Approximate geometric models, e.g. as created by reverse engineering, describe the approximate shape of an object, but do not record the underlying design intent. Automatically inferring geometric aspects of the design intent, represented by feature trees and geometric constraints, enhances the utility of such models for downstream tasks. One approach to design intent detection in such models is to decompose them into regularity features. Geometric regularities such as symmetries may then be sought in each regularity feature, and subsequently be combined into a global, consistent description of the model’s geometric design intent. This paper describes a systematic approach for finding such regularity features based on recovering broken symmetries in the model. The output is a tree of regularity features for subsequent use in regularity detection and selection. Experimental results are given to demonstrate the operation and efficiency of the algorithm.

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Abstract: In a collaborative computer-supported engineering environment, the interoperation of various applications will need a representation that goes beyond the current geometry-based representation, which is inadequate for capturing semantic information. The primary purpose of this study is to discuss a semantically based information exchange protocol that will facilitate seamless interoperability among current and next generation computer-aided design systems (CAD) and between CAD and other systems that use product data. An ontological approach is described to integrating computer-aided design (CAD) and computer-aided process planning (CAPP). Two commercial software applications are used to demonstrate the approach. This involves the development of a shared ontology and domain specific ontologies in the Knowledge Interchange Format (KIF) language. Domain specific ontologies – which are feature-based – are developed after a detailed analysis of the CAD and the CAPP software. Mapping between the domain ontologies and the shared ontology is achieved by several mapping rules. The approach

The precondition for process planning and process sequencing is existence of a valid feature precedence network. However, for complex designs with large number of features and relations between them, such network may be very complicated and very difficult to evaluate manually. This paper proposes Petri nets as a tool for modeling of feature interactions. The architecture of the prototype system is presented and the algorithm for creation of the Petri net that corresponds to the feature precedence network is described. The necessary transitions and places (based on feature model) for constraints between machining processes are identified. The feature precedence network is evaluated with the help of Petri net simulation. An implementation of the prototype system that generates and simulates Petri net for given model of manufacturing features and their relationships is described and its performance is shown on several examples.

The international standard ISO 10303 (STEP) is being extended to permit the exchange of procedurally defined shape models, with additional parameterization and constraint information, between CAD systems. The transfer of parameterized assembly models is an additional objective. Most of the essential new resources have already been published by ISO, and the remainder are well advanced in the standardization process. Because these are new capabilities, at present not quite complete, there are at present no commercial STEP translators making use of them. However, several proof-ofconcept trials have been performed or are in progress, using development versions of the STEP documentation. This paper reports in some detail on one of those trials, and comments on the experience gained. The conclusion is that the standardized exchange of CAD models containing ‘design intent ’ information has been successfully demonstrated, but that the development of translators for that purpose is not an easy task. One particular problem area is pinpointed, where further research is needed to find ways of improving the efficiency of such exchanges. CR Categories: H.2.5 [Information systems]: Heterogeneous databases—Data translation; H.5.3 [Information systems]: Group and organization interfaces—computer-supported cooperative work; J.6 [Computer applications]: Computer-aided engineering—Computer-aided design (CAD).

Abstract—This paper presents a new technique for detecting sharp features on point-sampled geometry. Sharp features of different nature and possessing angles varying from obtuse to acute can be identified without any user interaction. The algorithm works directly on the point cloud, no surface reconstruction is needed. Given an unstructured point cloud, our method first computes a Gauss map clustering on local neighborhoods in order to discard all points which are unlikely to belong to a sharp feature. As usual, a global sensitivity parameter is used in this stage. In a second stage, the remaining feature candidates undergo a more precise iterative selection process. Central to our method is the automatic computation of an adaptive sensitivity parameter, increasing significantly the reliability and making the identification more robust in the presence of obtuse and acute angles. The algorithm is fast and does not depend on the sampling resolution, since it is based on a local neighbor graph computation. Keywords—unstructured point sets, feature detection; sharp features; Gauss map clustering 1.

Finding design intent embodied as high-level geometric relations between a CAD model’s sub-parts facilitates various tasks such as model editing and analysis. This is especially important for boundary-representation models arising from, e.g., reverse engineering or CAD data transfer. These lack explicit information about design intent, and often the intended geometric relations are only approximately present. The novel solution to this problem presented is based on detecting approximate local incomplete symmetries, in a hierarchical decomposition of the model into simpler, more symmetric sub-parts. Design intent is detected as congruencies, symmetries and symmetric arrangements of the leaf-parts in this decomposition. All elementary 3D symmetry types and common symmetric arrangements are considered. They may be present only locally in subsets of the leaf-parts, and may also be incomplete, i.e. not all elements required for a symmetry need be present. Adaptive tolerance intervals are detected automatically for matching interpoint distances, enabling efficient, robust and consistent detection of approximate symmetries. Doing so avoids finding many spurious relations, reliably resolves ambiguities between relations, and reduces inconsistencies. Experiments show that detected relations reveal significant design intent.

We present an inference-based surface reconstruction algorithm that is capable of both identifying objects of interest amongst a cluttered scene, as well as reconstructing solid model representations even in the presence of occluded surfaces. As the underlying complexity of laser scanned environments increases, accurately reconstructing complete high-fidelity models from point cloud data becomes an increasingly more difficult problem. As the number of objects and the degree of clutter incorporated in these scenes increases, issues such as occlusion and partially visible surface features become more prevalent. For simulation purposes, there is a great need for accurate reconstructions of discrete solid models of each object residing in the scene. Our proposed approach incorporates a predictive modeling framework that uses a core set of prior knowledge, and applies this knowledge to the iterative identification and construction process. Our approach uses a local to global matching and fitting process driven by a set of rules developed from prior models. High quality surface patches, also obtained from the prior models, are then incrementally fitted and used to guide the reconstruction of each object. Through the use of these rules and patches, this approach is capable of reconstructing a solid representation even if a major portion of the object’s structure is occluded. To demonstrate the effectiveness of our approach, we provide the results of the algorithm executed on several datasets of increasing size and complexity. Categories and Subject Descriptors I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling – boundary representations, curve, surface,

As virtual environments become larger and more complex, there is an increasing need for more automated construction algorithms to support the development process. We present an approach for modeling solids by combining prior examples with a simple sketch. Our algorithm uses an inference-based approach to incrementally fit patches together in a consistent fashion to define the boundary of an object. This algorithm samples and extracts surface patches from input models, and develops a Petri net structure that describes the relationship between patches along an imposed parameterization. Then, given a new parameterized line or curve, we use the Petri net to logically fit patches together in a manner consistent with the input model. This allows us to easily construct objects of varying sizes and configurations using arbitrary articulation, repetition, and interchanging of parts. The result of our process is a solid model representation of the constructed object that can be integrated into a simulation-based environment.