Aircraft CAD model intelligent component recognition and segmentation method based on three-mode fusion

Abstract

The application discloses a method for intelligent component identification and segmentation of an aircraft CAD model based on three-mode fusion, comprising: obtaining an aircraft CAD model with a boundary representation structure; constructing a multi-modal training data set that fuses surface images, geometric parameters and component semantic labels; training a multi-modal identification model; using the model for preliminary identification; for low confidence or small area surfaces, starting a geometric rule auxiliary module based on geometric topology and parameterization rules for semantic completion and correction to form a final segmentation result; automatically extracting key geometric feature lines; and converting and outputting into a structured engineering semantic format that can be used by grid generation software. The application realizes full automation of component identification, solves the problems of low efficiency and consistency caused by manual dependence, improves the identification integrity and engineering practicability of complex geometry, and the output result can directly drive subsequent grid generation, thereby significantly improving the automation level of the digital simulation pre-processing process.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of computer-aided engineering and artificial intelligence, specifically to a method for intelligent component recognition and segmentation of aircraft CAD models based on three-modal fusion. Background Technology

[0002] With the widespread application of computational fluid dynamics and structural mechanics simulation in aircraft design, high-quality mesh generation has become a key bottleneck in simulation accuracy and efficiency. Currently, the mesh generation process usually includes: (1) dirty geometry processing: cleaning up geometric defects such as tiny gaps, overlapping surfaces, and discontinuous edges in the CAD model; (2) surface mesh generation: generating triangular or quadrilateral meshes on the model surface; (3) volume mesh generation: generating tetrahedral or hexahedral meshes based on the surface mesh.

[0003] In this process, component identification is a crucial preliminary step. On the one hand, different components (such as wings, fuselages, and engine nacelles) have different aerodynamic characteristics and structural functions, requiring differentiated mesh sizes and types. On the other hand, the connection relationships between components determine the mesh continuity, contact pair settings, and boundary condition definitions. Currently, this process relies entirely on engineer experience and manual operation. Engineers must first visually identify each component in CAD software, manually selecting, grouping, and marking them; then, based on the aerodynamic importance of the components (such as wing leading edges and engine air intakes), they manually set mesh refinement areas; finally, they manually determine whether gaps or overlaps between components constitute dirty geometry and repair them. Therefore, the existing technology has the following prominent drawbacks:

[0004] (1) Extremely low level of automation: Component identification relies entirely on manual labor, which is time-consuming and labor-intensive, becoming the main efficiency bottleneck in the mesh generation process;

[0005] (2) Consistency is difficult to guarantee: Different engineers have subjective differences in their understanding of the component boundaries, which leads to inconsistent mesh generation results and affects the comparability of simulations;

[0006] (3) Lack of dedicated identification tools: There is currently no automated method specifically for the identification of aircraft components. Existing CAD software only provides general geometric selection functions and cannot understand engineering semantics;

[0007] (4) Disconnected from the mesh generation process: Even if the parts are identified using general image segmentation methods, the results cannot be directly converted into geometric groups and feature lines that can be used by mesh generation software, and a lot of manual conversion and interpretation is still required. Summary of the Invention

[0008] The purpose of this invention is to solve the problems of low efficiency, poor consistency and automation breakpoints caused by relying on manual component identification in the aircraft mesh generation process. It provides a method that can automatically and accurately identify aircraft components and directly output semantic grouping and feature geometry that meet the requirements of mesh generation engineering, so as to realize the full-process automation from geometric model to computational mesh.

[0009] To achieve the above-mentioned objectives, this invention provides a method for intelligent component identification and segmentation of aircraft CAD models based on three-modal fusion, the method comprising:

[0010] Step S1: Obtain the CAD model of the aircraft to be processed, wherein the CAD model of the aircraft has a boundary representation structure;

[0011] Step S2: Based on the engineering requirements of mesh generation, perform engineering semantic annotation on the faces in the boundary representation structure of the aircraft CAD model to obtain component semantic labels; extract the geometric parameters of each face from the boundary representation structure; generate a multi-view 2D rendering image for each annotated face; associate and store the multi-view 2D rendering image, the geometric parameters, and the component semantic labels to form training samples.

[0012] Step S3: Train a multimodal recognition model using the training samples. The multimodal recognition model is configured to fuse input image features and language semantic information to output semantic labels for components on the surface of the aircraft CAD model.

[0013] Step S4: Input the aircraft CAD model to be identified into the trained multimodal recognition model, identify the surfaces in the aircraft CAD model, and obtain the preliminary surface-part semantic label mapping relationship;

[0014] Step S5: Based on the preliminary face-part semantic label mapping relationship, for faces whose recognition confidence output by the multimodal recognition model is lower than a preset threshold or whose geometric area ratio is less than a set threshold, the geometric rule auxiliary module is activated to perform semantic completion to obtain the final part segmentation result.

[0015] Step S6: Based on the final component segmentation result, extract the key geometric feature lines corresponding to each component from the boundary representation structure of the aircraft CAD model;

[0016] Step S7: Convert the final component segmentation results and the extracted key geometric feature lines into a structured engineering semantic format for the target mesh generation software to read and utilize, and then output it.

[0017] This method, through step S1, limits the processing object to CAD models with boundary representation structures, ensuring the feasibility of all subsequent geometric operations (topology analysis, parameter extraction). By utilizing steps S2-S3 to construct a multimodal dataset oriented towards mesh engineering semantics and training a dedicated model integrating visual, geometric, and linguistic information, the method gains the ability to understand engineering semantics, replacing manual identification by engineers. Steps S4-S5 employ a collaborative mechanism of AI recognition of main structural components and small feature geometric rule completion, utilizing AI to handle macroscopic patterns while using geometric rules to ensure detailed integrity, overcoming the limitations of single methods on complex engineering geometries. Steps S6-S7 automatically connect the semantic recognition results to the geometric kernel, extracting key geometric feature lines and converting them into a format directly readable by mesh software, thus completing the final step from recognition to application and eliminating manual conversion. This solves the end-to-end automation bottleneck problem in the aircraft CAD mesh generation process, where component recognition relies entirely on manual labor, resulting in extremely low efficiency, inconsistent results, and the inability to directly use the recognition results for subsequent engineering. It achieves a fully automated, high-precision processing flow from the original CAD model to mesh-generated ready engineering data, reducing the time required for manual work, which can take several weeks, to automated processing.

[0018] In step S2, the surfaces in the boundary representation structure of the aircraft CAD model are annotated with engineering semantics. Specifically, in the mesh preprocessing software environment, semantic labels are manually assigned to the surfaces contained in different components based on aerodynamic characteristics or structural functional differences.

[0019] Preferably, the geometric rule auxiliary module determines and corrects the semantic labels of the surface components based on the geometric topological relationships and parametric features of the aircraft CAD model, thereby forming the final component segmentation result.

[0020] Purely data-driven AI models are prone to low confidence or misjudgment when faced with small-sized, weakly textured, feature-fuzzy, or geometrically rare surfaces in the training data (such as slender wing trailing edges, tiny connecting protrusions, and hatches that smoothly transition into the main body). The above approach addresses this by introducing rule modules based on deterministic geometric knowledge to compensate for AI's shortcomings in such cases. General image segmentation models output pixel-level labels, which are difficult to automatically and error-free associate with the precise boundary representation (B-Rep) structure (faces, edges, vertices, and their topology) in CAD models. The above approach directly limits operations to the geometric and topological levels of the original CAD model, ensuring that any judgments and corrections are precisely applied to specific B-Rep surfaces, guaranteeing absolute geometric accuracy. By filling in gaps and correcting errors in the AI ​​recognition results, the final component segmentation results ensure that all geometric details affecting subsequent mesh generation and simulation analysis are covered, reducing the degradation of mesh quality or simulation accuracy caused by missing key small features. This approach eliminates reliance on a single AI model, creating a hybrid intelligence model that combines data-driven (AI) and knowledge-driven (rule-based) approaches. When faced with new aircraft models or those with unique geometries, even if the AI ​​model performs poorly initially, the method can still ensure the correct identification of basic and critical components through geometric rules, thus enhancing the method's generalization ability and practical stability.

[0021] Preferably, the multi-view 2D rendered image is generated as follows:

[0022] For each surface to be processed, multiple angles are used to wrap around it along its normal vector, and rendering is performed at each viewpoint to generate a series of two-dimensional view images.

[0023] Specifically, for each facet, multi-angle rendering is performed along its normal vector. This simulates the process of viewing a geometric facet from various directions, ensuring that the generated image fully reflects the facet's shape characteristics. This solves the problem of generating richly informative 2D image data from a single, planar aircraft CAD model facet, allowing the visual model to learn from it. It provides the visual model with multi-angle, comprehensive observation data, enhancing the model's robustness in recognizing component faces under different viewing angles and lighting conditions.

[0024] The preferred method for training the multimodal recognition model is as follows:

[0025] A neural network architecture is constructed, comprising a visual encoder, a language understanding module, and a cross-modal attention fusion module. First, the visual encoding module, based on Vision Transformer (ViT), is responsible for extracting features from the input image and outputting a visual token. Second, the language encoding module, based on a pre-trained large model, is responsible for encoding text prompts into text tokens. Finally, the cross-modal fusion module achieves bidirectional deep interaction between the visual and language tokens through a cross-attention mechanism. After multiple rounds of interaction, the features of the two modalities are aligned and fused in a unified semantic space to form a joint feature representation.

[0026] The neural network architecture is trained under supervision by using the multi-view two-dimensional rendered images and corresponding component semantic label texts in the training samples as input.

[0027] The multimodal recognition model is optimized using a combination loss function, which includes the classification cross-entropy loss function, the segmentation cross-union ratio loss function, and the boundary weighted loss function.

[0028] This paper presents a clear technical framework for fusing multimodal information through an architecture consisting of a visual encoder, a language module, and a cross-modal attention fusion module. Supervised learning is performed using a pre-constructed dataset, enabling the model to learn the mapping from images to semantic labels. A combined loss function is employed to simultaneously optimize classification accuracy, segmentation region consistency, and boundary precision, ensuring the quality of the model's output from multiple dimensions. The paper addresses the core technical path of model implementation by solving the problem of how to specifically construct and train a dedicated recognition model capable of understanding image-semantic associations. An optimized and trained intelligent model capable of accurately associating surface images with component semantic labels has been implemented.

[0029] Preferably, step S4 specifically involves identifying the main structural component surfaces in the aircraft CAD model; step S5 specifically involves semantic completion of local feature surfaces, connecting region surfaces, or low-confidence identification surfaces in the aircraft CAD model.

[0030] Steps S4 and S5 are defined as a hierarchical collaborative process: the first layer (AI) is responsible for the main structural components (efficiently handling regularities); the second layer (rules) is responsible for local features, connection surfaces, and other complex situations (precisely handling special characteristics). This design concept is an efficient way to solve engineering complexity problems. It realizes intelligent division of labor and collaboration in the recognition process, significantly improving the completeness of recognizing small and special features while ensuring the efficiency of recognizing the main structural components.

[0031] Preferably, the rules for determining the geometric topological relationships and parametric features of the aircraft CAD model include at least one of the following rules:

[0032] Rule 1: If a face is located at the geometric intersection of two identified main structural component faces, and the area of ​​the face is less than a set threshold relative to the area of ​​the adjacent main structural component face, then the component semantic label of the face is determined as a connecting feature face or an edge face.

[0033] Rule 2: If a surface is directly adjacent to the main wing, and the angle between its normal vector and the normal vector of the main wing's reference plane is greater than or equal to 45°, and this surface is located on the outermost side of the wing span, with an area between 0.5% and 5% of the main wing's area, then it is classified as a winglet or wingtip. The area proportion rule can identify small surfaces at boundaries (such as edges). The direction relationship rule can identify small surfaces that are not parallel to the main airflow direction (such as doors). This provides quantifiable and verifiable rule examples, enabling the system to automatically identify specific types of geometric features such as the wing trailing edge and doors.

[0034] Preferably, the extraction of key geometric feature lines corresponding to each component includes:

[0035] For the components identified by the multimodal recognition model, by analyzing the curvature distribution and boundary topology of its constituent surfaces, a geometric algorithm is used to extract the key geometric feature lines of the components, including its boundary lines and feature contour lines.

[0036] For the feature surfaces identified by the geometric rule assistance module, the corresponding key geometric feature lines are extracted based on their geometric definitions and their relationship with adjacent surfaces.

[0037] Specifically, for the main structural components identified by AI, geometric algorithms such as curvature and topology analysis are used to automatically extract their boundaries and contours. For small features identified by rules, extraction is performed directly based on their geometric definitions (such as the intersection of two surfaces). Both methods ensure that the output feature lines are accurately associated with the semantic labels of the components. This achieves automated integration from semantic segmentation to geometric feature extraction, outputting geometric data directly relied upon for operations such as mesh encryption and block division.

[0038] Preferably, in step S5, before or after forming the final component segmentation result, a post-processing step is also included:

[0039] The semantic labels of components are smoothed based on the adjacency relationship of surfaces to integrate the recognition results of the multimodal recognition model with the supplementary results of the geometric rule auxiliary module, thereby eliminating misjudgments of isolated surfaces; and the final component segmentation results are visualized and exported.

[0040] The label smoothing process is based on the adjacency relationship of faces, performing consistency checks and adjustments on the labels of adjacent faces to eliminate unreasonable abrupt changes or isolated points. The AI ​​results are fused with rule-based results, and visualization and export are supported, forming a complete closed loop from processing to delivery. Post-processing improves the topological consistency and visual smoothness of the segmentation results, and visualization and export functions make the results easy for engineers to verify and use.

[0041] The input to the multimodal recognition model includes natural language instructions; the method allows users to specify the type of part to be recognized or the segmentation requirements by inputting natural language instructions; the visual encoder adopts a Transformer-based architecture, the language understanding module adopts a pre-trained large language model, and the cross-modal attention fusion module adopts a cross-attention mechanism.

[0042] The model's input can include natural language commands, which are then translated into internal tasks that drive the model and geometry engine, achieving intelligent conversion from natural language to concrete operations. This enhances the system's usability and flexibility, allowing users to drive the system to perform specific tasks using natural language (such as segmenting all wing surfaces). The key modules in the architecture are concretized into corresponding selections, ensuring the feasibility of the solution: a visual encoder (ViT or other Transformer architectures), a language understanding module (QwenLM or other large models), and a fusion module (CrossAttn), ensuring the powerful capabilities of each module.

[0043] Preferably, in step S7, the structured engineering semantic format includes: a set of component geometric groups for the mesh generation software to recognize and call, a set of feature lines describing the key geometric features of the components, and optional mesh generation strategy parameters associated with preset components. By explicitly defining the output format as the structured engineering semantic format required by industrial software, including the component group set, feature line set, and mesh strategy parameters, the output of this method is no longer merely a display result, but rather instructions or data that can be directly read and applied by CAE software, thus achieving a closed loop of engineering value.

[0044] One or more technical solutions provided by this invention have at least the following technical effects or advantages:

[0045] (1) Full automation of component identification has been achieved, greatly improving the efficiency of mesh preprocessing;

[0046] Because this invention employs a visual-geometric-linguistic three-modal fusion recognition and hierarchical collaborative processing mechanism, it can automatically and accurately complete the identification and semantic segmentation of various aircraft components. The effect is that it shortens the process of identifying medium-sized passenger aircraft components, which originally relied entirely on manual identification and grouping by engineers and took 1-2 weeks, to automated processing, thus solving the primary efficiency bottleneck in the mesh generation process.

[0047] (2) Outputs engineering semantics and geometric features that can directly drive mesh generation;

[0048] Because the output of this invention is not a general image segmentation mask, but rather a face-part label mapping table and key geometric feature lines that are precisely associated with the original B-Rep model, the effect is that the recognition results can be directly converted into components, sets, or layers that mesh generation software can recognize, and key feature lines (such as wing leading / trailing edge lines) used for mesh partitioning and densification control are automatically extracted, achieving recognition-ready functionality and eliminating the large amount of manual conversion and interpretation work required in traditional methods.

[0049] (3) Improved the completeness of identification and engineering practicality of small-sized and weak-featured parts;

[0050] Because this invention employs a layered strategy of AI recognition of main structural components combined with geometric completion of small features, it intelligently completes small or inconspicuous parts such as the wing trailing edge and access hatches using geometric rules. The effect is that it significantly improves the integrity of the overall segmentation, ensuring that subsequent mesh generation covers all necessary geometric details, making the generated mesh more in line with the geometric integrity requirements of high-fidelity simulation.

[0051] (4) Improve grid quality by supporting smart grid strategy presets through semantic understanding;

[0052] Because the model training of this invention incorporates knowledge of mesh engineering, it possesses engineering semantic understanding capabilities. The effect is that the system can automatically recommend or apply preset mesh generation strategies based on component semantics (such as the leading edge of an airfoil), thereby generating higher-quality meshes more suitable for computation in key aerodynamic regions.

[0053] (5) Possesses strong domain generalization ability and knowledge accumulation value;

[0054] Because the core framework of this invention is not dependent on any specific aircraft model and supports incremental learning, the benefits are significant: only a small amount of data from new aircraft models needs to be labeled and fine-tuned to quickly adapt to new aircraft models, greatly reducing technical maintenance and expansion costs. Simultaneously, the system can distill the mesh generation experience of senior engineers into reusable models and rules, forming core knowledge assets. Attached Figure Description

[0055] The accompanying drawings, which are provided to further illustrate embodiments of the invention and constitute a part of this invention, are not intended to limit the scope of the invention.

[0056] Figure 1 This is a flowchart illustrating an intelligent component recognition and segmentation method for aircraft CAD models based on three-modal fusion.

[0057] Figure 2 This is a schematic diagram of the architecture of an intelligent component recognition and segmentation system for aircraft CAD models based on three-modal fusion. Detailed Implementation

[0058] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, where there is no conflict, the embodiments of the present invention and the features thereof can be combined with each other.

[0059] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0060] Those skilled in the art should understand that, in the disclosure of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the above terms should not be construed as limiting this invention.

[0061] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0062] Please refer to Figure 1 , Figure 1 This invention provides a method for intelligent component recognition and segmentation of aircraft CAD models based on three-modal fusion, comprising:

[0063] Step S1 - Model Acquisition and Format Confirmation: Acquire the CAD model of the aircraft to be processed, wherein the CAD model of the aircraft has a boundary representation structure;

[0064] Step S2 - Construction of a multimodal dataset oriented towards engineering semantics: Based on the engineering requirements of mesh partitioning, engineering semantic annotation is performed on the faces in the boundary representation structure of the aircraft CAD model to obtain component semantic labels; geometric parameters of each face are extracted from the boundary representation structure; for each labeled face, a multi-view 2D rendering image is generated; the multi-view 2D rendering image, the geometric parameters, and the component semantic labels are associated and stored to form training samples;

[0065] Step S3 - Multimodal Fusion Recognition Model Training: The multimodal recognition model is trained using the training samples. The multimodal recognition model is configured to fuse input image features and language semantic information to output semantic labels for components on the surface of the aircraft CAD model.

[0066] Step S4 - Initial identification of macroscopic components based on AI: Input the CAD model of the aircraft to be identified into the trained multimodal recognition model, identify the surfaces in the aircraft CAD model, and obtain the preliminary surface-component semantic label mapping relationship;

[0067] Step S5 - Collaborative completion of small features based on geometric rules: Based on the preliminary face-part semantic label mapping relationship, for faces whose recognition confidence output by the multimodal recognition model is lower than a preset threshold or whose geometric area ratio is less than a set threshold, the geometric rule auxiliary module is activated to perform semantic completion to obtain the final part segmentation result.

[0068] Step S6 - Automatic extraction of key geometric feature lines: Based on the final component segmentation results, extract the key geometric feature lines corresponding to each component from the boundary representation structure of the aircraft CAD model;

[0069] Step S7 - Structured Engineering Semantic Output: The final component segmentation results and extracted key geometric feature lines are converted into a structured engineering semantic format for the target mesh generation software to read and utilize, and then output.

[0070] This invention provides a fully automated and high-precision component identification and geometric feature extraction capability for mesh generation in computational fluid dynamics and finite element analysis, and is applicable to the digital simulation and manufacturing of complex equipment such as aviation, aerospace, and ships.

[0071] This invention provides a method for intelligent recognition, semantic segmentation, and geometric feature extraction of aircraft components based on a three-modal fusion of vision, geometry, and language. This invention addresses the inefficiencies, inconsistencies, and automation breakpoints caused by reliance on manual component recognition in the aircraft mesh generation process. Specifically, it includes:

[0072] (1) Eliminate the manual dependence in the component identification process and realize the automatic and accurate identification and semantic grouping of each component in the aircraft CAD model;

[0073] (2) Provide engineering semantic output that can be directly used for mesh generation, including component face sets and key feature lines (such as boundary lines and densification lines).

[0074] (3) Supports preset differentiated grid strategies for different components, enabling intelligent grid planning that is configured upon identification;

[0075] (4) Improve the automation level and processing efficiency of the entire mesh generation process, and provide reliable pre-processing tools for digital simulation.

[0076] This invention provides a joint modeling and processing method for a visual-geometric-linguistic three-modal approach. Its overall architecture comprises four parts: data construction, model training, inference and recognition, geometric processing, and mesh strategy generation. The specific steps are as follows:

[0077] (1) Data Construction: A Semantic Annotation System Oriented to Grid Requirements

[0078] 1) B-Rep Surface-Level Semantic Annotation: Using mesh generation software such as NNW-GridStar, and combining experience in aerodynamic and structural mesh generation, surface-level annotations are performed on aircraft CAD models represented in B-Rep format. This includes, but is not limited to: nose, forward fuselage, mid-fuselage, aft fuselage, upper wing surface, lower wing surface, vertical tail, horizontal tail, engine nacelle, etc. After annotation, each component is visualized in a different color for easy verification.

[0079] 2) Multi-view image generation: For each B-Rep face, perform 360-degree multi-view rendering around its normal vector to generate a series of main view images. Scaling, rotation, and lighting adjustments are supported to enhance view coverage and lighting robustness.

[0080] 3) Dataset Augmentation and Construction: Data diversity is enhanced using geometric transformations (rotation, translation, scaling), color perturbation, and noise injection. A structured multimodal dataset is constructed, with each data point including: B-Rep facet ID, multi-view image set, semantic labels, and original geometric parameters.

[0081] (2) Model training:

[0082] 1) Model Architecture Design: ViT (Vision Transformer) is used as the visual encoder to extract image features. CrossAttn (a cross-attention mechanism) is used to align visual features with linguistic features (from QwenLM). QwenLM serves as the language understanding and generation module, responsible for semantic mapping and prompt response.

[0083] 2) Supervised fine-tuning strategy: The input is the main view image of the face, the corresponding semantic label, and the structured prompt words (such as the face belongs to [category]).

[0084] 3) The loss function adopts a ternary combination: semantic cross-entropy loss (optimizes classification accuracy) + IoU loss (improves the overlap of segmented regions) + boundary weighted loss (enhances the accuracy of boundary pixel recognition).

[0085] The loss function is:

[0086] ;

[0087] in, For semantic cross-entropy loss, For IoU loss, For boundary-weighted loss, the weighting coefficients are initially set. , , Adjustments can be made based on subsequent experiments.

[0088] Semantic cross-entropy loss:

[0089] ;

[0090] In the formula, N is the total number of pixels, i.e., the total number of faces in the B-Rep model, C is the number of semantic categories of the component, and y i,c Let p be the true label of the i-th pixel belonging to category c. i,c Let be the probability predicted by the model that the i-th pixel belongs to category c.

[0091] The IoU loss is calculated by averaging the IoU for each part category:

[0092] ;

[0093] Boundary loss ( The initial setting is 0.3 (which can be adjusted based on experiments), where, L cls For boundary binary classification loss, L dist Loss for distance transformation:

[0094] ;

[0095] In the formula, This represents the true boundary label of the i-th pixel. This indicates that the pixel is located inside the component. This indicates that the pixel is located on the geometric boundary of two different components; This represents the probability that a pixel predicted by the model is a boundary. Based on the topological relationships of the B-Rep model, when two adjacent faces belong to different components, their shared edge is marked as a boundary.

[0096] Loss for distance transformation:

[0097] ;

[0098] In the formula, The total number of boundary pixels, This represents the distance from pixel i to the nearest real geometric boundary. This represents the distance from pixel i predicted by the model to the boundary. This forces the predicted boundary lines to be closer to the actual geometric boundaries, improving the geometric usability of the segmentation results in CAD models.

[0099] 4) The training employs progressive fine-tuning, first pre-training on large-scale general image-text data, and then fine-tuning on domain image-text pairs labeled with semantic tags for each component of the aircraft. This approach can utilize prior knowledge of general visual language while enabling the model to accurately grasp the fine-grained semantics of the aircraft's unique components, thus improving domain adaptability and recognition accuracy.

[0100] (3) Reasoning and recognition:

[0101] 1) Input: The CAD model of the aircraft to be processed (B-Rep) and natural language prompts (e.g., please identify all fuselage surfaces).

[0102] 2) Layered recognition processing flow:

[0103] The first layer is the overall component recognition: the model extracts visual features face by face, combines them with the semantics of prompt words to make inferences, and prioritizes the identification of the main structural components, i.e., large components. The main structural components refer to the components with independent functions on the aircraft, such as the entire wing, the entire fuselage, and the tail. For simplicity, they will be referred to as large components in the following descriptions. The primary semantic labels of each B-Rep face are output to form a face-component mapping table.

[0104] The second layer is small component / feature geometric completion: For small surfaces (such as surfaces belonging to the trailing edge of an wing or local protrusions) whose confidence level is lower than the set threshold or ignored by the model in the previous round of recognition, the geometric rule assistance module is activated instead of relying directly on the AI ​​model for secondary recognition.

[0105] Widgets / features mainly include the following types:

[0106] Connecting feature surfaces or edge surfaces: Surfaces located at the junction of two large components (such as the upper and lower surfaces of an wing), such as the trailing edge and leading edge of an wing.

[0107] Featured surfaces with opening, closing, or maintenance functions: surfaces surrounded by large components and whose orientation differs significantly from the main body, such as hatches and maintenance access covers.

[0108] Localized raised or recessed surfaces: such as rivet heads, sensor mounting bases, oil filler caps, and other small geometric features.

[0109] Low-confidence recognition surfaces: Surfaces that AI models cannot recognize with high confidence due to reasons such as blurred geometric features and insufficient training data coverage.

[0110] 3) Working principle of the geometric rule auxiliary module:

[0111] Input: The set of large components identified by AI (such as the upper surface of the left wing and the lower surface of the left wing) and all the B-Rep surfaces contained therein.

[0112] Processing: Based on the geometric topology and parametric information of the CAD model, preset geometric rules are executed. For example, Rule 1: If a surface belongs to the upper or lower surface of the wing, or is located at the boundary between the two, at the leading edge of the wing, has high curvature, and its spanwise length is close to the entire wing span, it is classified as the leading edge of the wing. Rule 2: If a surface is located at the boundary between the upper and lower surfaces of the wing, and its area is less than 1% of the overall wing area, it is classified as the trailing edge of the wing. Rule 3: If a surface is directly adjacent to the main wing but its normal is significantly deviated from the main wing plane, and it is located at the outermost point of the wing spanwise, with an area between 0.5% and 5% of the main wing area, it is classified as a winglet or wingtip. Rule 4: If a surface is surrounded by a large fuselage component, and its normal is approximately perpendicular to the main fuselage direction, and its area is small, it may be an access hatch or door, and further determination is made based on the geometric characteristics of its adjacent surfaces. Rule 5: If a surface belongs to a large component of the engine nacelle, is located at the foremost end of the nacelle, and has a closed annular boundary and a smooth Gaussian positive curvature transition, then it is classified as an air intake lip.

[0113] Output: Add annotations to the corresponding part categories for facets that conform to the geometric rules, or create new subcategory labels for them.

[0114] 4) Post-processing: Label smoothing is performed based on face adjacency relationships, integrating AI recognition results with geometric rule supplementary results to avoid misjudgment of isolated faces. Component-level result visualization and export are supported.

[0115] (4) Geometric treatment:

[0116] 1) Key Geometric Feature Line Extraction: Based on the complete identification of all components (including large components identified by AI and small features supplemented by geometric rules), boundary lines, curvature extrema lines, and feature contour lines of each component are extracted using B-Rep topology and geometric information. For large components identified by AI, geometric algorithms (such as curvature-based edge detection and topological connectivity analysis) are used for automated extraction. For small features identified by the geometric rule-assisted module, feature lines are extracted directly using their geometric definitions (such as the trailing edge line, i.e., the intersection of the upper and lower wing surfaces), resulting in higher accuracy.

[0117] 2) Output Interface: Feature lines are output in a format supported by NNW-GridStar. Seamless integration with simulation workflows is supported, improving CAE analysis efficiency.

[0118] The visual encoder (ViT) can be replaced by other mainstream networks (such as ConvNeXt, ResNet), and this embodiment of the invention does not impose any restrictions on it. The language model (QwenLM) can be replaced by other large language models (such as GLM, LLaMA), and this embodiment of the invention does not impose any restrictions on it. The specific judgment conditions and parameters of the geometric rules can be adjusted according to the characteristics of different components. The output interface can be adapted to different target mesh generation software (such as ANSA, HyperMesh).

[0119] Example 2;

[0120] Based on Example 1, please refer to Figure 2 , Figure 2 This is a schematic diagram of the architecture of an intelligent component recognition and segmentation system for aircraft CAD models based on three-modal fusion. Embodiment 2 of the present invention provides an intelligent component recognition and segmentation system for aircraft CAD models based on three-modal fusion. The system adopts a layered architecture and aims to serve the automated mesh generation in aircraft digital simulation, including:

[0121] The data construction layer, configured to build a multimodal training dataset oriented towards grid engineering semantics, includes:

[0122] Semantic annotation unit is used to perform engineering semantic annotation on the faces in the boundary representation structure of the aircraft CAD model and generate component semantic tags;

[0123] A geometric parameter extraction unit is used to extract the geometric parameters of each face from the boundary representation structure;

[0124] The multi-view rendering unit is used to generate multi-view 2D rendering surface images for each labeled surface;

[0125] The data management unit is used to associate and store the surface image, the corresponding geometric parameters, and the semantic labels of the components to form structured training samples;

[0126] A model training layer, connected to the data construction layer, is configured to train a multimodal recognition model using the multimodal training dataset. This layer includes:

[0127] The model architecture management unit is used to build and maintain a neural network architecture that includes a visual encoder, a language understanding module, and a cross-modal fusion module.

[0128] A training execution unit is used to perform supervised training on the neural network architecture by inputting the training samples;

[0129] An optimization unit is used to optimize the multimodal recognition model using a combined loss function;

[0130] The inference and recognition layer, connected to the model training layer, is configured to load the trained multimodal recognition model and perform intelligent recognition and segmentation on the input aircraft CAD model. This layer includes:

[0131] The first recognition sublayer is used to run the multimodal recognition model, perform preliminary recognition of the surfaces of the CAD model, and output preliminary surface-part semantic label mapping relationships;

[0132] The rule-assisted sublayer, as a geometric rule-assisted module, is used to determine, complete, and correct semantic labels for low-confidence or unrecognized surfaces output by the first recognition sublayer based on predefined geometric topology and parameterized rules.

[0133] The post-processing sub-layer is used to smooth and integrate the recognition results based on the face adjacency relationship, forming the final complete component segmentation result;

[0134] A geometry processing and output layer, connected to the inference and recognition layer, is configured to perform engineering processing and output of the recognition results. This layer includes:

[0135] The feature line extraction unit is used to automatically extract the key geometric feature lines corresponding to each component from the final component segmentation result based on the boundary representation structure of the CAD model;

[0136] The format conversion and output unit is used to convert the component segmentation results and key geometric feature lines into a structured engineering semantic format that can be read by the target mesh generation software and output it.

[0137] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0138] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An aircraft CAD model intelligent component recognition and segmentation method based on three-modal fusion, characterized in that, The method includes: Step S1: Obtain the CAD model of the aircraft to be processed, wherein the CAD model of the aircraft has a boundary representation structure; Step S2: Based on the engineering requirements of mesh generation, perform engineering semantic annotation on the faces in the boundary representation structure of the aircraft CAD model to obtain component semantic labels; extract the geometric parameters of each face from the boundary representation structure; generate a multi-view 2D rendering image for each annotated face; associate and store the multi-view 2D rendering image, the geometric parameters, and the component semantic labels to form training samples. Step S3: Train a multimodal recognition model using the training samples. The multimodal recognition model is configured to fuse input image features and language semantic information to output semantic labels for components on the surface of the aircraft CAD model. Step S4: Input the aircraft CAD model to be identified into the trained multimodal recognition model, identify the surfaces in the aircraft CAD model, and obtain the preliminary surface-part semantic label mapping relationship; Step S5: Based on the preliminary face-part semantic label mapping relationship, for faces whose recognition confidence output by the multimodal recognition model is lower than a preset threshold or whose geometric area ratio is less than a set threshold, the geometric rule auxiliary module is activated to perform semantic completion to obtain the final part segmentation result. Step S6: Based on the final component segmentation result, extract the key geometric feature lines corresponding to each component from the boundary representation structure of the aircraft CAD model; Step S7: Convert the final component segmentation results and the extracted key geometric feature lines into a structured engineering semantic format for the target mesh generation software to read and utilize, and then output it. The architecture of the multimodal recognition model is designed as follows: ViT is used as the visual encoder to extract image features, CrossAttn is used to align visual features with language features, and QwenLM is used as the language understanding and generation module to be responsible for semantic mapping and prompt response.

2. The tri-modal fusion based aircraft CAD model intelligent component identification and segmentation method according to claim 1, characterized in that, The geometric rule auxiliary module determines and corrects the semantic labels of the surface components based on the geometric topological relationships and parametric features of the aircraft CAD model, thus forming the final component segmentation result.

3. The intelligent component identification and segmentation method for aircraft CAD models based on three-modal fusion according to claim 1, characterized in that, The method for generating the multi-view 2D rendered image is as follows: For each surface to be processed, multiple angles are used to wrap around it along its normal vector, and rendering is performed at each viewpoint to generate a series of two-dimensional view images.

4. The intelligent component identification and segmentation method for aircraft CAD models based on three-modal fusion according to claim 1, characterized in that, The training method for the multimodal recognition model is as follows: Construct a neural network architecture that includes a visual encoder, a language understanding module, and a cross-modal attention fusion module; The neural network architecture is trained under supervision by using the multi-view two-dimensional rendered images and corresponding component semantic label texts in the training samples as input. The multimodal recognition model is optimized using a combination loss function, which includes the classification cross-entropy loss function, the segmentation cross-union ratio loss function, and the boundary weighted loss function.

5. The intelligent component identification and segmentation method for aircraft CAD models based on three-modal fusion according to claim 1, characterized in that, Step S4 specifically involves identifying the main structural component surfaces in the aircraft CAD model; Step S5 specifically involves semantic completion of local feature surfaces, connecting area surfaces, or low-confidence identification surfaces in the aircraft CAD model.

6. The intelligent component identification and segmentation method for aircraft CAD models based on three-modal fusion according to claim 5, characterized in that, The rules for determining the geometric topological relationships and parametric features of the aircraft CAD model include at least one of the following rules: Rule 1: If a face is located at the geometric intersection of two identified main structural component faces, and the area of ​​the face is less than a set threshold relative to the area of ​​the adjacent main structural component face, then the component semantic label of the face is determined as a connecting feature face or an edge face. Rule 2: If a face is surrounded by an identified main structural component face, and the spatial normal of the face and the main direction of the main structural component face meet the preset conditions, then based on the geometry of the face and the relationship with adjacent faces, the semantic label of the face is determined to be a hatch, cover, or other feature face with opening, closing, or maintenance functions.

7. The intelligent component identification and segmentation method for aircraft CAD models based on three-modal fusion according to claim 1, characterized in that, The extraction of key geometric feature lines corresponding to each component includes: For the components identified by the multimodal recognition model, by analyzing the curvature distribution and boundary topology of its constituent surfaces, a geometric algorithm is used to extract the key geometric feature lines of the components, including its boundary lines and feature contour lines. For the feature surfaces identified by the geometric rule assistance module, the corresponding key geometric feature lines are extracted based on their geometric definitions and their relationship with adjacent surfaces.

8. The intelligent component identification and segmentation method for aircraft CAD models based on three-modal fusion according to claim 1, characterized in that, In step S5, before or after forming the final component segmentation result, a post-processing step is also included: The semantic labels of components are smoothed based on the adjacency relationship of surfaces to integrate the recognition results of the multimodal recognition model with the supplementary results of the geometric rule auxiliary module, thereby eliminating misjudgments of isolated surfaces; and the final component segmentation results are visualized and exported.

9. The intelligent component identification and segmentation method for aircraft CAD models based on three-modal fusion according to claim 4, characterized in that, The input to the multimodal recognition model includes natural language instructions; the method allows users to specify the type of part to be recognized or segmentation requirements by inputting natural language instructions; the visual encoder adopts a Transformer-based architecture, the language understanding module adopts a pre-trained large language model, and the cross-modal attention fusion module adopts a cross-attention mechanism.

10. The intelligent component identification and segmentation method for aircraft CAD models based on three-modal fusion according to claim 1, characterized in that, In step S7, the structured engineering semantic format includes: a set of component geometric groups for the mesh generation software to identify and call, a set of feature lines describing the key geometric features of the components, and optional mesh partitioning strategy parameters associated with preset components.

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