Возможности визуального и статистического анализа для оценки применимости декомпозиции изделия при его послойном изготовлении

Представлены результаты исследования возможностей визуальной оценки эффективности применения структурной обратимой декомпозиции на основе анализа распределения элементарных объемов (воксельной 3D-модели) изделия по подпространствам, получаемым путем разбиения рабочего пространства. Апробация визуальной оценки и статистического анализа распределения элементарных объемов выполнялась с использованием моделей промышленных изделий.The results of a study of the possibilities of visual evaluation of effectiveness of structural reversible decomposition based on analysis of the distribution of elementary volumes (voxel 3D-model) of product on subspaces obtained by dividing the workspace are presented. Testing of visual evaluation and statistical analysis of the distribution of elementary volumes was performed using industrial product models

Computer aided inspection procedures to support smart manufacturing of injection moulded components

This work presents Reverse Engineering and Computer Aided technologies to improve the inspection of injection moulded electro-mechanical parts. Through a strong integration and automation of these methods, tolerance analysis, acquisition tool-path optimization and data management are performed. The core of the procedure concerns the automation of the data measure originally developed through voxel-based segmentation. This paper discusses the overall framework and its integration made according to Smart Manufacturing requirements. The experimental set-up, now in operative conditions at ABB SACE, is composed of a laser scanner installed on a CMM machine able to measure components with lengths in the range of 5÷250 mm, (b) a tool path optimization procedure and (c) a data management both developed as CAD-based applications

Automatic post-processing for tolerance inspection of digitized parts made by injection moulding

This paper presents the advancements of an automatic segmentation procedure based on the concept of Hierarchical Space Partitioning. It is aimed at tolerance inspection of electromechanical parts produced by injection moulding and acquired by laser scanning. After a general overview of the procedure, its application for recognising cylindrical surfaces is presented and discussed through a specific industrial test case

Evaluation of manufacturability for the effective decomposition of product when layered build

The possibility of evaluating the manufacturability of product on the basis of a statistical analysis of the elementary volumes distribution of original 3D model is considered. The proposed indicator allows for quantitative evaluation of the efficiency of applying structural reversible decomposition of a product in order to rationally place it in the workspace of layered build of additive technology installation. The definition of manufacturability index is carried out according to the proposed algorithm for analyzing the distribution of product material in workspace. The algorithm is performed by using voxel 3D-model of product. Approbation of the proposed evaluation algorithm is performed on the example of test models of industrial products. The estimated data for determining the manufacturability level is presented depending on division parts number of workspace with the product. The results show sufficiently high degree of confidence and informative for development of design and technological preparation of additive manufacturing of complex products

Intelligent Reverse-Engineering Segmentation: Automatic Semantic Recognition of Large 3D Digitalized Cloud of Points Dedicated to Heritage Objects

International audienceIn this article we present a multidisciplinary experimentation realized between a mechanical laboratory, a computer scientist laboratory and a museum. Our goal is to provide automatic tools for non-expert people who want to use 3D digitized elements. After scanning an objet, we obtain a huge amount of points. In order to manipulate it, it is necessary to decimate it. However, when doing this operation, we can optimize the algorithms for creating semantic topology; obviously we can do it automatically. Consequently, we are going to do what we name segmentation: we extract meaning from 3D points and meshes. Our experimentation deals with a physical mock-up of Nantes city that have been designed in 1900. After digitalization, we have created a software that can: 1. use the whole 3D cloud of points as an input; 2. fill a knowledge database with an intelligent segmentation of the 3D virtual models: ground, walls, roofs... This use case is the first step of our research. At the end, we aim to deploy our method to complex mechanical parts. Nowadays, when designing CAD parts we use as well as volume parts than surface parts or meshes. We know is it not necessary to reconstruct all the triangles. It is a lost of time and we can directly use cloud of points for CAD design. However, the design tree will not be updated. So, with our method, imagine that one day we can digitalize a motor and a system could automatically create the 3D mock-up and the design tree

Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies

In motion analysis and understanding it is important to be able to fit a suitable model or structure to the temporal series of observed data, in order to describe motion patterns in a compact way, and to discriminate between them. In an unsupervised context, i.e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion. In recent times, volumetric approaches in which the motion is captured from a number of cameras and a voxel-set representation of the body is built from the camera views, have gained ground due to attractive features such as inherent view-invariance and robustness to occlusions. Automatic, unsupervised segmentation of moving bodies along entire sequences, in a temporally-coherent and robust way, has the potential to provide a means of constructing a bottom-up model of the moving body, and track motion cues that may be later exploited for motion classification. Spectral methods such as locally linear embedding (LLE) can be useful in this context, as they preserve "protrusions", i.e., high-curvature regions of the 3D volume, of articulated shapes, while improving their separation in a lower dimensional space, making them in this way easier to cluster. In this paper we therefore propose a spectral approach to unsupervised and temporally-coherent body-protrusion segmentation along time sequences. Volumetric shapes are clustered in an embedding space, clusters are propagated in time to ensure coherence, and merged or split to accommodate changes in the body's topology. Experiments on both synthetic and real sequences of dense voxel-set data are shown. This supports the ability of the proposed method to cluster body-parts consistently over time in a totally unsupervised fashion, its robustness to sampling density and shape quality, and its potential for bottom-up model constructionComment: 31 pages, 26 figure

Analysis of face and segment level descriptors for robust 3D co-segmentation

Analysis of face and segment level descriptors for robust 3D co-segmentation