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

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號9466449
頁(從 - 到)6553-6560
頁數8
期刊IEEE Robotics and Automation Letters
6
發行號4
DOIs
出版狀態Published - 2021 10月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 生物醫學工程
  • 人機介面
  • 機械工業
  • 電腦視覺和模式識別
  • 電腦科學應用
  • 控制和優化
  • 人工智慧

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