MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters
Muhammad Adnan, Haw Ching Yang, Tsung-Han Kuo, Fan-Tien Cheng, Hong-Chuong Tran
Abstract
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.
BibTeX
@inproceedings{ral2021_mpibasedsystem2f,
title = {MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters},
author = {Muhammad Adnan and Haw Ching Yang and Tsung-Han Kuo and Fan-Tien Cheng and Hong-Chuong Tran},
booktitle = {RA-L 2021},
year = {2021}
}