自由曲面特征造型及其有效性维护的研究

对HUST-CAID（哈尔滨理工大学计算机辅助工业造型设计）系统研究的基础之上,引入自由曲面特征,运用自由曲面特征定义点构建几何形状轮廓曲线,实现特征参数化。其次,根据特征的分类和参数化,结合HUST-CAID系统的自身特点,完成特征类的定义,并在所构建的几何形状轮廓曲线的基础之上,使用扫掠法和蒙皮法技术构造出几何形状原型和最终的特征实例形状。最后,建立了一套自由曲面特征有效性维护机制。     

自由曲面特征模型中的数值和曲线参数*

成功的特征模型系统,在很大程度上取决于是否能给用户提供友好的方式来指定和改变特征模型,针对这一点,给出了两个新的功能：a）利用数值参数和曲线参数来指定和改变自由曲面特征模型;b）除了特征模型中现有的参数,其他参数可以添加到特征模型,包括特征之间的交叉曲线。为了提供这些功能,提出了一个新的三个层次的结构,最高层次为不可估计模型,最低层次为可评估的模型,中间层次为部分评估模型,后者链接最高层次的特征和最低层次的几何模型,并且也提供了最高层次和最低层次几何模型中的特征定义的交互。     

层次参数化的自由曲面特征表示与实现

为了解决自由曲面特征参数化问题,提出一种自由曲面特征参数分层实现框架.采用从高层到底层、从整体到局部的分层思想,将自由曲面特征的参数及映射关系划分为"3层参数和2级映射"结构来定义曲面特征,即高层语义参数表示整体形状,中间层参数表示特征线和底层参数表示细节形状,并在各层参数间建立映射和约束关系;同时支持设计者用Scheme命令快速实例化曲面特征.该方法避免了传统方式中由高层特征参数到底层数据间直接的复杂映射关系,且方便了曲面特征的实例化.实验结果表明,文中方法使得设计者能够从特征语义层直观地编辑修改自由曲面特征,是一种有效的自由曲面特征参数化表示和实现方法.     

基于特征造型的自由曲面特征及约束求解的研究

在对HUST-CAID（哈尔滨理工大学计算机辅助工业造型设计）系统进行研究的基础上，首先引入了自由曲面特征。然后结合HUST—CAID系统定义了自由曲面特征的分类和参数化，同时系统为用户提供了一组适当的参数，使用户能够直观的设计任意曲面，而不必参与曲面的底层设计。当用户修改参数时，系统就会自动提供相应的反馈。为了求解出与约束一致的图形，还给出了一种新颖的原型驱动约束求解算法，并在HUST—CAID系统中进行了仿真实验，结果显示，此算法能够有效地简化约束求解和特征造型的过程。     

双三次自由曲面轮廓线的研究

讨论了双三次Bezier曲面轮廓线的解析式,由于双三次自由曲面轮廓线的复杂性,提出了采用三次Bezier曲线逼近双三次Bezier曲面轮廓线的方法和分片曲面的预处理方法。     

附加自由曲面构造框架及其有效性维护的研究*

在对HUST-CAID(哈尔滨理工大学计算机辅助工业造型设计)系统进行研究的基础上,首先引入了自由曲面特征,然后结合HUST-CAID系统定义了自由曲面特征的分类和参数化,应用这种分类和参数化的方法创建了一个自由曲面特征,提出了一种基于FFDP配置的附加自由曲面特征的构造框架。此框架可以使自由曲面整体模型的构造完全参数化。最后建立了基于这个框架构造的自由曲面附加特征的有效性维护机制,从而达到对自由曲面附加特征进行有效性维护的目的。     

NURBS自由曲面造型的应用研究

本文论述了自由曲面（ＮＵＲＢＳ曲线和曲面）的理论和技术基础,并对ＣＡＤ领域的工程实际应用进行了研究和探索。     

自由曲面对象族研究

传统的边界表示方式导致自由曲面特征仅包含几何信息,很显然这种表达方式是很低效的。为此提出了一种有效描述自由曲面特征的新方法,该方法基于陈述式的对象族模型构建自由曲面对象族,使自由曲面具有高层次的语义信息,可以保证自由曲面特征无缝地融合到模型中去,同时满足自由曲面特征与其他特征间的约束关系。通过约束定义自由曲面特征具有的语义信息,在建模过程中功能属性被很好地维护,实现了对自由曲面特征在参数化层次上的描述,并证明了其可行性。     

参数曲线曲面自由变形的多项式因子方法

为得到理想的造型效果,提出一种空间参数曲线曲面自由变形的方法.首先引入基于多项式的伸缩因子,并构造了空间变形矩阵;然后将变形矩阵或伸缩因子作用于待变形曲线曲面方程,从而得到形变效果.实验结果表明,该方法计算简单、易于控制,可得到较好的变形效果.     

自由特征模型参数化研究

为了更高效直观地对自由特征形状进行建模,对自由特征形体及自由特征定义点进行参数化,提出了一种通过封装参数和约束创建自由特征体特征的方法。在自由特征形状的定义中,建立数值参数和最终特征形状几何属性的映射关系。在轨迹线上提取特征定义点,在各点处建立横截面,对横截面进行蒙皮和扫掠运算,生成和基曲面无缝连接的自由形状体特征。     

Creation of user-defined freeform feature from surface models based on characteristic curves

This paper proposes a novel method for the creation of user-defined freeform feature (UDFFF) from existing surface model. The developed methodology has four major steps: selecting the region of interest (ROI), obtaining the characteristic curves from the surface model, reconstructing the ROI surface, and parameterizing the UDFFF. To quickly create UDFFF on the ROI, the representation of freeform feature based on characteristic curves is proposed. First, the characteristic curves of ROI are obtained automatically or by a small amount of human–computer interaction. In particular, user can directly and easily draw the curves on surface to satisfy the designer's requirements. Second, from the set of characteristic curves that is obtained, freeform surface is reconstructed by a suitable surface generation method, e.g., sweeping, revolving, skinning, and filling. Finally, parameterization of the freeform feature is configured, which primarily involves establishing parameters and building the mappings between feature parameters and shape data. Moreover, according to the degrees of freedom, two types of parameterization of freeform features may be described: one (low degrees of freedom) can be accurately described by the characteristic curves and the other (high degrees of freedom) can be approximated with these curves. The proposed UDFFF creation method has been tested with examples based on the surface models. Experience with our prototype system indicates that it allows untrained users to create UDFFFs from the surface models.     

Local parameterization of freeform shapes using freeform feature recognition

Form feature modeling is a much used shape modeling technique that offers high-level control over a shape. When a feature-based interpretation of shape data is not available, e.g. when a shape is obtained by a laser range scanner or from a database of shapes, then the features must be reconstructed through feature recognition. Many methods for the recognition of machining features exist, but these methods cannot be used for freeform feature recognition, of which the complexity is much larger. In this paper, a new freeform feature recognition method is presented that is based on a new definition of the freeform feature concept. The method uses a three-step approach to feature recognition, in which first the global shape of a feature is matched to the target shape model. In a second step, this global shape is locally adapted to the target shape by adapting the definition of the feature. Finally, if the desired configuration of the feature has been determined, it can be used to reconstruct the target’s shape. In the first two steps, an evolutionary approach is taken to maximizing the similarity between the feature and the target shape. Finally, the target shape is reconstructed to incorporate the recognized feature. An extensive application example is given and the method is validated by applying it to a large number of artificially created test cases.     

自由特征模型约束求解研究*

在对自由特征模型参数化基础上,提出一种约束求解的新方法。该方法对自由特征形体定义点进行参数化,建立几何约束图,将约束问题分解为三角形约束和四面体约束两大类,分别进行求解,定义手向性规则,利用该规则将子问题组合为全局解。在自主开发的HUST-CAIDS（哈尔滨理工大学计算机辅助工业设计系统）中实现了本算法,并得到满足设计者需求的模型。     

形状的不变量特征提取与识别

目的 形状作为图像检索、目标识别等任务中的一种重要线索,一直是计算机视觉领域研究的重点课题。形状识别在实际应用中经常受到视角变化、非线性形变等因素的干扰,导致识别精度较低。针对这一状况,提出一种多尺度的不变量形状描述。方法 方法首先在多个尺度下对形状轮廓进行计算,提取5种不变量特征,以构建对形状的有效描述,然后利用动态时间规整（DTW）算法对形状描述进行匹配,计算形状之间的相似度,以完成形状的匹配与识别。结果 基于不变量多尺度的形状描述对于旋转、缩放、局部遮挡、铰接形变、类内差异,以及噪声等干扰具有很强的鲁棒性。同时,方法被用于对MPEG-7、Kimia99、Kimia216以及铰接形状数据库中的形状进行识别,取得了较高的识别精度,分别为91.79%、95.27%、91.33%,以及89.75%。此外,在MPEG-7数据库中进行形状识别的平均耗时为65 ms,优于大多数同类方法。结论 提出了一种基于不变量多尺度的形状描述方法。该方法能提取形状在不同尺度下的多种不变量特征,对形状进行有效描述,提高了形状描述对几何变换和非线性形变等干扰的鲁棒性以及形状匹配识别精度,适用于大多数应用场景下的目标识别任务。尤其是在旋转、缩放、类内差异、局部遮挡和铰接变形等干扰存在的情况下也能保持较高的识别正确率。     

基于特征面的分层特征识别

为降低特征识别的复杂度,提出基于特征实体、特征实面和特征虚面概念的层次性特征分类方法.通过构造2类神经网络输入矩阵,利用神经网络在特征识别中所具有的优势,实现基于特征面的分层特征识别方法.实例表明：该方法在识别去除材料的特征时比较有效,但识别特征的范围受到一定限制.     

面向虹膜识别的Gabor特征筛选研究

针对2D-Gabor虹膜特征并不稳定,影响虹膜识别率的问题,提出了一种从多尺度、多方向2D-Gabor小波提取的虹膜特征中,筛选稳定特征应用于虹膜识别的方法。对虹膜图像采用多通道Gabor小波提取虹膜图像特征,然后通过自定义筛选准则从多维特征中筛选出最优特征参数并编码,用Hamming距进行特征匹配识别。基于CASIA虹膜图像库进行实验,结果表明该方法扩大了类内匹配与类间匹配之间的Hamming距,降低了等错率,同时降低了编码的长度,加快了特征匹配速度。     

Volumetric feature recognition for machining components with freeform surfaces

This paper describes a procedure for the extraction of features of a part containing a combination of 2.5D features and freeform surfaces. This work invokes a previous algorithm that was designed to recognize machining features from 2.5D parts destined to be machined on a 3-axis milling machine. The essence of that algorithm was a volume decomposition based on a recursive descent into the part, yielding a feature graph that captured both the geometry and the spatial relationships of the features. This work augments the previous algorithm with the ability to handle a limited class of components having freeform surfaces. Freeform features are defined similar to the 2.5D features as comprising a planar contour, but substituting a bottom freeform surface for the depth. Covering faces, defined as projection of the freeform surface on the faces of the bounding box of the surface, are used as equivalent planar faces for performing the recursive descent. Inter-feature open edges are used to signal the relationship between the freeform feature and other neighboring features. Examples of molds and components that were machined using the proposed algorithms are also presented.