Search

We present an argument for using visual analytics to aid Grounded Theory methodologies in qualitative data analysis. Grounded theory methods involve the inductive analysis of data to generate novel insights and theoretical constructs. Making sense of unstructured text data is uniquely suited for visual analytics. Using natural language processing techniques such as parts-of-speech tagging, retrieving information content, and topic modeling, different parts of the data can be structured and semantically associated, and interactively explored, thereby providing conceptual depth to the guided discovery process. We review grounded theory methods and identify processes that can be enhanced through visual analytic techniques. Next, we develop an interface for qualitative text analysis, and evaluate our design with qualitative research practitioners who analyze texts with and without visual analytics support. The results of our study suggest how visual analytics can be incorporated into qualitative data analysis tools, and the analytic and interpretive benefits that can result. [ABSTRACT FROM AUTHOR]

Abstract: Conventional shape matching for engineering models primarily considers rigid shape similarity. They do not seek global shape similarity while considering large local deformations. However, engineering models created by some parametric-based design can involve large parametric changes. As a result, they do not share similarity in their global shape. Hence our goal is to develop shape representations for global matching of part models that can have large dissimilarity through stretching and/or bending. This paper presents a strategy of an integrated shape matching for contours of engineering drawings inspired by the divide-and-conquer paradigm. The original shape is decoupled into two levels of shape representations namely, higher-level structure and lower-level geometry. The higher-level structure matching is then achieved driven by optimal integrated solutions from matching of lower-level local geometry. Feature points are first extracted using curve evolution to attain the two levels of representations. In order to suit engineering semantics, a new significance function for a point is defined to suppress small features using discrete curve evolution. To conduct the integrated shape matching, a mechanism of using lookup tables is employed to associate these two levels of representations. Dynamic Time Warping and Elastic Matching are employed at different levels of shape representations in order to achieve the optimal integration. To demonstrate the advantages of the proposed work for engineering shapes, experiments for contour evolution, feature point registration, and shape-based similarity for retrieval are conducted. They are also compared with the existing methods. The experimental results show that the structure-oriented contour representation and matching are more meaningful and consistent from an engineering perspective. [Copyright &y& Elsevier]

Abstract: 3D shape retrieval and clustering is of current interest in several different fields, including mechanical engineering. Several new shape representations for 3D objects are continuing to emerge. Most shape representations are embedded in a variety of feature spaces. However, some of the recently reported shape representations are embedded in arbitrary metric spaces, i.e. distance spaces, rather than in multi-dimensional feature space. For such representations, the only operations available on the data objects are distance calculations between the objects. In addition, some of the view-based representations are embedded in non-metric spaces where the representations and the corresponding distances do not follow the triangle inequality. For shape clustering applications, most existing algorithms assume the shape representations either to be embedded in a multi-dimensional feature space or a metric distance space, making it difficult to evaluate several shape representations that do not conform to these assumptions. Therefore, two different approaches were evaluated for using the distance features of a shape to obtain clustering results. In the first method, the original distances are transformed into feature space using a multi-dimensional scaling approach for use with -means clustering. The second approach directly uses the original distances with a distance-based clustering algorithm. We compared the clustering effectiveness of these two approaches using a classified benchmark database of 3D models. The effect of using different shape descriptors and number of clusters was studied using four measures of clustering effectiveness. Several statistical methods, including the Rand Index and Mutual Information Index, were used to objectively evaluate the clustering efficacy. [Copyright &y& Elsevier]

Abstract: A global visibility map is a spherical image built to describe the complete set of global visible view directions for a surface. In this paper, we consider the computation of global visibility maps for regions on the boundary of a polyhedron. Both the self-occlusions introduced by a region and the global occlusions introduced by the rest of the surfaces on the boundary of the polyhedron are considered for computing a global visibility map. We show that the occluded view directions introduced between a pair of polyhedral surfaces can be computed from the spherical projection of the Minkowski sum of one surface and the reflection of the other. A suitable subset of the Minkowski sum, which shares the identical spherical projection with the complete Minkowski sum, is constructed to obtain the spherical images representing global occlusions. Our method has been successfully tested on many CAD models. It extends the previous methods for computing global visibility maps using convex decomposition, and it exhibits a better performance. [Copyright &y& Elsevier]

Most three-dimensional (3D) computer-aided design (CAD) models of mechanical parts, created during the design stage, have high shape complexity. The shape complexity required of CAD models reduces according to the field of application. Therefore, it is necessary to simplify the shapes of 3D CAD models, depending on their applications. Traditional simplification methods recognize simplification target shape based on a pre-defined algorithm. Such algorithm-based methods have difficulty processing unusual partial shapes not considered in the CAD model. This paper proposes a method that uses a network based on a generative adversarial network (GAN) to simplify the 3D CAD models of mechanical parts. The proposed network recognizes and removes simplification target shapes included in the 3D CAD models of mechanical parts. A 3D CAD model dataset was constructed to train the 3D CAD model simplification network. 3D CAD models are represented in voxel form in the dataset. Next, the constructed training dataset was used to train the proposed network. Finally, a 3D voxel simplification experiment was performed to evaluate the performance of the trained network. The experiment results showed that the network had an average error rate of 3.38% for the total area of the mechanical part and an average error rate of 14.61% for the simplification target area. • We present a learning-based method for simplifying mechanical 3D CAD models. • The proposed method utilizes an adversarial learning approach. • The generator generates a simplified CAD model from the original model. • The discriminator classifies the data created by the generator as either false or true. • Our method achieved a simplification performance of around 15% error rate. [ABSTRACT FROM AUTHOR]

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]

Abstract: Three-dimensional shape retrieval is a problem of current interest in several different fields, especially in the mechanical engineering domain. There exists a large body of work in developing representations for 3D shapes. However, there has been limited work done in developing domain-dependent benchmark databases for 3D shape searching. We propose a benchmark database for evaluating shape-based search methods relevant to the mechanical engineering domain. Twelve different shape descriptors belonging to three categories, namely: (1) feature vector-based, (2) histogram-based, and (3) view-based, are compared using the benchmark database. The main contributions of this paper are the development of a new engineering shape benchmark and an understanding of the effectiveness of different shape representations for classes of engineering parts. Overall, it was found that view-based representations yielded better retrieval results for a majority of shape classes, while no single method performed best for all shape categories. [Copyright &y& Elsevier]

Abstract: Estimates suggest that more than 75% of engineering design activity comprises reuse of previous design knowledge to address a new design problem. Reusing design knowledge has great potential to improve product quality, shorten lead time, and reduce cost. However, PLM systems, which address the issue of reuse by searching for keywords in filenames, part numbers or context attached to CAD models, do not provide a robust tool to search reusable knowledge. This paper presents a brief overview of a novel approach to search for 3D models. The system is built on a client–server–database architecture. The client takes in the query input from the user along with his search preferences and passes it to the server. The server converts the shape input into feature vectors and a unique skeletal graph representation. Details of the algorithms to perform these steps are presented here. Principal advantages of our graph representation are: (i) it preserves geometry and topology of the query model, (ii) it is considerably smaller than the B-Rep graph, and (iii) it is insensitive to minor perturbations in shape, but sensitive enough to capture the major features of a shape. The combined distance of feature vectors and skeletal graphs in the database provide an indirect measure of shape similarity between models. Critical database issues such as search system efficiency, semantic gap reduction and the subjectivity of the similarity definition are addressed. This paper reports our initial results in designing, implementing and running the shape search system. [Copyright &y& Elsevier]