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

• Published in: IEEE robotics and automation letters Vol. 6; no. 4; pp. 6553 - 6560
• Main Authors: Adnan, Muhammad, Yang, Haw-Ching, Kuo, Tsung-Han, Cheng, Fan-Tien, Tran, Hong-Chuong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3092762

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.