Computer Aided Reverse Engineering with Renishaw Digitizer for Digitization and Mazak for Model Fabrication

Application of reverse engineering (RE) is gaining its popularity in product design and manufacturing in recent years. It takes whatever methods, manual or computer-aided methods, to duplicate an existing object or system, either hardware or software. This report discovers about the process of exploring technical challenges to automatically generate computer-aided design (CAD) of an existing part using touch probe imaging techniques. This concept, computer-aided reverse engineering system has a potential for faster model duplication over traditional reverse engineering technologies. RENISHAW 3D Laser Digitizer was used to digitize the object and then the models were saved in IGES file format. CATIA CAD then has been used to create the solid model of the object, and finally the laminated object will be manufactured using MAZAK machine. In this project, a soccer boot has been reverse engineered. A prototype of the shoe was fabricated using the CNC codes that were obtained from UG NX3. The methodology of this process was presented, and this case study illustrated the RE approach. Technical challenges and future research directions in computer aided reverse engineering were identified. This approach has proved that, CAD Reverse Engineering increased the effectiveness in remodeling a product. These benefits include to reduce the time consume when generating the coordinates and also to get an accurate dimension of the object. It means that, complex contours of the shoe can be machined accurately by using this approach

Multi-view alignment with database of features for an improved usage of high-end 3D scanners

The usability of high-precision and high-resolution 3D scanners is of crucial importance due to the increasing demand of 3D data in both professional and general-purpose applications. Simplified, intuitive and rapid object modeling requires effective and automated alignment pipelines capable to trace back each independently acquired range image of the scanned object into a common reference system. To this end, we propose a reliable and fast feature-based multiple-view alignment pipeline that allows interactive registration of multiple views according to an unchained acquisition procedure. A robust alignment of each new view is estimated with respect to the previously aligned data through fast extraction, representation and matching of feature points detected in overlapping areas from different views. The proposed pipeline guarantees a highly reliable alignment of dense range image datasets on a variety of objects in few seconds per million of points

Computer Aided Reverse Engineering with Renishaw Digitizer for Digitization and Mazak for Model Fabrication

Application of reverse engineering (RE) is gaining its popularity in product design and manufacturing in recent years. It takes whatever methods, manual or computer-aided methods, to duplicate an existing object or system, either hardware or software. This report discovers about the process of exploring technical challenges to automatically generate computer-aided design (CAD) of an existing part using touch probe imaging techniques. This concept, computer-aided reverse engineering system has a potential for faster model duplication over traditional reverse engineering technologies. RENISHAW 3D Laser Digitizer was used to digitize the object and then the models were saved in IGES file format. CATIA CAD then has been used to create the solid model of the object, and finally the laminated object will be manufactured using MAZAK machine. In this project, a soccer boot has been reverse engineered. A prototype of the shoe was fabricated using the CNC codes that were obtained from UG NX3. The methodology of this process was presented, and this case study illustrated the RE approach. Technical challenges and future research directions in computer aided reverse engineering were identified. This approach has proved that, CAD Reverse Engineering increased the effectiveness in remodeling a product. These benefits include to reduce the time consume when generating the coordinates and also to get an accurate dimension of the object. It means that, complex contours of the shoe can be machined accurately by using this approach

Variational methods for shape and image registrations.

Estimating and analysis of deformation, either rigid or non-rigid, is an active area of research in various medical imaging and computer vision applications. Its importance stems from the inherent inter- and intra-variability in biological and biomedical object shapes and from the dynamic nature of the scenes usually dealt with in computer vision research. For instance, quantifying the growth of a tumor, recognizing a person\u27s face, tracking a facial expression, or retrieving an object inside a data base require the estimation of some sort of motion or deformation undergone by the object of interest. To solve these problems, and other similar problems, registration comes into play. This is the process of bringing into correspondences two or more data sets. Depending on the application at hand, these data sets can be for instance gray scale/color images or objects\u27 outlines. In the latter case, one talks about shape registration while in the former case, one talks about image/volume registration. In some situations, the combinations of different types of data can be used complementarily to establish point correspondences. One of most important image analysis tools that greatly benefits from the process of registration, and which will be addressed in this dissertation, is the image segmentation. This process consists of localizing objects in images. Several challenges are encountered in image segmentation, including noise, gray scale inhomogeneities, and occlusions. To cope with such issues, the shape information is often incorporated as a statistical model into the segmentation process. Building such statistical models requires a good and accurate shape alignment approach. In addition, segmenting anatomical structures can be accurately solved through the registration of the input data set with a predefined anatomical atlas. Variational approaches for shape/image registration and segmentation have received huge interest in the past few years. Unlike traditional discrete approaches, the variational methods are based on continuous modelling of the input data through the use of Partial Differential Equations (PDE). This brings into benefit the extensive literature on theory and numerical methods proposed to solve PDEs. This dissertation addresses the registration problem from a variational point of view, with more focus on shape registration. First, a novel variational framework for global-to-local shape registration is proposed. The input shapes are implicitly represented through their signed distance maps. A new Sumof- Squared-Differences (SSD) criterion which measures the disparity between the implicit representations of the input shapes, is introduced to recover the global alignment parameters. This new criteria has the advantages over some existing ones in accurately handling scale variations. In addition, the proposed alignment model is less expensive computationally. Complementary to the global registration field, the local deformation field is explicitly established between the two globally aligned shapes, by minimizing a new energy functional. This functional incrementally and simultaneously updates the displacement field while keeping the corresponding implicit representation of the globally warped source shape as close to a signed distance function as possible. This is done under some regularization constraints that enforce the smoothness of the recovered deformations. The overall process leads to a set of coupled set of equations that are simultaneously solved through a gradient descent scheme. Several applications, where the developed tools play a major role, are addressed throughout this dissertation. For instance, some insight is given as to how one can solve the challenging problem of three dimensional face recognition in the presence of facial expressions. Statistical modelling of shapes will be presented as a way of benefiting from the proposed shape registration framework. Second, this dissertation will visit th

Feature-based hybrid inspection planning for complex mechanical parts

Globalization and emerging new powers in the manufacturing world are among many challenges, major manufacturing enterprises are facing. This resulted in increased alternatives to satisfy customers\u27 growing needs regarding products\u27 aesthetic and functional requirements. Complexity of part design and engineering specifications to satisfy such needs often require a better use of advanced and more accurate tools to achieve good quality. Inspection is a crucial manufacturing function that should be further improved to cope with such challenges. Intelligent planning for inspection of parts with complex geometric shapes and free form surfaces using contact or non-contact devices is still a major challenge. Research in segmentation and localization techniques should also enable inspection systems to utilize modern measurement technologies capable of collecting huge number of measured points. Advanced digitization tools can be classified as contact or non-contact sensors. The purpose of this thesis is to develop a hybrid inspection planning system that benefits from the advantages of both techniques. Moreover, the minimization of deviation of measured part from the original CAD model is not the only characteristic that should be considered when implementing the localization process in order to accept or reject the part; geometric tolerances must also be considered. A segmentation technique that deals directly with the individual points is a necessary step in the developed inspection system, where the output is the actual measured points, not a tessellated model as commonly implemented by current segmentation tools. The contribution of this work is three folds. First, a knowledge-based system was developed for selecting the most suitable sensor using an inspection-specific features taxonomy in form of a 3D Matrix where each cell includes the corresponding knowledge rules and generate inspection tasks. A Travel Salesperson Problem (TSP) has been applied for sequencing these hybrid inspection tasks. A novel region-based segmentation algorithm was developed which deals directly with the measured point cloud and generates sub-point clouds, each of which represents a feature to be inspected and includes the original measured points. Finally, a new tolerance-based localization algorithm was developed to verify the functional requirements and was applied and tested using form tolerance specifications. This research enhances the existing inspection planning systems for complex mechanical parts with a hybrid inspection planning model. The main benefits of the developed segmentation and tolerance-based localization algorithms are the improvement of inspection decisions in order not to reject good parts that would have otherwise been rejected due to misleading results from currently available localization techniques. The better and more accurate inspection decisions achieved will lead to less scrap, which, in turn, will reduce the product cost and improve the company potential in the market