MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters

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 neur...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2021-10, Vol.6 (4), p.6553-6560
Main Authors: Adnan, Muhammad, Yang, Haw-Ching, Kuo, Tsung-Han, Cheng, Fan-Tien, Tran, Hong-Chuong
Format: Article
Language:English
Subjects:
Artificial neural networks
Control systems
Image classification
Intelligent Compensator
Laser fusion
Laser modes
LPBF
Manufacturing
melt pool image
Melting
Metal additive manufacturing
Polishing
Powder beds
Powder coating
Powders
Power lasers
Process control
Process controls
Process parameters
Shape
Systems 1 & 2
Thresholds
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3092762