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No description available.

Rapid prototyping systems have advanced significantly with respect to material capabilities, fabrication speed, and surface quality. However, build jobs are still manually activated one at a time. The result is non-productive machine time whenever an operator is not at hand to make a job changeover. A low-cost auxiliary system, named Continuous Layered Manufacturing (CLM), has been developed to automatically load and unload the FDM 1600 rapid prototyping system (Stratasys, Inc.). The modifications made to the FDM 1600 system are minimal. The door to the FDM 1600 build chamber is removed, and the .SML build files that are used to drive the FDM 1600 are modified at both ends to facilitate synchronized operation between the two systems. The CLM system is capable of running three consecutive build jobs without operator intervention. As long as an operator removes finished build jobs, and adds new build trays before at most every three build jobs, the FDM can operate near indefinitely. The impact of the CLM system on the productivity of the FDM 1600 rapid prototyping system is demonstrated by the expected reduction from the customary eight weeks down to a future three and one-half weeks required to complete the typical forty build jobs during a semester in the course ME 4644 Introduction to Rapid Prototyping at Virginia Tech. / Master of Science

The need for reliable and cost-effective testing procedures for Additive Manufacturing (AM) is growing. In this Dissertation, the development of a new computational-experimental method based on the realization of specific testing artifacts to address this need is presented. This research is focused on one of the widely utilized AM technologies, Fused Deposition Modeling (FDM), and can be extended to other AM technologies as well. In this method, testing artifacts are designed with simplified boundary conditions and computational domains that minimize uncertainties in the analyses. Testing artifacts are a combination of thin and thick cantilever structures, which allow measurement of natural frequencies, mode shapes, and dimensions as well as distortions and deformations. We apply Optical Non-Destructive Testing (ONDT) together with computational methods on the testing artifacts to predict their natural frequencies, thermal flow, mechanical properties, and distortions as a function of 3D printing parameters. The complementary application of experiments and simulations on 3D printed testing artifacts allows us to systematically investigate the density, porosity, moduli of elasticity, and Poisson’s ratios for both isotropic and orthotropic material properties to better understand relationships between these characteristics and the selected printing parameters. The method can also be adapted for distortions and residual stresses analyses. We optimally collect data using a design of experiments technique that is based on regression models, which yields statistically significant data with a reduced number of iterations. Analyses of variance of these data highlight the complexity and multifaceted effects of different process parameters and their influences on 3D printed part performance. We learned that the layer thickness is the most significant parameter that drives both density and elastic moduli. We also observed and defined the interactions among density, elastic moduli, and Poisson’s ratios with printing speed, extruder temperature, fan speed, bed temperature, and layer thickness quantitatively. This Dissertation also shows that by effectively combining ONDT and computational methods, it is possible to achieve greater understanding of the multiphysics that governs FDM. Such understanding can be used to estimate the physical and mechanical properties of 3D printed components, deliver part with improved quality, and minimize distortions and/or residual stresses to help realize functional components.

The need for reliable and cost-effective testing procedures for Additive Manufacturing (AM) is growing. In this Dissertation, the development of a new computational-experimental method based on the realization of specific testing artifacts to address this need is presented. This research is focused on one of the widely utilized AM technologies, Fused Deposition Modeling (FDM), and can be extended to other AM technologies as well. In this method, testing artifacts are designed with simplified boundary conditions and computational domains that minimize uncertainties in the analyses. Testing artifacts are a combination of thin and thick cantilever structures, which allow measurement of natural frequencies, mode shapes, and dimensions as well as distortions and deformations. We apply Optical Non-Destructive Testing (ONDT) together with computational methods on the testing artifacts to predict their natural frequencies, thermal flow, mechanical properties, and distortions as a function of 3D printing parameters. The complementary application of experiments and simulations on 3D printed testing artifacts allows us to systematically investigate the density, porosity, moduli of elasticity, and Poisson’s ratios for both isotropic and orthotropic material properties to better understand relationships between these characteristics and the selected printing parameters. The method can also be adapted for distortions and residual stresses analyses. We optimally collect data using a design of experiments technique that is based on regression models, which yields statistically significant data with a reduced number of iterations. Analyses of variance of these data highlight the complexity and multifaceted effects of different process parameters and their influences on 3D printed part performance. We learned that the layer thickness is the most significant parameter that drives both density and elastic moduli. We also observed and defined the interactions among density, elastic moduli, and Poisson’s ratios with printing speed, extruder temperature, fan speed, bed temperature, and layer thickness quantitatively. This Dissertation also shows that by effectively combining ONDT and computational methods, it is possible to achieve greater understanding of the multiphysics that governs FDM. Such understanding can be used to estimate the physical and mechanical properties of 3D printed components, deliver part with improved quality, and minimize distortions and/or residual stresses to help realize functional components.

Today's commercial rapid prototyping systems (i.e., solid freeform fabrication, layered manufacturing) rely on human intervention to load and unload build jobs. Hence, jobs are processed subject to both the machine's and the operator's schedules. In particular, first-in-first-out (FIFO) queuing of such systems will result in machine idle time whenever a build job has been completed and an operator is not available to unload that build job and start up the next one. These machine idle times can significantly affect the system throughput, and, hence, the effective cost rate. This thesis addresses this problem by rearranging the job queue to minimizing the machine idle time, subject to the machine's and operator's schedules. This is achieved by employing a general branch-and-bound search method, that, for efficiency, reduces the search space by identifying contiguous sequences and avoiding reshuffling of those sequences during the branching procedure. The effectiveness of this job scheduling optimization has been demonstrated using a sequence of 30 jobs extracted from the usage log for the FDM 1600 rapid prototyping system in the Department of Mechanical Engineering at Virginia Tech. / Master of Science

Additive manufacturing is gaining popularity at a rapid rate and has been a resourceful production process to reduce material usage, wastage (scrap) and manufacturing costs for various applications. The project conducted, emphasizes on Thermoplastic Elastomer (TPE SE6300C-65A) material, which is a highly versatile compound, and has the ability to exhibit properties of both rubber (Elastomers) in terms of flexibility and plastic (Polymers) in terms of recyclability. Cost reduction without compromising quality is one of the important factors for industries. The project involves the use of TPE pellets to extrude filaments that could be used for 3D Printing. Filament extrusion involves process variables like Nozzle Temperature, De-humidification of pellets, Diameter of the nozzle, Distance of collection, Cooling and Angle of inclination of the extruder. These process variables are optimized to accomplish the desired quality of filament. The filament produced through extrusion is further used to make products using Fused Deposition Modeling (FDM). FDM also involves numerous process variables like Layer Thickness, Build Orientation, Print Infill, Print Speed etc. In this study, different test specimens, in terms of geometrical shapes are printed from the material, TPE SE6300C-65A and tested in order to understand how the surface features as well as the dimensional accuracy change with different process variables. It is observed that the surface topography produced throughout FDM process is majorly affected by the angle of orientation of the printed part. The main goal of this thesis is to give the reader a better understanding on which process variable, such as layer thickness, temperature and print speed affect the surface roughness of the models and also a comparison between these three variables, highlighting which is more or less affecting. It is also observed the dimensional accuracy of the real specimen deviate from the value input into the CAD software. The results obtained in this study clearly suggest that there is a lot of opportunities for future improvements especially regarding the dimensional accuracy, it is imperative to achieve the highest precision possible in order to have commercial values for the FDM 3D printing.

<p>  </p> <p>This thesis is motivated by the author’s observation that no systematic methodology is available to characterize and model mechanical behaviors of 3D printed parts in terms of their elastic modulus and critical loading capacities. Note that the more controlled and steadier printing process is, the easier the mechanical properties parts can be predicted. This research focuses on the methods for the prediction and validation of mechanical properties of 3D printed parts, and the focus is the responses of the printed parts subjected to tensile loads. The mathematic models are derived to characterize the mechanical properties of a part along three principal directions, and the models are validated experimentally by following the American Society for Testing and Materials (ASTM) D638 testing standards. It is assumed that a unidirectional plane stress occurs to each lamina to (1) simplify a compliance matrix with a size 3 by 3 and (2) characterize the mechanical properties by the elastic modules and strengths in three principal directions. Two mathematical models are developed using the experimental data from the classical laminate theory and finite element analysis (FEA) by the SolidWorks. Both of the developed models are used to predict the ultimate tensile strength and Young’s modulus of the specimens that are printed by setting different raster angles on different layers. This thesis work aims to (1) gain a better understanding of the impact of printing parameters on the strengths of printed parts and (2) explore the feasibility of using the classical laminate theory to predict the mechanical properties of the parts printed with different raster angles and patterns. To validate the proposed mathematic models, parts by FDM are tested by following the ASTM testing standards; moreover, it testifies if the selected ASTM-D638 is suitable to test 3D printed parts by FDM. </p>

No description available.

This project was a test of concept for an external heating system for Big Area Additive Manufacturing (BAAM) Fused Deposition Modeling (FDM) 3D printers. To goal of the heating system was to prevent or mitigate delamination and warping in BAAM FDM prints by propelling warm air onto printed layers while not interfering with prior functions of the 3D printer.