A Deep Learning-based Approach to Extraction of Filler Morphology in SEM Images with the Application of Automated Quality Inspection

Authors

Md. Fashiar Rahman, The University of Texas at El PasoFollow
Tzu-Liang Bill Tseng, The University of Texas at El PasoFollow
Jianguo Wu, The University of Texas at El PasoFollow
Yuxin Wen, Chapman UniversityFollow
Yirong Lin, The University of Texas at El PasoFollow

Document Type

Article

Publication Date

3-18-2022

Abstract

Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system can simultaneously classify, detect, and segment fillers in SEM images, making it suitable for morphology analysis of fillers and automatic quality inspection. We also propose a novel SEM image simulation procedure to overcome the data scarcity for training a deep CNN architecture. The proposed filler detection system is trained on the simulated images. It is shown that the trained network can detect and segment fillers with higher accuracy even in the overlapping and obscure situations. The performance and robustness of the proposed system are evaluated using both simulated and real microscopic images.

Comments

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Artificial Intelligence for Engineering Design, Analysis and Manufacturing, volume 36, in 2022 following peer review. The definitive publisher-authenticated version is available online at https://doi.org/10.1017/S0890060421000330.

The Creative Commons license below applies only to this version of the article.

Recommended Citation

M. F. Rahman, T.-L. (B. Tseng, J. Wu, Y. Wen, and Y. Lin, “A deep learning-based approach to extraction of filler morphology in SEM images with the application of automated quality inspection,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, vol. 36, p. e15, 2022. https://doi.org/10.1017/S0890060421000330

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.