EN
TR
A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS
Abstract
This review explores the integration of machine learning (ML) and artificial neural networks (ANNs) in optimizing alloy production modeling and print control within Laser Engineered Net Shaping (LENS), a key additive manufacturing process. It investigates theoretical foundations, methodologies, case studies, and emerging trends to enhance process efficiency, improve product quality, and accelerate production cycles. A comprehensive literature review was conducted across academic databases and industry reports using keywords such as “machine learning,” “artificial neural networks,” and “Laser Engineered Net Shaping.” Both theoretical and experimental perspectives were analyzed to provide a well-rounded discussion. Findings indicate that ML and ANN models enhance understanding of alloy production, optimizing configurations and reducing defects. Real-time ML-driven optimization enables adaptive adjustments to process parameters, ensuring improved quality and accuracy. ANNs effectively predict key alloy microstructure properties, supporting informed decision-making and process refinement. Integrating ML and ANNs into LENS facilitates adaptive manufacturing, dynamically responding to changing conditions and alloy compositions.
Keywords
References
- Ahlers, D., Wasserfall, F., Hendrich, N., & Zhang, J. (2019). 3D printing of nonplanar layers for smooth surface generation. IEEE International Conference Automative Science Enginerring. https://doi.org/10.1109/COASE.2019.8843116
- Al Faruque, M. A., Chhetri, S. R., Canedo, A., & Wan, J. (2016). Acoustic Side-Channel Attacks on Additive Manufacturing Systems. 2016 ACM/IEEE 7th Int Conf Cyber-Physical Syst ICCPS 2016-Proceedings 2016. https://doi.org/10.1109/ICCPS.2016.7479068
- Alabi, M. O. (2018). Big data, 3D printing technology, and industry of the future. International Journal of Big Data and Anal Healthcare, 2, 1–20. https://doi.org/10.4018/ijbdah.2017070101
- Aoyagi, K., Wang, H., Sudo, H., & Chiba, A. (2019). Simple method to construct process maps for additive manufacturing using a support vector machine. Additive Manufacturing, 27, 353–362. https://doi.org/10.1016/J.ADDMA.2019.03.013
- Banga, S., Gehani, H., & Bhilare, S. (2018). 3D topology optimization using convolutional neural networks. ArxivOrg. https://doi.org/10.48550/arXiv.1808.07440
- Bendsoe, M. (1999). Material interpolation schemes in topology optimization. Amsterdam: Springer. https://doi.org/10.1007/s004190050248
- Bennett, J., Dudas, R., Jian Cao, J. and Ehmann, K. (2016). “Control of heating and cooling for direct laser deposition repair of cast iron components.” Northwestern University, Evanston, IL uluslararası esnek otomasyon sempozyumu (ISFA) , IEEE (2016). https://doi.org/10.1109/ISFA.2016.7790166
- Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R., & Teti, R. (2019). Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Annals, 68, 451–454. https://doi.org/10.1016/J.CIRP.2019.03.021
Details
Primary Language
English
Subjects
Neural Networks, Additive Manufacturing
Journal Section
Review
Publication Date
March 3, 2025
Submission Date
December 3, 2024
Acceptance Date
December 27, 2024
Published in Issue
Year 2025 Volume: 28 Number: 1
APA
Karaömerlioğlu, F., & Ucar, M. (2025). A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 551-582. https://doi.org/10.17780/ksujes.1594930
AMA
1.Karaömerlioğlu F, Ucar M. A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS. KSU J. Eng. Sci. 2025;28(1):551-582. doi:10.17780/ksujes.1594930
Chicago
Karaömerlioğlu, Filiz, and Mustafa Ucar. 2025. “A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 28 (1): 551-82. https://doi.org/10.17780/ksujes.1594930.
EndNote
Karaömerlioğlu F, Ucar M (March 1, 2025) A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 28 1 551–582.
IEEE
[1]F. Karaömerlioğlu and M. Ucar, “A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS”, KSU J. Eng. Sci., vol. 28, no. 1, pp. 551–582, Mar. 2025, doi: 10.17780/ksujes.1594930.
ISNAD
Karaömerlioğlu, Filiz - Ucar, Mustafa. “A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 28/1 (March 1, 2025): 551-582. https://doi.org/10.17780/ksujes.1594930.
JAMA
1.Karaömerlioğlu F, Ucar M. A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS. KSU J. Eng. Sci. 2025;28:551–582.
MLA
Karaömerlioğlu, Filiz, and Mustafa Ucar. “A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, vol. 28, no. 1, Mar. 2025, pp. 551-82, doi:10.17780/ksujes.1594930.
Vancouver
1.Filiz Karaömerlioğlu, Mustafa Ucar. A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS. KSU J. Eng. Sci. 2025 Mar. 1;28(1):551-82. doi:10.17780/ksujes.1594930
Cited By
Adaptive nickel–titanium shape memory alloy for smart systems: Mechanisms, manufacturing, and applications across biomedical, aerospace, civil, and energy
Materials Today Communications
https://doi.org/10.1016/j.mtcomm.2025.114564Emerging Material Systems and Alloy Design Strategies in Metal Additive Manufacturing: Current Trends and Future Directions
Scientia. Technology, Science and Society
https://doi.org/10.59324/stss.2026.3(2).06