The spatial synthesis problem addressed in this paper is the configuration of rectangles in 2D space, where the sides of the rectangles are parallel to an orthogonal coordinate system. Variables are the locations of the edges of the rectangles and their orientations. Algebraic constraints on these variables define a layout and constitute a constraint satisfaction problem. We give a new O(n2) algorithm for incremental path-consistency, which is applied after adding each algebraic constraint. Problem requirements are formulated as spatial relations between the rectangles, for example, adjacency, minimum distance, and nonoverlap. Spatial relations are expressed by Boolean combinations of the algebraic constraints; called disjunctive constraints. Solutions are generated by backtracking search, which selects a disjunctive constraint and instantiates its disjuncts. The selected disjuncts describe an equivalence class of configurations that is a significantly different solution. This method generates the set of significantly different solutions that satisfy all the requirements. The order of instantiating disjunctive constraints is critical for search efficiency. It is determined dynamically at each search state, using functions of heuristic measures called textures. Textures implement fail-first and prune-early strategies. Extensions to the model, that is, 3D configurations, configurations of nonrectangular shapes, constraint relaxation, optimization, and adding new rectangles during search are discussed.

The ability to understand the implications of the geometry of solid objects is an important aspect of intelligent behavior. This paper presents work designed to enable reasoning with relatively loose conceptualizations of geometry. The method operates by comparing target geometry to known geometry, and involves the manipulation of models based upon prototypes. In particular, three techniques of simplification, approximation, and transformation are discussed. Finally, an application of the method to the domain of stress concentration prediction is presented.

The design of a new car is guided by a set of directives indicating the target market, specific engineering, and aesthetic constraints, which may also include the preservation of the company brand identity or the restyling of products already on the market. When creating a new product, designers usually evaluate other existing products to find sources of inspiration or to possibly reuse successful solutions. In the perspective of an optimized styling workflow, great benefit could be derived from the possibility of easily retrieving the related documentation and existing digital models both from internal and external repositories. In fact, the rapid growth of resources on the Web and the widespread adoption of computer-assisted design tools have made available huge amounts of data, the utilization of which could be improved by using more selective retrieval methods. In particular, the retrieval of aesthetic elements may help designers to create digital models conforming to specific styling properties more efficiently. The aim of our research is the definition of a framework that supports (semi)automatic extraction of semantic data from three-dimensional models and other multimedia data to allow car designers to reuse knowledge and design solutions within the styling department. The first objective is then to capture and structure the explicit and implicit elements contributing to the definition of car aesthetics, which can be realistically tackled through computational models and methods. The second step is the definition of a system architecture that is able to transfer such semantic evaluation through the automatic annotation of car models.

We describe an approach that uses causal and geometric reasoning to construct explanations for the purposes of the geometric features on the parts of a mechanical device. To identify the purpose of a feature, the device is simulated with and without the feature. The simulations are then translated into a “causal-process” representation, which allows qualitatively important differences to be identified. These differences reveal the behaviors caused and prevented by the feature and thus provide useful cues about the feature's purpose. A clear understanding of the feature's purpose, however, requires a detailed analysis of the causal connections between the caused and prevented behaviors. This presents a significant challenge because one has to understand how a behavior that normally takes place affects (or is affected by) another behavior that is normally absent. This article describes techniques for identifying such elusive relationships. These techniques employ a set of rules that can determine if one behavior enables or disables another that is spatially and temporally far away. They do so by geometrically examining the traces of the causal processes in the device's configuration space. Using the results of this analysis, our program can automatically generate text output describing how the feature performs its function.

Solid models are the critical data elements in modern computer-aided design environments, because they describe the shape and form of manufactured artifacts. Their growing ubiquity has created new problems in how to effectively manage the many models that are now stored in the digital libraries for large design and manufacturing enterprises. Existing techniques from the engineering literature and industrial practice, such as group technology, rely on human-supervised encodings and classification; techniques from the multimedia database and computer graphics/vision communities often ignore the manufacturing attributes that are most significant in the classification of models. This paper presents our approach to comparing the manufacturing similarity assessments of solid models of mechanical parts based on machining features. Our technical approach is threefold: perform machining feature extraction, construct a model dependency graph (MDG) from the set of machining features, and partition the models in a database using a measure of similarity based on the MDGs. We introduce two heuristic search techniques for comparing MDGs and present empirical experiments to validate our approach using our testbed, the National Design Repository.