Carbon fiber fuselage structure layering optimization design method and system

Abstract

The invention discloses a carbon fiber fuselage structure layering optimization design method and system, and relates to the technical field of material structure intelligent design, and the method comprises the steps: importing a three-dimensional CAD model of a fuselage structure, collecting physical field input parameters, integrating and generating a digital engineering package, and carrying out the temperature field calculation through a thermal coupling solver based on the digital engineering package, the method comprises the following steps: acquiring temperature distribution of a fuselage, mapping the temperature distribution of the fuselage into a thermal stress field through a thermoelastic constitutive equation, performing vector superposition on the thermal stress field and a mechanical load to generate a comprehensive stress cloud picture, starting asymmetric layering design for a high-stress region based on the comprehensive stress cloud picture, and verifying mechanical balance of layering angle combination through a balance optimization algorithm. And obtaining a layering optimization scheme. The thermal deformation sensitivity is accurately evaluated by using the thermal-mechanical coupling solver and the finite element disturbance analysis method, and thermal deformation can be effectively predicted and controlled, so that the dimensional stability and the surface quality of a final product are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent design of material structures, and in particular to a carbon fiber fuselage structure layup optimization design method and system. Background Art

[0002] Carbon fiber composites, due to their superior strength-to-weight ratio, specific stiffness, and fatigue resistance, have become a core material in modern airframe structural design. In recent years, with the aviation industry's increasing demand for lightweighting and high strength, carbon fiber layup optimization design methods have garnered extensive attention and research. Traditional layup design is typically based on empirical rules or simple finite element analysis, manually adjusting ply angles and thicknesses to meet mechanical performance requirements.

[0003] With the development of computer-aided engineering (CAD) technology, optimization algorithms have gradually been introduced into layup design. However, some shortcomings remain. For one thing, existing technologies are limited in controlling thermal deformation and fail to fully consider the impact of layup angle combinations on thermal deformation sensitivity, making it difficult to achieve high-precision deformation control. Furthermore, current layup optimization schemes ignore potential process defects that may occur during actual manufacturing, such as excessively large layup angle gradients or irregular curvature radii. These issues not only reduce the quality of the finished product but can also significantly increase manufacturing costs. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a carbon fiber fuselage structure ply optimization design method to solve the problems of inaccurate control of thermal deformation and process defects in the manufacturing process.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a carbon fiber fuselage structure layup optimization design method, which includes importing a three-dimensional CAD model of the fuselage structure, collecting physical field input parameters, and integrating and generating a digital engineering package;

[0008] Based on the digital engineering package, a thermomechanical coupling solver is used to calculate the temperature field and obtain the fuselage temperature distribution. The fuselage temperature distribution is mapped into a thermal stress field using the thermoelastic constitutive equation, and vector superposition is performed with the mechanical load to generate a comprehensive stress cloud map.

[0009] Based on the comprehensive stress cloud map, asymmetric layup design is initiated for high-stress areas. The mechanical balance of the layup angle combination is verified using a balanced optimization algorithm to obtain a layup optimization solution.

[0010] Import the ply optimization plan into the thermal-mechanical coupling solver, perform secondary temperature field calculations, detect thermal deformation, adjust the weight coefficients of the ply angle combination based on the thermal deformation, and output the secondary ply optimization plan.

[0011] Based on the secondary optimization scheme for layup, the feasibility of layup is verified using the automatic fiber placement machine process database. After verification, a manufacturing-ready package is output using an angle gradient transition strategy.

[0012] The manufacturing-ready package is loaded into the digital twin engine to simulate the actual layup process and output the final layup process parameter set.

[0013] As a preferred solution of the carbon fiber fuselage structure layup optimization design method described in the present invention, the physical field input parameters include mechanical load, temperature field parameters, material anisotropy parameters, fuselage vibration parameters and manufacturing process parameters.

[0014] As a preferred solution of the carbon fiber fuselage structure ply optimization design method of the present invention, the comprehensive stress cloud diagram has the following specific steps:

[0015] The material anisotropy parameters are loaded into the thermal-mechanical coupling solver, and the temperature field distribution on the fuselage surface is obtained using the transient heat conduction algorithm;

[0016] Extract the thermal expansion tensor from the anisotropic parameters of the material, and apply the thermoelastic constitutive equation based on the thermal expansion tensor to map the temperature field distribution into the thermal stress field;

[0017] The thermal stress field and the mechanical load are vector-superimposed using the weighted tensor superposition algorithm to generate a three-dimensional comprehensive stress tensor field. Based on the three-dimensional comprehensive stress tensor field, the comprehensive stress cloud map is obtained using the maximum principal stress algorithm.

[0018] As a preferred solution of the carbon fiber fuselage structure ply optimization design method of the present invention, the ply optimization solution comprises the following specific steps:

[0019] Based on the comprehensive stress cloud map, the DBSCAN clustering algorithm is used to extract the regional coordinates of high-stress areas and classify them into tension, compression, and shear dominant areas. The ply-driven method is then used to design an asymmetric ply strategy for each dominant area.

[0020] After completing the design of the asymmetric ply in the high-stress area, the NSGA-II multi-objective balance optimization algorithm is used to dynamically adjust the ply angle and thickness, and the mechanical balance is verified through ABD matrix calculation, and the ply optimization plan is finally output.

[0021] As a preferred solution of the carbon fiber fuselage structure layup optimization design method of the present invention, wherein:

[0022] The specific steps of the secondary optimization scheme of the ply are as follows:

[0023] The ply optimization solution is imported into the thermomechanical coupling solver through a parameterized script interface. A secondary temperature field calculation is performed using a dynamic heat conduction algorithm to generate an updated three-dimensional temperature gradient field. Based on this updated three-dimensional temperature gradient field, the thermal deformation is detected using a displacement extreme value extraction algorithm.

[0024] Based on the thermal deformation, the partial derivative matrix of the ply angle is obtained through the finite element perturbation analysis method, and the deformation sensitivity gradient analysis is performed to generate the deformation sensitivity weight distribution map;

[0025] Based on the generated deformation sensitivity weight distribution map, the ply angle parameters are adjusted through the gradient descent adjustment algorithm, and the ply secondary optimization scheme is output through the multi-objective optimization framework.

[0026] As a preferred solution of the carbon fiber fuselage structure layup optimization design method of the present invention, the manufacturing ready package has the following specific steps:

[0027] The automatic fiber placement machine process database is used to perform multi-rule joint verification on the secondary optimization plan of the ply:

[0028] A dynamic curvature radius algorithm is used to eliminate illegal areas, a sliding window method is used to detect the gradient of adjacent ply angle jumps in real time, and a random forest classification model is used to predict the probability of process defects.

[0029] After completing the multi-rule joint verification, based on the angle gradient transition strategy, the transition layer insertion algorithm and the B-spline curve path fitting method are used to generate the NC code for the wire laying machine, input it into the digital twin engine, and generate a manufacturing-ready package.

[0030] As a preferred solution of the carbon fiber fuselage structure layup optimization design method of the present invention, wherein: the output of the final layup process parameter set is carried out in the following specific steps:

[0031] In the digital twin engine, the laying process of the fiber laying machine is simulated through kinematic modeling and real-time physical simulation engine, and the laying speed, heating temperature and pressure parameters are iteratively adjusted through a multi-objective optimization algorithm to output the final laying process parameter set.

[0032] In a second aspect, the present invention provides a carbon fiber fuselage structure ply optimization design system, including a parameter integration module, a thermal stress coupling module, a primary optimization module, a secondary optimization module, a verification module, and a process simulation module.

[0033] Parameter integration module, used to import the 3D CAD model of the fuselage structure, collect physical field input parameters, and integrate them to generate a digital engineering package;

[0034] The thermal-stress coupling module is used to calculate the temperature field using a thermal-mechanical coupling solver based on a digital engineering package to obtain the fuselage temperature distribution. This temperature distribution is then mapped into a thermal stress field using the thermoelastic constitutive equation and vector-superimposed with the mechanical load to generate a comprehensive stress cloud map.

[0035] The initial optimization module is used to initiate asymmetric layup design for high-stress areas based on the comprehensive stress cloud map, and verify the mechanical balance of the layup angle combination through the equilibrium optimization algorithm to obtain the layup optimization solution;

[0036] The secondary optimization module is used to import the ply optimization plan into the thermal-mechanical coupling solver, perform secondary temperature field calculations, detect thermal deformation, adjust the weight coefficients of the ply angle combination based on the thermal deformation, and output the secondary optimization plan for the ply.

[0037] The verification module verifies the feasibility of layup based on the secondary optimization scheme using the automatic fiber placement machine process database. After verification, it uses the angle gradient transition strategy to output a manufacturing-ready package.

[0038] The process simulation module is used to load the manufacturing-ready package into the digital twin engine, simulate the actual layup process, and output the final layup process parameter set.

[0039] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the carbon fiber fuselage structure layup optimization design method as described in the first aspect of the present invention is implemented.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the carbon fiber fuselage structure layup optimization design method as described in the first aspect of the present invention is implemented.

[0041] The beneficial effects of the present invention are as follows: by utilizing a thermomechanical coupling solver and a finite element perturbation analysis method, an accurate assessment of the sensitivity to thermal deformation is achieved. By dynamically adjusting the weight coefficients of the ply angle combinations and employing a gradient descent adjustment algorithm, thermal deformation can be effectively predicted and controlled, thereby improving the dimensional stability and surface quality of the final product. Secondly, in response to the problem of process defects in the manufacturing process, the present invention combines the automatic wire laying machine process database with a multi-rule joint verification mechanism to ensure the manufacturing feasibility of the ply scheme. At the same time, a random forest classification model is used to predict the probability of potential process defects, and the ply design is optimized based on an angle gradient transition strategy, which significantly reduces the risk of process defects in the manufacturing process and improves the quality of finished products and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 Flowchart of the optimal design method for carbon fiber fuselage structure layup.

[0044] Figure 2 Schematic diagram of a system for optimizing the layup of carbon fiber fuselage structures.

[0045] Figure 3 Flowchart of the process of obtaining the comprehensive stress diagram.

[0046] Figure 4 Flowchart of the output process of the final layup process parameter set. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0050] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a carbon fiber fuselage structure layup optimization design method, comprising the following steps:

[0051] S1. Import the 3D CAD model of the fuselage structure, collect physical field input parameters, and integrate them to generate a digital engineering package.

[0052] The specific steps include:

[0053] Using CATIA computer-aided design software, the fuselage structural design drawings were converted into a digital 3D CAD model to ensure that every detail was accurately captured and expressed. Next, to fully understand the fuselage's response in the actual operating environment, a series of physical field input parameters needed to be collected, including mechanical loads, temperature field parameters, material anisotropy parameters, fuselage vibration, and manufacturing process parameters.

[0054] The data collection process relies on a variety of existing technologies. Specifically, mechanical testing equipment, such as strain gauges and force sensors, are installed at the stressed parts of the fuselage. The deformation and stress response of the structure under external loads are measured, and the magnitude and distribution of the mechanical loads are recorded to collect mechanical load data.

[0055] Place temperature sensors in the fuselage structure area, and use thermal imaging technology to scan the fuselage surface with an infrared thermal imager to obtain its temperature distribution under different working conditions and collect temperature field parameters;

[0056] Based on laboratory testing methods, by applying different mechanical loads to material samples and recording their responses, combined with finite element analysis technology, the anisotropic characteristic data inside the material is derived to collect the anisotropic parameters of the material;

[0057] Accelerometers and spectrum analyzers are placed at different locations on the fuselage to monitor vibration signals in real time. Signal processing techniques are used to extract key frequency components and their corresponding amplitude information to collect fuselage vibration parameters. Finally, through a historical process database and on-site monitoring equipment, the process parameters of the automatic wire placement machine during the actual manufacturing process, such as placement speed, heating temperature, and pressure, are collected and recorded to form a complete set of manufacturing process parameters. The collected physical field input parameters provide a solid data foundation for the subsequent generation of digital engineering packages.

[0058] The collected physical field input parameters are used through data mapping technology to accurately associate mechanical load data, temperature field parameters, material anisotropy parameters, fuselage vibration parameters, environmental humidity field parameters and manufacturing process parameters with the geometric coordinates of the 3D CAD model: the mechanical load is converted into nodal force distribution through the finite element preprocessor, and the discrete temperature field parameters are integrated through the interpolation algorithm to generate a continuous temperature gradient field. The material anisotropy parameters are bound layer by layer through the composite material property assignment tool (such as ANSYS Composite PrepPos), the fuselage vibration parameters are calibrated through the finite element analysis method, and the manufacturing constraints of the manufacturing process parameters are extracted through the Java process rule parsing tool. The mechanical load, temperature field parameters, material anisotropy parameters, fuselage vibration parameters, environmental humidity field parameters and manufacturing process parameters are hierarchically structured and packaged in HDF5 format. At the same time, the geometric matching is verified by the curvature continuity detection algorithm, and the missing data is supplemented by the Kriging interpolation algorithm. Then, various data files are integrated through a standardized compression format. Finally, efficient data call is achieved through the hierarchical index interface to generate a digital engineering package.

[0059] S2. Based on the digital engineering package, a thermomechanical coupling solver is used to calculate the temperature field and obtain the temperature distribution of the fuselage. The fuselage temperature distribution is mapped into a thermal stress field through the thermoelastic constitutive equation, and vector superposition is performed with the mechanical load to generate a comprehensive stress cloud map.

[0060] The specific steps include:

[0061] After loading the digital engineering package into the thermal-mechanical coupling solver, the first step is to analyze the anisotropic parameters of the material and read the thermal conductivity tensor through the property extraction interface to calculate the temperature field distribution. The thermal conductivity tensor reflects the difference in thermal conductivity of carbon fiber materials in different directions. It is accurately assigned according to the layup angle and the main axis direction of the material. After the thermal conductivity tensor is assigned, the transient heat conduction algorithm is used to calculate the temperature field. In the specific operation, the transient heat conduction calculation needs to consider the temperature change in the time dimension. The finite volume method is usually used to discretize the spatial domain of the thermal conductivity tensor to ensure that it can better handle complex geometric shapes and material discontinuities. The implicit Euler format algorithm is used to advance the thermal conductivity tensor in time. The unconditional stability of the implicit Euler format algorithm can avoid the strict restrictions on the time step of the explicit method.

[0062] After spatial domain discretization and time advancement are completed, transient temperature field data containing the temperature gradient distribution of the fuselage surface and internal structure are generated. At the same time, the pressure distribution and boundary layer characteristics of the aircraft surface are calculated using aerodynamic thermodynamics simulation software to obtain aerodynamic heat flow calculation results. The transient temperature field data and aerodynamic heat flow calculation results are integrated using the boundary condition fusion method, and the high-temperature area is refined using local grid encryption technology. Combined with the real-time update and iteration of the material anisotropic parameters, the temperature field distribution of the fuselage surface is finally obtained.

[0063] After obtaining the temperature field distribution, the temperature field distribution results need to be mapped into a thermal stress field. In the specific operation, the thermal expansion tensor is first extracted from the material anisotropy parameters through the material property parser. The thermal expansion tensor is a key parameter for calculating thermal strain. The thermal expansion tensor is directional and needs to be assigned according to the material principal axis direction of each ply. Based on the assigned thermal expansion tensor, the thermoelastic constitutive equation is used to gradually apply temperature loads to the carbon fiber material using the incremental method to obtain the temperature change. The specific mathematical formula is:

[0064]

[0065] Where ΔT represents the temperature change, T represents the temperature, T1 represents the sum of the main diagonal element values ​​of the tensor, ε represents the thermal strain tensor, k represents the correction factor, α represents the thermal expansion tensor, T1(ε) represents the sum of the main diagonal element values ​​of the thermal strain tensor, k(T) represents the correction factor of temperature, and T1(α) represents the sum of the main diagonal element values ​​of the thermal expansion tensor;

[0066] It should be noted that the specific value of k(T) depends on the properties of the material, the operating temperature range, and the experimental analysis results, and is generally between 0 and 2;

[0067] The thermal strain field is calculated within each temperature increment based on the acquired temperature change. During the calculation process, the thermoelastic equilibrium equation is established through finite element discretization based on the principle of virtual work, and the temperature field distribution is mapped into the thermal strain field using the efficient conjugate gradient method. The calculation of the thermal strain field requires special attention to the strain coordination conditions at the ply interface to ensure deformation continuity and physical rationality between plies at different angles.

[0068] After obtaining the thermal stress field, the mesh consistency of the thermal stress field and the mechanical load is checked using a mesh matching algorithm to ensure that the thermal stress field and the stress field of the mechanical load have the same unit topology. In the case of mesh inconsistency, a stress field interpolation technique based on shape functions is used to achieve mesh consistency. After ensuring mesh consistency, the vector superposition of the thermal stress field and the mechanical load is performed using a weighted tensor superposition algorithm. In the specific operation, the material stiffness tensor is extracted from the material anisotropic parameters using a material property parser, the thermal stress component is obtained using a thermoelastic solver, and the mechanical stress component is obtained through static finite element analysis. The material stiffness tensor is normalized to establish a weight coefficient matrix reflecting the anisotropic characteristics of the material.

[0069] Based on the weight coefficient matrix, the thermal stress component and the mechanical stress component are tensor-decomposed using the tensor eigenvalue decomposition algorithm to extract the normal stress and shear stress components respectively. The normal stress component and the shear stress component are then weighted and synthesized according to the weights. The shear stress weight in the high-temperature area is reduced (taking into account the material softening effect), while the normal stress component in the high-stiffness direction is given a higher weight. The Gaussian integration point algorithm is used in the weighted synthesis process to ensure the continuity of the stress components. After the weighted synthesis is completed, a three-dimensional comprehensive stress tensor field containing six independent components is generated.

[0070] Based on the three-dimensional comprehensive stress tensor field, a comprehensive stress cloud map is generated. In the specific operation, based on the three-dimensional comprehensive stress tensor field, a finite element unit is generated through the finite element pre-processor, and a three-dimensional stress tensor is generated through the thermal-mechanical coupling solver. Then, the Gaussian integration points of all finite element units are traversed through the maximum principal stress algorithm. The eigenvalue of the three-dimensional stress tensor at each Gaussian integration point is solved by the Jacobi iteration method to extract the maximum value in the three-dimensional stress tensor, and the maximum value is used as the representative stress value of the Gaussian integration point;

[0071] After solving the eigenvalues ​​of all Gaussian integral points, bilinear interpolation is used to map each stress value to the unit node to generate the nodal stress field. Lagrangian shape functions are used to achieve a smooth transition of the nodal stress field. Adaptive color scale generation technology is used to render the nodal stress field cloud map. The color distribution range is automatically adjusted according to the stress gradient. Local color scale encryption is used for high stress concentration areas (such as opening edges and connection joints). At the same time, an edge detection algorithm is used to identify stress mutation boundaries. Combined with isosurface extraction technology, clear stress contours are generated.

[0072] Based on the stress contours, a parallel computing-accelerated graphics rendering pipeline is generated through the CUDA parallel computing framework. The comprehensive stress cloud map finally generated by the parallel computing-accelerated graphics rendering pipeline includes a dynamic color scale, maximum stress value annotation, and a magnified view of key areas. It supports arbitrary cross-section display of the 3D model and stress distribution animation playback.

[0073] S3. Based on the comprehensive stress cloud map, asymmetric ply design is initiated for high-stress areas. The mechanical balance of the ply angle combination is verified through a balanced optimization algorithm to obtain a ply optimization solution.

[0074] The specific steps include:

[0075] When conducting asymmetric ply design based on a comprehensive stress cloud map, the DBSCAN clustering algorithm is first used to accurately identify high-stress areas and reasonably partition them to obtain the regional coordinate sets of the high-stress areas. In specific operations, the DBSCAN clustering algorithm uses parameter optimization tools (such as the multi-objective optimization component in ANSYS Workbench) to set an appropriate neighborhood search radius and minimum sample number threshold. Based on the set neighborhood search radius, the algorithm automatically scans the entire stress cloud map through density reachability analysis, identifies areas with stress values ​​exceeding the minimum sample number threshold as high-stress areas, and uses spatial indexing technology to obtain the regional coordinate sets of the high-stress areas. These are then divided into several independent clusters using the Euclidean distance metric.

[0076] During the clustering process, the principal stress directions within each independent cluster were analyzed using a principal stress direction statistical algorithm. The first principal stress direction was then obtained using an eigenvalue decomposition algorithm based on the principal stress direction. The angle between the first principal stress direction and the material axis was then statistically analyzed, and the K-means clustering technique was used to further subdivide each independent cluster into tension-dominated, compression-dominated, and shear-dominated regions. The tension-dominated region was identified when the first principal stress direction was consistent with the material axis. The compression-dominated region was identified when the first principal stress direction was perpendicular to the material axis. The shear-dominated region was characterized by a maximum shear stress significantly higher than the normal stress.

[0077] After completing the division of each dominant zone, the layup-driven technology uses a rule engine to adopt asymmetric layup strategies for different dominant zones: the tension-dominant zone mainly arranges zero-degree plies to fully utilize the high-strength characteristics of the fiber direction. The compression-dominant zone increases the proportion of 45-degree plies to improve in-plane shear stiffness and anti-buckling capacity. The shear-dominant zone adopts an alternating arrangement of positive and negative 45-degree plies to optimize shear performance. The layup-driven technology automatically generates the initial layup plan based on the layup strategy through parametric modeling tools (such as the layup parameterization tool in CATIA Composites). It also sets a reasonable layup transition zone between adjacent high-stress areas, ensuring a smooth transition of mechanical properties through gradual angle changes and thickness adjustments to avoid sudden stiffness changes.

[0078] After completing the design of the asymmetric plies in the high-stress area, the NSGA-II multi-objective equilibrium optimization algorithm was used to iteratively improve the initial layup plan. Specifically, during initialization, the NSGA-II algorithm randomly generated multiple layup plans using the Latin hypercube sampling method to form an initial population. Each layup plan in the initial population contained a complete sequence of ply angles and thickness distributions.

[0079] Each layup solution is evaluated using a multi-objective evaluation technique based on the initial population. The ABD matrix is ​​first calculated using classical laminate theory. The balance between tensile and compressive stiffness is then determined through stiffness matrix decomposition. The proportional relationship between in-plane and bending stiffness is calculated using stiffness component ratios. The values ​​of various coupling effects are analyzed using coupled stiffness terms. The weight index of each layup solution is calculated through layup parameter integration. Process feasibility is then assessed using process constraint checking techniques. This includes factors such as whether the layup angle change rate exceeds the maximum allowable value of the automated placement machine and whether the minimum curvature radius meets material placement requirements.

[0080] A multi-objective fitness function was constructed using a weighted linear combination method based on the balance between tensile and compressive stiffness, the proportional relationship between in-plane and bending stiffness, the values ​​of various coupling effects, the weight index of the layup scheme, and process feasibility. Based on this multi-objective fitness function, the mechanical balance was verified using the non-dominated sorting mechanism of the NSGA-II algorithm.

[0081] At the same time, during each generation of evolution of the NSGA-II multi-objective balance optimization algorithm, the NSGA-II multi-objective balance optimization algorithm selects parent individuals through a tournament selection mechanism and generates a child population using simulated binary crossover and polynomial mutation operations. Binary crossover intelligently reorganizes the ply angle sequence of the parent individuals, while polynomial mutation applies adaptive perturbations to the ply thickness parameters to dynamically adjust the ply angle and thickness.

[0082] Next, all parent individuals, offspring populations, and initial populations are divided into multiple frontiers based on their merits using the Pareto dominance relationship. An improved crowding formula is defined based on ply angle continuity constraints and thickness gradient restrictions. This improved crowding formula is used through adaptive neighborhood search technology to ensure that the parent individuals, offspring populations, and initial populations on each frontier are evenly distributed while meeting the ply process requirements.

[0083] Based on multiple frontiers, the NSGA-II multi-objective equilibrium optimization algorithm evolves over multiple generations using an elite retention strategy. In each generation, it dynamically adjusts the angle and thickness combinations (e.g., increasing the proportion of 0° plies for solutions with insufficient stiffness and optimizing the ±45° ply thickness distribution for overweight solutions) and outputs a set of Pareto optimal solutions.

[0084] The Pareto optimal solution strikes the best balance between weight, stiffness, and process feasibility. The final decision is made by generating a layup optimization scheme based on the optimal solution through a multi-criteria decision-making method. This layup optimization scheme not only meets the mechanical performance requirements but also takes into account the manufacturing process limitations, providing a reliable basis for the detailed design of the carbon fiber fuselage structure.

[0085] S4. Import the ply optimization plan into the thermal-mechanical coupling solver, perform secondary temperature field calculations, and detect thermal deformation. Based on the thermal deformation, adjust the weight coefficient of the ply angle combination and output the secondary ply optimization plan.

[0086] The specific steps include:

[0087] When importing the ply optimization solution into the thermomechanical coupling solver, the parametric script interface first obtains the ply angle sequence and thickness distribution parameters through the CATIA CAA secondary development interface. The format is then converted using the ANSYS ACT custom converter. This conversion process requires precise matching of the topological relationship between the material axis and the mesh to ensure that the angle sequence and thickness distribution parameters of each ply element are accurately mapped to the corresponding mesh area.

[0088] After the format conversion is complete, the thermal coupling solver initiates the dynamic heat conduction calculation. Specifically, the nonlinear transient thermal analysis algorithm uses a time step control strategy to automatically increase the time step in areas with drastic temperature changes to improve the accuracy of the temperature field evolution calculation. At the same time, in the ply transition region, the anisotropic heat conduction algorithm automatically adjusts the heat flow calculation direction to reflect the changes in thermal conductivity performance caused by the gradual change in ply angle. During the temperature field evolution calculation, the thermal coupling solver monitors the temperature field convergence in real time. When it detects that the local temperature field convergence gradient exceeds the convergence threshold, it automatically triggers the mesh refinement program and inserts new calculation nodes in the area with drastic temperature changes.

[0089] Based on the new computational nodes, an updated three-dimensional temperature gradient field is generated using cubic spline interpolation technology. The convergence threshold is defined by thermal-mechanical coupling sensitivity analysis based on the material thermal expansion coefficient and ply angle distribution characteristics.

[0090] Based on the updated three-dimensional temperature gradient field, the node displacement and temperature changes are obtained through the thermal-mechanical coupling solver. The displacement extreme value extraction algorithm establishes the mapping relationship between the node displacement and temperature change through least squares regression analysis. Based on this mapping relationship between the node displacement and temperature change, the displacement extreme value points are identified through the Delaunay triangulation area scanning technology. The complete displacement change history of the displacement extreme value points over time is recorded, and the thermal deformation is obtained through the numerical differentiation algorithm.

[0091] Based on the thermal deformation, the finite element perturbation analysis method is used to obtain the partial derivative matrix of the ply angle. Specifically, each ply angle parameter is slightly perturbed using the central difference method. After each perturbation, the thermomechanical coupling solver is used to perform the thermomechanical coupling calculation again to obtain new displacement field data. By comparing the displacement field data before and after the perturbation, a complete ply angle partial derivative matrix is ​​constructed using the matrix assembly algorithm. Each element in the ply angle partial derivative matrix quantifies the control effect of a specific ply angle adjustment on local thermal deformation through the sensitivity coefficient.

[0092] After generating the ply angle partial derivative matrix, deformation sensitivity gradient analysis is performed using the gradient projection method to generate deformation sensitivity distribution data. Based on the generated deformation sensitivity distribution data, the ply angle partial derivative matrix is ​​converted into a visual deformation sensitivity weight distribution diagram using the Matplotlib visualization library. The deformation sensitivity weight distribution diagram is displayed in the form of a heat map. The figure uses a gradient from blue to red to represent the sensitivity of different areas. The red area represents the area with high sensitivity during the ply angle adjustment process, and the blue area represents the area with low sensitivity during the ply angle adjustment process. At the same time, the visualization algorithm will automatically mark the location of the sensitivity mutation, which usually corresponds to the ply transition zone or the area with discontinuous material properties.

[0093] Based on the deformation sensitivity weight distribution map, the ply parameter optimization is carried out through the gradient descent adjustment algorithm. In the specific operation, when the gradient descent adjustment algorithm is initialized, the high-sensitivity areas in the deformation sensitivity weight distribution map are first identified through the regional clustering analysis algorithm, and high adjustment weights are assigned to these high-sensitivity areas. At the same time, during the iteration process of the gradient descent adjustment algorithm, the sensitivity gradient direction in the high-sensitivity area is obtained through the finite difference analysis method, and the gradient descent adjustment algorithm is used to adjust the ply angle parameters along the sensitivity gradient direction through the step size control strategy. At the same time, the displacement changes of key nodes (such as displacement extreme points, temperature gradient mutation points, and ply transition zones) are monitored. When it is detected that the adjustment of a certain ply angle leads to an improvement in displacement, the gradient descent adjustment algorithm will increase the adjustment weight of the ply angle, and vice versa.

[0094] During the ply angle parameter adjustment process, the gradient descent adjustment algorithm enforces the ply process constraints through constraint processing technology, ensuring that the ply angle change rate and thickness gradient remain within the manufacturable range. A multi-objective optimization framework based on Pareto frontier theory coordinates the entire ply angle parameter adjustment process and maintains three independent optimization objective queues through a dynamic priority algorithm: stiffness performance queue, thermal deformation control queue, and process feasibility queue.

[0095] After each layup parameter adjustment, the multi-objective optimization framework will evaluate the impact of the layup parameter adjustment on the three optimization target queues through multi-criteria decision analysis. The layup parameter adjustment will only be retained when it is beneficial to at least two optimization target queues and does not harm the third optimization target queue. The multi-objective optimization framework also sets up a target balance monitoring mechanism through a performance deviation monitoring algorithm. When the optimization of a target queue lags significantly, the optimization priority of the target queue will be automatically increased. Based on the retained layup parameters, the parametric modeling engine outputs a secondary layup optimization plan.

[0096] S5. Based on the secondary optimization scheme for laying, the feasibility of laying is verified through the automatic wire laying machine process database. After passing the verification, the manufacturing-ready package is output using the angle gradient transition strategy.

[0097] The specific steps include:

[0098] In the process of implementing the manufacturing-ready package, the ply secondary optimization plan is first imported into the automatic fiber placement machine process database through a parameterized data interface for multi-rule joint verification. In the specific operation, the entire ply secondary optimization plan is scanned using the dynamic curvature radius algorithm, and the area where the curvature exceeds the minimum allowable value of the automatic fiber placement machine is identified using the rate extreme value detection algorithm. At the same time, the hash marking algorithm marks it as a violation area, where the minimum allowable value is defined by the minimum layable curvature radius parameter specified in the automatic fiber placement machine equipment technical specification. At the same time, the sliding window method moves along the ply path with a fixed width, and the difference angle calculator calculates the rate of change of adjacent ply angles in real time. The gradient extraction algorithm obtains the angle jump gradient of adjacent plies.

[0099] When it is detected that the gradient of the angle jump between adjacent plies exceeds the gradient threshold, an immediate warning is issued. The gradient threshold is defined based on the maximum allowable angle change rate of adjacent plies specified in the composite material process specification. At the same time, by utilizing historical physical field input parameters, a random forest classification model is constructed using the Scikit-learn machine learning framework and trained using the feature weighted training method. The trained random forest classification model comprehensively evaluates multiple factors such as fiber orientation, ply sequence, and thickness change through a multi-dimensional feature fusion evaluator, and outputs the process defect probability value for each area;

[0100] After completing the multi-rule joint verification, the angle gradient transition strategy begins to be implemented. In specific operations, the transition layer insertion algorithm first analyzes the area that needs to be optimized to determine the insertion position and number of transition layers. The transition layer insertion algorithm prioritizes high-risk areas with higher process defect probability values ​​and adds transition layers in high-risk areas through an adaptive layer allocation algorithm. The added transition layers smooth the angle changes through linear interpolation of angles. At the same time, using B-spline curve path fitting technology, the discrete layer path points are converted into continuous and smooth curves through node parameterization methods to ensure that the automatic fiber placement head can smoothly perform the placement action.

[0101] During the path fitting process, B-spline curve path fitting technology considers material tension compensation and placement speed optimization to avoid fiber wrinkling or stretching at turns. The layer insertion algorithm and B-spline curve path fitting technology are combined to generate optimized layup path data. This optimized layup path data is then converted into NC code for the fiber placement machine through an NC code generator. Multiple verifications are implemented during the NC code generation process, including command syntax checking, motion range verification, and process parameter compliance confirmation.

[0102] After receiving the generated NC code for the wire laying machine, the digital twin engine constructs a virtual manufacturing environment through 3D scene reconstruction technology, completely reproducing the equipment configuration and process conditions of the actual production workshop. The digital twin engine performs full-process simulation, simulating the actual motion trajectory and laying process of the automatic wire laying machine, and detects possible interference or collision through collision detection algorithms, and generates a simulation verification report. Finally, the optimized laying path data, NC code files and simulation verification reports are integrated through manufacturing data integration and packaging technology to generate a manufacturing-ready package. The entire manufacturing-ready package generation process is fully automated, and no human intervention is required from the input of the secondary optimization plan for laying to the output of the ready package, ensuring the accuracy and efficiency of process conversion.

[0103] S6. Load the manufacturing-ready package into the digital twin engine, simulate the actual layup process, output the final layup process parameter set, and optimize the layup design.

[0104] The specific steps include:

[0105] Optimize the layup process in the digital twin engine. Specifically, a dedicated data parser is used to load and parse the manufacturing-ready package, which is then converted into an internal format that can be processed by the digital twin engine using a structured data conversion algorithm.

[0106] After the format conversion is completed, kinematic modeling technology is used to construct a composite material constitutive model through a multi-body dynamics simulation framework. During the construction of the composite material constitutive model, a parametric joint modeling method is used to fully reproduce all the motion degrees of freedom of the mechanical structure of the wire placement machine, including the six-axis motion capability of the wire placement head and the dynamic characteristics of the wire feeding mechanism. After the composite material constitutive model is completed, the real-time physical simulation engine is initialized through the physical engine protocol, and the composite material constitutive model and temperature field data are loaded through the distributed data loader, providing a physical basis for subsequent simulations.

[0107] During the layup process simulation phase, the NC code is converted into action instructions through a G-code interpreter. Kinematic modeling technology is used to drive the material constitutive model to execute the layup action according to the action instructions. While executing the layup action, the real-time physical simulation engine is used to advance the calculation with a fixed simulation step size: the stress and strain state vector of the carbon fiber during the layup process is calculated using the finite element method, the heating temperature field distribution value is calculated using the heat conduction equation, and the interaction force between the fiber placement head and the mold surface is calculated based on the contact mechanics algorithm. Based on the stress and strain state vector, the temperature field distribution size, and the interaction force value between the fiber placement head and the mold surface, a multi-physics field coupling analysis is used to generate a layup process status report;

[0108] Based on the generated layup process status report, the multi-objective optimization algorithm dynamically adjusts process parameters. Specifically, it constructs a weighted objective function through a target weight allocation algorithm. Based on this weighted objective function, it uses a dynamic priority algorithm to maintain three parallel optimization objectives: placement quality index, production efficiency, and energy consumption control. Laying parameters are adjusted using a gradient descent method to maximize motion efficiency while ensuring fiber impregnation quality. After each layup parameter adjustment, the digital twin engine quickly performs local resimulation and evaluates the effectiveness of the layup parameter adjustment through multi-criteria decision analysis based on the three parallel optimization objectives.

[0109] Based on the effect of ply parameter adjustment, heating temperature control is achieved through the PID algorithm and simulation verification is completed through the physical field convergence verification method. The digital twin engine integrates the execution results of the simulation verification through parameter normalization technology and outputs the final ply process parameter set.

[0110] This embodiment also provides a carbon fiber fuselage structure ply optimization design system, including: parameter integration module, thermal stress coupling module, primary optimization module, secondary optimization module, verification module, process simulation module,

[0111] Parameter integration module, used to import the 3D CAD model of the fuselage structure, collect physical field input parameters, and integrate them to generate a digital engineering package;

[0112] The thermal-stress coupling module is used to calculate the temperature field using a thermal-mechanical coupling solver based on a digital engineering package to obtain the fuselage temperature distribution. This temperature distribution is then mapped into a thermal stress field using the thermoelastic constitutive equation and vector-superimposed with the mechanical load to generate a comprehensive stress cloud map.

[0113] The initial optimization module is used to initiate asymmetric layup design for high-stress areas based on the comprehensive stress cloud map, and verify the mechanical balance of the layup angle combination through the equilibrium optimization algorithm to obtain the layup optimization solution;

[0114] The secondary optimization module is used to import the ply optimization plan into the thermal-mechanical coupling solver, perform secondary temperature field calculations, detect thermal deformation, adjust the weight coefficients of the ply angle combination based on the thermal deformation, and output the secondary optimization plan for the ply.

[0115] The verification module verifies the feasibility of layup based on the secondary optimization scheme using the automatic fiber placement machine process database. After verification, it uses the angle gradient transition strategy to output a manufacturing-ready package.

[0116] The process simulation module is used to load the manufacturing-ready package into the digital twin engine, simulate the actual layup process, and output the final layup process parameter set.

[0117] This embodiment also provides a computer device suitable for the carbon fiber fuselage structure layup optimization design method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the carbon fiber fuselage structure layup optimization design method proposed in the above embodiment.

[0118] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0119] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the carbon fiber fuselage structure ply optimization design method proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0120] In summary, the present invention achieves an accurate assessment of thermal deformation sensitivity through: a thermomechanical coupling solver and a finite element perturbation analysis method. By dynamically adjusting the weight coefficients of the ply angle combinations and adopting a gradient descent adjustment algorithm, thermal deformation can be effectively predicted and controlled, thereby improving the dimensional stability and surface quality of the final product. Secondly, in response to the problem of process defects in the manufacturing process, the present invention combines the automatic wire laying machine process database with a multi-rule joint verification mechanism to ensure the manufacturing feasibility of the ply scheme. At the same time, the random forest classification model is called to predict the probability of potential process defects, and the ply design is optimized based on the angle gradient transition strategy, which greatly reduces the risk of process defects in the manufacturing process and improves the quality of the finished product and production efficiency.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A carbon fiber fuselage structure layup optimization design method, characterized by: include, Import the 3D CAD model of the fuselage structure, collect physical field input parameters, and integrate them to generate a digital engineering package; Based on the digital engineering package, a thermomechanical coupling solver is used to calculate the temperature field and obtain the fuselage temperature distribution. The fuselage temperature distribution is mapped into a thermal stress field using the thermoelastic constitutive equation, and vector superposition is performed with the mechanical load to generate a comprehensive stress cloud map. Based on the comprehensive stress cloud map, asymmetric layup design is initiated for high-stress areas. The mechanical balance of the layup angle combination is verified using a balanced optimization algorithm to obtain a layup optimization solution. Import the ply optimization plan into the thermal-mechanical coupling solver, perform secondary temperature field calculations, detect thermal deformation, adjust the weight coefficients of the ply angle combination based on the thermal deformation, and output the secondary ply optimization plan. Based on the secondary optimization scheme for layup, the feasibility of layup is verified using the automatic fiber placement machine process database. After verification, a manufacturing-ready package is output using an angle gradient transition strategy. The manufacturing-ready package is loaded into the digital twin engine to simulate the actual layup process and output the final layup process parameter set.

2. The carbon fiber fuselage structure layup optimization design method according to claim 1, wherein: The physical field input parameters include mechanical loads, temperature field parameters, material anisotropy parameters, fuselage vibration parameters, and manufacturing process parameters.

3. The carbon fiber fuselage structure layup optimization design method according to claim 1, wherein: The specific steps of the comprehensive stress cloud diagram are as follows: The material anisotropy parameters are loaded into the thermal-mechanical coupling solver, and the temperature field distribution on the fuselage surface is obtained using the transient heat conduction algorithm; Extract the thermal expansion tensor from the anisotropic parameters of the material, and apply the thermoelastic constitutive equation based on the thermal expansion tensor to map the temperature field distribution into the thermal stress field; The thermal stress field and the mechanical load are vector-superimposed using the weighted tensor superposition algorithm to generate a three-dimensional comprehensive stress tensor field. Based on the three-dimensional comprehensive stress tensor field, the comprehensive stress cloud map is obtained using the maximum principal stress algorithm.

4. The carbon fiber fuselage structure layup optimization design method according to claim 1, wherein: The specific steps of the ply optimization scheme are as follows: Based on the comprehensive stress cloud map, the DBSCAN clustering algorithm is used to extract the regional coordinates of high-stress areas and classify them into tension, compression, and shear dominant areas. The ply-driven method is then used to design an asymmetric ply strategy for each dominant area. The NSGA-II multi-objective balance optimization algorithm is used to dynamically adjust the ply angle and thickness, and the mechanical balance is verified through ABD matrix calculation, and the ply optimization plan is finally output.

5. The carbon fiber fuselage structure layup optimization design method according to claim 4, characterized in that: The specific steps of the secondary optimization scheme of the ply are as follows: The ply optimization solution is imported into the thermomechanical coupling solver through a parameterized script interface. A secondary temperature field calculation is performed using a dynamic heat conduction algorithm to generate an updated three-dimensional temperature gradient field. Based on this updated three-dimensional temperature gradient field, the thermal deformation is detected using a displacement extreme value extraction algorithm. Based on the thermal deformation, the partial derivative matrix of the ply angle is obtained through the finite element perturbation analysis method, and the deformation sensitivity gradient analysis is performed to generate the deformation sensitivity weight distribution map; Based on the generated deformation sensitivity weight distribution map, the ply angle parameters are adjusted through the gradient descent adjustment algorithm, and the ply secondary optimization scheme is output through the multi-objective optimization framework.

6. The carbon fiber fuselage structure layup optimization design method according to claim 5, characterized in that: The manufacturing ready package, the specific steps are as follows, The automatic fiber placement machine process database is used to perform multi-rule joint verification on the secondary optimization plan of the ply: A dynamic curvature radius algorithm is used to eliminate illegal areas, a sliding window method is used to detect the gradient of adjacent ply angle jumps in real time, and a random forest classification model is used to predict the probability of process defects. After completing the multi-rule joint verification, based on the angle gradient transition strategy, the transition layer insertion algorithm and the B-spline curve path fitting method are used to generate the NC code for the wire laying machine, input it into the digital twin engine, and generate a manufacturing-ready package.

7. The carbon fiber fuselage structure layup optimization design method according to claim 6, characterized in that: The specific steps of outputting the final ply process parameter set are as follows: In the digital twin engine, the laying process of the fiber laying machine is simulated through kinematic modeling and real-time physical simulation engine, and the laying speed, heating temperature and pressure parameters are iteratively adjusted through a multi-objective optimization algorithm to output the final laying process parameter set.

8. A carbon fiber fuselage structure layup optimization design system based on the carbon fiber fuselage structure layup optimization design method according to any one of claims 1 to 7, characterized in that: Including parameter integration module, thermal stress coupling module, primary optimization module, secondary optimization module, verification module, process simulation module, Parameter integration module, used to import the 3D CAD model of the fuselage structure, collect physical field input parameters, and integrate them to generate a digital engineering package; The thermal-stress coupling module is used to calculate the temperature field using a thermal-mechanical coupling solver based on a digital engineering package to obtain the fuselage temperature distribution. This temperature distribution is then mapped into a thermal stress field using the thermoelastic constitutive equation and vector-superimposed with the mechanical load to generate a comprehensive stress cloud map. The initial optimization module is used to initiate asymmetric layup design for high-stress areas based on the comprehensive stress cloud map, and verify the mechanical balance of the layup angle combination through the equilibrium optimization algorithm to obtain the layup optimization solution; The secondary optimization module is used to import the ply optimization plan into the thermal-mechanical coupling solver, perform secondary temperature field calculations, detect thermal deformation, adjust the weight coefficients of the ply angle combination based on the thermal deformation, and output the secondary optimization plan for the ply. The verification module verifies the feasibility of layup based on the secondary optimization scheme using the automatic fiber placement machine process database. After verification, it uses the angle gradient transition strategy to output a manufacturing-ready package. The process simulation module is used to load the manufacturing-ready package into the digital twin engine, simulate the actual layup process, and output the final layup process parameter set.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the carbon fiber fuselage structure layup optimization design method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the carbon fiber fuselage structure layup optimization design method according to any one of claims 1 to 7 are implemented.

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