Feature-based validation reasoning for intent-driven engineering design

Feature based modelling represents the future of CAD systems. However, operations such as modelling and editing can corrupt the validity of a feature-based model representation. Feature interactions are a consequence of feature operations and the existence of a number of features in the same model. Feature interaction affects not only the solid representation of the part, but also the functional intentions embedded within features. A technique is thus required to assess the integrity of a feature-based model from various perspectives, including the functional intentional one, and this technique must take into account the problems brought about by feature interactions and operations. The understanding, reasoning and resolution of invalid feature-based models requires an understanding of the feature interaction phenomena, as well as the characterisation of these functional intentions. A system capable of such assessment is called a feature-based representation validation system. This research studies feature interaction phenomena and feature-based designer's intents as a medium to achieve a feature-based representation validation system. [Continues.

Idealized models for FEA derived from generative modeling processes based on extrusion primitives

International audienceShape idealization transformations are very common when adapting a CAD component to FEA requirements. Here, an idealization approach is proposed that is based on generative shape processes used to decompose an initial B-Rep object, i.e. extrusion processes. The corresponding primitives form the basis of candidate sub domains for idealization and their connections conveyed through the generative processes they belong to, bring robustness to set up the appropriate connections between idealized sub domains. Taking advantage of an existing construction tree as available in a CAD software does not help much because it may be complicated to use it for idealization processes. Using generative processes attached to an object that are no longer reduced to a single construction tree but to a graph containing all non trivial construction trees, is more useful for the engineer to evaluate variants of idealization. From this automated decomposition, each primitive is analyzed to define whether it can idealized or not. Subsequently, geometric interfaces between primitives are taken into account to determine more precisely the idealizable sub domains and their contours when primitives are incrementally merged to come back to the initial object

Manufacturing Feature Recognition With 2D Convolutional Neural Networks

Feature recognition is a critical sub-discipline of CAD/CAM that focuses on the design and implementation of algorithms for automated identification of manufacturing features. The development of feature recognition methods has been active for more than two decades for academic research. However, in this domain, there are still many drawbacks that hinder its practical applications, such as lack of robustness, inability to learn, limited domain of features, and computational complexity. The most critical one is the difficulty of recognizing interacting features, which arises from the fact that feature interactions change the boundaries that are indispensable for characterizing a feature. This research presents a feature recognition method based on 2D convolutional neural networks (CNNs). First, a novel feature representation scheme based on heat kernel signature is developed. Heat Kernel Signature (HKS) is a concise and efficient pointwise shape descriptor. It can present both the topology and geometry characteristics of a 3D model. Besides informative and unambiguity, it also has advantages like robustness of topology and geometry variations, translation, rotation and scale invariance. To be inputted into CNNs, CAD models are discretized by tessellation. Then, its heat persistence map is transformed into 2D histograms by the percentage similarity clustering and node embedding techniques. A large dataset of CAD models is built by randomly sampling for training the CNN models and validating the idea. The dataset includes ten different types of isolated v features and fifteen pairs of interacting features. The results of recognizing isolated features have shown that our method has better performance than any existing ANN based approaches. Our feature recognition framework offers the advantages of learning and generalization. It is independent of feature selection and could be extended to various features without any need to redesign the algorithm. The results of recognizing interacting features indicate that the HKS feature representation scheme is effective in handling the boundary loss caused by feature interactions. The state-of-the-art performance of interacting features recognition has been improved

A surface texture modeling system for solid freeform fabrication

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1997.Includes bibliographical references (leaves 83-84).Solid Freeform Fabrication, SFF, is a set of manufacturing processes that fabricates parts as a bonded stack of individual layers. The Three Dimensional Printing process, 3DPTM process, is an SFF technology developed at MIT. It builds layers by ink jet printing binder onto the surface of a bed of powder. The bed of powder is lowered and fresh powder is spread onto the bed. As subsequent cross sections of the part are printed, the part exists, submerged in the powder bed. Access to the individual layers as they are fabricated gives access to the interior structure of the part. This approach allows the part to have high geometric complexity. In this work a designer centric Computer Aided Design system is proposed to allow the interactive creation of functional surface texture on mechanical parts. This system is structured to behave like a VLSI CAD system, which offers substantial process capabilities. The requirements for a Mechanical CAD, MCAD, system to behave like VLSI CAD are determined to be: 1. That the informational model of the unit cell of texture be separable into distinct logical subsets.2. That manipulations on either subset not violate the logical consistency of the other subset. This thesis shows that geometric dimensions and tolerances carry the essential information of the model of a unit cell of functional texture. A variety of Unit Cell editors are evaluated according to their ability to meet the desired system criteria. A tool, Swiss Solid Geometry, SSG, for the design of unit cells of functional texture is developed, that fulfills requirement #1. SSG is an approach to MCAD modeling that combines geometric primitives in the manner of Constructive Solid Geometry, however the primitives of SSG, are different. They consist of simple objects such as lines, but includes the spatial envelope around them of a fixed offset. Also, they are used to represent both positive and negative regions of space. The placement of the individual replications is established by a mesh, that covers the intended 3D surface region. A meshing algorithm is developed that regularizes the mesh by directly utilizing the dimensional tolerances specified in the process of Unit Cell design. The geometric dimensions are instantiated as standalone geometric entities that push and pull on the nodes of the mesh in order to bring their length into dimensional tolerance. This method fulfills requirement #2, and it is implemented into a CAM software called Vari 4. The modularity of the CAM software, Vari 4, is described in detail.by John G. Nace.S.M

Idealized models for FEA derived from generative modeling processes based on extrusion primitives

International audienceShape idealization transformations are very common when adapting a CAD component to FEA requirements. Here, an idealization approach is proposed that is based on generative shape processes used to decompose an initial B-Rep object, i.e. extrusion processes. The corresponding primitives form the basis of candidate sub domains for idealization and their connections conveyed through the generative processes they belong to, bring robustness to set up the appropriate connections between idealized sub domains. Taking advantage of an existing construction tree as available in a CAD software does not help much because it may be complicated to use it for idealization processes. Using generative processes attached to an object that are no longer reduced to a single construction tree but to a graph containing all non trivial construction trees, is more useful for the engineer to evaluate variants of idealization. From this automated decomposition, each primitive is analyzed to define whether it can idealized or not. Subsequently, geometric interfaces between primitives are taken into account to determine more precisely the idealizable sub domains and their contours when primitives are incrementally merged to come back to the initial object

The design and analysis of boundary data structures

The thesis is concerned with the efficient interrogation of CAD data. CAD data finds use in diverse range of applications which necessitates extension and integration of the CAD data base. By an exhaustive categorization of such application requirements and analysis of various CAD techniques, it is shown that boundary data structures are the most suitable in CAD, CAM and advanced robotic applications. Several boundary data structures have been proposed since the classic Winged edge data structure, these aimed at reducing the storage requirement and increasing information retrieval speeds. In this thesis methodologies are developed which enable us to discover compact and fast access time schemes and analyze and fine tune for individual applications. We demonstrate how the application of the optimality concepts can lead us to the discovery of more efficient data structures than popular data structures. All the boundary data structures proposed to date have been based on the underlying assumption that all the data resides in main memory. We show that in an integrated CAD environment (characterized by virtual a memory environment or a data base environment), these data structures are inefficient in both storage and time. We propose a new data structure shaped like A which is the most compact as well as more efficient in access time, under certain conditions of real memory and virtual memory. Experiments reveal a paradoxical phenomenon: access time increases with storage, violating the classic law of storage vs. time. Recently non-manifold boundary geometric modeling has become popular to meet the growing needs such as uniform treatment of wire frame, surface and solid modeling and design by features. We introduce a uniform terminology and notation to distinguish and critically analyze several non-manifold boundary data structures. It is hoped to fulfill the need for a ready reference for the design of efficient boundary data structures. The other aspects dealt with are the validity and conversion of Boundary data structures. To verify the concepts developed, in practice, a whole suite of fast algorithms have been implemented for model manipulation, visualization and data conversion

Part grouping for efficient process planning

A framework to provide automated part grouping has been investigated in order to overcome the limitations found in existing part grouping techniques. The work is targeted at: exploration of criteria for feature-based part grouping to make the process planning activity efficient; determination of the optimal number of part families in the part grouping process; development of an experimental hybrid process planning system (HYCAPP); investigation of the effects of improved part grouping on manufacturing cell design. The research work has explored the creation of a feature-based component data model and manufacturing system capability data model, and checked the limitations inherent in existing part grouping techniques i.e. part grouping: around methods; based on part geometry; based on machining processes; and based on machines. [Continues.

A Feature Based Approach to Automated Design of Multi-Piece Sacrificial Molds

This report describes a feature-based approach to automated design of multi-piece sacrificial molds. We use multi-piece sacrificial molds to create complex 3D polymer/ceramic parts. Multi-piece molds refer to molds that contain more than two mold components or subassemblies. Our methodology has the following three benefits over the state-of-the-art. First, by using multi-piece molds we can create complex 3D objects that are impossible to create using traditional two piece molds. Second, we make use of sacrificial molds. Therefore, using multi-piece sacrificial molds, we can create parts that pose disassembly problems for permanent molds. Third, mold design steps are significantly automated in our methodology. Therefore, we can create the functional part from the CAD model of the part in a matter of hours and so our approach can be used in small batch manufacturing environments. The basic idea behind our mold design algorithm is as follows. We first form the desired gross mold shape based on the feature-based description of the part geometry. If the desired gross mold shape is not manufacturable as a single piece, we decompose the gross mold shape into simpler shapes to make sure that each component is manufacturable using CNC machining. During the decomposition step, we account for tool accessibility to make sure that (1) each component is manufacturable, and (2) components can be assembled together to form the gross mold shape. Finally, we add assembly features to mold component shapes to facilitate easy assembly of mold components and eliminate unnecessary degree of freedoms from the final mold assembly

Improving architectural 3D reconstruction by constrained modelling

Institute of Perception, Action and BehaviourThis doctoral thesis presents new techniques for improving the structural quality of automatically-acquired architectural 3D models. Common architectural properties such as parallelism and orthogonality of walls and linear structures are exploited. The locations of features such as planes and 3D lines are extracted from the model by using a probabilistic technique (RANSAC). The relationships between the planes and lines are inferred automatically using a knowledge-based architectural model. A numerical algorithm is then used to optimise the position and orientations of the features taking constraints into account. Small irregularities in the model are removed by projecting the irregularities onto the features. Planes and lines in the resulting model are therefore aligned properly to each other, and so the appearance of the resulting model is improved. Our approach is demonstrated using noisy data from both synthetic and real scenes