Sciammarella, Federico M.
M.S. (Master of Science)
Department of Mechanical Engineering
Metal Additive Manufacturing with the Direct Energy Deposition (DED) process allows for rapid prototyping and low-volume custom manufacturing of complex geometries at a lower cost than traditional manufacturing methods. Direct energy deposition is a complex process having many inter-related process parameters that effect the overall quality and mechanical properties of the build. When new powder feedstocks are used, calibrating these process parameters is typically done empirically in trials that can contain more than a hundred different combinations of parameters. This time-consuming process can be reduced by performing an analytical analysis on how the energy from the laser transfers into the metal using the physical properties of the powder paired with empirical relations to predict where optimal process parameters will occur.
Typical builds use a fixed process parameter set and do not feature any in-situ process control. Previous research has shown that combining the variables of powder flow and travel speed into a ratio allows for consistent results. For this thesis the relation was improved upon to compare the bead volume per pass to the powder flow improving the relation and allowing powder flow to be predicted. The energy analysis was used to improve build quality of different parameter sets resulting in reduction of porosity and nearly fully dense parts. A methodology for process parameter prediction is introduced and validated using the analytical and empirical relations proved in this thesis. The ability to increase bead size allows for the process to be more efficient in time, energy, and powder usage, lessening time per part and cost for industry. The ability to have beads of different dimensions also allows for large deposition to occur followed by smaller deposits for fine features with uniform properties.
Pulscher, Daniel John, "Energy Analysis For Process Parameter Prediction of Direct Energy Deposition Metal Additive Manufacturing" (2019). Graduate Research Theses & Dissertations. 7571.
Northern Illinois University
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