In situ monitoring and control of material growth for high resolution electron beam induced deposition

2007 ◽  
Vol 25 (6) ◽  
pp. 2210 ◽  
Author(s):  
W. F. van Dorp ◽  
C. W. Hagen ◽  
P. A. Crozier ◽  
P. Kruit
Keyword(s):  
Electron Beam ◽  
High Resolution ◽  
In Situ Monitoring ◽  
Monitoring And Control ◽  
Material Growth ◽  
Resolution Electron ◽  
Electron Beam Induced Deposition ◽  
And Control

In-situ monitoring and control for MOCVD growth of AlGaAs and InGaAs

1997 ◽  
Vol 26 (10) ◽  
pp. 1145-1153 ◽  
Author(s):  
A. Kussmaul ◽  
S. Vernon ◽  
P. C. Colter ◽  
R. Sudharsanan ◽  
A. Mastrovito ◽  
...  
Keyword(s):  
In Situ Monitoring ◽  
Monitoring And Control ◽  
Mocvd Growth ◽  
And Control

In situ monitoring and control of multicomponent gas-phase streams for growth of GaN via MOCVD

2000 ◽  
Vol 212 (1-2) ◽  
pp. 11-20 ◽  
Author(s):  
M.C Johnson ◽  
K Poochinda ◽  
N.L Ricker ◽  
J.W Rogers Jr ◽  
T.P Pearsall
Keyword(s):  
Gas Phase ◽  
In Situ Monitoring ◽  
Monitoring And Control ◽  
Multicomponent Gas ◽  
And Control

scholarly journals Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 11910
Author(s):  
Dalia Mahmoud ◽  
Marcin Magolon ◽  
Jan Boer ◽  
M.A Elbestawi ◽  
Mohammad Ghayoomi Mohammadi
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
In Situ Monitoring ◽  
Sensor Data ◽  
Learning Approaches ◽  
Monitoring And Control ◽  
In Situ Sensors ◽  
Applications Of Machine Learning ◽  
And Control

One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

In situ monitoring and control of InGaP growth on GaAs in MOVPE

1998 ◽  
Vol 195 (1-4) ◽  
pp. 223-227 ◽  
Author(s):  
M. Zorn ◽  
T. Trepk ◽  
P. Kurpas ◽  
M. Weyers ◽  
J.-T. Zettler ◽  
...  
Keyword(s):  
In Situ Monitoring ◽  
Monitoring And Control ◽  
And Control
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