The various manufacturers of digitization systems speak of the effectiveness and accuracy of their tools under optimal conditions, but actual experimentation with simple or complex objects and different materials yields results that on occasions refute the effectiveness of those systems. In order to help choose a digitization system on the basis of its accuracy and the quality of the distribution of points and triangular meshes, in the field of reverse engineering, we compared five digitization techniques (three versions of the laser scanner, a fringe projection version and an X-ray version): (1) an ordered point cloud obtained with a laser incorporated in a CMM, (2) a disordered point cloud obtained with a manual laser the position of which is determined with a Krypton Camera, (3) an Exascan manual laser with targets, (4) an ordered point cloud obtained by high precision Computerized Tomography (CT) and (5) an Atos fringe projection scanner with targets. Each of the three calibrated pieces (a sphere, a cylinder and a gauge block) was measured five times by the five digitization systems to confirm the accuracy of the measurement. A comparison was also made of the meshes generated by the five software packages (Focus-Inspection, Metris, VxScan, Mimics and Atos) of the five digitization systems for the three calibrated pieces and two more complex pieces (a bone and an automobile window winder pulley) to determine meshing quality. Finally, all the pieces were meshed by triangulation in the Catia V5 DSE (Digitized Shape Editor) module in order to test the quality of the points distribution.

Comparative study of different digitization techniques and their accuracy

Today's product designer is being asked to develop high quality, innovative products at an ever increasing pace. To meet this need, an intensive search is underway for advanced design methodologies that facilitate the acquisition of design knowledge and creative ideas for later reuse. Additionally, designers are embracing a wide range of 3D digital design applications, such as 3D digitization, 3D CAD and CAID, reverse engineering (RE), CAE analysis and rapid prototyping (RP). In this paper, we propose a reverse engineering innovative design methodology called Reverse Innovative Design (RID). The RID methodology facilitates design and knowledge reuse by leveraging 3D digital design applications. The core of our RID methodology is the definition and construction of feature-based parametric solid models from scanned data. The solid model is constructed with feature data to allow for design modification and iteration. Such a construction is well suited for downstream analysis and rapid prototyping. In this paper, we will review the commercial availability and technological developments of some relevant 3D digital design applications. We will then introduce three RE modelling strategies: an autosurfacing strategy for organic shapes; a solid modelling strategy with feature recognition and surface fitting for analytical models; and a curve-based modelling strategy for accurate reverse modelling. Freeform shapes are appearing with more frequency in product development. Since their ''natural'' parameters are hard to define and extract, we propose construction of a feature skeleton based upon industrial or regional standards or by user interaction. Global and local product definition parameters are then linked to the feature skeleton. Design modification is performed by solving a constrained optimization problem. A RID platform has been developed and the main RE strategies and core algorithms have been integrated into SolidWorks as an add-in product called ScanTo3D. We will use this system to demonstrate our RID methodology on a collection of innovative consumer product design examples.

Reverse innovative design - an integrated product design methodology

This research investigates the use of highresolution three-dimensional terrestrial laser scanners as tools to capture geometric range data of complex scenes for structural engineering applications. Laser scanning technology is continuously improving, with commonly available scanners now able to capture over 1,000,000 points per second with an accuracy of ∼0.1 mm. This research focuses on developing the foundation toward the use of laser scanning to structural engineering applications, including structural health monitoring, collapse assessment, and post-hazard response assessment. One of the keys to this work is to establish a process for extracting important information from raw laser-scanned data sets such as the location, orientation, and size of objects in a scene, and location of damaged regions on a structure. A methodology for processing range data to identify objects in the scene is presented. Previous work in this area has created an initial foundation of basic data processing steps. Existing algorithms, including sharp feature detection and segmentation are implemented and extended in this work. Additional steps to remove extraneous and outlying points are added. Object detection based on a predefined library is developed allowing generic description of objects. The algorithms are demonstrated on synthetic scenes as well as validated on range data collected from an experimental test specimen and a collapsed bridge. The accuracy of the object detection is presented, demonstrating the applicability of the methodology. These additional steps and modifications to existing algorithms are presented to advance the performance of data processing on laser scan range data sets for future application in structural engineering appli∗To whom correspondence should be addressed. E-mail: Jf.hajjar@neu.edu. cations such as robust determination of damage location and finite element modeling.

Data Processing of Point Clouds for Object Detection for Structural Engineering Applications

A framework for 3D model reconstruction in reverse engineering

Reverse Engineering (RE) is a long-term goal of engineering and computer science; it aims at the reconstruction of CAD models from measured data by means of 3D mathematical surfaces and geometrical features representing the geometry of a physical part. In the last two decades, reviews and surveys have occasionally covered this topic, but a systematic dissertation of modeling methods from a mechanical engineering point of view is still missing. The purpose of this paper is to fill this gap; starting from a general description of the overall RE framework (acquisition, segmentation, classification, fitting), both an up-to-date survey and a categorization of available modeling techniques and tools working on 3D data are provided. The main aspects of various strategies are discussed as well, in order to highlight strengths and weaknesses characterizing different approaches. Moreover, an overview of commercial software for RE is presented, considering both dedicated solutions and packages supplied as ad...

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Reverse engineering modeling methods and tools: a survey

We presented two integrated solution schemes, sectional feature based strategy and surface feature based strategy, for modeling industrial components from point cloud to surfaces without using triangulation. For the sectional feature based strategy, slicing, curve feature recognition and constrained fitting are introduced. This strategy emphasizes the advanced feature architecture patterns from 2D to 3D in reverse engineering. The surface feature based strategy relies on differential geometric attributes estimation and diverse feature extraction techniques. The methods and algorithms such as attributes estimation based on 4D Shepard surface, symmetry plane extraction, quadric surface recognition and optimization, extruded and rotational surface extraction, and blend feature extraction with probability and statistic theory are proposed. The reliable three-dimensional feature fabricated the valid substratum of B-rep model faultlessly. All the algorithms are implemented in RE-SOFT, a reverse engineering software developed by Zhejiang University. The proposed strategies can be used to capture the original design intention accurately and to complete the reverse modeling process conveniently. Typical industrial components are used to illustrate the validation of our feature-based strategies.

Yinling Ke

Papers

Feature-based reverse modeling strategies