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This article presents a simulation program for previewing and emulating three-dimensional printing. The proposed simulator adopts a voxel-based virtual manufacturing approach to fabricate the target model in a virtual volume space. A mass-diffusion procedure coupling with adaptive filament deposition is used to generate the printing roads of the virtual manufacturing model. Thus, the resultant virtual model resembles the printed object better. As the virtual model has been built, the simulator performs line-drawing, animation and volume rendering to reveal the progression of the printing and the geometric features of the model, including its outer appearance, surface roughness and internal structures. Hence, more geometrical features of the model are shown. During the virtual manufacturing process, semantic error checking is also carried out to detect potential printing errors which may cause supporting problems, collisions, overand under-extrusion, and other printing failures. Two efficient algorithms, using three rasters of voxels, are developed to conduct the kernel computations such that the run-time semantic error checking is simplified and the memory usage is reduced. Equipped with advanced modelling, debugging and graphics techniques, the proposed simulator can be used as a virtual three-dimensional printer as well as a debugger in additive manufacturing procedures. Users can utilize it to preview the resultant model and to evaluate the printing process such that potential errors can be prevented and the quality of the printed parts can be enhanced.

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Layered manufacturing techniques have been successfully employed to construct scanned objects from three-dimensional medical image data sets. The printed physical models are useful tools for anatomical exploration, surgical planning, teaching, and related medical applications. Before fabricating scanned objects, we have to first build watertight geometrical representations of the target objects from medical image data sets. Many algorithms had been developed to fulfill this duty. However, some of these methods require extra efforts to resolve ambiguity problems and to fix broken surfaces. Other methods cannot generate legitimate models for layered manufacturing. To alleviate these problems, this article presents a modeling procedure to efficiently create geometrical representations of objects from computerized tomography scan and magnetic resonance imaging data sets. The proposed procedure extracts the iso-surface of the target object from the input data set at the first step. Then it converts the iso-surface into a three-dimensional image and filters this three-dimensional image using morphological operators to remove dangling parts and noises. At the next step, a distance field is computed in the three-dimensional image space to approximate the surface of the target object. Then the proposed procedure smooths the distance field to soothe sharp corners and edges of the target object. Finally, a boundary representation is built from the distance field to model the target object. Compared with conventional modeling techniques, the proposed method possesses the following advantages: (1) it reduces human efforts involved in the geometrical modeling process. (2) It can construct both solid and hollow models for the target object, and wall thickness of the hollow models is adjustable. (3) The resultant boundary representation guarantees to form a watertight solid geometry, which is printable using three-dimensional printers. (4) The proposed procedure allows users to tune the precision of the geometrical model to compromise with the available computational resources.

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It's advantageous for computational scientists to have the capability to perform interactive visualization on their desktop workstations. For data on large unstructured meshes, this capability is not generally available. In particular, particle tracing on unstructured grids can result in a high percentage of non-contiguous memory accesses and therefore may perform very poorly with virtual memory paging schemes. The alternative of visualizing a lower resolution of the data degrades the original high-resolution calculations. This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk les for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that during the streamline construction only a very small amount of data are brought i n to the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is signicantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-20 megabytes. Our test results also show that this approach i s m uch more ecient than relying on virtual memory and operating system's paging algorithms.

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