GA Based Feature Recognition of Step File for CAD/CAM Integration

Feature-based method has been successfully applied in several fields of manufacturing. However, most of the applications use the solid modeling method that cannot meet the requirements of a product design that needs a free-form surface or a complicated surface. This research utilizes the Genetic Algorithm (GA) technique for feature recognition of STEP file. A GA model is proposed for optimizing the coordinates which is used for feature recognition. It is proposed as an input for automatic feature recognition in Computer Aided Design and Manufacturing (CAD/CAM) application. These methods accomplish their task based on recognition of features as GA made up. This technique used standard for exchange of product information (STEP) formats for geometrical data extraction representation to matching the coordinate from STEP file to decide the correct or optimize solution. Genetic operator such as selection, crossover and mutation are performed repeatedly to acquire the optimal sequences of coordinates. Even though the result of this processes are optimal, some coordinates are not placed in the correct position

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

Segmentaçao de imagens de profundidade utilizando curvaturas de superfícies e um método de estimativa robusto

Orientadora : Olga R. P. BellonDissertaçao (mestrado) - Universidade Federal do ParanáNeste trabalho são apresentadas contribuições para o aperfeiçoamento da segmentação de imagens de profundidade, um processo de fundamental importância a sistemas de visão computacional e ainda um dos maiores desafios nesta área de pesquisa. O principal objetivo é desenvolver técnicas de segmentação que preservem melhor a topologia dos objetos em cena, de modo a auxiliar processos posteriores de representação, modelagem, reconhecimento e reconstrução de objetos, ajudando a diminuir algumas limitações na utilização de sistemas de visão computacional. O problema da segmentação de imagens de profundidade foi abordado em duas formas diferentes e as principais contribuições apresentadas são: (1) dois métodos de deteção de bordas inéditos baseados em valores das curvaturas de superfície H e K e integrando dados de profundidade e de intensidade luminosa correspondentes à mesma cena: e (2) um novo método de segmentação de imagens de profundidade utilizando um algoritmo genético e um método de estimativa robusto, aperfeiçoados, para a extração de superfícies planas das imagens. Através da utilização de uma mesma base de imagens, os resultados experimentais foram comparados positivamente aos resultados obtidos por outros quatro métodos de segmentação de imagens de profundidade, considerados a principal referência no assunto de acordo com a literatura. Os métodos de deteção de bordas integrando dados de intensidade luminosa preservam melhor as formas e localizações de bordas dos objetos em cena e podem ser utilizados para melhorar os resultados obtidos por outros métodos de segmentação. O método de segmentação por extração de superfícies planas foi avaliado quantitativamente, utilizando um conjunto de métricas relacionadas a segmentações manualmente geradas, e apresentou um melhor desempenho na preservação da topologia dos objetos, principalmente, pelo fato de melhor segmentar regiões pequenas das imagens. As contribuições apresentadas constituem avanços relevantes para o aperfeiçoamento da segmentação de imagens de profundidade e estão sendo utilizadas como suporte a um projeto mais amplo, o SRIC3D, em desenvolvimento pelo grupo de pesquisa IMAGO.This work is a contribution to the improvement of the range image segmentation process, which is of fundamental importance to computer vision systems and still one of the greatest challenges in this research field. The main objective is to develop segmentation techniques to better preserve the objects topology in the imaged scenes in order to support object representation, modeling, recognition and reconstruction processes, while also helping to make feasible new applications of computer vision systems. The range image segmentation problem was approached in two different ways and the main contributions presented here are: (1) two original edge detection techniques based on H and K surface curvature values and integrating range and light intensity data corresponding to the same scene; and (2) a novel range image segmentation method employing an improved genetic algorithm and a robust estimator to extract planar surfaces from the range images. By the use of a same image database, the experimental results were positively compared to the ones obtained by other four range image segmentation methods which are considered the main reference in this subject, according to the literature. The edge detection techniques, integrating range and intensity data, better preserve shapes and edge locations of the imaged objects and may be applied to improve the performance of other segmentation methods. The segmentation method based on planar surface extraction was quantitatively evaluated, using a set of metrics related to ground truth segmentations, and presented a better performance in preserving object topology, mainly, because of the better segmentation of small image regions. The presented contributions are relevant advances to the improvement of the range image segmentation process and are already being used as support for another project - the Content-based Image Retrieval System of 3D Digital Replicas from Physical Objects. SRIC3D - under development by the IMAGO research group

Quadric surface extraction using genetic algorithms

This article presents a new surface extraction method based on genetic algorithms (GA). The proposed method is capable of extracting all types of quadric surfaces (including flat surfaces) with a single surface representation. This representation is first fitted to predefined subsets of data points by a least square fitting algorithm. The evolution of the surface representations is achieved by repetitive application of crossover and mutation operations until a termination condition is met. The expression is finally classified into a specific quadric surface according to a classification table. The proposed method can be used for CAD model reconstruction of 3D objects composed of plane and quadric surfaces.link_to_subscribed_fulltex