TY - GEN
T1 - Sensory Data Fusion Using Machine Learning Methods for In-Situ Defect Registration in Additive Manufacturing
T2 - 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
AU - Akhavan, Javid
AU - Manoochehri, Souran
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In-situ control to predict and mitigate defects in Additive Manufacturing (AM) could significantly increase these technologies' quality and reliability. Thorough knowledge of the AM processes is needed to develop such a controller. Recent studies utilized various methods to acquire data from the process, build insight into the process, and detect anomalies within the process. However, each sensory method has its unique limitations and capabilities. Sensor fusion techniques based on Machine Learning (ML) methods can combine all the data acquisition sources to form a holistic monitoring system for better data aggregation and enhanced detection. This holistic approach could also be used to train a controller on top of the fusion system to master the AM production and increase its reliance. This article summarizes recent studies on sensor utilization, followed by ML-based sensor fusion and control strategies.
AB - In-situ control to predict and mitigate defects in Additive Manufacturing (AM) could significantly increase these technologies' quality and reliability. Thorough knowledge of the AM processes is needed to develop such a controller. Recent studies utilized various methods to acquire data from the process, build insight into the process, and detect anomalies within the process. However, each sensory method has its unique limitations and capabilities. Sensor fusion techniques based on Machine Learning (ML) methods can combine all the data acquisition sources to form a holistic monitoring system for better data aggregation and enhanced detection. This holistic approach could also be used to train a controller on top of the fusion system to master the AM production and increase its reliance. This article summarizes recent studies on sensor utilization, followed by ML-based sensor fusion and control strategies.
KW - Additive Manufacturing (AM)
KW - Data Fusion
KW - Defect Classification
KW - In-situ Process Control
KW - Insitu Defect Detection
KW - Machine Learning
KW - Sensor Fusion
UR - http://www.scopus.com/inward/record.url?scp=85133822139&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133822139&partnerID=8YFLogxK
U2 - 10.1109/IEMTRONICS55184.2022.9795815
DO - 10.1109/IEMTRONICS55184.2022.9795815
M3 - Conference contribution
AN - SCOPUS:85133822139
T3 - 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
BT - 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
A2 - Chakrabarti, Satyajit
A2 - Paul, Rajashree
A2 - Gill, Bob
A2 - Gangopadhyay, Malay
A2 - Poddar, Sanghamitra
Y2 - 1 June 2022 through 4 June 2022
ER -