Article: Artificial Neural Network Algorithms for 3D Printing

Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts' properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

Citing Articles

Artificial Neural Network Modeling of Mechanical Properties of 3D-Printed Polyamide 12 and Its Fiber-Reinforced Composites.

Fetecau C, Stan F, Boazu D Polymers (Basel). 2025; 17(5).

PMID: 40076169 PMC: 11902404. DOI: 10.3390/polym17050677.

Innovative 3D printing technologies and advanced materials revolutionizing orthopedic surgery: current applications and future directions.

Cong B, Zhang H Front Bioeng Biotechnol. 2025; 13:1542179.

PMID: 40008034 PMC: 11850356. DOI: 10.3389/fbioe.2025.1542179.

Optimization of Fast Non-Local Means Noise Reduction Algorithm Parameter in Computed Tomographic Phantom Images Using 3D Printing Technology.

Kim H, Lim S, Park M, Kim K, Kang S, Lee Y Diagnostics (Basel). 2024; 14(15).

PMID: 39125465 PMC: 11312005. DOI: 10.3390/diagnostics14151589.

Decision Support Tool in the Selection of Powder for 3D Printing.

Szczupak E, Malysza M, Wilk-Kolodziejczyk D, Jaskowiec K, Bitka A, Glowacki M Materials (Basel). 2024; 17(8).

PMID: 38673229 PMC: 11052508. DOI: 10.3390/ma17081873.

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions.

Kim R, Abisado M, Villaverde J, Sampedro G Sensors (Basel). 2023; 23(15).

PMID: 37571604 PMC: 10422627. DOI: 10.3390/s23156821.

References

1.

Rawat W, Wang Z . Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review. Neural Comput. 2017; 29(9):2352-2449. DOI: 10.1162/NECO_a_00990. View

2.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

3.

Mahmood M, Popescu A, Mihailescu I . Metal Matrix Composites Synthesized by Laser-Melting Deposition: A Review. Materials (Basel). 2020; 13(11). PMC: 7321634. DOI: 10.3390/ma13112593. View

4.

Caiazzo F, Caggiano A . Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning. Materials (Basel). 2018; 11(3). PMC: 5873023. DOI: 10.3390/ma11030444. View

5.

Fang Z, Wang R, Wang M, Zhong S, Ding L, Chen S . Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network. Materials (Basel). 2020; 13(8). PMC: 7216274. DOI: 10.3390/ma13081963. View

6.

Sireesha M, Lee J, Kranthi Kiran A, Babu V, Kee B, Ramakrishna S . A review on additive manufacturing and its way into the oil and gas industry. RSC Adv. 2022; 8(40):22460-22468. PMC: 9081453. DOI: 10.1039/c8ra03194k. View

7.

Ye D, Fuh J, Zhang Y, Hong G, Zhu K . In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks. ISA Trans. 2018; 81:96-104. DOI: 10.1016/j.isatra.2018.07.021. View

8.

Abiodun O, Jantan A, Omolara A, Dada K, Mohamed N, Arshad H . State-of-the-art in artificial neural network applications: A survey. Heliyon. 2018; 4(11):e00938. PMC: 6260436. DOI: 10.1016/j.heliyon.2018.e00938. View

9.

Wu S, Chow T . Self-organizing and self-evolving neurons: a new neural network for optimization. IEEE Trans Neural Netw. 2007; 18(2):385-96. DOI: 10.1109/TNN.2006.887556. View

10.

Sidambe A . Biocompatibility of Advanced Manufactured Titanium Implants-A Review. Materials (Basel). 2017; 7(12):8168-8188. PMC: 5456424. DOI: 10.3390/ma7128168. View

11.

Rajendra Acharya U, Oh S, Hagiwara Y, Tan J, Adam M, Gertych A . A deep convolutional neural network model to classify heartbeats. Comput Biol Med. 2017; 89:389-396. DOI: 10.1016/j.compbiomed.2017.08.022. View