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    Aesthetic and functional analysis for product model validation in reverse engineering applications

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    Reverse engineering techniques are broadly used in the development process of products with meaningful aesthetic properties. Many stylists prefer to evaluate the product shape on the basis of a full-scale hand made physical mock-up. Such model has then to be converted into a 3D CAD model, to begin the product engineering and production processes. A critical aspect of the RE process is that the physical mock-up, made by the stylist, usually does not take into account the engineering production constraints. The surface reconstruction activity must then be followed by a modelling phase, where the engineering designer modifies the reconstructed model shape in order to make it suitable for production. At the end of this process, no matter how accurate and precise the surface reconstruction phase has been, the product model will be different from the original mock-up, and then it needs to be submitted to the stylist for the validation of its shape. For the stylist, getting used to working with physical models, the shape validation on a virtual model is not a trivial task. The objective of this research is to develop a software tool to support the identification of aesthetic and functional regions of the product model shape, that have been modified with respect to the original mock-up. The proposed approach is based on the decomposition of a 3D surface analysis problem into a simpler 2D curves analysis problem. This approach simulates the traditional method used by stylists to evaluate the quality of shapes. The implementation of the developed algorithms has been performed using a commercial software package (I-deas FreeForm by EDS/Unigraphics) and it has been successfully applied on real test case

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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