Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning

Léopold Le Roux, Chao Liu, Ze Ji, Pierre Kerfriden, Daniel Gage, Felix Feyer, Carolin Körner, Samuel Bigot

Research output: Journal article publicationConference articleAcademic researchpeer-review


Additive manufacturing (AM) has gained high research interests in the past but comes with some drawbacks, such as the difficulty to do in-situ quality monitoring. In this paper, deep learning is used on electron-optical images taken during the Electron Beam Melting (EBM) process to classify the quality of AM layers to achieve automatized quality assessment. A comparative study of several mainstream Convolutional Neural Networks to classify the images has been conducted. The classification accuracy is up to 95 %, which demonstrates the great potential to support in-process layer quality control of EBM.And the error analysis has shown that some human misclassification were correctly classified by the Convolutional Neural Networks.

Original languageEnglish
Pages (from-to)342-347
Number of pages6
JournalProcedia CIRP
Publication statusPublished - 3 May 2021
Externally publishedYes
Event14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020 - Naples, Italy
Duration: 15 Jul 202017 Jul 2020


  • Additive Manufacturing
  • Artificial Intelligence
  • Image recognition
  • Quality control
  • Transfert learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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