TOWARDS A DATA MODEL FOR PLM APPLICATION IN BIO-MEDICAL IMAGING

International audienceBio-Medical Imaging (BMI) is currently confronted to data issues similar to those of the manufacturing industry twenty years ago. In particular, the need for data sharing and reuse has never been so strong to foster major discoveries in neuroimaging. Some data management systems have been developed to meet the requirements of BMI large-scale research studies. However, many efforts to integrate the data provenance along a research study, from the specifications to the published results, are to be done. Product Lifecycle Management (PLM) systems are designed to comply with manufacturing industry expectations of providing the right information at the right time and in the right context. Consequently PLM systems are proposed to be relevant for the management of BMI data. From a need analysis led with the GIN research group, the BMI-LM data model is designed: it is PLM-oriented, generic (enabling the management of many types of data such as imaging, clinical, psychology or genetics), flexible (enabling users’ customisation) and it covers the whole stages of a BMI study from specifications to publication. The test implementation of the BMI-LM model into a PLM system is detailed. The preliminary feed-back of the GIN researchers is discussed in this paper: the BMI-LM data model and the PLM concepts are relevant to manage BMI data, but PLM systems interfaces are unsuitable for BMI researchers

A three-level signature by graph for Reverse Engineering of mechanical assemblies

Several approaches exist to provide Reverse Engineering solutions on mechanical parts. Mechanical assemblies and the expertise information retrieved at the same time with the model geometry are not really taken into account in the literature. Thus, the main challenge of this contribution is to propose a methodology to retrieve the Digital Mock-Up of a mechanical assembly from its meshed data (from digitalization). The output DMU consists of expertise information and parameterized CAD models. The methodology proposed relies on a signature by a three-level graph. It enables to provide an adequate level of details by identifying the corresponding functional surfaces in meshed data. The first-level graph is a connectivity graph; the intermediate level is the same as the first with the geometric type of face added to each node (plane, cylinder and sphere) and the deepest level corresponds to a precedence graph. This one provides information such as functional surfaces and position between them (perpendicularity, coaxiality etc.). The solutions developed and the results are presented in this paper. The methodology is illustrated thanks to an industrial use-case with a scan of an assembly with a connecting rod and a piston. The conclusion and perspectives will complete this paper

Knowledge capture and reuse through expert’s activity monitoring in engineering design

This paper deals with artificial intelligence driven product engineering support. Many software systems are available to support the product lifecycle, especially during product design, such as CAD, PDM, CAE, SDM, etc. Most product development process is performed using these systems, which through their rich user interfaces allow skilled professionals to express their expertise and knowledge using the tools and functions the software is willing to provide them. At the end of the day, the result of their work is a model, built through a user interface, and stored in a repository. The goal of our research is to reverse engi-neer the user’s knowledge by analysing his/her actions with the software system, based on the assumption that the process will itself be meta-knowledge driven and that we will focus on engineering software which provide semantically rich user interfaces. The aim of this paper is to investigate the idea of building reusa-ble expert knowledge from actions on engineering software user interfaces. It first outlines existing works from different fields and identifies remaining issues. It then suggest an approach to address these issues and put together an operational system

Towards new processes to reverse engineering digital mock-ups from a set of heterogeneous data

Reverse-Engineering techniques are commonly used to generate or update the CAD model of a single physical object. However, the reverse engineering of a whole assembly is still very tedious and time-consuming. This is mainly due to the fact that the complete definition of the final digital mock-up relies on the integration of multiple sources of heterogeneous data, such as point clouds, images, schemes or any type of digital representations which are not yet fully supported by actual software. Thus, having new methods and tools to better process and integrate those multi-representations would speed up the reconstruction process which could therefore become adapted to the reconstruction of large mechanical assemblies such as in automotive field. This paper addresses such a difficult problem. Actually, starting from an analysis of three different use-cases, we first highlight the lack of software solutions for the considered problematic. Then, the proposed process-workflow is introduced together with the advanced mechanisms involved in the reconstruction. In our approach, the signatures of the components play a key role in the identification of the relationships and matching procedures between the heterogeneous data. This process-workflow is illustrated on an example in the automotive domain

A three-level signature by graph for Reverse Engineering of mechanical assemblies

Several approaches exist to provide Reverse Engineering solutions on mechanical parts. Mechanical assemblies and the expertise information retrieved at the same time with the model geometry are not really taken into account in the literature. Thus, the main challenge of this contribution is to propose a methodology to retrieve the Digital Mock-Up of a mechanical assembly from its meshed data (from digitalization). The output DMU consists of expertise information and parameterized CAD models. The methodology proposed relies on a signature by a three-level graph. It enables to provide an adequate level of details by identifying the corresponding functional surfaces in meshed data. The first-level graph is a connectivity graph; the intermediate level is the same as the first with the geometric type of face added to each node (plane, cylinder and sphere) and the deepest level corresponds to a precedence graph. This one provides information such as functional surfaces and position between them (perpendicularity, coaxiality etc.). The solutions developed and the results are presented in this paper. The methodology is illustrated thanks to an industrial use-case with a scan of an assembly with a connecting rod and a piston. The conclusion and perspectives will complete this paper

Définition d'un processus de rétro-conception de produit par intégration des connaissances de son style de vie

Cette thèse porte sur la rétro-conception (RC) d un objet mécanique. Cette activité consiste, à partir d un objet et du nuage de points issu de sa numérisation, à générer un modèle CAO de cet objet. L état de l art sur la reconnaissance de formes dans un nuage de points propose des approches qui permettent d obtenir un modèle CAO quasi inexploitable (paramétrage géométriques et non métier) pour une activité de re-conception. Cette thèse définit une méthodologie de RC visant à fournir un modèle CAO paramétré incluant les vues métier comme la fabrication et les aspects fonctionnels. Ainsi, l activité de reconception pourra être accélérée car les vues métier, autrement dit, les intentions de conception, auront été mises en lumière. En conception de produit, des solutions telles que les systèmes d information technique visant à assurer la gestion des vues métier (connaissances sur le produit) existent. Cette thèse propose d adapter cette solution à la RC. Une méthode nommée Knowledge Based Reverse Engineering a été créée. Elle permet d analyser l objet mécanique selon des vues métier. Ces vues sont incarnées par des entités géométriques dont les paramètres métier sont extraits du nuage de points. En sortie, une CAO paramétrée incluant les vues métier (fonctionnelle, fabrication etc.) peut être créée. Ce travail est validé par des exemples industriels et implémenté dans un démonstrateur : KBRE systemThis thesis concerns the reverse engineering (RE) of a mechanical object. This activity consists in generating a CAD model of this object from the 3D points cloud from its digitalisation. The state of the art on the geometrical recognition in a point cloud suggests approaches which allow obtaining an almost unusable CAD model (geometrical parameters with not design intents) for a redesign activity. This thesis defines a methodology of RE that provides a parameterised CAD model that includes design intents such as the manufacturing and the functional aspects. So, an activity of redesign can be accelerated because the design intents will have been brought to light. In design product domain, solutions like Knowledge Based Engineering ensure the de-sign intents management. This thesis suggests adapting these solutions to RE. A method called Knowledge Based Reverse Engineering was created. It allows analysing the mechanical object according to the design intents. These design intents are embodied by geometrical features. Their parameters are extracted from the 3D points cloud. A CAD model including design intents (manufacturing, functional requirements) can be created. This work is illustrated by industrial examples and implemented in a viewer called KBRE sys-temTROYES-SCD-UTT (103872102) / SudocSudocFranceF

From a 3D point cloud to a real CAD model of mechanical parts, a product knowledge based approach

International audienceReverse engineering is not a new domain but, according to users, from results are obtained with the current approaches are not good enough. Starting from the 3D point cloud of the original mechanical part, the surface/solid based approach allows to obtain a software solution which purpose is to find an automatic way for surface rebuilding. Based on basic segmentation and free from surface fitting methodologies, Geometric features can be extracted from a point cloud from a 3D digitalisation of a real model. Then, all these features are rebuilt and connected with each others using expert knowledge to add some design features in a very long process in order to obtain a kind of CAD model a little more useful than a meshed model. As far as we can see, there are no industrial approaches for the automatic conversion of a 3D point cloud into a CAD model with parameters or formulas. In this article, we depict a new research theme which will lead to the reconstruction of a CAD Model from the 3D point cloud with a knowledge-based approach

Prise en compte de connaissances métiers pour la rétro-conception de pièces mécaniques - le projet PHENIX

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KBRE: A Knowledge Based Reverse Engineering for Mechanical Components

International audienceThis paper focuses on Reverse Engineering (RE) in mechanical design. RE is an activity which consists in creating a full CAD model from a 3D point cloud. The aim of RE is to enable an activity of redesign in order to improve, repair or update a given mechanical part. Nowadays, CAD models obtained using modern software applications are generally “frozen” because they are sets of triangles of free form surfaces. In such models, there are not functional parameters but only geometric parameters. This paper proposes the KBRE (Knowledge Based Reverse Engineering) methodology which allows managing and fitting manufacturing and/or functional features. Specific geometric algorithms are described. They allow extracting design intents in a point cloud in order to fit these features

Reverse Engineering for Manufacturing Approach: Based on the Combination of 3D and Knowledge Information

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