Browse > Article
http://dx.doi.org/10.4150/KPMI.2021.28.3.208

Characterization and Classification of Pores in Metal 3D Printing Materials with X-ray Tomography and Machine Learning  

Kim, Eun-Ah (3D printing materials research center, Korea Institute of Materials Science)
Kwon, Se-Hun (Materials Science and Engineering, Pusan national university)
Yang, Dong-Yeol (3D printing materials research center, Korea Institute of Materials Science)
Yu, Ji-Hun (3D printing materials research center, Korea Institute of Materials Science)
Kim, Kwon-Ill (C51)
Lee, Hak-Sung (3D printing materials research center, Korea Institute of Materials Science)
Publication Information
Journal of Powder Materials / v.28, no.3, 2021 , pp. 208-215 More about this Journal
Abstract
Metal three-dimensional (3D) printing is an important emerging processing method in powder metallurgy. There are many successful applications of additive manufacturing. However, processing parameters such as laser power and scan speed must be manually optimized despite the development of artificial intelligence. Automatic calibration using information in an additive manufacturing database is desirable. In this study, 15 commercial pure titanium samples are processed under different conditions, and the 3D pore structures are characterized by X-ray tomography. These samples are easily classified into three categories, unmelted, well melted, or overmelted, depending on the laser energy density. Using more than 10,000 projected images for each category, convolutional neural networks are applied, and almost perfect classification of these samples is obtained. This result demonstrates that machine learning methods based on X-ray tomography can be helpful to automatically identify more suitable processing parameters.
Keywords
Machine Learning; Additive manufacturing; Metal 3D printing; X-ray tomography; Computer vision;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. I. Kwon, M. C. Kang, Y. C. Kim, C. J. Bae and S. B. Lee: A Study on the 3D Printing Industry Revitalization Plan, KISTI (2021).
2 C. L. A. Leung, S. Marussi, R. C. Atwood, M. Towrie, P. J. Withers and P. D. Lee: Nat. Commun., 9 (2018) 1355.   DOI
3 B. Dutta and F. H. Froes: Additive Manufacturing of Titanium Alloys, Butterworth-Heinemann, (2016) 20.
4 Y. S. Eom, D. W. Kim, K. T. Kim, S. S. Yang, J. H. Choe, I. J. Son and J. H. Yu: J. Korean Powder Metall. Inst., 27 (2020) 103.   DOI
5 Wohlers Report: 3D printing and Additive Manufacturing Global state of the Industry, Wohlers Associates, 25 (2020).
6 S. M. H. Hojjatzadeh, N. D. Parab, W. Yan, Q. Guo, L. Xiong, C. Zhao, M. Qu, L. I. Escano, X. Xiao, K. Fezzaa, W. Everhart, T. Sun and L. Chen: Nat. Commun., 10 (2019) 3088.   DOI
7 H. S. Lee, D. K. Kim, Y. I. Kim, J. E. Nam, Y. Son, T. S. Kim and B. Lee: J. Korean Powder Metall. Inst., 27 (2020) 44.   DOI
8 C. Wang, X. P. Tan, S. B. Tor and C. S. Lim: Addit. Manuf., 36 (2020) 101538.
9 J. H. Hwang: J. Welding and Joining, 35 (2017) 29.   DOI
10 https://github.com/macromancer/material_defect_detector/blob/master/preprocess_images.ipynb
11 R. Boyer, G. Welsch and E. W. Collings: Materials Properties Handbook: Titanium Alloys, ASM International, (1994) 125.
12 A. A. Martin, N. P. Calta, S. A. Khairallah, J. Wang, P. J. Depond, A. Y. Fong, V. Thampy, G. M. Guss, A. M. Kiss, K. H. Stone, C. J. Tassone, J. N. Weker, M. F. Toney, T. V. Buuren and M. J. Matthews: Nat. Commun., 10 (2019) 1987.   DOI