TY - JOUR
T1 - MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters
AU - Adnan, Muhammad
AU - Yang, Haw Ching
AU - Kuo, Tsung Han
AU - Cheng, Fan Tien
AU - Tran, Hong Chuong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2021/10
Y1 - 2021/10
N2 - Determining thresholds of the primary control loops (System 1) of an additive manufacturing (AM) process is challenging when realizing System 1 with its fast and intuitive capability for adapting to different metal powers, machine configurations, and process parameters. Based on the convolution neural network and long short-term memory models, this letter presents a secondary tuning loop (System 2) to classify the types of melt-pool images (MPIs) from a coaxial camera online, suggest polishing parameters, and determine the control thresholds of System 1 offline. Case studies indicate that the thresholds and parameters of System 1 including smoke discharging, powder coating, and laser polishing of control loops of a laser powder bed fusion (LPBF) machine can be more deliberatively and logically decided by the proposed MPI-based System 2.
AB - Determining thresholds of the primary control loops (System 1) of an additive manufacturing (AM) process is challenging when realizing System 1 with its fast and intuitive capability for adapting to different metal powers, machine configurations, and process parameters. Based on the convolution neural network and long short-term memory models, this letter presents a secondary tuning loop (System 2) to classify the types of melt-pool images (MPIs) from a coaxial camera online, suggest polishing parameters, and determine the control thresholds of System 1 offline. Case studies indicate that the thresholds and parameters of System 1 including smoke discharging, powder coating, and laser polishing of control loops of a laser powder bed fusion (LPBF) machine can be more deliberatively and logically decided by the proposed MPI-based System 2.
UR - http://www.scopus.com/inward/record.url?scp=85111115184&partnerID=8YFLogxK
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U2 - 10.1109/LRA.2021.3092762
DO - 10.1109/LRA.2021.3092762
M3 - Article
AN - SCOPUS:85111115184
SN - 2377-3766
VL - 6
SP - 6553
EP - 6560
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 4
M1 - 9466449
ER -