TY - CONF
T1 - System identification and feedback control for directed-energy, metal-based additive manufacturing
AU - Seltzer, D. M.
AU - Wang, X.
AU - Nassar, A. R.
AU - Schiano, J. L.
AU - Reutzel, E. W.
N1 - Funding Information:
The authors gratefully acknowledge the contributions by Zachary Lassman, Mike Gidaro, Seth Gregor, and Chris Howland from the School of Electrical Engineering and Computer Science at the Pennsylvania State University. This work was supported by the College of Engineering Research Initiative at The Pennsylvania State University and the Office of Naval Research, under Contract No. N00014-11-1-0668. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the Office of Naval Research.
Funding Information:
This work was supported by the College of Engineering Research Initiative at The Pennsylvania State University and the Office of Naval Research, under Contract No. N00014-11-1-0668. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the Office of Naval Research.
PY - 2020
Y1 - 2020
N2 - Additive manufacturing of metal parts is a complex process where many variables determine part quality. In addition to manipulated process variables, such as travel speed, feedstock flow pattern, and energy distribution, other exogenous inputs also determine part quality. For example, changing build geometry and a growing global temperature. In addition, there are random external disturbances such as spatter on a cover lens. Both manipulated process variables and exogenous inputs affect dimensional tolerance, microstructure, and other properties that determine the final part quality. Our long term aim is to improve part quality through real-time regulation of measurable process variables using vision-based feedback control. As a starting point, we present a process model that relates scanning speed and laser power to build height and melt pool width. These results demonstrate the necessity for using multi-input multi-output feedback control techniques and provide information for refining the frame rate and spectral sensitivity of the imaging system.
AB - Additive manufacturing of metal parts is a complex process where many variables determine part quality. In addition to manipulated process variables, such as travel speed, feedstock flow pattern, and energy distribution, other exogenous inputs also determine part quality. For example, changing build geometry and a growing global temperature. In addition, there are random external disturbances such as spatter on a cover lens. Both manipulated process variables and exogenous inputs affect dimensional tolerance, microstructure, and other properties that determine the final part quality. Our long term aim is to improve part quality through real-time regulation of measurable process variables using vision-based feedback control. As a starting point, we present a process model that relates scanning speed and laser power to build height and melt pool width. These results demonstrate the necessity for using multi-input multi-output feedback control techniques and provide information for refining the frame rate and spectral sensitivity of the imaging system.
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M3 - Paper
AN - SCOPUS:85084932689
SP - 592
EP - 601
T2 - 26th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2015
Y2 - 10 August 2015 through 12 August 2015
ER -