Saeid Motavalli

Review of reverse engineering approaches

Reverse engineering is accomplished in three steps, part digitizing, features extraction, and CAD modeling. Part digitization is accomplished by variety of contact and non-contact digitizers. Feature extraction is normally achieved by segmenting the digitized data and capturing surface features such as edges. Part modeling is accomplished by fitting variety of surfaces to the segmented data points. This paper reviews various techniques for part digitization, segmentation and surface modeling.

Multi-criteria assembly sequencing

This paper describes the elements of a multi-criteria optimization formulation of the assembly sequencing problem. The criteria considered are: total assembly time and number of reorientations. These two criteria are combined using multi-attribute utility theory to derive a single objective function. The combined objective function has been formulated subject to the precedence constraints of the assembly process. The model has been solved using simulated annealing. The elements of the developed algorithms and their performances are demonstrated in this paper.

Automatic segmentation of digitized data for reverse engineering applications

Reverse engineering is the process of developing a Computer Aided Design (CAD) model and a manufacturing database for an existing part. This process is used in CAD modeling of part prototypes, in designing molds, and in automated inspection of parts with complex surfaces. The work reported in this paper is on the automatic segmentation of 3-Dimensional (3-D) digitized data captured by a laser scanner or a Coordinate Measuring Machine (CMM) for reverse engineering applications. Automatic surface segmentation of digitized data is achieved using a combination of region and edge based approaches. It is assumed that the part surface contains planar as well as curved surfaces that are embedded in a base surface. The part surface should be visible to a single scanning probe (21/2D object). Neural network algorithms are developed for surface segmentation and edge detection. A back propagation network is used to segment part surfaces into surface primitives which are homogenous in their intrinsic differential geometric properties. The method is based on the computation of Gaussian and mean curvatures of the surface. They are obtained by locally approximating the object surface using quadratic polynomials. The Gaussian and mean curvatures are used as input to the neural network which outputs an initial region-based segmentation in the form of a curvature sign map. An edge based segmentation is also performed using the partial derivatives of depth values. Here, the output of the Laplacian operator and the unit surface normal are computed and used as input to a Self-Organized Mapping (SOM) network. This network is used to find the edge points on the digitized data. The combination of the region based and the edge based approaches, segment the data into primitive surface regions. The uniqueness of our approach is in automatic calculation of the threshold level for segmentation, and on the adaptability of the method to various noise levels in the digitized data. The developed algorithms and sample results are described in the paper.

Sensor fusion for monitoring machine tool conditions

A unique multisensory tool monitoring system using machine vision and vibration sensors has been developed for turning operations. Vibration signals from the tool are monitored on-line using a piezoelectric accelerometer mounted on the tool holder and the tool condition is monitored periodically using a vision sensory system. Images of the tool are frame grabbed between cuts. The developed image processing algorithm uses neural network mathematics to measure the tool wear in the captured images. The inputs to the neural network are a set of vectors defining the characteristics of the tool surface such as surface curvature. Features such as area, perimeter, width and height of the worn area are measured to identify the extent of the tool wear. The system is unique in that it combines an indirect tool monitoring technique, vibration monitoring, with a direct visual monitoring technique. The addition of the vision system increases the reliability of the monitoring system by detecting false signals received f...

Computer-aided manufacturing of implants for the repair of large cranial defects: an improvement of the stereolithography technique.

The objective of this study is to demonstrate the utility of geometric modeling in cranioplasty; in other words, to use geometric modeling to generate a prototype that will be used as the base structure of a composite prosthesis for covering cranial defects. This geometric model is easy to manipulate and can be modified. To achieve this goal, the top surface of a cranial bone flap is digitized using a portable coordinate measurement machine. Intentionally, a sub-surface of the bone flap, representing the skull defect, was not digitized. A geometric model of the bone flap is generated that includes the undigitized region. With the technique described in this paper the authors generated the geometric model of the undigitized region (the skull defect). The geometric model of the bone flap is further manipulated and a series of conical cavities are introduced. Prototypes of the geometric models are manufactured using stereolithography. The clinical implications of this technique are discussed.

Design model generation for reverse engineering using multi-sensors

Reverse engineering is the process of creating a design model and a manufacturing database for an existing part or prototype. The applications of reverse engineering are in redesigning of existing parts/tools or prototype parts where the CAD model of the part is not available. Reverse engineering, for the most part, is performed as an interactive process where the designer identifies the surface features from digitized data and then models the surfaces accordingly. This paper presents the algorithms and implementation results for a reverse engineering system which is intended to automatically create CAD representations of part prototypes. An integrated sensory system combining contact and non-contact sensors has been developed to digitize parts surfaces. The sensory system fuses data from machine vision and a coordinate measuring machine (CMM) in order to automatically digitize the part surface. Machine vision is used to capture the orthographic views of the part. The images of these orthographic views are processed and vectorized to create five views of the part in the form of an engineering drawing. The system utilizes the generated orthographic projections to automatically drive the CMM to capture a grid of point coordinates from the part surface. The CMM digitization process is guided by the segmentation provided from the orthographic views. The segmented data from the part surface is input to the surface modeling module of the system where parametric surfaces are fitted through the digitized points. The surfaces are then extended and intersected using the Hermite approximation method to develop the 3-D CAD model of the part. Accuracy and automation is achieved by combining global shape information obtained from part images with the accurate point data acquired by a CMM. Algorithms for surface segmentation, part digitization, surface extension, and surface intersection modeling are described in this paper.

Object-oriented modelling of a feature-based reverse engineering system

Reverse engineering, as applied to manufacturing, is the process of extracting design and manufacturing data from an existing part. Typical applications of reverse engineering are in the areas of automated inspection and redesign of complex tools and existing parts. A review of research in reverse engineering reveals that most of the existing systems rely extensively on human interaction. A reverse engineering system has been developed which aims at automating the process of feature-based design model generation for existing parts. A database structure for the system based on the object-oriented paradigm has been designed. The object-oriented approach proved to be suitable for handling the heterogenous data structures involved in reverse engineering. This paper describes the elements of the reverse engineering system and the integration of various components of the system using object-oriented database structure.

Feature-based modeling: an object oriented approach

Abstract Feature-based models have created an elegant way of integrating CAD and CAM. These models have the capability of storing the design in terms of features which can also relate to manufacturing processes. The integration process requires an extensive data structure capable of integrating various types of data including geometric, tolerancing information and manufacturing related data. The object-oriented paradigm, as a powerful modeling tool, has recently gained popularity in the design and implementation of emerging data-intensive applications which include Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM). It provides an elegant means for mastering the complexity of large systems by decomposing them into hierarchies of data abstractions. This paper presents the result of the authors research in modeling integrated feature-based CAD/CAM systems using object-oriented database structure. The proposed object-oriented model supports data integrity and data abstraction required by the system. The key elements of the developed model are data and behavior schemes of features and their interaction. Designing and developing such schemes require a sophisticated modeling approach to support complicated data manipulations as well as behavior modeling in the same fashion.

A system for reverse engineering parts using orthographic of prismatic images

Reverse engineering is the process of creating a CAD model and a manufacturing database for an existing part or a prototype. This process is necessary in redesigning existing parts and for automated inspection. In this research, a unique system has been developed which creates 3Dwireframe models for prismatic parts using four orthographic images captured by a machine vision system. Two cameras are used where one camera captures the top view of the object while the other camera captures the four side views by rotating the object in steps of 90 degrees. The images are processed and orthographic views are created. A3D wireframe model of the object is then created by stereo matching feature points in various orthographic views. This paper describes the developed feature extraction procedure, camera calibration method and the matching technique. Sample results are also given.

Building a 3D wireframe CAD model for an existing prismatic part

Reverse engineering is the process of creating a CAD model and a manufacturing database for an existing part or a prototype. This process is necessary in redesign of existing parts and for automated inspection. In this paper a unique approach to reverse engineering is proposed. Here the part to be modeled is viewed from two othogonal view points. One camera captures the top view of the object while the other camera captures the four side views by rotating the object in steps of 90 degrees. The images are processed and the orthographic views are created. A 3D line drawing of the object is then recreated by matching points in the orthographic views. This paper describes the developed feature extraction procedure, camera calibration method, and the matching technique. Sample results are also given.