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Development of a Relational Energy Balance for Additive Manufacturing

Author:
Park, Joshua
Published:
[University Park, Pennsylvania] : Pennsylvania State University, 2015.
Physical Description:
1 electronic document
Additional Creators:
Martukanitz, Richard, Todd, Judith, and Segall, Albert E.
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Open Access.
Summary:
This thesis presents a proposed phenomenological, quasi-empirical model for the heattransfer of select additive manufacturing techniques through the use of an analytical energybalance, as well as results from related directed energy deposition experiments. Two sets ofexperiments were designed to measure deposition track geometries and melt-pool temperaturesfor Ti-6Al-4V and Inconel 625 alloys across a variety of processing conditions.Parameter variation experiments were conducted to examine the deposition trackgeometries associated with various combinations of several key user-established processingparameters. Full-factorial design was utilized for the processing parameters of laser power,velocity, and spot size in order to allow the study of the effect that each factor had on theresponse variable of melt-pool penetration depth for both alloys.A second set of experiments examined the thermal responses experienced in thedeposition melt pool and on the substrate surface during a laser deposition process. Both Ti-6Al-4V and Inconel 625* samples demonstrated melt-pool temperatures above the liquidustemperatures referenced in literature. These measured temperatures were recorded and used toestablish not only thermophysical property data for the model, but also as direct inputs into theheat transfer equations inside the Energy Output term of the proposed energy balance model.Thirdly, an analytical energy balance was developed to examine the response that selectkey processing parameters have on a directed energy deposition track's geometry. The melt-poolpenetration were plotted as a response variable against laser power and velocity. Finally, theresults of the model were compared with the experimental data to demonstrate the model'spotential for predicting responses in penetration data trends.
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Dissertation Note:
M.S. Pennsylvania State University 2015.
Technical Details:
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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