Defect Modeling During the SLM Process for Manufacturing Microwave Devices

Shuai Li; Xiue Bao; Giovanni Gugliandolo; Haoyun Yuan; Jinkai Li; Linxiang Shao; Minghe Du; Nicola Donato; Zlatica Marinkovic; Giovanni Crupi; Lili Fang; Liming Si; Houjun Sun

doi:10.1109/MetroXRAINE58569.2023.10405715

Defect Modeling During the SLM Process for Manufacturing Microwave Devices

Shuai Li^*, Xiue Bao^*, Giovanni Gugliandolo, Haoyun Yuan^*, Jinkai Li, Linxiang Shao^*, Minghe Du, Nicola Donato, Zlatica Marinkovic, Giovanni Crupi, Lili Fang^*, Liming Si^*, Houjun Sun^*

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper intends to address the issue of crack formation and other flaws during the selective laser melting (SLM) additive manufacturing process. To achieve this objective, image processing, 3D modeling, and deep-learning techniques are employed to generate a 3D defect model, while data statistics are utilized for enhancing and optimizing the entire additive manufacturing process, including adjusting manufacturing process parameters, optimizing strategies, reducing defects, and improving the yield rate of SLM. After training and adjustment, the crack recognition accuracy of the final model can reach 92.3%.

Original language	English
Title of host publication	2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	412-416
Number of pages	5
ISBN (Electronic)	9798350300802
DOIs	https://doi.org/10.1109/MetroXRAINE58569.2023.10405715
Publication status	Published - 2023
Event	2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Milano, Italy Duration: 25 Oct 2023 → 27 Oct 2023

Publication series

Name	2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings

Conference

Conference	2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023
Country/Territory	Italy
City	Milano
Period	25/10/23 → 27/10/23

Keywords

3D printing
Additive manufacturing
deep learning
defect identification
three-dimensional reconstruction

Access to Document

10.1109/MetroXRAINE58569.2023.10405715

Cite this

Li, S., Bao, X., Gugliandolo, G., Yuan, H., Li, J., Shao, L., Du, M., Donato, N., Marinkovic, Z., Crupi, G., Fang, L., Si, L., & Sun, H. (2023). Defect Modeling During the SLM Process for Manufacturing Microwave Devices. In 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings (pp. 412-416). (2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MetroXRAINE58569.2023.10405715

Li, Shuai ; Bao, Xiue ; Gugliandolo, Giovanni et al. / Defect Modeling During the SLM Process for Manufacturing Microwave Devices. 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 412-416 (2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings).

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title = "Defect Modeling During the SLM Process for Manufacturing Microwave Devices",

abstract = "This paper intends to address the issue of crack formation and other flaws during the selective laser melting (SLM) additive manufacturing process. To achieve this objective, image processing, 3D modeling, and deep-learning techniques are employed to generate a 3D defect model, while data statistics are utilized for enhancing and optimizing the entire additive manufacturing process, including adjusting manufacturing process parameters, optimizing strategies, reducing defects, and improving the yield rate of SLM. After training and adjustment, the crack recognition accuracy of the final model can reach 92.3%.",

keywords = "3D printing, Additive manufacturing, deep learning, defect identification, three-dimensional reconstruction",

author = "Shuai Li and Xiue Bao and Giovanni Gugliandolo and Haoyun Yuan and Jinkai Li and Linxiang Shao and Minghe Du and Nicola Donato and Zlatica Marinkovic and Giovanni Crupi and Lili Fang and Liming Si and Houjun Sun",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 ; Conference date: 25-10-2023 Through 27-10-2023",

year = "2023",

doi = "10.1109/MetroXRAINE58569.2023.10405715",

language = "English",

series = "2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "412--416",

booktitle = "2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings",

address = "United States",

}

Li, S, Bao, X, Gugliandolo, G, Yuan, H, Li, J, Shao, L, Du, M, Donato, N, Marinkovic, Z, Crupi, G, Fang, L , Si, L & Sun, H 2023, Defect Modeling During the SLM Process for Manufacturing Microwave Devices. in 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings. 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 412-416, 2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023, Milano, Italy, 25/10/23. https://doi.org/10.1109/MetroXRAINE58569.2023.10405715

Defect Modeling During the SLM Process for Manufacturing Microwave Devices. / Li, Shuai; Bao, Xiue; Gugliandolo, Giovanni et al.
2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 412-416 (2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Defect Modeling During the SLM Process for Manufacturing Microwave Devices

AU - Li, Shuai

AU - Bao, Xiue

AU - Gugliandolo, Giovanni

AU - Yuan, Haoyun

AU - Li, Jinkai

AU - Shao, Linxiang

AU - Du, Minghe

AU - Donato, Nicola

AU - Marinkovic, Zlatica

AU - Crupi, Giovanni

AU - Fang, Lili

AU - Si, Liming

AU - Sun, Houjun

PY - 2023

Y1 - 2023

N2 - This paper intends to address the issue of crack formation and other flaws during the selective laser melting (SLM) additive manufacturing process. To achieve this objective, image processing, 3D modeling, and deep-learning techniques are employed to generate a 3D defect model, while data statistics are utilized for enhancing and optimizing the entire additive manufacturing process, including adjusting manufacturing process parameters, optimizing strategies, reducing defects, and improving the yield rate of SLM. After training and adjustment, the crack recognition accuracy of the final model can reach 92.3%.

AB - This paper intends to address the issue of crack formation and other flaws during the selective laser melting (SLM) additive manufacturing process. To achieve this objective, image processing, 3D modeling, and deep-learning techniques are employed to generate a 3D defect model, while data statistics are utilized for enhancing and optimizing the entire additive manufacturing process, including adjusting manufacturing process parameters, optimizing strategies, reducing defects, and improving the yield rate of SLM. After training and adjustment, the crack recognition accuracy of the final model can reach 92.3%.

KW - 3D printing

KW - Additive manufacturing

KW - deep learning

KW - defect identification

KW - three-dimensional reconstruction

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DO - 10.1109/MetroXRAINE58569.2023.10405715

M3 - Conference contribution

AN - SCOPUS:85185821635

T3 - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings

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EP - 416

BT - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

Y2 - 25 October 2023 through 27 October 2023

ER -

Li S, Bao X, Gugliandolo G, Yuan H, Li J, Shao L et al. Defect Modeling During the SLM Process for Manufacturing Microwave Devices. In 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 412-416. (2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023 - Proceedings). doi: 10.1109/MetroXRAINE58569.2023.10405715

Defect Modeling During the SLM Process for Manufacturing Microwave Devices

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