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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number9466449
Pages (from-to)6553-6560
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume6
Issue number4
DOIs
Publication statusPublished - 2021 Oct

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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