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Abstract: Product design is a highly involved, often ill-defined, complex and iterative process, and the needs and specifications of the required artifact get more refined only as the design process moves toward its goal. An effective computer support tool that helps the designer make better-informed decisions requires efficient knowledge representation schemes. In today’s world, there is a virtual explosion in the amount of raw data available to the designer, and knowledge representation is critical in order to sift through this data and make sense of it. In addition, the need to stay competitive has shrunk product development time through the use of simultaneous and collaborative design processes, which depend on effective transfer of knowledge between teams. Finally, the awareness that decisions made early in the design process have a higher impact in terms of energy, cost, and sustainability, has resulted in the need to project knowledge typically required in the later stages of design to the earlier stages. Research in design rationale systems, product families, systems engineering, and ontology engineering has sought to capture knowledge from earlier product design decisions, from the breakdown of product functions and associated physical features, and from customer requirements and feedback reports. VR (Virtual reality) systems and multidisciplinary modeling have enabled the simulation of scenarios in the manufacture, assembly, and use of the product. This has helped capture vital knowledge from these stages of the product life and use it in design validation and testing. While there have been considerable and significant developments in knowledge capture and representation in product design, it is useful to sometimes review our position in the area, study the evolution of research in product design, and from past and current trends, try and foresee future developments. The goal of this paper is thus to review both our understanding of the field and the support tools that exist for the purpose, and identify the trends and possible directions research can evolve in the future. [Copyright &y& Elsevier]

Abstract: We present a novel interaction system, “Shape-It-Up”, for creative expression of 3D shapes through the naturalistic integration of human hand gestures with a modeling scheme dubbed intelligent generalized cylinders (IGC). To achieve this naturalistic integration, we propose a novel paradigm of shape–gesture–context interplay (SGCI) wherein the interpretation of gestures in the spatial context of a 3D shape directly deduces the designer’s intent and the subsequent modeling operations. Our key contributions towards SGCI are threefold. Firstly, we introduce a novel representation (IGC) of generalized cylinders as a function of the spatial hand gestures (postures and motion) during the creation process. This representation allows for fast creation of shapes while retaining their aesthetic features like symmetry and smoothness. Secondly, we define the spatial contexts of IGCs as proximity functions of their representational components, namely cross-sections and the skeleton with respect to the hands. Finally, we define a natural association of modification and manipulation of the IGCs by combining the hand gestures with the spatial context. Using SGCI, we implement intuitive hand-driven shape modifications through skeletal bending, sectional deformation and sectional scaling schemes. The implemented prototype involves human skeletal tracking and hand posture classification using the depth data provided by a low-cost depth sensing camera (Kinect™). With Shape-It-Up, our goal is to make the designer an integral part of the shape modeling process during early design, in contrast to the case for current CAD tools which segregate 3D sweep geometries into procedural 2D inputs in a non-intuitive and onerous process requiring extensive training. We conclusively demonstrate the modeling of a wide variety of 3D shapes within a few seconds. [Copyright &y& Elsevier]

Abstract: The process of re-creating CAD models from actual physical parts, formally known as digital shape reconstruction (DSR) is an integral part of product development, especially in re-design. While, the majority of current methods used in DSR are surface-based, our overarching goal is to obtain direct parameterization of 3D meshes, by avoiding the actual segmentation of the mesh into different surfaces. As a first step towards reverse modeling physical parts, we extract (1) locally prominent cross-sections (PCS) from triangular meshes, and (2) organize and cluster them into sweep components, which form the basic building blocks of the re-created CAD model. In this paper, we introduce two new algorithms derived from Locally Linear Embedding (LLE) (Roweis and Sauk, 2000 ) and Affinity Propagation (AP) (Frey and Dueck, 2007 ) for organizing and clustering PCS. The LLE algorithm analyzes the cross-sections (PCS) using their geometric properties to build a global manifold in an embedded space. The AP algorithm, then clusters the local cross sections by propagating affinities among them in the embedded space to form different sweep components. We demonstrate the robustness and efficiency of the algorithms through many examples including actual laser-scanned (point cloud) mechanical parts. [Copyright &y& Elsevier]