Multiple Representation Approach to Geometric Model Construction From Range Data

A method is presented for constructing geometric design data from noisy 3-D sensor measurements of physical parts. In early processing phase, RLTS regression filters stemming from robust estimation theory are used for separating the desired part of the signal in contaminated sensor data from undesired part. Strategies for producing a complete 3-D data set from partial views are studied. Multiple representations are used in model construction because there is no single representation that would be most appropriate in all situations. In particular, surface triangulation, NURBS, and super-ellipsoids are employed in order to represent efficiently polygonal and irregular shapes, free form surfaces and standard primitive solids. The size of the required control point mesh for spline description is estimated using a surface characterization process. Surfaces of arbitrary topology are modeled using triangulation and trimmed NURBS. A user given tolerance value is driving refinement of the obtained surface model. The resulting model description is a procedural CAD model which can convey structural information in addition to low level geometric primitives. The model is translated to IGES standard product data exchange format to enable data sharing with other processes in concurrent engineering environment. Preliminary results on view registration using simulated data are shown. Examples of model construction using both real and simulated data are also given

Rapid Prototyping Using Three-Dimensional Computer Vision

A method for building model data for CAD and CAM purposes from physical instances using three-dimensional sensor data is presented. These techniques are suitable for Reverse Engineering of industrial parts, and can be used as a design aid as well. The nature of the reverse engineering task is quantitative, and the emphasis is on accurate recovery of the geometry of the part, whereas the object recognition task is qualitative, and aims to recognize similar shapes. The proposed method employs multiple representation to build a CAD model for the part, and to produce useful information for part analysis and process planning. The model building strategy is selected based on the obtained surface and volumetric data descriptions and their quality. A novel, robust non-linear filtering method is presented to attenuate noise from sensor data. Volumetric description is obtained by recovering a superquadric model for the whole data set. A surface characterization process is used to determine the complexity of the underlying surface. A substantial data compression can be obtained by approximating huge amount sensor data by B-spline surfaces. As a result a Boundary Representation model for Alpha-1 solid modeling system is constructed. The model data is represented both in Alpha-1 modeling language and IGES product data exchange format. Experimental results for standard geometric shapes and for sculptured free-form surfaces are presented using both real and synthetic range data

Automatic feature extraction from conventional CAD model to support feature-based design approach for the sheet metal stamping industries

Despite the continuing improvement in computer aided design (CAD) systems and improvements in computer aided manufacturing (CAM), the process planning activity has still not been completely integrated into the CAD/CAM cycle. Particularly in sheet metal stamping industries human interpretation of CAD data is required to extract the geometry and technological information of a component. As a result most CAD systems are used as advanced drafting and drawing management tools by designers. Thus the responsibility for interpreting the design data required for extracting the manufacturing part features still resides with the process planner. Which has increase possibilities of entering errors with design data. A need, therefore, exists to develop expert system for automatic features extraction from a CAD database. An application software was developed for automatic feature extraction from conventional CAD model database to impliment feature-based design approach for the sheet metal stamping industries

Requirements for a geometry programming language for CFD applications

A number of typical problems faced by the aerodynamicist in using computational fluid dynamics are presented to illustrate the need for a geometry programming language. The overall requirements for such a language are illustrated by examples from the Boeing Aero Grid and Paneling System (AGPS). Some of the problems in building such a system are also reviewed along with suggestions as to what to look for when evaluating new software problems

Modeling of an automatic CAD-based feature recognition and retrieval system for group technology application

In recent time, many researches have come up with new different approaches and means for Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) integration. Computer-Aided Process Planning (CAPP) is considered to be a bridge that connects these both technologies. CAPP may involve such an important technique as automatic feature extraction - a procedure that is engaged in process plans generation to be used in producing a designed part. Also in terms of CAD, the feature extraction procedure facilitates a cooperative design and process planning within the entire product development process. The main objective of the thesis is to present a new automatic feature extraction and classification system that is able to process mechanical rotational and non-rotational parts from the Opitz Code System point of view. The implemented system takes Standard for Exchange of Product data (STEP) - a neutral product representation format as input and extracts features of parts required for further manufacturing. The STEP format is used to provide geometrical and topological information about machining parts. A methodology to extract shape features was developed based on these geometrical and topological data. As output, the proposed system codes the extracted part features to Opitz Code System. CAD product files were taken from official manufacturers of mechanical parts in order to evaluate the developed system

A feature-based reverse engineering system using artificial neural networks

Reverse Engineering (RE) is the process of reconstructing CAD models from scanned data of a physical part acquired using 3D scanners. RE has attracted a great deal of research interest over the last decade. However, a review of the literature reveals that most research work have focused on creation of free form surfaces from point cloud data. Representing geometry in terms of surface patches is adequate to represent positional information, but can not capture any of the higher level structure of the part. Reconstructing solid models is of importance since the resulting solid models can be directly imported into commercial solid modellers for various manufacturing activities such as process planning, integral property computation, assembly analysis, and other applications. This research discusses the novel methodology of extracting geometric features directly from a data set of 3D scanned points, which utilises the concepts of artificial neural networks (ANNs). In order to design and develop a generic feature-based RE system for prismatic parts, the following five main tasks were investigated. (1) point data processing algorithms; (2) edge detection strategies; (3) a feature recogniser using ANNs; (4) a feature extraction module; (5) a CAD model exchanger into other CAD/CAM systems via IGES. A key feature of this research is the incorporation of ANN in feature recognition. The use of ANN approach has enabled the development of a flexible feature-based RE methodology that can be trained to deal with new features. ANNs require parallel input patterns. In this research, four geometric attributes extracted from a point set are input to the ANN module for feature recognition: chain codes, convex/concave, circular/rectangular and open/closed attribute. Recognising each feature requires the determination of these attributes. New and robust algorithms are developed for determining these attributes for each of the features. This feature-based approach currently focuses on solving the feature recognition problem based on 2.5D shapes such as block pocket, step, slot, hole, and boss, which are common and crucial in mechanical engineering products. This approach is validated using a set of industrial components. The test results show that the strategy for recognising features is reliable

A CT Database for Research, Development and Education: Concept and Potential

Both in radiology and in surgery, numerous applications are emerging that enable 3D visualization of data from various imaging modalities. In clinical practice, the patient's images are analyzed on work stations in the Radiology Department. For specific preclinical and educational applications, however, data from single patients are insufficient. Instead, similar scans from a number of individuals within a collective must be compiled. The definition of standardized acquisition procedures and archiving formats are prerequisite for subsequent analysis of multiple data sets. Focusing on bone morphology, we describe our concept of a computer database of 3D human bone models obtained from computed tomography (CT) scans. We further discuss and illustrate deployment areas ranging from prosthesis design, over virtual operation simulation up to 3D anatomy atlases. The database of 3D bone models described in this work, created and maintained by the AO Development Institute, may be accessible to research institutes on reques

Towards automation of forensic facial reconstruction

Forensic facial reconstruction is a blend of art and science thus computerizing the process leads to numerous solutions. However, complete automation remains a challenge. This research concentrates on automating the first phase of forensic facial reconstruction which is automatic landmark detection by model fitting and extraction of feature points. Detection of landmarks is a challenging task since the skull orientation in a 3D scanned data cloud is generally arbitrary and unknown. To address the issue, well defined skull and mandible models with known geometric structure, features and orientation are (1) aligned and (2) fit to the scanned data. After model fitting is complete, landmarks can be extracted, within reasonable tolerance, from the dataset. Several methods exist for automatic registration (alignment); however, most suffer ambiguity or require interaction to manage symmetric 3D objects. A new alternative 3D model to data registration technique is introduced which works successfully for both symmetric and non-symmetric objects. It takes advantage of the fact that the model and data have similar shape and known geometric features. Therefore, a similar canonical frame of reference can be developed for both model and data. Once the canonical frame of reference is defined, the model can be easily aligned to data by a euclidian transformation of its coordinate system. Once aligned, the model is scaled and deformed globally to accommodate the overall size the object and bring the model in closer proximity to the data. Lastly, the model is deformed locally to better fit the scanned data. With fitting completed, landmark locations on the model can be utilized to isolate and select corresponding landmarks in the dataset. The registration, fitting and landmark detection techniques were applied to a set of six mandible and three skull body 3D scanned datasets. Results indicate the canonical axes formulation is a good candidate for automatic registration of complex 3D objects. The alternate approach posed for deformation and surface fitting of datasets also shows promise for landmark detection when using well constructed NURBS models. Recommendations are provided for addressing the algorithms limitations and to improve its overall performance

Hybridized Isogeometric Method for Elliptic Problems on CAD Surfaces with Gaps

We develop a method for solving elliptic partial differential equations on surfaces described by CAD patches that may have gaps/overlaps. The method is based on hybridization using a three-dimensional mesh that covers the gap/overlap between patches. Thus, the hybrid variable is defined on a three-dimensional mesh, and we need to add appropriate normal stabilization to obtain an accurate solution, which we show can be done by adding a suitable term to the weak form. In practical applications, the hybrid mesh may be conveniently constructed using an octree to efficiently compute the necessary geometric information. We prove error estimates and present several numerical examples illustrating the application of the method to different problems, including a realistic CAD model

Strategies Toward Automation of Overset Structured Surface Grid Generation

An outline of a strategy for automation of overset structured surface grid generation on complex geometries is described. The starting point of the process consists of an unstructured surface triangulation representation of the geometry derived from a native CAD, STEP, or IGES definition, and a set of discretized surface curves that captures all geometric features of interest. The procedure for surface grid generation is decomposed into an algebraic meshing step, a hyperbolic meshing step, and a gap-filling step. This paper will focus primarily on the high-level plan with details on the algebraic step. The algorithmic procedure for the algebraic step involves analyzing the topology of the network of surface curves, distributing grid points appropriately on these curves, identifying domains bounded by four curves that can be meshed algebraically, concatenating the resulting grids into fewer patches, and extending appropriate boundaries of the concatenated grids to provide proper overlap. Results are presented for grids created on various aerospace vehicle components