Reverse engineering is of great interests for computer aided verification of freeform shapes. Accurate model is crucial to assure the validity of geometry assessment. Freeform reconstruction is especially challenging when the parts are thin and/or self-occlusion. An integrated measuring system was proposed by incorporating laser scanner into hardware mechanism on reconstructing model of complex shape. Mathematical model mapping the transformation relationship between system components and cylinder-based algorithm for mechanism calibration was presented. The proposed system was validated on blade measurement. Experimental results showed that the proposed system and calibration method were able to provide a reliable measurement on freeform shape.
Increasing diversity of types and decreasing batch sizes along with a growing complexity of products manufactured by forming technology result in new challenges for developers and designers. The construction of a full parametric model of a deep drawing tool in a 3D CAD system is usually considered time-consuming and associated with high cost, and thus discourages many designers. In order to render this type of modeling easier and faultless, a new method for the model-driven design of deep drawing tools is developed. For this purpose the analysis of fully parametric 3D CAD models of deep drawing tools is necessary. This analyzing contributes to the newly developed graphical domain-specific language, which makes the modeling of deep drawing tools more flexible and time-efficient.
The value of (semi-)automatically recognizing the meaningful regions on a Computer Aided Design (CAD) model is well documented and has been traditionally studied as Geometric feature recognition. However, the effective tagging of the regions, especially tagging with contextually meaningful (i.e. semantic) keywords, has been elusive. In this paper, we focus on the non-geometric aspects of the tagging problem and present a semantic tagging framework. Specifically, given the contextually augmented instances created from the unidentified regions on a CAD model, the framework tags the instances with semantic keywords defined in the semantic model. This will provide the direct link between a CAD model and semantic models, and thus allow high-level reasoning to support design and manufacturing tasks. The feasibility of the approach has been verified by applying the developed framework to tag the regions on turbine blade models. This paper concludes with the future challenges of the approach.
High relief is a sculpture where more than half of 3D figure is attached onto a background plane. The main problem of high relief modeling from 3D object is how to transform the 3D geometry within limited depth range. This paper presents a novel method to generate high reliefs, which benefits from the technique of Laplacian-based mesh deformation. Given a 3D object as input, we first select a set of handle points on the input model and compute their offset distances to the background. Taking these handle points as constraints, we then optimize the depth field by solving a bi-Laplacian-based linear system. The deformed object is ensured to attach onto the background with preserved depth structure and geometrical details. Our method is effective in dealing with different types of input shapes, even the ones with topology-disconnected components. Experimental results and comparisons with previous method demonstrate the effectiveness of the proposed method.
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