TY - JOUR
T1 - Heterogeneous sensor-based condition monitoring in directed energy deposition
AU - Montazeri, Mohammad
AU - Nassar, Abdalla R.
AU - Stutzman, Christopher B.
AU - Rao, Prahalada
N1 - Funding Information:
Funding at The Pennsylvania State University: This work uses data collected at the Applied Research Laboratory at the Pennsylvania State University under programs supported by the Office of Naval Research , under Contract No. N00014-11-1-0668 and Air Force Research Laboratory through America Makes under agreement number FA8650-12-2-7230 . 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, Air Force Research Laboratory, or America Makes. Funding at the University of Nebraska-Lincoln: One of the authors (PKR) thanks the National Science Foundation for funding his research through the following grants CMMI-1719388 , CMMI-1739696 and CMMI-1752069 (CAREER) at University of Nebraska-Lincoln. Specifically, the concept of using spectral graph theory for modeling and monitoring in metal additive manufacturing applications was funded through CMMI-1752069 towards a correct-as-you-build smart additive manufacturing paradigm.
Funding Information:
Funding at The Pennsylvania State University: This work uses data collected at the Applied Research Laboratory at the Pennsylvania State University under programs supported by the Office of Naval Research, under Contract No. N00014-11-1-0668 and Air Force Research Laboratory through America Makes under agreement number FA8650-12-2-7230. 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, Air Force Research Laboratory, or America Makes. Funding at the University of Nebraska-Lincoln: One of the authors (PKR) thanks the National Science Foundation for funding his research through the following grants CMMI-1719388, CMMI-1739696 and CMMI-1752069 (CAREER) at University of Nebraska-Lincoln. Specifically, the concept of using spectral graph theory for modeling and monitoring in metal additive manufacturing applications was funded through CMMI-1752069 towards a correct-as-you-build smart additive manufacturing paradigm.
PY - 2019/12
Y1 - 2019/12
N2 - The objective of this work is to detect in situ the occurrence of lack-of-fusion defects in titanium alloy (Ti-6Al-4 V) parts made using directed energy deposition (DED) additive manufacturing (AM). We use data from two types of in-process sensors, namely, a spectrometer and an optical camera which are integrated into an Optomec MR-7 DED machine. Both sensors are focused on capturing the dynamic phenomena around the melt pool region. To detect lack-of-fusion defects, we fuse (combine) the data from the in-process sensors invoking the concept of Kronecker product of graphs. Subsequently, we use the features derived from the graph Kronecker product as inputs to a machine learning algorithm to predict the severity (class or level) of average length of lack-of-fusion defects within a layer, which is obtained from offline X-ray computed tomography of the test parts. We demonstrate that the severity of lack-of-fusion defects is classified with statistical fidelity (F-score) close to 85% for a two-level classification scenario, and approximately 70% for a three-level classification scenario. Accordingly, this work demonstrates the use of heterogeneous in-process sensing and online data analytics for in situ detection of defects in DED metal AM process.
AB - The objective of this work is to detect in situ the occurrence of lack-of-fusion defects in titanium alloy (Ti-6Al-4 V) parts made using directed energy deposition (DED) additive manufacturing (AM). We use data from two types of in-process sensors, namely, a spectrometer and an optical camera which are integrated into an Optomec MR-7 DED machine. Both sensors are focused on capturing the dynamic phenomena around the melt pool region. To detect lack-of-fusion defects, we fuse (combine) the data from the in-process sensors invoking the concept of Kronecker product of graphs. Subsequently, we use the features derived from the graph Kronecker product as inputs to a machine learning algorithm to predict the severity (class or level) of average length of lack-of-fusion defects within a layer, which is obtained from offline X-ray computed tomography of the test parts. We demonstrate that the severity of lack-of-fusion defects is classified with statistical fidelity (F-score) close to 85% for a two-level classification scenario, and approximately 70% for a three-level classification scenario. Accordingly, this work demonstrates the use of heterogeneous in-process sensing and online data analytics for in situ detection of defects in DED metal AM process.
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U2 - 10.1016/j.addma.2019.100916
DO - 10.1016/j.addma.2019.100916
M3 - Article
AN - SCOPUS:85074173782
VL - 30
JO - Additive Manufacturing
JF - Additive Manufacturing
SN - 2214-8604
M1 - 100916
ER -