Volume decomposition for two-piece rigid casting

2021 ◽  
Vol 40 (6) ◽  
pp. 1-14
Author(s):  
Thomas Alderighi ◽  
Luigi Malomo ◽  
Bernd Bickel ◽  
Paolo Cignoni ◽  
Nico Pietroni
Keyword(s):  
Volume Decomposition

scholarly journals Volume Decomposition for Hierarchical Skeletonization

2009 ◽  
Vol 8 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Xiaopeng Zhang ◽  
Jianfei Liu ◽  
Marc Jaeger ◽  
Zili Li
Keyword(s):  
Initial Data ◽  
Hierarchical Structure ◽  
Shape Description ◽  
Shape Features ◽  
Volume Data ◽  
Volume Decomposition ◽  
The Hierarchical Structure

Hierarchical skeletons and shape components are important shape features, and they are useful for shape description and shape understanding. Techniques to extract shape components and hierarchical skeletons from volume data are analyzed in this paper based on multiple distance transformations. The application of volume decomposition for the extraction of hierarchical skeletons is emphasized and specified here. This work includes an establishment of the hierarchical structure of the object volume, a decomposition of the volume into simple sub-volumes, an extraction of compact skeleton segments corresponding to each independent sub-volume, and a connection of these skeleton segments into a hierarchical structure reflecting the organization the initial data

Form Feature Recognition Using Base Volume Decomposition

1994 ◽  
Author(s):  
James K. Coles ◽  
Richard H. Crawford ◽  
Kristin L. Wood
Keyword(s):  
Real World ◽  
Feature Recognition ◽  
Recognition Method ◽  
Mechanical Parts ◽  
Feature Representations ◽  
Volume Decomposition ◽  
Boundary Representations ◽  
New Feature ◽  
Form Feature

Abstract A new feature recognition method is presented that generates volumetric feature representations from conventional boundary representations of mechanical parts. Recognition is accomplished by decomposing the known total feature volume of a part into a set of smaller volumes through analytic face extension. The decomposed volumes are combined to generate an initial set of features. Alternative sets of features are generated by maintaining and evaluating information on intersections of the initial feature set. The capabilities of the method are demonstrated through both a hypothetical and a real world design example. The method’s ability to locate features despite interactions with other features, and its ability to generate alternative sets of features, distinguishes it from existing recognition techniques.

Maximal Volume Decomposition and its Application to Feature Recognition

1995 ◽  
Author(s):  
Parag Dave ◽  
Hiroshi Sakurai
Keyword(s):  
Decomposition Method ◽  
Feature Recognition ◽  
Graph Matching ◽  
Raw Material ◽  
Maximal Volume ◽  
Object A ◽  
Finished Part ◽  
Volume Decomposition ◽  
Minimal Cells ◽  
Volume Difference

Abstract A method has been developed that decomposes an object having both planar and curved faces into volumes, called maximal volumes, using the halfspaces of the object. A maximal volume has as few concave edges as possible without introducing additional halfspaces. The object is first decomposed into minimal cells by extending the faces of the object. These minimal cells are then composed to form maximal volumes. The combinations of such minimal cells that result in maximal volumes are searched efficiently by examining the relationships among those minimal cells. With this decomposition method, a delta volume, which is the volume difference between the raw material and the finished part, is decomposed into maximal volumes. By subtracting maximal volumes from each other in different orders and applying graph matching to the resulting volumes, multiple interpretations of features can be generated.

Destructive Modeling by Volume Decomposition and Its Applications

2003 ◽  
Author(s):  
Yoonhwan Woo ◽  
Sang Hun Lee
Keyword(s):  
Decomposition Method ◽  
Solid Model ◽  
Solid Models ◽  
Volume Decomposition ◽  
Number Of Cells ◽  
Natural Way

Adding simple volumes, which are often called primitives, is a natural way to construct complex solid models. Conversely, cell-based volume decomposition is the existing method to decompose a complex solid model into simpler volumes that are often the primitives used to create the model. One problem of this volume decomposition is that it generates a large number of cells, many of which are unnecessary for the decomposition. In this paper, a volume decomposition method that improves the performance by avoiding generating the unnecessary cells is presented. Some possible applications are also presented to attest the usefulness of this volume decomposition method.

scholarly journals Ground and Volume Decomposition as a Proxy for AGB from P-Band SAR Data

2020 ◽  
Vol 12 (2) ◽  
pp. 240 ◽  
Author(s):  
Francesco Banda ◽  
Mauro Mariotti d’Alessandro ◽  
Stefano Tebaldini
Keyword(s):  
Tropical Forests ◽  
Forest Structure ◽  
Synthetic Aperture ◽  
Above Ground Biomass ◽  
Volume Scattering ◽  
Interferometric Phase ◽  
Volume Decomposition ◽  
Sar Data

In this work, the role of volume scattering obtained from ground and volume decomposition of P-band synthetic aperture radar (SAR) data as a proxy for biomass is investigated. The analysis here presented originates from the BIOMASS L2 activities, part of which were focused on strengthening the physical foundations of the SAR-based retrieval of forest above-ground biomass (AGB). A critical analysis of the observed strong correlation between tomographic intensity and AGB is done in order to propose simplified AGB proxies to be used during the interferometric phase of BIOMASS. In particular, the aim is to discuss whether, and to what extent, volume scattering obtained from ground/volume decomposition can provide a reasonable alternative to tomography. To do this, both are tested on P-band data collected at Paracou during the TropiSAR campaign and cross-validated against in-situ AGB measurements. Results indicate that volume backscattered power as obtained by ground/volume decomposition is weakly correlated to AGB, notwithstanding different solutions for volume scattering are tested, and support the conclusion that forest structure actually plays a non-negligible role in AGB retrieval in dense tropical forests.

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