Investigating Statistical Correlation Between Multi-Modality In-Situ Monitoring Data for Powder Bed Fusion Additive Manufacturing

Zhuo Yang, M. Adnan, Yan Lu, Fan Tien Cheng, Haw Ching Yang, Milica Perisic, Yande Ndiaye

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

3 Citations (Scopus)

Abstract

In-situ measurements provide vast information for additive manufacturing process understanding and real-time control. Data from various monitoring techniques observe different characteristics of a build process. Fusing multi-modal in-situ monitoring data can significantly enhance process anomaly detection, part defect prediction, and build failure diagnosis, thus improving AM part quality control. This paper compares the powder bed fusion in-process observations from two types of AM in-situ monitoring, coaxial melt pool imaging, and layerwise imaging, and investigates the correlation between the two observations for a build of parts with multiple geometric features and scan patterns. All data were collected from an open architecture powder bed fusion AM testbed. Data analysis shows that both datasets exhibit significant statistical changes when new features are introduced during the build. However, further machine learning-based modeling indicates that statistical features extracted from the two data sets do not correlate very well. Discussions are provided on how the statistical analysis of the observations from the two modality monitoring system can be utilized for data fusion strategy development, especially toward improving process anomaly detection.

Original languageEnglish
Title of host publication2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
PublisherIEEE Computer Society
Pages283-290
Number of pages8
ISBN (Electronic)9781665490429
DOIs
Publication statusPublished - 2022
Event18th IEEE International Conference on Automation Science and Engineering, CASE 2022 - Mexico City, Mexico
Duration: 2022 Aug 202022 Aug 24

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2022-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Country/TerritoryMexico
CityMexico City
Period22-08-2022-08-24

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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