Sensory Data Fusion Using Machine Learning Methods for In-Situ Defect Registration in Additive Manufacturing: A Review

Javid Akhavan, Souran Manoochehri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Scopus citations

Abstract

In-situ control to predict and mitigate defects in Additive Manufacturing (AM) could significantly increase these technologies' quality and reliability. Thorough knowledge of the AM processes is needed to develop such a controller. Recent studies utilized various methods to acquire data from the process, build insight into the process, and detect anomalies within the process. However, each sensory method has its unique limitations and capabilities. Sensor fusion techniques based on Machine Learning (ML) methods can combine all the data acquisition sources to form a holistic monitoring system for better data aggregation and enhanced detection. This holistic approach could also be used to train a controller on top of the fusion system to master the AM production and increase its reliance. This article summarizes recent studies on sensor utilization, followed by ML-based sensor fusion and control strategies.

Original languageEnglish
Title of host publication2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
EditorsSatyajit Chakrabarti, Rajashree Paul, Bob Gill, Malay Gangopadhyay, Sanghamitra Poddar
ISBN (Electronic)9781665486842
DOIs
StatePublished - 2022
Event2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022 - Toronto, Canada
Duration: 1 Jun 20224 Jun 2022

Publication series

Name2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022

Conference

Conference2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
Country/TerritoryCanada
CityToronto
Period1/06/224/06/22

Keywords

  • Additive Manufacturing (AM)
  • Data Fusion
  • Defect Classification
  • In-situ Process Control
  • Insitu Defect Detection
  • Machine Learning
  • Sensor Fusion

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