Deep Learning Based Automatic Porosity Detection of Laser Powder Bed Fusion Additive Manufacturing

Syed Ibn Mohsin, Behzad Farhang, Peng Wang, Yiran Yang, Narges Shayesteh, Fazleena Badurdeen

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

1 Scopus citations

Abstract

Laser Powder Bed Fusion (LPBF) is a widely utilized additive manufacturing process. Despite its popularity, LPBF has been found to have limitations in terms of the reliability and repeatability of its parts. To address these limitations, a deep learning model based on You Only Look Once (YOLO) was adapted to automate the detection of defect areas from scanning electron microscopic images of LPBF-manufactured parts. The data on the defect areas are then integrated into an Artificial Neural Network to correlate the process parameters with defects. The results show that the development of defects is stochastic in nature with respect to the input process parameters. The high variability of defects generated from the same process parameters makes it difficult to reliably predict the quality of the parts using only a process data-driven approach. This highlights the importance of in-situ monitoring of the system for reliable prediction of part quality.

Original languageEnglish
Title of host publicationFlexible Automation and Intelligent Manufacturing
Subtitle of host publicationEstablishing Bridges for More Sustainable Manufacturing Systems - Proceedings of FAIM 2023
EditorsFrancisco J. G. Silva, Raul D.S.G. Campilho, António B. Pereira
Pages328-335
Number of pages8
DOIs
StatePublished - 2024
Event32nd International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2023 - Porto, Portugal
Duration: Jun 18 2023Jun 22 2023

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference32nd International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2023
Country/TerritoryPortugal
CityPorto
Period6/18/236/22/23

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Additive Manufacturing
  • Machine Learning
  • Powder Bed Fusion

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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