A key advantage of Additive Manufacturing (AM) is the opportunity to design and fabricate complex structures that cannot be made via traditional means. However, this potential is significantly constrained by the use of a facet-based geometry representation (e.g., the STL and the AMF file formats); which do not contain any volumetric information and often, designing/slicing/printing complex geometries exceeds the computational power available to the designer and the AM system itself. To enable efficient design and fabrication of complex/multi-material complex structures, several algorithms are presented that represent and process solid models as a set of voxels (three-dimensional pixels). Through this, one is able to efficiently realize parts featuring complex geometries and functionally graded materials. This thesis specifically aims to explore applications in three distinct fields namely, (i) Design for AM, (ii) Design for Manufacturing (DFM) education, and (iii) Reverse engineering from imaging data wherein voxel-based representations have proven to be superior to the traditional AM digital workflow. The advantages demonstrated in this study cannot be easily achieved using traditional AM workflows, and hence this work emphasizes the need for development of new voxel based frameworks and systems to fully utilize the capabilities of AM. / MS / Additive Manufacturing(AM) (also referred to as 3D Printing) is a process by which 3D objects are constructed by successively forming one-part cross-section at a time. Typically, the input file format for most AM systems is in the form of surface representation format (most commonly. STL file format). A STL file is a triangular representation of a 3-dimensional surface geometry where the part surface is broken down logically into a series of small triangles (facets). A key advantage of Additive Manufacturing is the opportunity to design and fabricate complex structures that cannot be made easily via traditional manufacturing techniques. However, this potential is significantly constrained by the use of a facet-based (triangular) geometry representation (e.g., the STL file format described above); which does not contain any volumetric (for e.g. material, texture, color etc.) information. Also, often, designing/slicing/printing complex geometries using these file formats can be computationally expensive. To enable more efficient design and fabrication of complex/multi-material structures, several algorithms are presented that represent and process solid models as a set of voxels (three-dimensional pixels). A voxel represents the smallest representable element of volume. For binary voxel model, a value of ‘1’ means that voxel is ‘on’ and value of 0 means voxel is ‘off’. Through this, one is able to efficiently realize parts featuring complex geometries with multiple materials. This thesis specifically aims to explore applications in three distinct fields namely, (i) Design for AM, (ii) Design for Manufacturing (DFM) education, and (iii) Fabricating models (Reverse engineering) directly from imaging data. In the first part of the thesis, a software tool is developed for automated manufacturability analysis of a part that is to be produced by AM. Through a series of simple computations, the tool provides feedback on infeasible features, amount of support material, optimum orientation and manufacturing time for fabricating the part. The results from this tool were successfully validated using a simple case study and comparison with an existing pre-processing AM software. Next, the above developed software tool is implemented for teaching instruction in a sophomore undergraduate classroom to improve students’ understanding of design constraints in Additive Manufacturing. Assessments are conducted to measure students’ understanding of a variety of topics in manufacturability both before and after the study to measure the effectiveness of this approach. The third and final part of this thesis aims to explore fabrication of models directly from medical imaging data (like CT Scan and MRI). A novel framework is proposed which is validated by fabricating three distinct medical models: a mouse skull, a partial human skull and a horse leg directly from corresponding CT Scan data. The advantages demonstrated in this thesis cannot be easily achieved using traditional AM workflows, and hence this work emphasizes the need for development of new voxel based frameworks and systems to fully utilize the capabilities of AM.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/89524 |
| Date | 20 November 2017 |
| Creators | Tedia, Saish |
| Contributors | Mechanical Engineering, Williams, Christopher B., Zheng, Xiaoyu, Bairaktarova, Diana |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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