Page 82 - IJAMD-2-2
P. 82
International Journal of AI for
Materials and Design Prediction of AM defect based on DL
Availability of data selective laser melting (SLM). Int J Adv Manuf Technol.
2025;9:1-32.
The dataset in this paper is available from the author of
reference upon appropriate request. doi: 10.1007/s00170-024-14920-x
10
10. Wang GW. Microstructure and Mechanical Properties of
References Oxide Dispersion Strengthened Nickel-Based Superalloys by
1. Raihan AS, Harper A, Era IZ, et al. A data-efficient sequential Laser Additive Manufacturing. [Dissertation, Zhongnan
learning framework for melt pool defect classification in laser University, China]; 2023. Available from: https://www.cnki.
powder bed fusion. J Manuf Processes. 2025;145:201-210. net [Last accessed on 2025 May 28].
doi: 10.1016/j.jmapro.2025.03.118 11. Bramer M. Data for Data Mining. Principles of Data Mining.
London: Springer; 2016. p. 9-19.
2. Ni C, Zhu J, Zhang B, et al. Recent advance in laser
powder bed fusion of Ti-6Al-4V alloys: Microstructure, doi: 10.1007/978-1-4471-7307-6
mechanical properties and machinability. Virtual Phys 12. Alabdulwahab S, Moon B. Feature selection methods
Prototyp. 2025;20(1):e2446952. simultaneously improve the detection accuracy and model
doi: 10.1080/17452759.2024.2446952 building time of machine learning classifiers. Symmetry.
2020;12(9):1424.
3. Nabavi SF, Dalir H, Farshidianfar A. A comprehensive
review of recent advances in laser powder bed fusion doi: 10.3390/sym12091424
characteristics modeling: Metallurgical and defects. Int J 13. Maseer ZK, Yusof R, Bahaman N, Mostafa SA, Foozy CF.
Adv Manuf Technol. 2024;132(5):2233-2269. Benchmarking of machine learning for anomaly based
doi: 10.1007/s00170-024-13491-1 intrusion detection systems in the CICIDS2017 dataset.
IEEE Access. 2021;9:22351-22370.
4. Ero O, Taherkhani K, Hemmati Y, Toyserkani E. An
integrated fuzzy logic and machine learning platform doi: 10.1109/ACCESS.2021.3056614
for porosity detection using optical tomography imaging 14. Mohammed JZ, Wagner M. Data Mining and Analysis:
during laser powder bed fusion. Int J Extrem Manuf. Fundamental Concepts and Algorithms. Cambridge:
2024;6(6):065601. Cambridge University Press; 2014.
doi: 10.1088/2631-7990/ad65cd 15. Lewis ND. Deep Learning Made Easy with R. A Gentle
5. Gu Z, Mani Krishna KV, Parsazadeh M, et al. Deep learning- Introduction for Data Science. South Carolina: CreateSpace
based melt pool and porosity detection in components Independent Publishing Platform; 2016.
fabricated by laser powder bed fusion. Prog Addit Manuf. 16. Elman JL. Finding structure in time. Cogn Sci.
2025;10(1):53-70. 1990;14(2):179-211.
doi: 10.1007/s40964-024-00603-2
doi: 10.1207/s15516709cog1402_1
6. Zhao J, Yang Z, Chen Q, et al. Real-time detection 17. Jordan MI. Serial order: A parallel distributed processing
of powder bed defects in laser powder bed fusion approach. Adv Psychol. 1997;121:471-495.
using deep learning on 3D point clouds. Virtual Phys
Prototyp. 202531;20(1):e2449171. 18. Jang H, Plis SM, Calhoun VD, Lee JH. Task-specific
feature extraction and classification of fMRI volumes
doi: 10.1080/17452759.2024.2449171 using a deep neural network initialized with a deep belief
7. Pouyanfar S, Sadiq S, Yan Y, et al. A survey on deep learning: network: Evaluation using sensorimotor tasks. Neuroimage.
Algorithms, techniques, and applications. ACM Comput 2017;145:314-328.
Surv (CSUR). 2018;51(5):1-36.
doi: 10.1016/j.neuroimage.2016.04.003
doi: 10.1145/3234150
19. Ghasemi F, Mehridehnavi A, Fassihi A, Pérez-Sánchez H.
8. Narasimharaju SR, Zeng W, See TL, Zhu Z, Scott P, Jiang X, Deep neural network in QSAR studies using deep belief
Lou S. A comprehensive review on laser powder bed fusion network. Appl Soft Comput. 2018;62:251-258.
of steels: Processing, microstructure, defects and control
methods, mechanical properties, current challenges and doi: 10.1016/j.asoc.2017.09.040
future trends. J Manuf Processes. 2022;75:375-414. 20. Hinton GE. Training products of experts by minimizing
contrastive divergence. Neural Comput. 2002;14:1771-800.
doi: 10.1016/j.jmapro.2021.12.033
doi: 10.1162/089976602760128018
9. Ganta MG, Kurek M. Influence of post-processing methods
on the fatigue performance of materials produced by 21. Hinton G. A practical guide to training restricted Boltzmann
Volume 2 Issue 2 (2025) 76 doi: 10.36922/IJAMD025060005

