A part recognition based computer aided assembly system

In this paper, a new approach has been developed to recognize the CAD models through their face adjacency relations and attributes and to automatically assemble the recognized parts in a CAD environment. Adjacent faces and the face attributes belonging to each face of parts designed in a CAD platform are derived by using of standard for the exchange of product data (STEP) file. They are represented in a square matrix format named face oriented relation matrix (FORM). An expert system was developed and embedded within the system. A knowledge base of the expert system is generated using a text editor. Reasoning the face adjacency relations and the face attributes in the knowledge base and FORM, the parts are recognized. Then, a reference face belonging to recognized parts is determined and they are positioned and assembled in assembly file through their reference faces. Part recognition approach developed in this study is applied to a computer aided assembly system. But it may be useful and practical for different CAD/CAM applications such as process planning and group technology too. The algorithm has been applied to a diesel automobile engine which has complex parts to demonstrate its efficiency and capability.     

Feature-based representational scheme of a solid modeler for providing dimensioning and tolerancing information

Presently available CAD data models have no problem handling the basic shape design of an object as required but they are not properly manufacturing-oriented. It is not possible to represent the dimensioning and tolerancing information or other technological information (such as material data, the surface roughness and accuracy data, feature information, etc.). This paper discusses the requirements of a new CAD data model and proposes a feature-based representation scheme based on the hybrid CSG/B-Rep data structure. This hybrid structure exploits the advantages of both CSG and B-Rep models in representing tolerance information. A relational graph structure (face-based data model of B-Rep) of the object is maintained at each hierarchical level of object construction for associating tolerance and other attributes.     

CAD mesh model segmentation by clustering

CAD mesh models have been widely employed in current CAD/CAM systems, where it is quite useful to recognize the features of the CAD mesh models. The first step of feature recognition is to segment the CAD mesh model into meaningful parts. Although there are lots of mesh segmentation methods in literature, the majority of them are not suitable to CAD mesh models. In this paper, we design a mesh segmentation method based on clustering, dedicated to the CAD mesh model. Specifically, by the agglomerative clustering method, the given CAD mesh model is first clustered into the sparse and dense triangle regions. Furthermore, the sparse triangle region is separated into planar regions, cylindrical regions, and conical regions by the Gauss map of the triangular faces and Hough transformation; the dense triangle region is also segmented by the mean shift operation performed on the mean curvature field defined on the mesh faces. Lots of empirical results demonstrate the effectiveness and efficiency of the CAD mesh segmentation method in this paper.     

一元化特征建模技术研究

特征建模是实现ＣＡＤ／ＣＡＭ以及并行工程中信息集成的键。一元化征建模技术融特征设计和特征识别两者一体，设计阶段的初始设计特征进入应用领域后，经过特征识别器表成领域专用特征。     

基于T-Spline的全自动几何拓扑修复方法

从高质量曲面网格生成的需求出发,提出了一种基于T-Spline的全自动几何拓扑修复方法.本文方法创新性主要可归纳为:1）对原有计算机辅助设计（Computer aided design,CAD）几何模型不进行任何修改保留其本真,自动识别CAD几何模型中常见不必要的几何特征,成功解决了CAD几何模型中存在的几何瑕疵,如短边、窄面、退化边、退化面、非连续光滑边界及尖锐特征等,利用新生成的"虚边"、"虚面"处理几何瑕疵,同时通过虚拓扑重构CAD几何模型的B-Rep;2）开发了一套CAD/CAE集成系统,统一了几何模型与计算分析模型,实现计算机辅助工程（Computer aided engineering,CAE）与CAD两者的无缝集成,所有拓扑修复操作及后续CAE分析计算均在同一环境下进行,避免了几何模型在CAE与CAD系统间进行转换时造成的数据丢失.该方法能够对复杂实体实现全自动几何拓扑修复及网格生成,实验表明,在保证不失真的前提下,修复后的几何模型能够生成质量良好的网格且能降低网格的生成规模,验证了本文方法的实用性和有效性,以满足工程实际分析的需要.     

B-Rep model simplification by automatic fillet/round suppressing for efficient automatic feature recognition

The CAD models of real-world mechanical parts usually have many fillets and rounds that are essentially important to ensure the manufacturability and assembability. In feature-based modeling, fillets and rounds are often referred as secondary features that are used to modify the local details of the primary features such as holes, slots and pockets. Although the major shape of the primary features may not be affected, fillets and rounds can greatly change the geometric and topological patterns of the primary features. The geometric and topological variations can result in inefficient feature semantics classification in feature recognition. When feature interactions occur, it may become even worse to identify the regular patterns of the primary features. In addition, the fillets and rounds consist of no-linear surfaces such as cylindrical surfaces, spherical surfaces and toroidal surfaces, which bring the difficulties in volumetric feature extraction by half-space partition. In order to facilitate volumetric feature extraction and feature semantics classification, we pre-process the input B-Rep models by suppressing fillets and rounds before the feature recognition. Thus the input B-Rep models can be simplified without altering the major shapes of primary features, the targets of the feature recognition. The B-Rep simplification can be viewed as the reverse process of the edge blending in feature-based design. In this paper, several issues on fillet/round suppressing are discussed and a relatively general and robust approach is proposed to suppress blendings in B-Rep models of mechanical parts before the surface feature recognition and volumetric feature extraction.     

Fast global and partial reflective symmetry analyses using boundary surfaces of mechanical components

Axisymmetry and planar reflective symmetry properties of mechanical components can be used throughout a product development process to restructure the modeling process of a component, simplify the computation of tool path trajectories, assembly trajectories, etc. To this end, the restructured geometric model of such components must be at least as accurate as the manufacturing processes used to produce them, likewise their symmetry properties must be extracted with the same level of accuracy to preserve the accuracy of their geometric model. The proposed symmetry analysis is performed on a B-Rep CAD model through a divide-and-conquer approach over the boundary of a component with faces as atomic entities. As a result, it is possible to identify rapidly all global symmetry planes and axisymmetry as well as local symmetries. Also, the corresponding algorithm is fast enough to be inserted in CAD/CAM operators as part of interactive modeling processes, it performs at the same level of tolerance than geometric modelers and it is independent of the face and edge parameterizations.     

Repairing CAD model errors based on the design history

For users of CAD data, few things are as frustrating as receiving unusable, poor quality data. Users often waste time fixing or rebuilding such data from scratch on the basis of paper drawings. While previous studies use the boundary representation (B-Rep) of CAD models, we propose an approach to repairing CAD model errors that is based on the design history. CAD model errors can be corrected by an interdependency analysis of the feature commands or of the parametric data of each feature command, as well as by a reconstruction of the feature commands through rule-based reasoning of an expert system. Unlike other correction methods based on B-Rep models, our method repairs parametric feature models without translating them to a B-Rep shape, and it also preserves parametric information.     

Automatic recognition of features from freeform surface CAD models

This paper reports the design and implementation of a system for automatic recognition of features from freeform surface CAD models of sheet metal parts represented in STL format. The developed methodology has three major steps viz. STL model preprocessing, Region segmentation and automated Feature recognition. The input CAD model is preprocessed to get a healed and topology enriched STL model. A new hybrid region segmentation algorithm based on both edge- and region-based approaches has been developed to segment the preprocessed STL model into meaningful regions. Geometrical properties of facets, edges and vertices such as gauss and mean curvature at vertices, orientations of facet normals, shape structure of triangles, dihedral edge angle (angle between facets), etc. have been computed to identify and classify the regions. Feature on a freeform surface is defined as a set of connected meaningful regions having a particular geometry and topology which has some significance in design and manufacturing. Feature recognition rules have been formulated for recognizing a variety of protrusion and depression features such as holes, bends, darts, beads, louvres, dimples, dents, ridges/channels (blind and through) etc. occurring on automotive sheet metal panels. The developed system has been extensively tested with various industrial sheet metal parts and is found to be robust and consistent. The features data can be post processed and linked to various downstream CAD/CAM applications like automated process planning, sheet metal tool design, refinement of FEM meshes and product redesign.     

Mixed feature selection based on granulation and approximation

Feature subset selection presents a common challenge for the applications where data with tens or hundreds of features are available. Existing feature selection algorithms are mainly designed for dealing with numerical or categorical attributes. However, data usually comes with a mixed format in real-world applications. In this paper, we generalize Pawlak’s rough set model into δ neighborhood rough set model and k-nearest-neighbor rough set model, where the objects with numerical attributes are granulated with δ neighborhood relations or k-nearest-neighbor relations, while objects with categorical features are granulated with equivalence relations. Then the induced information granules are used to approximate the decision with lower and upper approximations. We compute the lower approximations of decision to measure the significance of attributes. Based on the proposed models, we give the definition of significance of mixed features and construct a greedy attribute reduction algorithm. We compare the proposed algorithm with others in terms of the number of selected features and classification performance. Experiments show the proposed technique is effective.     

Generalizing the advancing front method to composite surfaces in the context of meshing constraints topology

Being able to automatically mesh composite geometry is an important issue in the context of CAD–FEA integration. In some specific contexts of this integration, such as using virtual topology or meshing constraints topology (MCT), it is even a key requirement. In this paper, we present a new approach to automatic mesh generation over composite geometry. The proposed mesh generation approach is based on a generalization of the advancing front method (AFM) over curved surfaces. The adaptation of the AFM to composite faces (composed of multiple boundary representation (B-Rep) faces) involves the computation of complex paths along these B-Rep faces, on which progression of the advancing front is based. Each mesh segment or mesh triangle generated through this progression on composite geometry is likely to lie on multiple B-Rep faces and consequently, it is likely to be associated with a composite definition across multiple parametric spaces. Collision tests between new front segments and existing mesh elements also require specific and significant adaptations of the AFM, since a given front segment is also likely to lie on multiple B-Rep faces. This new mesh generation approach is presented in the context of MCT, which requires being able to handle composite geometry along with non-manifold boundary configurations, such as edges and vertices lying in the interior domain of B-Rep faces.     

语义特征建模系统中参数范围计算的研究

在语义特征CAD系统中,特征参数的确定是一个十分耗时且需要反复实验和调整的过程。为了在创建特征的同时能够迅速为设计者给出特征参数值的适当范围,提出了一种改进的方法。该方法使用特征语义表示法来描述产品特征的各种信息,简化了“细胞元”模型管理特征的各个面的策略,通过提取每个特征面的语义并计算特征面的语义约束条件,最终确定特征参数的精确范围。该算法不仅可以有效提高确定参数范围的速度和精确度,还可以大大提高语义特征CAD系统的性能。实验表明该算法具有更强的适应性和实用性。     

A database system of mechanical components based on geometric and topological similarity. Part I: representation

A database of mechanical components is an important issue for some manufacturing activities such as cost estimation, process planning, and design by case-based reasoning. In this paper, we give the representation scheme of the CAD model in such a database. Components are represented using attributed graphs in which the nodes correspond to the surfaces of the component and the links correspond to the edges of the component. The graph is based on the standard for the exchange of product information (STEP) physical file of the component. STEP file should be unique for a single component regardless of the underlying CAD system. The process of creating the graph of a component constitutes two sub-tasks: (i) importing the CAD model from the CAD system in STEP format and (ii) transforming the STEP data into attributed graph-based representation. The graph and its attributes describe the topology of the component completely together with some geometric data that are not dependent on any coordinate system such as surface type and curve type. These geometric data are helpful in the retrieval and matching processes in the database.     

Defeaturing CAD models using a geometry-based size field and facet-based reduction operators

We propose a method to automatically defeature a CAD model by detecting irrelevant features using a geometry-based size field and a method to remove the irrelevant features via facet-based operations on a discrete representation. A discrete B-Rep model is first created by obtaining a faceted representation of the CAD entities. The candidate facet entities are then marked for reduction using a geometry-based size field. This is accomplished by estimating local mesh sizes based on geometric criteria. If the field value at a facet entity goes below a user-specified threshold value then it is identified as an irrelevant feature and is marked for reduction. The reduction of marked facet entities is performed using various facet operators. Care is taken to retain a valid geometry and topology of the discrete model throughout the procedure. The original model is not altered as the defeaturing is performed on a separate discrete model. Associativity between the entities of the discrete model and that of original CAD model is maintained in order to decode the attributes and boundary conditions applied on the original CAD entities onto the mesh via the entities of the discrete model. Example models are presented to illustrate the effectiveness of the proposed approach.     

A novel classifier for multivariate instance using graph class signatures

Applications like identifying different customers from their unique buying behaviours, determining ratingsof a product given by users based on different sets of features, etc. require classification using class-specific subsets of features. Most of the existing state-of-the-art classifiers for multivariate data use complete feature set for classification regardless of the different class labels. Decision tree classifier can produce class-wise subsets of features. However, none of these classifiers model the relationship between features which may enhance classification accuracy. We call the class-specific subsets of features and the features’ interrelationships as class signatures. In this work, we propose to map the original input space of multivariate data to the feature space characterized by connected graphs as graphs can easily model entities, their attributes, and relationships among attributes. Mostly, entities are modeled using graphs, where graphs occur naturally, for example, chemical compounds. However, graphs do not occur naturally in multivariate data. Thus, extracting class signatures from multivariate data is a challenging task. We propose some feature selection heuristics to obtain class-specific prominent subgraph signatures. We also propose two variants of class signatures based classifier namely: 1) maximum matching signature (gMM), and 2) score and size of matched signatures (gSM). The effectiveness of the proposed approach on real-world and synthetic datasets has been studied and compared with other established classifiers. Experimental results confirm the ascendancy of the proposed class signatures based classifier on most of the datasets.     

A hybrid approach to feature segmentation of triangle meshes

Segmentation of a polygonal mesh is a method of breaking the mesh down into ‘meaningful’ connected subsets of meshes called regions or features. Several methods have been proposed in the past and they are either vertex based or edge based. The vertex method used here is based on the watershed segmentation scheme which appears prominently in the image segmentation literature and was later applied to the 3D segmentation problem [9] and [10]. Its main drawback is that it is a vertex based method and no hard boundaries (edges) are created for the features or regions. Edge based methods rely on the dihedral angle between polygon faces to determine if the common edge should be classified as a Feature Edge. However, this method results in many disconnected edges and thereby incomplete feature loops.We propose a hybrid method which takes advantage of both methods mentioned earlier and create regions with complete feature loops. Satisfactory results have been achieved for both CAD parts as well as other laser scanned objects such as bones and ceramic vessels.     

交通标志识别特征提取研究综述

交通标志识别(TSR)是智能交通系统(ITS)的一个重要研究方向,而特征提取是交 通标志识别研究中的重点。聚焦交通标志识别的特征提取,综述了常见的人工特征(颜色直方图、 尺度不变特征变换特征、局部二值模式特征、方向梯度直方图特征、Haar-like 特征、Gabor 小 波特征、Canny 特征等)和深度特征(提取自 AlexNet,VGG16,Inception 等),并在同一数据集 (GTSRB)上提取多种特征,采用相同分类器,通过相同评价指标体系进行定量比较与分析,并 以图表方式,针对不同特征和不同交通标志类别,进行直观的性能比较研究,以期为交通标志 识别时特征向量的选择和深入研究提供参考。     

Computing constant offsets of a NURBS B-Rep

An approach for the automatic offset of a NURBS B-Rep has been presented which can be used for a class of manifold B-Reps. The approach offsets each of the trimmed surfaces (faces) of the B-Rep and then removes the gaps and intersections between offset faces automatically, if any. The offset B-Rep is then created by sewing all the updated offset faces. The practical approach and the treatment of the subject presented are unique and have not been reported earlier. The approach works under the assumption that the number of faces in both base and offset B-Reps is the same. It further assumes that the faces are at least G₁ continuous and non-self intersecting after constant offset. The present approach has given accurate and robust offsets of many lay-up surfaces (B-Reps) in the composite laminate modeling. The approach has better error control. Some of the offsets of lay-up surfaces that are generated using this approach have been presented.     

Feature Model Visualization

Feature modelling is now the predominant way of modelling products. Feature visualization is an important aspect here that can still be considerably improved. In this paper, an integrated way of visualizing feature models is presented, using new techniques for both the geometry and the structure of models. For the geometry of feature models, techniques are presented to visualize a selected subset of form features in a way that clearly distinguishes them from the rest of the model, as well as functional information such as closure faces of subtractive form features. For the structure of features models, techniques are presented to visualize several types of graphs. The different visualization techniques are used in an integrated way. Implementation of some of the techniques requires a non‐manifold representation of the geometry of the feature model. This representation, and some other implementation aspects, are briefly described. Throughout the paper, numerous examples of images of feature models are given which show that the new visualization techniques can indeed improve the effectiveness of feature modelling. ACM CSS: I.3.7 Three‐Dimensional Graphics and Realism—visible line/surface algorithms, J.6 Computer‐AidedEngineering—feature modelling