Real-time control for large scale additive manufacturing using thermal images

Feifan Wang, Feng Ju, Kyle Rowe, Nils Hofmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Large scale additive manufacturing has process mechanisms similar to Fused Filament Fabrication (FFF) and can print parts with large sizes, such as car bodies. This capability provides large scale additive manufacturing great application potentials in a variety of industries, including aerospace, automotive manufacturing, and construction. To make large scale additive manufacturing a viable manufacturing solution, both production efficiency and product quality need to be considered. Specifically, the printing process is subject to constraints on print surface temperature to guarantee good product quality. In this paper, we propose a novel method for controlling layer time during the printing process by using thermal images. Specifically, several thin wall test components are printed by the Thermwood Large Scale Additive Manufacturing (LSAM™) machine, and the print surface temperature is monitored by a FLIR™ thermal camera. A regression model based on real-time thermal imaging data is built to predict the surface temperature. Then a layer time control method is proposed based on the temperature prediction model. By comparing the proposed method to the fixed layer time policy, the experiment results suggest that the control method by using real-time data can siginificantly reduce the layer time, and subsequently the total printing time, while satisfying the quality requirement.

Original languageEnglish (US)
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages36-41
Number of pages6
ISBN (Electronic)9781728103556
DOIs
StatePublished - Aug 2019
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: Aug 22 2019Aug 26 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
CountryCanada
CityVancouver
Period8/22/198/26/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Real-time control for large scale additive manufacturing using thermal images'. Together they form a unique fingerprint.

  • Cite this

    Wang, F., Ju, F., Rowe, K., & Hofmann, N. (2019). Real-time control for large scale additive manufacturing using thermal images. In 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 (pp. 36-41). [8843264] (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2019.8843264