TY - GEN
T1 - REAL-TIME MONITORING AND GAUSSIAN PROCESS-BASED ESTIMATION OF THE MELT POOL PROFILE IN DIRECT ENERGY DEPOSITION
AU - Lyu, Jiaqi
AU - Akhavan, Javid
AU - Mahmoud, Youmna
AU - Xu, Ke
AU - Krishna, Chaitanya
AU - Vallabh, Prasad
AU - Manoochehri, Souran
N1 - Publisher Copyright:
Copyright © 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - A comprehensive understanding of the melt pool behavior during directed energy deposition (DED) has become essential in identifying process anomalies and controlling the process quality. Previous studies focused on predicting the melt pool characteristics by solely using the process parameters, in this study we use real-time melt pool images to predict the melt pool characteristics. A CMOS camera is used to capture coaxial images of the melt pool during the deposition of single-track prints to improve the prediction model. Multiple regression models are trained and compared to estimate the melt pool profile (width, depth, and height) as a function of process parameters (namely, the laser power, the powder feed rate, and the scanning speed) and features from the ellipse fitting of the real-time melt pool images. A novel image processing algorithm is proposed to extract the major axis, minor axis, and tilt. The sensitivity analysis demonstrated that combining process parameters and coaxial images can improve the prediction performance. The Gaussian Process regression showed the best performance among all the employed regression models.
AB - A comprehensive understanding of the melt pool behavior during directed energy deposition (DED) has become essential in identifying process anomalies and controlling the process quality. Previous studies focused on predicting the melt pool characteristics by solely using the process parameters, in this study we use real-time melt pool images to predict the melt pool characteristics. A CMOS camera is used to capture coaxial images of the melt pool during the deposition of single-track prints to improve the prediction model. Multiple regression models are trained and compared to estimate the melt pool profile (width, depth, and height) as a function of process parameters (namely, the laser power, the powder feed rate, and the scanning speed) and features from the ellipse fitting of the real-time melt pool images. A novel image processing algorithm is proposed to extract the major axis, minor axis, and tilt. The sensitivity analysis demonstrated that combining process parameters and coaxial images can improve the prediction performance. The Gaussian Process regression showed the best performance among all the employed regression models.
KW - Direct energy deposition
KW - Gaussian process regression
KW - Image processing
KW - Melt pool profile
KW - Real-time monitoring
KW - SS316L
UR - http://www.scopus.com/inward/record.url?scp=85176804777&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85176804777&partnerID=8YFLogxK
U2 - 10.1115/msec2023-105104
DO - 10.1115/msec2023-105104
M3 - Conference contribution
AN - SCOPUS:85176804777
T3 - Proceedings of ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023
BT - Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering
T2 - ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023
Y2 - 12 June 2023 through 16 June 2023
ER -