This paper presents a preliminary comparison between the role of computer-aided design (CAD) and sketching in engineering through a case study of a senior design project and interviews with industry and academia. The design team consisted of four senior level mechanical engineering students each with less than 1 year of professional experience are observed while completing an industry sponsored mechanical engineering capstone design project across a 17 week semester. Factors investigated include what CAD tools are used, when in the design process they are implemented, the justification for their use from the students' perspectives, the actual knowledge gained from their use, the impact on the final designed artifact, and the contributions of any sketches generated. At each design step, comparisons are made between CAD and sketching. The students implemented CAD tools at the onset of the project, generally failing to realize gains in design efficiency or effectiveness in the early conceptual phases of the design process. As the design became more concrete, the team was able to recognize clear gains in both efficiency and effectiveness through the use of computer assisted design programs. This study is augmented by interviews with novice and experienced industry users and academic instructors to align the trends observed in the case study with industry practice and educational emphasis. A disconnect in the perceived capability of CAD tools was found between novice and experienced user groups. Opinions on the importance of sketching skills differed between novice educators and novice industry professionals, suggesting that there is a change of opinion as to the importance of sketching formed when recent graduates transition from academia to industry. The results suggest that there is a need to emphasize the importance of sketching and a deeper understanding as to the true utility of CAD tools at each stage of the design process.

We present a multimodal spatial data access framework designed to serve the informational and computational requirements of architectural design assistance systems that are intended to provide intelligent spatial decision support and analytical capabilities. The framework focuses on multiperspective semantics, qualitative and artifactual spatial abstractions, and industrial conformance and interoperability within the context of the industry foundation classes. The framework provides qualitative and cognitively adequate representational mechanisms, and the formal interpretation of the structural form of indoor spaces that are not directly provided by conventional computer-aided design based or quantitative models of space. We illustrate the manner in which these representations directly provide the spatial abstractions that are needed to enable the implementation of intelligent analytical capabilities in design assistance tools. We introduce the framework, and also provide detailed use cases that illustrate the usability of the framework and the manner of its utilization within architectural design assistance systems.

Reflective actions in collaborative design can potentially improve design performance and results. This paper quantitatively reexamines the relationships between reflective activities and design performance during the collaborative design process in terms of reflection in action. Twenty sets of protocol data were encoded by a modified version of Valkenburg and Dorst's coding scheme. Using statistical testing, the relationship between the design performance and the number of activities plus the transitions was examined. A significant statistical correlation was found between the percentage of mature framing (setting up of a desired goal with sufficient follow-ups) and the overall performance. These quantitative results verify the qualitative findings of the previous study.

The hierarchical construction of solid models with current computer-aided design systems provide little support in creating and editing free-form surfaces commonly encountered in industrial design. In this work, we propose a new design exploration method that enables sketch-based editing of free-form surface geometries where specific modifications can be applied at different levels of detail. This multilevel detail approach allows the designer to work from existing models and make alterations at coarse and fine representations of the geometry, thereby providing increased conceptual flexibility during modeling. At the heart of our approach lies a multiscale representation of the geometry obtained through a spectral analysis on the discrete free-form surface. This representation is accompanied by a sketch-based surface editing algorithm that enables edits to be made at different levels. The seamless transfer of modifications across different levels of detail facilitates a fluid exploration of the geometry by eliminating the need for a manual specification of the shape hierarchy. We demonstrate our method with several design examples.

The tool switching problem (ToSP) is well known in the domain of flexible manufacturing systems. Given a reconfigurable machine, the ToSP amounts to scheduling a collection of jobs on this machine (each of them requiring a different set of tools to be completed), as well as the tools to be loaded/unloaded at each step to process these jobs, such that the total number of tool switches is minimized. Different exact and heuristic methods have been defined to deal with this problem. In this work, we focus on memetic approaches to this problem. To this end, we have considered a number of variants of three different local search techniques (hill climbing, tabu search, and simulated annealing), and embedded them in a permutational evolutionary algorithm. It is shown that the memetic algorithm endowed with steepest ascent hill climbing search yields the best results, performing synergistically better than its stand-alone constituents, and providing better results than the rest of the algorithms (including those returned by an effective ad hoc beam search heuristic defined in the literature for this problem).

Although many approaches to digital ink recognition have been proposed, most lack the flexibility and adaptability to provide acceptable recognition rates across a variety of problem spaces. This project uses a systematic approach of data mining analysis to build a gesture recognizer for sketched diagrams. A wide range of algorithms was tested, and those with the best performance were chosen for further tuning and analysis. Our resulting recognizer, RATA.Gesture, is an ensemble of four algorithms. We evaluated it against four popular gesture recognizers with three data sets; one of our own and two from other projects. Except for recognizer–data set pairs (e.g., PaleoSketch recognizer and PaleoSketch data set) the results show that it outperforms the other recognizers. This demonstrates the potential of this approach to produce flexible and accurate recognizers.