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

doi:10.1016/j.procir.2021.03.050

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 publication › Conference article › Academic research › peer-review

7 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	342-347
Number of pages	6
Journal	Procedia CIRP
Volume	99
DOIs	https://doi.org/10.1016/j.procir.2021.03.050
Publication status	Published - 3 May 2021
Externally published	Yes
Event	14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020 - Naples, Italy Duration: 15 Jul 2020 → 17 Jul 2020

Keywords

Additive Manufacturing
Artificial Intelligence
Image recognition
Quality control
Transfert learning

ASJC Scopus subject areas

Control and Systems Engineering
Industrial and Manufacturing Engineering

More information

10.1016/j.procir.2021.03.050

Cite this

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title = "Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning",

abstract = "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.",

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T1 - Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning

AU - Roux, Léopold Le

AU - Liu, Chao

AU - Ji, Ze

AU - Kerfriden, Pierre

AU - Gage, Daniel

AU - Feyer, Felix

AU - Körner, Carolin

AU - Bigot, Samuel

N1 - Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement n°820774. Publisher Copyright: © 2021 The Authors. Published by Elsevier B.V.

PY - 2021/5/3

Y1 - 2021/5/3

N2 - 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.

AB - 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.

KW - Additive Manufacturing

KW - Artificial Intelligence

KW - Image recognition

KW - Quality control

KW - Transfert learning

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DO - 10.1016/j.procir.2021.03.050

M3 - Conference article

AN - SCOPUS:85106439688

SN - 2212-8271

VL - 99

SP - 342

EP - 347

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020

Y2 - 15 July 2020 through 17 July 2020

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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