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 - Funding Information:
Manuscript received February 28, 2021; accepted June 10, 2021. Date of publication June 28, 2021; date of current version July 19, 2021. This letter was recommended for publication by Associate Editor F. Ju and Editor J. Yi upon evaluation of the reviewers’ comments. This work was supported in part by the "Intelligent Manufacturing Research Center (iMRC)" from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, ROC, and also imparted by the Ministry of Science and Technology, Taiwan, ROC under Contracts MOST 109-2218-E-HE992-005 and 109-2622-E-992-020-CC2. (Corresponding author: Haw-Ching Yang.) Muhammad Adnan, Tsung-Han Kuo, and Fan-Tien Cheng are with the Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan 701, Taiwan (e-mail: ad.gujjar@yahoo.com; tsunghankuo@ imrc.ncku.edu.tw; chengft@mail.ncku.edu.tw).
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.
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U2 - 10.1109/LRA.2021.3092762
DO - 10.1109/LRA.2021.3092762
M3 - Article
AN - SCOPUS:85111115184
VL - 6
SP - 6553
EP - 6560
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 4
M1 - 9466449
ER -