Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | 100916 |
| Journal | Additive Manufacturing |
| Volume | 30 |
| DOIs | |
| State | Published - Dec 2019 |
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All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Materials Science(all)
- Engineering (miscellaneous)
- Industrial and Manufacturing Engineering
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Heterogeneous sensor-based condition monitoring in directed energy deposition. / Montazeri, Mohammad; Nassar, Abdalla R.; Stutzman, Christopher B.; Rao, Prahalada.
In: Additive Manufacturing, Vol. 30, 100916, 12.2019.Research output: Contribution to journal › Article
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
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.
UR - http://www.scopus.com/inward/record.url?scp=85074173782&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074173782&partnerID=8YFLogxK
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 -