An In-Situ Deep Learning-Based Defect Detection Technology for Additive Manufacturing Process

Wei Wang, Peiren Wang, Hanzhong Zhang, Xiaoyi Chen, Min Chen, Ji Li*

*Corresponding author for this work

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

Abstract

A trend in the additive manufacturing (AM) area is to deliver full-functional end-use products rather than discrete parts. Therefore, multiple manufacturing processes, additive or traditional, need to be combined to enable complex structure building, various material processing, and end-use functionality actualization. 3D electronics is a type of important product that such AM technologies can produce. However, the whole manufacturing procedure is much more complex than ever before, and any defect that occurs during this period may fail the product. To address the challenge, this paper proposes a deep learning-based defect detection technology to monitor the AM fabrication procedure in situ. An improved YOLOv8 algorithm is developed to train the defect detection model to identify and evaluate the defect image. A dataset of 3550 defects in four categories was created by selecting typical defects of the extrusion 3D printing process to testify to the practicability of this method. Experimental results showed that the improved YOLOv8 model achieves a mean average precision (mAP50) of 91.7% with a frame rate of 71.9 frames per second. In the future, this technology can be deployed on AM apparatus for real-time quality monitoring during the fabrication period. Moreover, depending on the detection results, printing settings can be adjusted real-timely to enhance the success rate of the printing process.

Original languageEnglish
Title of host publication2023 8th International Conference on Integrated Circuits and Microsystems, ICICM 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages65-70
Number of pages6
ISBN (Electronic)9798350318517
DOIs
Publication statusPublished - 2023
Event8th International Conference on Integrated Circuits and Microsystems, ICICM 2023 - Nanjing, China
Duration: 20 Oct 202323 Oct 2023

Publication series

Name2023 8th International Conference on Integrated Circuits and Microsystems, ICICM 2023

Conference

Conference8th International Conference on Integrated Circuits and Microsystems, ICICM 2023
Country/TerritoryChina
CityNanjing
Period20/10/2323/10/23

Keywords

  • additive manufacturing
  • deep learning
  • defect detection
  • machine vision

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