Details
| Original language | English |
|---|---|
| Pages (from-to) | 25-28 |
| Number of pages | 4 |
| Journal | Procedia CIRP |
| Volume | 94 |
| Early online date | 15 Sept 2020 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 11th CIRP Conference on Photonic Technologies, LANE 2020 - Virtual, Online Duration: 7 Sept 2020 → 10 Sept 2020 |
Abstract
Laser Powder Bed Fusion (LPBF) is widely considered a key enabling technology of the future. In order to realize its full potential, however, reproducibility and in-process quality inspection capabilities have to meet industrial requirements. The application of novel sensor technologies such as hyperspectral cameras and intelligent data evaluation methods like machine learning models will allow more reliable manufacturing processes. This article discusses the value of snapshot hyperspectral imaging as a means to predict process states and defects with the help of machine learning algorithms. The imaging technology is presented and its characteristic advantage of providing spectral as well as spatial resolution is weighed against the drawbacks of low temporal resolution and reduced spatial resolution. Besides, different methods and configurations for in-process data acquisition and subsequent data labeling are explained. Finally, the utilization of this monitoring approach to the LPBF-processing of magnesium alloys is discussed and results are presented.
Keywords
- Additive Manufacturing, Hyperspectral Imaging, Laser Powder Bed Fusion, Machine Learning, Process Monitoring
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 94, 2020, p. 25-28.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Snapshot hyperspectral imaging for quality assurance in Laser Powder Bed Fusion
AU - Gerdes, N.
AU - Hoff, C.
AU - Hermsdorf, J.
AU - Kaierle, S.
AU - Overmeyer, L.
N1 - Funding Information: The authors gratefully acknowledge the funding by the German Research Foundation (DFG) within the priority program (SPP) 2122 “Ma terials for Additive Manufacturing (MATframe)”.
PY - 2020
Y1 - 2020
N2 - Laser Powder Bed Fusion (LPBF) is widely considered a key enabling technology of the future. In order to realize its full potential, however, reproducibility and in-process quality inspection capabilities have to meet industrial requirements. The application of novel sensor technologies such as hyperspectral cameras and intelligent data evaluation methods like machine learning models will allow more reliable manufacturing processes. This article discusses the value of snapshot hyperspectral imaging as a means to predict process states and defects with the help of machine learning algorithms. The imaging technology is presented and its characteristic advantage of providing spectral as well as spatial resolution is weighed against the drawbacks of low temporal resolution and reduced spatial resolution. Besides, different methods and configurations for in-process data acquisition and subsequent data labeling are explained. Finally, the utilization of this monitoring approach to the LPBF-processing of magnesium alloys is discussed and results are presented.
AB - Laser Powder Bed Fusion (LPBF) is widely considered a key enabling technology of the future. In order to realize its full potential, however, reproducibility and in-process quality inspection capabilities have to meet industrial requirements. The application of novel sensor technologies such as hyperspectral cameras and intelligent data evaluation methods like machine learning models will allow more reliable manufacturing processes. This article discusses the value of snapshot hyperspectral imaging as a means to predict process states and defects with the help of machine learning algorithms. The imaging technology is presented and its characteristic advantage of providing spectral as well as spatial resolution is weighed against the drawbacks of low temporal resolution and reduced spatial resolution. Besides, different methods and configurations for in-process data acquisition and subsequent data labeling are explained. Finally, the utilization of this monitoring approach to the LPBF-processing of magnesium alloys is discussed and results are presented.
KW - Additive Manufacturing
KW - Hyperspectral Imaging
KW - Laser Powder Bed Fusion
KW - Machine Learning
KW - Process Monitoring
UR - http://www.scopus.com/inward/record.url?scp=85093365775&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2020.09.006
DO - 10.1016/j.procir.2020.09.006
M3 - Conference article
AN - SCOPUS:85093365775
VL - 94
SP - 25
EP - 28
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 11th CIRP Conference on Photonic Technologies, LANE 2020
Y2 - 7 September 2020 through 10 September 2020
ER -