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^*

^*Corresponding author for this work

Mechanical, Biomedical & Design Engineering

Research output: Contribution to journal › Conference article › peer-review

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
Early online date	3 May 2021
DOIs	https://doi.org/10.1016/j.procir.2021.03.050
Publication status	Published - 2021
Event	14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020 - Naples, Italy Duration: 15 Jul 2020 → 17 Jul 2020

Bibliographical note

© 2021 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
Peer-review under responsibility of the scientific committee of the 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering 15-17 July 2020

Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement n°820774.

Keywords

Additive Manufacturing
Artificial Intelligence
Image recognition
Quality control
Transfert learning

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10.1016/j.procir.2021.03.050

Automatised quality assessment in additive layer manufacturing
© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering 15-17 July 2020
Final published version, 953 KBLicence: CC BY-NC-ND 3.0

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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note = "{\textcopyright} 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering 15-17 July 2020 Funding Information: This project has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement n°820774. ; 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020 ; Conference date: 15-07-2020 Through 17-07-2020",

year = "2021",

doi = "10.1016/j.procir.2021.03.050",

language = "English",

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journal = "Procedia CIRP",

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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 - © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering 15-17 July 2020 Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement n°820774.

PY - 2021

Y1 - 2021

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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U2 - 10.1016/j.procir.2021.03.050

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

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Keywords

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