printing product is obtained. Printing stress matching data are obtained, and a self-adaptive melt extrusion control mechanism is designed; and finally, the control mechanism is optimized and adjusted to form a self-adaptive melt extrusion optimization control mechanism, the self-adaptive melt extrusion optimization control mechanism is sent to a 3D printer control center, and the melt extrusion control method is executed. According to the invention, the melt extrusion control technology is optimized, so that the melt extrusion control technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the field of melt extrusion control technology, and in particular to a 3D printing melt extrusion control method and system. Background Art

[0002] Melt extrusion control in the 3D printing process is still a technical problem, especially when printing high-precision and high-complexity products, the stability of the melt and the force control during the extrusion process have become key factors affecting the printing quality and efficiency. Melt extrusion control is directly related to the melting state, fluidity and curing process of the printing material. Any improper control will lead to printing failure, surface defects or insufficient structural strength. Previous 3D printing control methods mostly rely on experience and basic control algorithms. These methods are usually poorly adaptable to changes in the printing environment and materials, and are difficult to cope with complex printing tasks. In practical applications, fluctuations in parameters such as melt temperature, speed, and pressure during the printing process lead to uneven or excessive extrusion of the melt, thereby affecting the printing quality. Therefore, it is particularly important to develop a melt extrusion control method that can be adaptively adjusted according to real-time printing conditions. Adaptive melt extrusion control technology can make real-time adjustments according to various dynamic changes during the printing process, thereby effectively avoiding printing defects and improving printing accuracy and product quality. However, a traditional 3D printing melt extrusion control method has the problem of inaccurate matching of melt temperature and force during 3D printing, resulting in large melt extrusion control errors. Summary of the invention

[0003] Based on this, it is necessary to provide a 3D printing melt extrusion control method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a 3D printing melt extrusion control method is provided, the method comprising the following steps: Step S1: obtaining a 3D printed product drawing; marking the printing process of the 3D printed product drawing to obtain a 3D printed forming process; analyzing the product structure and morphology trend based on the 3D printed forming process to obtain product structure and morphology trend data; Step S2: Perform product segment printing melt temperature matching simulation on product structure morphology trend data to obtain structure segment melt temperature matching data; perform local printing force matching based on structure segment melt temperature matching data to obtain structure segment printing force matching data; perform adaptive melt extrusion control design based on structure segment printing force matching data to obtain adaptive melt extrusion control mechanism; Step S3: Optimizing and adjusting the adaptive melt extrusion control mechanism to obtain an adaptive melt extrusion optimization control mechanism; sending the adaptive melt extrusion optimization control mechanism to a 3D printer control center to execute the melt extrusion control method.

[0005] Preferably, step S1 comprises the following steps: Step S11: Obtain 3D printing product drawings; Step S12: performing data cleaning on the 3D printed product drawings to obtain 3D printed product cleaning drawings; Step S13: marking the 3D printing product cleaning drawing with a printing molding process to obtain a 3D printing molding process; Step S14: Analyze the product structure and morphology trend of the 3D printed product cleaning drawing based on the 3D printing molding process to obtain product structure and morphology trend data.

[0006] Preferably, step S2 comprises the following steps: Step S21: obtaining initial printing parameters, wherein the initial printing parameters include initial parameters of the material melt and initial printing speed; Step S22: performing a product segment printing melt temperature matching simulation on the product structure morphology trend data according to the material melt initial parameters in the printing initial parameters, and obtaining structure segment melt temperature matching data; Step S23: performing local printing force matching on the product structure morphology trend data according to the printing initial speed and structure segment melt temperature matching data in the printing initial parameters to obtain structure segment printing force matching data; Step S24: Based on the structural segment melt temperature matching data and the structural segment printing force matching data, an adaptive melt extrusion control design is performed to obtain an adaptive melt extrusion control mechanism.

[0007] Preferably, step S22 includes the following steps: Step S221: performing thin-wall structure recognition on the product structure morphology trend data to obtain thin-wall structure morphology trend data; performing bridging overhang structure recognition on the product structure morphology trend data to obtain product bridging overhang structure data; Step S222: performing layer gradient stacking effect analysis based on the thin-wall structure morphology trend data to obtain thin-wall layer stacking effect data; Step S223: simulating the heat accumulation rate according to the thin-wall structure morphology trend data and the thin-wall lamination stacking effect data to obtain the heat accumulation rate of the thin-wall structure; Step S224: performing thermal deformation nonlinear temperature interval fitting according to the heat accumulation rate of the thin-wall structure and the thin-wall lamination stacking effect data to obtain the thermal deformation nonlinear temperature interval; Step S225: performing thin-wall printing melt temperature matching on the thermal deformation nonlinear temperature interval according to the material melt initial parameters in the printing initial parameters, and obtaining thin-wall printing melt temperature matching data; Step S226: performing bridge overhang printing melt dynamic temperature matching on the product bridge overhang structure data according to the material melt initial parameters in the printing initial parameters, to obtain bridge overhang melt dynamic temperature matching data; Step S227: Based on the thin-wall printing melt temperature matching data and the bridge overhang melt dynamic temperature matching data, a product segment printing melt temperature matching simulation is performed to obtain the structure segment melt temperature matching data.

[0008] Preferably, step S226 includes the following steps: Evaluate the bridge support tension between two points on the product bridge overhang structure data to obtain the product bridge support tension; According to the product bridge support tension, the bending section strength of the product bridge overhang structure data is evaluated to obtain the bending section strength data; According to the initial parameters of the material melt in the initial printing parameters, the thermal expansion / contraction deformation error between different product sections is simulated and evaluated for the product bridge support tension and bending section strength data, and the thermal expansion / contraction deformation error data between different sections are obtained; Based on the thermal expansion / contraction deformation error data, the material melt initial parameters in the printing initial parameters are matched with the melt temperature increase interval and cooling rate between different cross sections to obtain the melt temperature increase interval and cooling rate matching data between different cross sections; The dynamic temperature matching of the melt in bridge overhang printing is performed according to the melt temperature increase range and cooling rate matching data between different cross sections, and the dynamic temperature matching data of the melt in bridge overhang printing is obtained.

[0009] Preferably, step S23 includes the following steps: Step S231: performing curved surface structure recognition on the product structure morphology trend data to obtain structural curved surface morphology data; performing bending angle recognition on the product structure morphology trend data according to the structural curved surface morphology data to obtain the bending angle; Step S232: performing structural density distribution difference analysis according to the structural surface morphology data and the bending angle to obtain structural density distribution difference data; Step S233: performing melt rheological property analysis based on the structural segmented melt temperature matching data to obtain structural segmented melt rheological property data; Step S234: performing internal stress release constraint analysis during the shearing process according to the structural density distribution difference data and the structural segmented melt rheological property data to obtain internal stress release constraint data during the shearing process; Step S235: performing local printing force matching based on the internal stress release constraint data and the initial printing speed in the initial printing parameters to obtain structure segment printing force matching data.

[0010] Preferably, step S234 includes the following steps: Perform local density gradient calculation on the structural density distribution difference data to obtain density gradient distribution data; Decomposing the shear stress tensor according to the density gradient distribution data and the structural segmented melt rheological property data, the anisotropic stress component data is obtained; The imbalanced internal stress distribution is simulated during the shear cooling process on the anisotropic stress component data to obtain the imbalanced internal stress distribution data; Adjust the strength of the stress-bearing printing layers based on the internal stress distribution imbalance data to obtain the strength adjustment data of the stress-bearing printing layers; Optimize the stress-bearing printing speed according to the internal stress distribution imbalance data to obtain stress-bearing printing speed optimization data; According to the stress printing interlayer strength adjustment data and the stress printing speed optimization data, the internal stress release constraint analysis in the stress shearing process is performed to obtain the internal stress release constraint data in the stress shearing process.

[0011] Preferably, step S24 comprises the following steps: Step S241: normalizing the structure segment melt temperature matching data and the structure segment printing force matching data respectively, to obtain the structure segment melt temperature matching normalized data and the structure segment printing force matching normalized data respectively; Step S242: performing logic learning on the structural segment melt temperature matching normalized data and the structural segment printing force matching normalized data respectively, to obtain segment melt temperature logic learning data and segment printing force logic learning data; Step S243: Based on the strategy gradient algorithm, adaptive melt extrusion control design is performed on the segmented melt temperature logic learning data and the segmented printing force logic learning data to obtain an adaptive melt extrusion control mechanism.

[0012] Preferably, step S3 comprises the following steps: Step S31: testing and simulating the adaptive melt extrusion control mechanism to obtain melt extrusion control test simulation data; Step S32: using a preset melt extrusion error identification model to perform error identification and collection on melt extrusion control test simulation data to obtain melt extrusion control test error data; Step S33: performing error parameter balance optimization on the melt extrusion control test error data to obtain error parameter balance optimization data; Step S34: Optimizing and adjusting the adaptive melt extrusion control mechanism based on the error parameter balance optimization data to obtain an adaptive melt extrusion optimization control mechanism; sending the adaptive melt extrusion optimization control mechanism to the 3D printer control center to execute the melt extrusion control method.

[0013] Preferably, the present invention further provides a 3D printing melt extrusion control system for executing the 3D printing melt extrusion control method as described above, the 3D printing melt extrusion control system comprising: The morphology trend analysis module is used to obtain 3D printing product drawings; mark the printing and molding process of the 3D printing product drawings to obtain the 3D printing molding process; analyze the product structure morphology trend based on the 3D printing molding process to obtain the product structure morphology trend data; The adaptive melt extrusion control design module is used to simulate the product segment printing melt temperature matching based on the product structure morphology trend data to obtain the structure segment melt temperature matching data; perform local printing force matching based on the structure segment melt temperature matching data to obtain the structure segment printing force matching data; perform adaptive melt extrusion control design based on the structure segment printing force matching data to obtain the adaptive melt extrusion control mechanism; The mechanism adjustment and optimization module is used to optimize and adjust the adaptive melt extrusion control mechanism to obtain an adaptive melt extrusion optimization control mechanism; the adaptive melt extrusion optimization control mechanism is sent to the 3D printer control center to execute the melt extrusion control method.

[0014] The beneficial effect of the present invention is that by obtaining the 3D printing product drawings and marking the printing molding process, the scheme can establish a clear printing process framework for each printed product. By analyzing the product structure morphology trend, the product structure morphology trend data can be obtained. This process ensures the accurate capture of every detail in the printing process and provides basic data support for subsequent temperature matching and force matching. This structured analysis makes each link of the printing process traceable and controllable, which helps to achieve high-precision printing. By performing segmented printing melt temperature matching simulation on the product structure morphology trend data, the scheme can optimize the matching according to the temperature requirements of different parts, ensure that the temperature of the melt is stable and uniform during the printing process, and avoid printing defects caused by temperature fluctuations. At the same time, local printing force matching is performed according to the temperature matching results to further optimize the force distribution of the material during the printing process and ensure the stability and strength of the structure. Combined with these data, an adaptive melt extrusion control mechanism is designed, which can adjust the melt extrusion parameters according to real-time feedback to ensure that the melt state of each part is under optimal conditions, thereby improving the printing quality. By optimizing and adjusting the adaptive melt extrusion control mechanism, the control accuracy and efficiency are further improved. By finely optimizing the melt extrusion process, it is possible to cope with more complex printing tasks and more variable printing conditions, minimize errors in the printing process, and improve the final quality of the product. The optimized control mechanism is sent to the 3D printer control center to achieve precise melt extrusion control. In this way, not only the stability of the printing process is improved, but also the printing time is significantly shortened, material waste is reduced, and feasibility is provided for large-scale production and printing of complex products. Therefore, the present invention is an optimization treatment of a traditional 3D printing melt extrusion control method, which solves the problem that a traditional 3D printing melt extrusion control method has inaccurate matching of melt temperature and force during 3D printing, thereby causing large errors in melt extrusion control, improves the accuracy of matching melt temperature and force during 3D printing, and reduces the error of melt extrusion control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the steps of a 3D printing melt extrusion control method; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. DETAILED DESCRIPTION

[0016] See also Figures 1 to 3 , a 3D printing melt extrusion control method, the method comprising the following steps: Step S1: obtaining a 3D printed product drawing; marking the printing process of the 3D printed product drawing to obtain a 3D printed forming process; analyzing the product structure and morphology trend based on the 3D printed forming process to obtain product structure and morphology trend data; Step S2: Perform product segment printing melt temperature matching simulation on product structure morphology trend data to obtain structure segment melt temperature matching data; perform local printing force matching based on structure segment melt temperature matching data to obtain structure segment printing force matching data; perform adaptive melt extrusion control design based on structure segment printing force matching data to obtain adaptive melt extrusion control mechanism; Step S3: Optimizing and adjusting the adaptive melt extrusion control mechanism to obtain an adaptive melt extrusion optimization control mechanism; sending the adaptive melt extrusion optimization control mechanism to a 3D printer control center to execute the melt extrusion control method.

[0017] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a 3D printing melt extrusion control method of the present invention. In this example, the 3D printing melt extrusion control method includes the following steps: Step S1: obtaining a 3D printed product drawing; marking the printing process of the 3D printed product drawing to obtain a 3D printed forming process; analyzing the product structure and morphology trend based on the 3D printed forming process to obtain product structure and morphology trend data; In an embodiment of the present invention, the required 3D printing product drawings are imported into the three-dimensional modeling software. Computer-aided design (CAD) software is used for analysis to extract the product's appearance features, internal structure, support requirements and key geometric parameters. The imported product model is subjected to topological analysis to identify the model's boundaries, curvature changes, support structure requirements and stacking directions. The three-dimensional model is segmented layer by layer using a spatial layering algorithm based on contour tracking, the outer contour data of each layer is extracted, and the corresponding layer thickness information is generated. Combined with the fluid dynamics analysis method, the printing path of each layer is marked to form a complete printing molding process. Based on the formed printing molding process, the curvature change analysis method is used to analyze the overall structural morphology trend of the product. For areas with large curvature changes, the curvature change rate is extracted using a three-dimensional gradient calculation method, and the structural morphology trend is calculated in combination with the direction vector of the morphological change. For linear or slowly varying surface areas, the segmented fitting method is used to model the morphological trend and generate product structural morphology trend data. The data includes the curvature characteristics, geometric distribution laws and printing path optimization information of each part of the product, which serves as the basis for subsequent printing parameter optimization.

[0018] Step S2: Perform product segment printing melt temperature matching simulation on product structure morphology trend data to obtain structure segment melt temperature matching data; perform local printing force matching based on structure segment melt temperature matching data to obtain structure segment printing force matching data; perform adaptive melt extrusion control design based on structure segment printing force matching data to obtain adaptive melt extrusion control mechanism; In the embodiment of the present invention, based on the product structure morphology trend data, the finite element analysis (FEA) method is used to simulate the heat conduction characteristics of different regions, and the temperature gradient distribution of different materials during the printing process is calculated using the three-dimensional steady-state heat conduction equation. For temperature-sensitive areas, a local temperature gradient adjustment algorithm is used to perform real-time heat flow correction based on the data fed back by the actual temperature sensor to obtain the structural segmented melt temperature matching data. For thin-walled structures or large-span structures, heat conduction optimization parameters are added to prevent the accumulation of internal residual stresses caused by excessive temperature differences. Computational fluid dynamics (CFD) is used to simulate the melt extrusion process, and local force matching analysis is performed in combination with the temperature matching data obtained in the previous step. For stress-concentrated areas, structural mechanics simulation is used to calculate the melt force distribution, and the filling density in the printing path is adjusted to reduce the local internal stress peak. For suspended structures or bridging areas, the interlayer stress balance calculation method is used to optimize the printing path and layer thickness matching strategy to form structural segmented printing force matching data. Combined with temperature matching data and force matching data, a melt extrusion control strategy is designed based on an adaptive control algorithm. The melt flow rate sensor and temperature sensor are built into the extruder nozzle, and the melt flow rate and temperature are controlled by feedback adjustment PID to achieve dynamic melt extrusion control. In view of the change in material viscosity during printing, a variable step flow control method is used to adjust the extrusion flow according to the real-time melt temperature to form an adaptive melt extrusion control mechanism.

[0019] Step S3: Optimizing and adjusting the adaptive melt extrusion control mechanism to obtain an adaptive melt extrusion optimization control mechanism; sending the adaptive melt extrusion optimization control mechanism to a 3D printer control center to execute the melt extrusion control method.

[0020] In the embodiment of the present invention, the adaptive melt extrusion control mechanism is optimized. First, the printing stability under different extrusion rates is simulated by offline simulation, and the extrusion uniformity index on different printing paths is calculated. The adaptive iterative learning control (AILC) method is adopted to correct the melt flow error layer by layer to reduce the accumulation of extrusion errors caused by external interference. Based on the sensor data in the actual printing process, the nonlinear relationship model between parameters such as extrusion temperature, flow rate, and ambient temperature is established by using the data regression analysis method. For the case where the nozzle temperature fluctuates greatly, a dynamic filtering compensation algorithm is adopted to make the melt temperature adjustment process smoother. For the matching relationship between different printing layer thicknesses and extrusion rates, a parameter optimization method based on gradient descent is adopted to reduce the interlayer non-uniformity and improve the melt deposition stability. The optimized adaptive melt extrusion control mechanism has undergone multiple rounds of verification tests to ensure that it can operate stably under different ambient temperatures and printing material conditions. Finally, the optimized control mechanism is imported into the 3D printer control center, and the melt extrusion control method is executed by the embedded control unit to achieve high-precision and high-stability 3D printing process control.

[0021] Preferably, step S1 comprises the following steps: Step S11: Obtain 3D printing product drawings; Step S12: performing data cleaning on the 3D printed product drawings to obtain 3D printed product cleaning drawings; Step S13: marking the 3D printing product cleaning drawing with a printing molding process to obtain a 3D printing molding process; Step S14: Analyze the product structure and morphology trend of the 3D printed product cleaning drawing based on the 3D printing molding process to obtain product structure and morphology trend data.

[0022] In an embodiment of the present invention, in a computer-aided design (CAD) environment, a three-dimensional model file of a target 3D printed product is loaded. A model file stored in a standard industrial format (such as STL, OBJ, STEP or IGES) is used, and a geometric integrity check is performed on it. First, a geometric analysis tool is used to calculate the surface area, volume and boundary topology information of the model to ensure that the file does not lose key data. Then, a Boolean operation method is used to detect whether the model has overlapping geometric bodies or open boundaries. If redundant overlapping parts are found, Boolean subtraction is applied for correction. In the case of openings, an iterative filling algorithm is used to automatically generate closed surfaces, and the normal vector directions of adjacent triangular facets are matched to ensure the continuity and closure of the printed data. After completing the basic geometric check, the layer thickness information, support demand analysis data and surface smoothness information of the three-dimensional model are extracted for subsequent processing. The obtained 3D printed product drawings are cleaned for data, and firstly topological optimization is performed to eliminate redundant geometric structures. A spatial search tree (such as an octree or a kd tree) is used to classify the facets, merge repeated or overlapping facets, and eliminate existing isolated points. Then, the quality of the triangular facets is evaluated, and the curvature analysis method is used to detect abnormally sharp facets. If a curvature mutation point is found, the Laplace smoothing algorithm is used to adjust the coordinates of the adjacent vertices to make them consistent with the overall morphological trend. For the STL file format, the consistency of the normal vector direction of the facet is checked. By calculating the normal vector angle of adjacent triangular faces, it is determined whether there is a problem of normal vector flipping. If an abnormality occurs, the normal vector recalculation method is used to correct it based on the average normal vector of the adjacent facets. For the existing non-manifold boundary problem, the boundary ring detection algorithm is used to identify the unclosed area, and the closed surface is generated by interpolation to ensure data integrity. In addition, structural optimization is performed for small features or suspended structures in the model. If the feature size is smaller than the printing resolution (such as FDM printing accuracy of 0.1mm), the feature is removed to prevent printing failure. For suspended structures, the support generation algorithm is used to calculate the optimal support point position and mark the area that requires additional support. After completing all cleaning steps, the 3D printed product cleaning drawing is output, and its volume, mass center and support requirements are recalculated to ensure the accuracy of subsequent printing parameter settings. Based on the cleaning drawings of 3D printed products, the printing process is marked. First, the layer thickness analysis method is used to calculate the optimal printing layer thickness, and the appropriate layering scheme is determined according to the device resolution (such as 0.05mm, 0.1mm, etc.). Then, the contour detection algorithm is used to extract the boundary information of each layer, calculate the printing path, and adjust the filling direction based on the dynamic path optimization method to reduce the stress concentration generated during the printing process. The printing paths are classified and marked, including contour printing paths, filling printing paths, support printing paths, etc. For the contour path, the equidistant offset algorithm is used to generate the outer contour, and the smoothness of the print head path is ensured while maintaining the geometric accuracy of the model.For the filling path, honeycomb filling, linear filling or grid filling algorithm is used to select the appropriate filling method according to the structural characteristics of the model. For the support structure, the support optimization method based on the gravity direction is adopted to ensure that the support structure can provide the necessary stability and reduce the impact on the model surface when it is removed later. In addition, the printing sequence optimization is also required during the marking process of the printing molding process. The cooling time of different areas is calculated using a method based on thermodynamic analysis, and the printing sequence is adjusted to minimize local heat accumulation and reduce the possibility of warping or cracking. Finally, the complete 3D printing molding process data is output, including printing path, support information, layer thickness distribution, etc., to provide a basis for subsequent printing parameter setting. Based on the 3D printing molding process, the product structure morphology trend analysis is carried out. First, the curvature analysis method is used to calculate the curvature distribution of each part of the model. The Gaussian curvature and principal curvature calculation methods are used to determine whether the model surface is concave, convex or flat, and the curvature change trend of each area is marked. For high curvature areas, their structural features are identified to determine whether it is necessary to optimize the printing path or increase the support structure. Secondly, the structural morphology trend is divided. The principal component analysis (PCA) method is used to extract the main direction of the model and divide the model into multiple regions with similar morphological characteristics. For linear regions, the main axis direction is extracted and the angle between the printing path and the direction is calculated to optimize the printing path direction. For curved surface regions, the local curvature fitting method is used to analyze the surface trend and adjust the printing layer thickness to meet the structural requirements of different forms. Then, the printing deformation analysis is performed. The thermal stress simulation method is used to calculate the trend of material cooling and shrinkage during printing and predict the deformation area. For areas with a high risk of deformation, the printing parameters are adjusted, including reducing the extrusion speed, increasing the cooling time or adjusting the filling method to reduce the impact of thermal stress. For areas where interlayer separation occurs, interlayer bonding strategies are added, such as staggered filling or increasing the local melting temperature to improve the interlayer bonding strength. Finally, the product structure morphology trend data is output, including curvature distribution, main axis direction, printing path optimization suggestions, deformation risk assessment and other information, providing accurate data support for subsequent printing temperature control, melt extrusion control and other links.

[0023] Preferably, step S2 comprises the following steps: Step S21: obtaining initial printing parameters, wherein the initial printing parameters include initial parameters of the material melt and initial printing speed; Step S22: performing a product segment printing melt temperature matching simulation on the product structure morphology trend data according to the material melt initial parameters in the printing initial parameters, and obtaining structure segment melt temperature matching data; Step S23: performing local printing force matching on the product structure morphology trend data according to the printing initial speed and structure segment melt temperature matching data in the printing initial parameters to obtain structure segment printing force matching data; Step S24: Based on the structural segment melt temperature matching data and the structural segment printing force matching data, an adaptive melt extrusion control design is performed to obtain an adaptive melt extrusion control mechanism.

[0024] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: obtaining initial printing parameters, wherein the initial printing parameters include initial parameters of the material melt and initial printing speed; In the embodiment of the present invention, before executing the 3D printing task, it is necessary to first obtain the initial printing parameters, including the initial parameters of the material melt and the initial printing speed. The initial parameters of the material melt mainly include physical parameters such as melting temperature, viscosity, thermal diffusivity, specific heat capacity, density, thermal conductivity, etc. In order to accurately obtain these parameters, a thermogravimetric analyzer (TGA) is used to determine the thermal decomposition temperature range of the material, and a differential scanning calorimeter (DSC) is used to determine the melting temperature range to ensure that no decomposition or condensation occurs in the subsequent melt extrusion process. The viscosity is measured by a capillary rheometer, the flow characteristics of the melt are tested at different shear rates, the shear viscosity curve is obtained, and the non-Newtonian flow behavior of the melt is analyzed in combination with the power law equation. For the thermal diffusivity and thermal conductivity, a laser thermal conductivity analyzer (LFA) is used to test the thermal diffusivity of the material, and the thermal conductivity is calculated in combination with the material density and specific heat capacity to ensure that the melt can be evenly heated and cooled during the printing process. The acquisition of the initial printing speed is based on the equipment performance and the characteristics of the printed material. A closed-loop speed control system is used, and a preliminary experiment is first carried out under different printing conditions to determine the matching relationship between the extrusion rate and the print head movement speed. The real-time speed of the nozzle movement is measured by a displacement sensor, and the deposition of the extruded melt is recorded with a high-speed camera to analyze its uniformity. Based on the experimental data, the optimal initial printing speed is calculated so that the accumulation morphology of the extruded melt matches the theoretical layer thickness to avoid over-extrusion or under-extrusion. All the initial printing parameters obtained will be used for subsequent melt temperature matching and force matching analysis to optimize the stability and consistency of the printing process.

[0025] Step S22: performing a product segment printing melt temperature matching simulation on the product structure morphology trend data according to the material melt initial parameters in the printing initial parameters, and obtaining structure segment melt temperature matching data; In the embodiment of the present invention, after obtaining the initial printing parameters, the product structure morphology trend data is segmented and the temperature matching simulation is performed based on the initial parameters of the material melt. First, the curvature distribution analysis method is used to extract different morphological regions according to the product structure morphology trend data, including plane regions, curved surface regions, sharp angle regions, suspended structure regions, etc. The K-means clustering method is used to classify the model by region, and the characteristic size, heat conduction path and local heating conditions of each region are determined. For different regions, the finite element heat conduction model is used to calculate the temperature field distribution after melt deposition, and the cooling rate and interlayer bonding strength of the melt are analyzed. For the plane region, the thermal diffusion rate of the melt is calculated to ensure that the temperature is uniform after the melt is deposited, and the nozzle temperature is adjusted to be slightly higher than the glass transition temperature of the material to prevent warping and deformation. For the curved surface region, the effect of curvature change on heat conduction is considered, the printing layer thickness and heating temperature are adjusted to ensure that the melt can be fully spread, and a stable heat flow field is maintained through a real-time temperature control feedback system. For the sharp angle region, the effect of cooling shrinkage on angle accuracy is analyzed, and the melt fluidity of the corner is controlled by a local temperature compensation strategy to prevent corner shrinkage and cracking. For the suspended structure area, combined with hot air convection analysis, the heat transfer effect of the support structure is calculated, and the extrusion temperature is adjusted to optimize the interlayer adhesion. Finally, combined with multi-region temperature matching data, the structure segment melt temperature matching data is generated to provide a basis for subsequent printing force matching.

[0026] Step S23: performing local printing force matching on the product structure morphology trend data according to the printing initial speed and structure segment melt temperature matching data in the printing initial parameters to obtain structure segment printing force matching data; In the embodiment of the present invention, after obtaining the structural segmented melt temperature matching data, the product structure morphology trend data is subjected to local force matching analysis to optimize the stability of melt deposition during printing. First, the residual stress distribution between the layers after melt deposition is calculated using mechanical finite element analysis (FEA). The stress distribution curve of the melt deposition path is fitted by the least squares method to identify the high stress area, and whether the printing parameters need to be adjusted according to the yield strength of the material. In the plane area, the tensile and shear stresses after melt deposition are calculated to ensure that the melt extrusion speed matches the printing speed, so that the deposition line width is stable and interlayer peeling is avoided. In the curved surface area, the effect of curvature on melt flow is analyzed, and the travel acceleration of the nozzle is adjusted so that the melt can be evenly distributed along the curved surface to improve the interlayer bonding strength. In the sharp angle area, the force concentration effect is calculated, and a variable speed printing strategy is adopted to reduce the extrusion rate in the corner area to make the melt better fused and prevent local stress concentration from causing cracking. In the suspended structure area, the gravity deformation of the melt during printing is simulated, and the printing order and strength of the support material are adjusted to prevent the collapse of the support structure. Finally, based on the force matching analysis results, the printing speed, extrusion pressure and interlayer bonding strategy were adjusted to obtain the force matching data for structure segment printing, providing support for subsequent melt extrusion control.

[0027] Step S24: Based on the structural segment melt temperature matching data and the structural segment printing force matching data, an adaptive melt extrusion control design is performed to obtain an adaptive melt extrusion control mechanism.

[0028] In an embodiment of the present invention, after obtaining the structural segment melt temperature matching data and the structural segment printing force matching data, an adaptive melt extrusion control design is performed to realize the dynamic adjustment of the melt extrusion parameters. First, a relationship model between melt extrusion pressure and temperature is established, and the flow characteristics of the melt in the nozzle are calculated based on the principle of Newtonian fluid dynamics, and the extrusion pressure is adjusted in combination with real-time temperature monitoring data. An adaptive fuzzy control algorithm is used to adjust the extrusion rate according to temperature fluctuations to ensure that the melt flow rate is stable and prevent excessive extrusion or blockage. In the plane area, a constant extrusion pressure is set, and a closed-loop control method is used to maintain the consistency of the melt deposition thickness. In the curved surface area, the extrusion pressure is adjusted in real time to match the curvature change of the printing path to ensure that the melt is evenly distributed. In the sharp angle area, the extrusion rate is reduced, and the flow time of the melt is increased to fully fuse the melt and improve the corner accuracy. In the suspended structure area, a layered heating strategy is adopted to increase the interlayer bonding strength and reduce structural deformation by heating layer by layer. Finally, the melt temperature matching and force matching data are integrated to establish an adaptive melt extrusion control mechanism to achieve precise dynamic adjustment of printing parameters to ensure the stability and consistency of printing quality.

[0029] Preferably, step S22 includes the following steps: Step S221: performing thin-wall structure recognition on the product structure morphology trend data to obtain thin-wall structure morphology trend data; performing bridging overhang structure recognition on the product structure morphology trend data to obtain product bridging overhang structure data; Step S222: performing layer gradient stacking effect analysis based on the thin-wall structure morphology trend data to obtain thin-wall layer stacking effect data; Step S223: simulating the heat accumulation rate according to the thin-wall structure morphology trend data and the thin-wall lamination stacking effect data to obtain the heat accumulation rate of the thin-wall structure; Step S224: performing thermal deformation nonlinear temperature interval fitting according to the heat accumulation rate of the thin-wall structure and the thin-wall lamination stacking effect data to obtain the thermal deformation nonlinear temperature interval; Step S225: performing thin-wall printing melt temperature matching on the thermal deformation nonlinear temperature interval according to the material melt initial parameters in the printing initial parameters, and obtaining thin-wall printing melt temperature matching data; Step S226: performing bridge overhang printing melt dynamic temperature matching on the product bridge overhang structure data according to the material melt initial parameters in the printing initial parameters, to obtain bridge overhang melt dynamic temperature matching data; Step S227: Based on the thin-wall printing melt temperature matching data and the bridge overhang melt dynamic temperature matching data, a product segment printing melt temperature matching simulation is performed to obtain the structure segment melt temperature matching data.

[0030] In the embodiment of the present invention, when the thin-walled structure is identified for the product structure morphology trend data, the mesh data of the three-dimensional model is first preprocessed to extract the information of all triangular facets. Based on the topological analysis method, the local curvature, surface area ratio and thickness gradient change of each mesh unit are calculated. A feature extraction method based on regional growth is adopted to screen out continuous facets with a smaller thickness by setting a thickness threshold, and the extracted thin-walled area is optimized by using a morphological operator. For the thin-walled area finally identified, the flatness is corrected based on the normal vector consistency detection, and the thin-walled structure morphology trend data is output. For the identification of the bridging overhang structure, based on the gradient change in the layer thickness direction, the average normal angle of the model surface is first calculated, and the area with an overhang angle exceeding 45 degrees is marked in combination with the gravity direction vector. The local geometric curvature is calculated by the Laplace operator, and the surface mutation area is judged to identify the suspended bridging area. Further, the span length of the bridging area is calculated by the spatial frequency analysis method based on Fourier transform, and the bridging overhang structure is screened out. Finally, the identified bridging overhang structure data is stored and passed to the next step. When analyzing the lamination gradient stacking effect based on the thin-walled structure morphological trend data, the stacking curvature analysis model based on the layer thickness direction is first constructed. The thin-walled structure is divided into multiple equal-height slices, and the boundary point set of each layer is fitted using the spline interpolation method, and the interlayer thickness gradient change is calculated. The interlayer overlap rate is calculated based on the interpolation results, and the interlayer bonding strength is evaluated in combination with the local micro-section analysis. The interlayer stress gradient distribution is simulated using the finite element method to determine the stacking stress difference of each layer. At the same time, based on the multi-scale fractal analysis method, the surface roughness of the lamination is quantitatively calculated to extract the interlayer fluctuation characteristics. By calculating the angle between the interlayer fluctuation frequency and the stacking direction, the area that causes the reduced layer adhesion is identified. Finally, the stacking gradient data of each layer is integrated to output the thin-walled lamination stacking effect data. When simulating the heat accumulation rate based on the thin-walled structure morphological trend data and the thin-walled lamination stacking effect data, a local heat transfer model based on the heat conduction equation is first established. The thin-walled structure is meshed using the finite difference method, and the temperature transfer equation is established in each mesh unit. The boundary conditions are defined so that the model conforms to the actual heat source distribution during the printing process, including the heating power of the melt nozzle, the heat transfer between the printed layers, and the air convection heat transfer coefficient. For the thin-walled area, the heat accumulation rate inside the thin-walled structure is calculated based on the combined analysis method of steady-state and transient heat conduction. The implicit time stepping method is used to simulate the temperature field at different time points during the printing process, and the interlayer heat accumulation rate is calculated in combination with the thin-wall layer stacking effect data. Finally, the calculated interlayer heat accumulation rate data is stored as input for subsequent thermal deformation analysis.According to the heat accumulation rate of thin-walled structures and the stacking effect data of thin-walled layers, when fitting the nonlinear temperature interval of thermal deformation, the nonlinear regression analysis method is first used to fit the thin-wall deformation data at different temperatures. Typical temperature interval points are selected, and the thermal deformation response curve is obtained by curve fitting the experimental data. The least squares fitting method is used to segment the temperature-deformation curve, and the key temperature change interval is determined by combining the glass transition temperature point and the interlaminar stress gradient data. The fitting curve is differentially analyzed to calculate the deformation rate change, and the nonlinear growth point is screened out, and finally the thermal deformation nonlinear temperature interval data is output. According to the initial parameters of the material melt in the initial printing parameters, when matching the melt temperature of thin-wall printing in the thermal deformation nonlinear temperature interval, the relationship model between melt temperature and flow rate is first established based on the dynamic viscosity change curve of the material. The fluid dynamics simulation method is used to calculate the shear rate and interlaminar wetting performance of the melt at different temperatures. Combined with the thermal deformation nonlinear temperature interval data, the numerical interpolation method is used to calculate the melt temperature range suitable for thin-wall printing. According to the different layer heights and thicknesses of the thin-walled area, the melt temperature is dynamically adjusted, and the melt temperature matching data for thin-wall printing is output. When the dynamic temperature matching of the bridge overhang printing melt is performed on the product bridge overhang structure data according to the material melt initial parameters in the initial printing parameters, the heat transfer equation of the bridge area is first constructed based on the span, thickness and printing speed of the bridge overhang structure. The finite element method is used to calculate the temperature gradient of the bridge overhang part, and the dynamic temperature control strategy at different positions is determined in combination with the thermal stability model of the printed melt. For bridge areas of different spans, a variable step control strategy is adopted to adjust the melt nozzle temperature in real time to maintain the optimal melt viscosity range during the printing process. Based on the support requirements of the bridge area, the melt cooling time at different printing speeds is calculated, and the temperature distribution of the bridge area is ensured to be uniform in combination with the printing path optimization algorithm. Finally, the dynamic temperature matching data of the bridge overhang melt is output. Based on the melt temperature matching data of thin-wall printing and the dynamic temperature matching data of bridge overhang melt, when simulating the melt temperature matching of product segment printing, the product is first divided into multiple hot sections, and the temperature control parameters of each section are determined by the aforementioned matching data. The finite element simulation method based on thermal fluid dynamics is used to simulate the melt flow of each section. According to the printing temperature requirements of different structural areas, the layered progressive iterative calculation method is used to optimize the temperature matching of each segment melt. For the bridging area, the range of nozzle temperature variation is adjusted based on the thermal stability evaluation of the overhang structure; for the thin-wall area, a dynamic temperature adjustment strategy is adopted to prevent deformation caused by heat accumulation. Finally, the structural segment melt temperature matching data is output to provide a basis for subsequent printing control.

[0031] Preferably, step S226 includes the following steps: Evaluate the bridge support tension between two points on the product bridge overhang structure data to obtain the product bridge support tension; According to the product bridge support tension, the bending section strength of the product bridge overhang structure data is evaluated to obtain the bending section strength data; According to the initial parameters of the material melt in the initial printing parameters, the thermal expansion / contraction deformation error between different product sections is simulated and evaluated for the product bridge support tension and bending section strength data, and the thermal expansion / contraction deformation error data between different sections are obtained; Based on the thermal expansion / contraction deformation error data, the material melt initial parameters in the printing initial parameters are matched with the melt temperature increase interval and cooling rate between different cross sections to obtain the melt temperature increase interval and cooling rate matching data between different cross sections; The dynamic temperature matching of the melt in bridge overhang printing is performed according to the melt temperature increase range and cooling rate matching data between different cross sections, and the dynamic temperature matching data of the melt in bridge overhang printing is obtained.

[0032] In the embodiment of the present invention, the bridge support tension is evaluated for the product bridge overhang structure data. First, the overhang span of the bridge segment needs to be extracted based on the printing path and geometric morphology data. By analyzing the product bridge overhang structure data, the coordinate information of the span start and end points is identified, and a mechanical analysis model of the bridge overhang area is established. The finite difference method is used to segmentally calculate the gravity effect, melt flow influence and deformation in the solidification stage of the bridge area. According to the viscoelastic properties of the material and the stress relaxation characteristics of the melt at high temperature, the tensile stress and shear stress of the bridge overhang structure during the melt deposition process are calculated based on the Hertz contact mechanics theory. In order to accurately evaluate the bridge support tension, it is necessary to further use the Euler-Bernoulli beam theory, combine the cross-sectional geometric dimensions of the bridge segment, the initial temperature of the melt and the cooling rate, establish the deflection equation, calculate the strain energy of the bridge overhang area and solve the critical tension. Through experimental verification, a high-precision strain measurement device is used to compare and analyze the bridge support tension under different printing speeds and temperature conditions, and finally obtain the product bridge support tension data under different printing conditions. To evaluate the bending section strength of the product bridge overhang structure data, it is necessary to first determine the stress state of the bridge section at different deposition time points in combination with the calculation results of the bridge support tension. The beam bending theory is used to establish a cross-section bending stress calculation model based on the material yield strength, elastic modulus and bridge span of the overhanging part. Mechanical analysis is performed on different printing layer thicknesses and interlayer bonding strengths, and the stress concentration factor is used to adjust the stress distribution near the bridge support point. Based on the cooling rate data of the printed melt, the influence function of thermal expansion and contraction on the section strength is established, and combined with the experimentally measured cooling curve of the bridge melt, the bending strength change trend under different temperature conditions is derived. Finally, through dynamic loading experiments, the bending strength of the bridge section under different printing parameters is measured and compared to obtain the bending section strength data. Based on the bridge support tension and bending section strength data, the thermal expansion / contraction deformation error between different product sections is simulated and evaluated. First, the thermal expansion coefficient of the material is experimentally measured, and the expansion behavior of the melt in different temperature ranges is recorded using a high-temperature dilatometer. Based on Fourier heat conduction theory, the temperature gradient between different layers during the melt cooling process is calculated, and combined with the thermal convection and radiation heat transfer models in the actual printing environment, the thermal deformation calculation formula between cross sections is constructed. A comprehensive analysis is conducted on different layer thicknesses, printing speeds, and melt flow characteristics. The finite element method is used to simulate the stress and deformation generated in different cross sections during heating and cooling to obtain the thermal expansion / contraction deformation error data between different cross sections. The thermal expansion / contraction deformation error data is analyzed to establish a melt temperature matching strategy between cross sections. The numerical fitting method is used to perform regression analysis on the melt temperature distribution under different printing speeds, layer thicknesses, and material parameters to obtain the temperature rise range between different cross sections.Combined with the cooling rate data measured in the experiment, the second-order interpolation method is used to optimize the matching of the cooling curve, adjust the printing path and the melt deposition rate, and make the temperature distribution between different sections tend to be uniform. Finally, the melt temperature rise interval and cooling rate matching data between different sections are obtained. Based on the melt temperature rise interval and cooling rate matching data between different sections, the printing temperature control strategy of the bridge overhang area is adjusted. The PID control method is used to adjust the melt extrusion temperature in real time so that the bridge overhang part maintains the best melt fluidity and interlayer bonding strength during the printing process. Combined with the bridge support tension data, the moving speed of the print head and the melt deposition rate are dynamically adjusted to prevent the melt from collapsing in areas with large spans or uneven interlayer bonding.

[0033] Preferably, step S23 includes the following steps: Step S231: performing curved surface structure recognition on the product structure morphology trend data to obtain structural curved surface morphology data; performing bending angle recognition on the product structure morphology trend data according to the structural curved surface morphology data to obtain the bending angle; Step S232: performing structural density distribution difference analysis according to the structural surface morphology data and the bending angle to obtain structural density distribution difference data; Step S233: performing melt rheological property analysis based on the structural segmented melt temperature matching data to obtain structural segmented melt rheological property data; Step S234: performing internal stress release constraint analysis during the shearing process according to the structural density distribution difference data and the structural segmented melt rheological property data to obtain internal stress release constraint data during the shearing process; Step S235: performing local printing force matching based on the internal stress release constraint data and the initial printing speed in the initial printing parameters to obtain structure segment printing force matching data.

[0034] In the embodiment of the present invention, the surface structure recognition of the product structure morphology trend data needs to extract the product surface geometry information from the three-dimensional model data. First, the discrete differential geometry method is used to calculate the Gaussian curvature and mean curvature of the three-dimensional mesh model, so as to distinguish the surface area from the plane area. For the model represented by the triangular mesh, the main curvature direction of each vertex is calculated, the noise interference is smoothed by the second-order Laplace-Beltrami operator, and the surface segmentation is performed in combination with the main curvature direction field. The partition growth algorithm is used to extract the boundary of the surface area according to the change trend of the Gaussian curvature value, and finally the structural surface morphology data is obtained. Based on the structural surface morphology data, the bending angle of the product structure morphology trend data is identified. First, the extracted surface morphology area is sliced, and multiple equally spaced profile curves are generated along the structural trend. The Fourier descriptor method is used to analyze the bending trend of the profile curve, and the arc length parameterization method is used to calculate the local curvature change rate. The adjacent surface segments are matched, and the corner angle is calculated based on the curvature continuity and tangential deviation. For complex structures, such as multi-directional variable curvature regions, the main direction calculation method of differential geometry is used to extract the local surface normal direction, calculate the angle between the two adjacent surface normals, and then obtain the bending angle. The structural density distribution difference analysis is based on the structural surface morphology data and the bending angle. First, the voxel grid subdivision method is used to divide the three-dimensional model into multiple uniform volume units, and the material distribution density in each voxel is calculated. The density calculation adopts the local volume integral method, and the local density value of the area is calculated by statistically calculating the material filling rate in each voxel. For different curvature regions, the density gradient calculation method is used to evaluate the density change trend between different structural surfaces. For high curvature corner regions, the density gradient change rate of local voxels is calculated, and the structural density change characteristics are analyzed in combination with the material deposition path. In the process of density distribution difference analysis, the weighted average interpolation method is used to refine the calculation of the area with large density changes to ensure the accuracy of the density distribution analysis. Finally, based on the density distribution difference calculation results, the structural density distribution difference data is obtained. The melt rheological properties analysis is based on the structural segmented melt temperature matching data. First, the viscoelastic behavior of the printed melt was experimentally determined. A capillary rheometer was used to measure the shear viscosity of the melt at different temperatures, and the data was curve fitted to establish the relationship between the melt temperature and the rheological properties. For the interlayer bonding area, a steady-state shear experiment was used to determine the shear modulus of the melt, and combined with the density distribution data, the change law of the rheological properties of the melt in different structural segments was analyzed. For different structural regions, the non-Newtonian fluid dynamics theory was used to calculate the flow rate of the melt at different temperatures and pressures. For high curvature areas, the dual viscosity model was used, combined with the shear rate data, to analyze the rheological behavior of the melt in the transition area. Finally, the rheological property data of the melt in different structural segments were obtained through rheological property calculations.The internal stress release constraint analysis during the shearing process is based on the structural density distribution difference data and the structural segmented melt rheological property data. First, a shearing mechanical analysis model is established for the stress of the melt during printing. The finite element analysis method is used to simulate the stress of different density distribution areas, and the internal stress distribution of the melt under different stress conditions during printing is calculated. In the high-density area, the residual stress caused by the uneven bonding between the layers is calculated, and the stress transfer path between different printing layers is analyzed in combination with the melt cooling rate. For areas with large curvature changes, the shear stress analysis method is used to calculate the deformation rate of the melt during the stress process, and the elastic-plastic mechanical model is used to calculate the stress release after stress. Based on the stress release analysis results, the constraint optimization method is used to adjust the printing path of different areas to reduce the internal stress accumulation under the shear force. Finally, the internal stress release constraint data during the shearing process is obtained to provide support for the subsequent local printing force matching. The local printing force matching is based on the internal stress release constraint data and the initial printing speed in the initial printing parameters. First, according to the internal stress data generated during the shearing process, the stress distribution characteristics of different areas are calculated. For areas with high stress accumulation, a dynamic printing speed adjustment method is adopted. By adjusting the printing path planning, the print head reduces the extrusion rate in the area with greater stress, thereby improving the deposition stability of the melt. For different structural areas, a local force control strategy is adopted to calculate the mechanical state of different areas during the printing process, and the acceleration and deceleration of the printing path are adjusted in combination with the print head motion trajectory optimization method to reduce the force fluctuation during the printing process. For areas with large curvature, the interlayer filling density adjustment method is adopted to increase the filling rate of high curvature areas to enhance the local force uniformity. Finally, through verification, the stress state of the printed melt is compared and analyzed at different printing speeds and printing paths, and the structural segmented printing force matching data is obtained, providing basic data for optimizing 3D printing melt extrusion control.

[0035] Preferably, step S234 includes the following steps: Perform local density gradient calculation on the structural density distribution difference data to obtain density gradient distribution data; Decomposing the shear stress tensor according to the density gradient distribution data and the structural segmented melt rheological property data, the anisotropic stress component data is obtained; The imbalanced internal stress distribution is simulated during the shear cooling process on the anisotropic stress component data to obtain the imbalanced internal stress distribution data; Adjust the strength of the stress-bearing printing layers based on the internal stress distribution imbalance data to obtain the strength adjustment data of the stress-bearing printing layers; Optimize the stress-bearing printing speed according to the internal stress distribution imbalance data to obtain stress-bearing printing speed optimization data; According to the stress printing interlayer strength adjustment data and the stress printing speed optimization data, the internal stress release constraint analysis in the stress shearing process is performed to obtain the internal stress release constraint data in the stress shearing process.

[0036] In an embodiment of the present invention, the local density gradient calculation is performed based on the structural density distribution difference data. First, a three-dimensional voxel grid division method is used to divide the printed model area into equally spaced voxel units, and the material density of each voxel unit is numerically extracted. The density value is calculated by calculating the material filling rate in the unit volume, and the octree segmentation algorithm is used to subdivide the high-density area to improve the calculation accuracy. On the basis of the voxel grid, the three-dimensional Sobel operator is used to calculate the density gradient in each direction. The specific calculation method is to perform directional difference operations on the density values ​​of adjacent voxels to obtain the density change rate in the x-axis, y-axis and z-axis directions. For complex geometric structures, a local weighted interpolation method is used, combined with Gaussian kernel smoothing, to eliminate noise interference in the density gradient calculation. In the high-density transformation area, the second-order derivative of the density gradient is calculated to determine the acceleration of the density change, so as to determine whether there is a mutation in the local density distribution. Finally, the density gradient data is stored in the form of a three-dimensional tensor, and the density gradient distribution data is generated for subsequent shear stress analysis. The shear stress tensor decomposition is based on the density gradient distribution data and the rheological properties data of the structural segmented melt. First, the non-Newtonian fluid mechanics method is used to calculate the shear stress in different regions according to the rheological properties data of the melt. The shear stress calculation is based on the melt viscosity model, using the experimentally measured shear rate-viscosity curve combined with the density gradient distribution data to calculate the stress components in different directions. For anisotropic structural regions, the stress tensor decomposition method is used to decompose the overall shear stress matrix into principal stress components. The decomposition process uses the generalized eigenvalue decomposition algorithm to calculate the principal stresses in each direction and extract the maximum principal stress direction to determine the main force trend of the melt in the local area. For complex structures, such as interlayer bonding areas or high-curvature surfaces, the finite element method is used for local mesh subdivision, and the stress components are calculated on each unit to improve the decomposition accuracy. Finally, anisotropic stress component data are generated and used for subsequent internal stress distribution analysis. The simulation of the imbalance of internal stress distribution during shear cooling is based on the anisotropic stress component data. First, a three-dimensional thermal-mechanical coupling finite element model is established to numerically simulate the stress changes of the melt during cooling. The transient temperature field analysis method is used to calculate the temperature gradient change of the melt from extrusion to cooling and solidification, and the thermal stress evolution of the material is calculated in combination with the thermal expansion coefficient. During the cooling process, the cooling rates in different regions are different, resulting in an imbalance in the internal stress distribution inside the melt. In order to analyze this phenomenon, the layer-by-layer thermal stress accumulation calculation method is used to calculate the stress change of each layer of the melt during the cooling process, and the finite difference method is used to solve the thermal stress diffusion equation to determine the stress distribution between different layers. In the local shear stress area, combined with the stress relaxation model, the stress release of the melt at different cooling stages is analyzed, and the final internal stress distribution imbalance data is obtained through numerical iterative calculation. The strength adjustment between the stress-bearing printing layers is based on the internal stress distribution imbalance data.First, the influence of interlayer bonding strength on internal stress distribution is analyzed, and the interlayer bonding strength calculation method is used to determine the bonding force distribution between different printing layers. Combined with the interlayer shear strength curve measured by the experiment, the interlayer bonding force under different temperature conditions is calculated, and the interlayer filling method of the high stress concentration area is adjusted based on the printing path optimization algorithm. In the process of interlayer strength adjustment, the local heating control method is used to increase the interlayer bonding temperature in the specific area to enhance the bonding strength. At the same time, the micro-scale interlayer filling optimization technology is used to adjust the melt deposition path so that the melt flow direction in the interlayer bonding area is consistent with the main stress direction, thereby reducing the risk of interlayer separation. Finally, the stress printing interlayer strength adjustment data is generated and used to optimize the printing path. The stress printing speed optimization is based on the imbalance data of internal stress distribution. First, the influence of different printing speeds on internal stress is analyzed, and the dynamic simulation method is used to calculate the influence of printing speed changes on melt shear stress and interlayer bonding strength. For high stress accumulation areas, a dynamic speed adjustment strategy is adopted to reduce the printing speed to reduce stress concentration. In the process of speed optimization, the temperature gradient change of the melt at different printing speeds is calculated by combining the heat conduction analysis method, and the acceleration parameters of the print head are adjusted to make the stress release of the melt uniform during the cooling process. In the high curvature area, the path optimization method is used to adjust the motion trajectory of the print head to make the force distribution more uniform. Finally, the force printing speed optimization data is generated and used for the subsequent internal stress release constraint analysis. The internal stress release constraint analysis in the force shear process is based on the force printing interlayer strength adjustment data and the force printing speed optimization data. First, the force shear constraint model is established to calculate the force state in different areas, and analyze the influence of interlayer strength and printing speed on internal stress release. The stress-deformation analysis method is used to calculate the deformation rate of the melt under different printing parameters, and the stress release effect is verified by combining experimental data. In the process of internal stress release constraint analysis, the optimization algorithm is used to adjust the printing path so that the melt releases internal stress during the deposition process and reduces the shear stress accumulation. For high stress areas, the printing parameters are dynamically adjusted in combination with real-time monitoring data to ensure uniform stress release. Finally, the internal stress release constraint data during the shearing process is obtained and used to optimize the 3D printing melt extrusion control.

[0037] Preferably, step S24 comprises the following steps: Step S241: normalizing the structure segment melt temperature matching data and the structure segment printing force matching data respectively, to obtain the structure segment melt temperature matching normalized data and the structure segment printing force matching normalized data respectively; Step S242: performing logic learning on the structural segment melt temperature matching normalized data and the structural segment printing force matching normalized data respectively, to obtain segment melt temperature logic learning data and segment printing force logic learning data; Step S243: Based on the strategy gradient algorithm, adaptive melt extrusion control design is performed on the segmented melt temperature logic learning data and the segmented printing force logic learning data to obtain an adaptive melt extrusion control mechanism.

[0038] In an embodiment of the present invention, the normalization process is performed based on the structural segment melt temperature matching data and the structural segment printing force matching data. First, the melt temperature data of different printing structure segments are extracted, and the data is derived from the printing nozzle temperature sensor, the printing path temperature monitoring point, and the thermal coupling calculation data during the melt flow process. For the printing force matching data, the shear stress, interlayer bonding force and force direction vector information measured during the printing process of each layer are extracted, and the data is derived from the stress sensor measurement results and the mechanical simulation calculation value. The normalization process uses the maximum and minimum normalization method to map the melt temperature data and the printing force data to the [0,1] interval respectively. The specific operation includes calculating the maximum and minimum values ​​in the data set, and performing numerical conversion according to the normalization formula so that the data are compared on the same scale. For extreme values ​​and outliers, a local weighted regression smoothing algorithm is used for smoothing to avoid excessive data deviation during the normalization process. In the normalization process, in order to ensure the calculation accuracy, a segmented normalization strategy is adopted for the high-resolution area, that is, in the area where the local melt temperature changes drastically, the normalized segment density is increased to improve the data characterization accuracy. In the normalization process of printing force matching data, the shear stress vector is split and normalized so that the force information in each direction can be processed independently. Finally, the structural segment melt temperature matching normalized data and the structural segment printing force matching normalized data are obtained for use in the subsequent logic learning step. The logic learning is based on the structural segment melt temperature matching normalized data and the structural segment printing force matching normalized data. First, a two-layer recursive neural network (RNN) is constructed to learn the melt temperature change law and printing force characteristic distribution of different structural segments. The first layer of the network is used to extract the local temperature change pattern, and the second layer is used to analyze the global temperature evolution trend, and the temperature change trend model is established through the time series prediction method. In the process of segment printing force logic learning, the long short-term memory network (LSTM) is used to train the normalized force data. The network input layer accepts data such as force direction, shear stress value and interlayer bonding strength, and calculates the force state of different printing layers through bidirectional propagation. The gradient descent optimization algorithm is used to adjust the network weights so that the network can accurately predict the force conditions of different printing areas. In order to improve the accuracy of logic learning, the self-attention mechanism is used to calculate the correlation between the data, and the dot product operation is performed through the normalized feature matrix to obtain the weights of the key influencing factors. During the calculation process, the time dependence of the melt temperature characteristics and the force characteristics are encoded into the hidden variables to ensure that the model can capture dynamic change information. Finally, the segmented melt temperature logic learning data and the segmented printing force logic learning data are obtained as the input for the subsequent melt extrusion control design. The adaptive melt extrusion control design is based on the policy gradient algorithm.First, a reinforcement learning model is established, in which the state space includes segmented melt temperature logic learning data and segmented printing force logic learning data, the action space includes extrusion speed adjustment, nozzle temperature adjustment and interlayer deposition path optimization, and the reward function is constructed based on the printing quality evaluation index. In the training process of the policy gradient algorithm, the Monte Carlo policy gradient (REINFORCE) method is used to calculate the policy gradient. The specific steps include: sampling the melt temperature and force characteristic data under the current printing conditions, calculating the reward value under the current policy, and adjusting the extrusion parameters using the gradient ascent method. In order to avoid the local optimal problem, the entropy regularization term is introduced to improve the policy exploration ability. In the adaptive control design process, a segmented dynamic adjustment strategy is adopted. For areas with large local forces, the control system reduces the extrusion speed and increases the melt temperature to enhance the interlayer bonding strength. For areas with large temperature gradients, the nozzle heating power is adjusted to reduce the accumulation of thermal stress during printing. Finally, based on the strategy trained by the reinforcement learning model, an adaptive melt extrusion control mechanism is generated and applied to the 3D printing melt extrusion control process.

[0039] Preferably, step S3 comprises the following steps: Step S31: testing and simulating the adaptive melt extrusion control mechanism to obtain melt extrusion control test simulation data; Step S32: using a preset melt extrusion error identification model to perform error identification and collection on melt extrusion control test simulation data to obtain melt extrusion control test error data; Step S33: performing error parameter balance optimization on the melt extrusion control test error data to obtain error parameter balance optimization data; Step S34: Optimizing and adjusting the adaptive melt extrusion control mechanism based on the error parameter balance optimization data to obtain an adaptive melt extrusion optimization control mechanism; sending the adaptive melt extrusion optimization control mechanism to the 3D printer control center to execute the melt extrusion control method.

[0040] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: testing and simulating the adaptive melt extrusion control mechanism to obtain melt extrusion control test simulation data; In an embodiment of the present invention, the test simulation is performed based on an adaptive melt extrusion control mechanism. In the test environment, the trained adaptive melt extrusion control strategy is loaded, and the test parameters are set, including the print nozzle diameter, melt temperature, extrusion speed, print path length and layer thickness, etc. A numerical simulation method is used to construct a three-dimensional printing simulation scene in the finite element simulation software, and the melt flow boundary conditions are defined so that the dynamic behavior of the melt under different extrusion rates, temperatures and shear stresses can be accurately described. During the simulation process, a gridded calculation domain is established, and a fluid dynamics solver is used to calculate the flow velocity distribution, temperature change and extrusion pressure of the melt. For different areas on the printing path, the flow uniformity, interlayer bonding and shear stress change trend of the melt are dynamically monitored. A high-precision data sampling method is used to record key physical quantities at different time steps, and generate melt extrusion control test simulation data. During the test simulation, a variety of process parameters were used for comparative testing, such as setting three different melt temperatures (180°C, 200°C, 220°C) and four different extrusion rates (5 mm / s, 10 mm / s, 15 mm / s, 20 mm / s), and recording the melt flow state and extrusion pressure fluctuations under each set of parameters. Finally, the melt extrusion control test simulation data was sorted out based on the simulation data and stored in the database for subsequent error identification and processing.

[0041] Step S32: using a preset melt extrusion error identification model to perform error identification and collection on melt extrusion control test simulation data to obtain melt extrusion control test error data; In an embodiment of the present invention, error identification is performed based on a preset melt extrusion error identification model, which uses an error feature extraction method to analyze the deviations generated during the melt extrusion process. In the process of constructing the error identification model, error categories are defined based on empirical data, including insufficient extrusion, excessive extrusion, flow channel blockage, uneven interlayer bonding, etc. The specific operation of error identification includes first loading the melt extrusion control test simulation data and screening the data using an abnormality detection method. In view of the changing trends of extrusion rate, melt temperature and shear stress, the frequency domain characteristics are analyzed by Fourier transform, and the time point when the error occurs is determined in combination with the threshold judgment method. For interlayer bonding errors, a boundary tracking algorithm is used to detect interlayer melt accumulation and calculate layer thickness deviations. In the process of error data collection, an error weight allocation method is used to assign different weights according to the importance of the error type. For example, the error weight of insufficient extrusion is set to 0.7, the error weight of excessive extrusion is set to 0.5, and the error weight of flow channel blockage is set to 0.9. Finally, the error data is stored in a structured format and organized into melt extrusion control test error data for use in error parameter optimization.

[0042] Step S33: performing error parameter balance optimization on the melt extrusion control test error data to obtain error parameter balance optimization data; In an embodiment of the present invention, the error parameter balance optimization is performed based on the melt extrusion control test error data. First, the main influencing factors in the error data are analyzed, and the key parameters are adjusted by a multi-objective optimization method. During the optimization process, the error correction objective function is defined, and gradient optimization is performed for variables such as extrusion rate, melt temperature, and shear stress. The optimization algorithm uses a particle swarm optimization algorithm (PSO) to search and adjust the error parameters. An initial particle swarm is set, including candidate solutions for multiple different parameter combinations, and the optimal parameter combination is found through iterative calculation. For the problem of insufficient extrusion, the nozzle pressure compensation parameter is adjusted to increase the melt supply; for the problem of excessive extrusion, the extrusion speed feedback control parameter is adjusted to stabilize the extrusion rate. During the error parameter balance optimization process, an adaptive step size adjustment method is adopted, and a larger step size is used to accelerate the convergence speed in the early stage of error correction, and a small step size is used in the later stage to improve the optimization accuracy. Finally, the error parameter balance optimization data is output and stored in the control system for adjustment of the adaptive melt extrusion optimization control mechanism.

[0043] Step S34: Optimizing and adjusting the adaptive melt extrusion control mechanism based on the error parameter balance optimization data to obtain an adaptive melt extrusion optimization control mechanism; sending the adaptive melt extrusion optimization control mechanism to the 3D printer control center to execute the melt extrusion control method.

[0044] In the embodiment of the present invention, the optimization adjustment is performed based on the error parameter balance optimization data, and the core of the adjustment includes melt extrusion rate compensation, temperature adjustment strategy optimization and shear stress feedback control improvement. First, the control parameters are updated, the optimized extrusion compensation parameters are imported into the adaptive melt extrusion control mechanism, and the PID controller gain is reset so that the extrusion system can respond to the error change in real time. During the optimization process, a predictive control method is adopted so that the control system can judge the extrusion error trend in advance and adjust the extrusion rate. In terms of nozzle heating control, a temperature gradient adaptive adjustment strategy is introduced to dynamically change the heating power of the nozzle in different printing path areas, thereby reducing the problem of uneven melt flow caused by the temperature gradient. In terms of shear stress control, a feedback compensation strategy is adopted to measure the melt extrusion pressure in real time, calculate the stress change at the nozzle outlet, and dynamically adjust the pressure compensation parameters. For the flow channel blockage problem, a periodic flow pulse test method is added, that is, the extrusion rate is periodically adjusted during the printing process, and the melt flow response is analyzed to determine whether there is a local blockage phenomenon. Finally, the optimized adaptive melt extrusion optimization control mechanism is obtained and transmitted to the 3D printer control center through the data interface. The control center loads the optimized control mechanism and applies it to actual printing tasks to achieve precise melt extrusion control.

[0045] Preferably, the present invention further provides a 3D printing melt extrusion control system for executing the 3D printing melt extrusion control method as described above, the 3D printing melt extrusion control system comprising: The morphology trend analysis module is used to obtain 3D printing product drawings; mark the printing and molding process of the 3D printing product drawings to obtain the 3D printing molding process; analyze the product structure morphology trend based on the 3D printing molding process to obtain the product structure morphology trend data; The adaptive melt extrusion control design module is used to simulate the product segment printing melt temperature matching based on the product structure morphology trend data to obtain the structure segment melt temperature matching data; perform local printing force matching based on the structure segment melt temperature matching data to obtain the structure segment printing force matching data; perform adaptive melt extrusion control design based on the structure segment printing force matching data to obtain the adaptive melt extrusion control mechanism; The mechanism adjustment and optimization module is used to optimize and adjust the adaptive melt extrusion control mechanism to obtain an adaptive melt extrusion optimization control mechanism; the adaptive melt extrusion optimization control mechanism is sent to the 3D printer control center to execute the melt extrusion control method.

[0046] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A 3D printing melt extrusion control method, characterized in that: The following steps are involved: Step S1: obtaining a 3D printed product drawing; marking the printing process of the 3D printed product drawing to obtain a 3D printed forming process; analyzing the product structure and morphology trend based on the 3D printed forming process to obtain product structure and morphology trend data; Step S2: Perform product segment printing melt temperature matching simulation on product structure morphology trend data to obtain structure segment melt temperature matching data; perform local printing force matching based on structure segment melt temperature matching data to obtain structure segment printing force matching data; perform adaptive melt extrusion control design based on structure segment printing force matching data to obtain adaptive melt extrusion control mechanism; Step S3: Optimizing and adjusting the adaptive melt extrusion control mechanism to obtain an adaptive melt extrusion optimization control mechanism; sending the adaptive melt extrusion optimization control mechanism to a 3D printer control center to execute the melt extrusion control method.

2. The 3D printing melt extrusion control method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtain 3D printing product drawings; Step S12: performing data cleaning on the 3D printed product drawings to obtain 3D printed product cleaning drawings; Step S13: marking the 3D printing product cleaning drawing with a printing molding process to obtain a 3D printing molding process; Step S14: Analyze the product structure and morphology trend of the 3D printed product cleaning drawing based on the 3D printing molding process to obtain product structure and morphology trend data.

3. The 3D printing melt extrusion control method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: acquiring initial printing parameters, wherein the initial printing parameters include initial parameters of the material melt and initial printing speed; Step S22: performing product segment printing melt temperature matching simulation on the product structure morphology trend data according to the material melt initial parameters in the printing initial parameters, and obtaining structure segment melt temperature matching data; Step S23: performing local printing force matching on the product structure morphology trend data according to the printing initial speed and structure segment melt temperature matching data in the printing initial parameters to obtain structure segment printing force matching data; Step S24: Based on the structural segment melt temperature matching data and the structural segment printing force matching data, an adaptive melt extrusion control design is performed to obtain an adaptive melt extrusion control mechanism.

4. The 3D printing melt extrusion control method according to claim 3, characterized in that: Step S22 includes the following steps: Step S221: performing thin-wall structure recognition on the product structure morphology trend data to obtain thin-wall structure morphology trend data; performing bridging overhang structure recognition on the product structure morphology trend data to obtain product bridging overhang structure data; Step S222: performing layer gradient stacking effect analysis based on the thin-wall structure morphology trend data to obtain thin-wall layer stacking effect data; Step S223: simulating the heat accumulation rate according to the thin-wall structure morphology trend data and the thin-wall lamination stacking effect data to obtain the heat accumulation rate of the thin-wall structure; Step S224: performing thermal deformation nonlinear temperature interval fitting according to the heat accumulation rate of the thin-wall structure and the thin-wall lamination stacking effect data to obtain the thermal deformation nonlinear temperature interval; Step S225: performing thin-wall printing melt temperature matching on the thermal deformation nonlinear temperature interval according to the material melt initial parameters in the printing initial parameters, and obtaining thin-wall printing melt temperature matching data; Step S226: performing bridge overhang printing melt dynamic temperature matching on the product bridge overhang structure data according to the material melt initial parameters in the printing initial parameters, to obtain bridge overhang melt dynamic temperature matching data; Step S227: Based on the thin-wall printing melt temperature matching data and the bridge overhang melt dynamic temperature matching data, a product segment printing melt temperature matching simulation is performed to obtain the structure segment melt temperature matching data.

5. The 3D printing melt extrusion control method according to claim 4, characterized in that: Step S226 includes the following steps: Evaluate the bridging support tension between two points on the product bridging overhang structure data to obtain the product bridging support tension; According to the product bridge support tension, the bending section strength of the product bridge overhang structure data is evaluated to obtain the bending section strength data; According to the initial parameters of the material melt in the initial printing parameters, the thermal expansion / contraction deformation error between different product sections is simulated and evaluated for the product bridge support tension and bending section strength data, and the thermal expansion / contraction deformation error data between different sections are obtained; Based on the thermal expansion / contraction deformation error data, the material melt initial parameters in the printing initial parameters are matched with the melt temperature increase interval and cooling rate between different cross sections to obtain the melt temperature increase interval and cooling rate matching data between different cross sections; The dynamic temperature matching of the melt in bridge overhang printing is performed according to the melt temperature increase range and cooling rate matching data between different cross sections, and the dynamic temperature matching data of the melt in bridge overhang printing is obtained.

6. The 3D printing melt extrusion control method according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing curved surface structure recognition on the product structure morphology trend data to obtain structural curved surface morphology data; performing bending angle recognition on the product structure morphology trend data according to the structural curved surface morphology data to obtain the bending angle; Step S232: performing structural density distribution difference analysis according to the structural surface morphology data and the bending angle to obtain structural density distribution difference data; Step S233: performing melt rheological property analysis based on the structural segmented melt temperature matching data to obtain structural segmented melt rheological property data; Step S234: performing internal stress release constraint analysis during the shearing process according to the structural density distribution difference data and the structural segmented melt rheological property data to obtain internal stress release constraint data during the shearing process; Step S235: performing local printing force matching based on the internal stress release constraint data and the initial printing speed in the initial printing parameters to obtain structure segment printing force matching data.

7. The 3D printing melt extrusion control method according to claim 6, characterized in that: Step S234 includes the following steps: Perform local density gradient calculation on the structural density distribution difference data to obtain density gradient distribution data; Decomposing the shear stress tensor according to the density gradient distribution data and the structural segmented melt rheological property data, the anisotropic stress component data is obtained; The imbalanced internal stress distribution is simulated during the shear cooling process on the anisotropic stress component data to obtain the imbalanced internal stress distribution data; Adjust the strength of the stressed printing layers based on the internal stress distribution imbalance data to obtain the strength adjustment data of the stressed printing layers; Optimize the stress-bearing printing speed according to the internal stress distribution imbalance data to obtain stress-bearing printing speed optimization data; According to the stress printing interlayer strength adjustment data and the stress printing speed optimization data, the internal stress release constraint analysis in the stress shearing process is performed to obtain the internal stress release constraint data in the stress shearing process.

8. The 3D printing melt extrusion control method according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: normalizing the structure segment melt temperature matching data and the structure segment printing force matching data respectively, to obtain the structure segment melt temperature matching normalized data and the structure segment printing force matching normalized data respectively; Step S242: performing logic learning on the structural segment melt temperature matching normalized data and the structural segment printing force matching normalized data respectively, to obtain segment melt temperature logic learning data and segment printing force logic learning data; Step S243: Based on the strategy gradient algorithm, adaptive melt extrusion control design is performed on the segmented melt temperature logic learning data and the segmented printing force logic learning data to obtain an adaptive melt extrusion control mechanism.

9. The 3D printing melt extrusion control method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: testing and simulating the adaptive melt extrusion control mechanism to obtain melt extrusion control test simulation data; Step S32: using a preset melt extrusion error identification model to perform error identification and collection on melt extrusion control test simulation data to obtain melt extrusion control test error data; Step S33: performing error parameter balance optimization on the melt extrusion control test error data to obtain error parameter balance optimization data; Step S34: Optimizing and adjusting the adaptive melt extrusion control mechanism based on the error parameter balance optimization data to obtain an adaptive melt extrusion optimization control mechanism; sending the adaptive melt extrusion optimization control mechanism to the 3D printer control center to execute the melt extrusion control method.

10. A 3D printing melt extrusion control system, characterized in that: Used to execute the 3D printing melt extrusion control method according to claim 1, the 3D printing melt extrusion control system comprises: The morphology trend analysis module is used to obtain 3D printing product drawings; mark the printing and molding process of the 3D printing product drawings to obtain the 3D printing molding process; analyze the product structure morphology trend based on the 3D printing molding process to obtain the product structure morphology trend data; The adaptive melt extrusion control design module is used to simulate the product segment printing melt temperature matching based on the product structure morphology trend data to obtain the structure segment melt temperature matching data; perform local printing force matching based on the structure segment melt temperature matching data to obtain the structure segment printing force matching data; perform adaptive melt extrusion control design based on the structure segment printing force matching data to obtain the adaptive melt extrusion control mechanism; The mechanism adjustment and optimization module is used to optimize and adjust the adaptive melt extrusion control mechanism to obtain an adaptive melt extrusion optimization control mechanism; the adaptive melt extrusion optimization control mechanism is sent to the 3D printer control center to execute the melt extrusion control method.

Images