Page 61 - IJAMD-2-2

International Journal of AI for
Materials and Design ML-driven optimization in additive manufacturing

doi: 10.1080/17452759.2023.2251453 reinforced poly-lactic acid composites. Prog Addit Manuf.
153. Cai Y, Wang Y, Chen H, Xiong J. Searching optimal process 2024;10:2585-2599.
parameters for desired layer geometry in wire-laser directed doi: 10.1007/s40964-024-00768-w
energy deposition based on machine learning. Virtual Phys
Prototyp. 2024;19(1):e2352066. 163. Yi J, Deng B, Peng F, et al. Study on the parameters
optimization of 3D printing continuous carbon fiber-
doi: 10.1080/17452759.2024.2352066 reinforced composites based on CNN and NSGA-II. Compos
154. Pulido-Victoria LA, Flores-Tlacuahuac A, Panales- Part A Appl Sci Manuf. 2025;190:108657.
Pérez A., Lara-Ceniceros TE, Ávila-López MA, Bonilla- doi: 10.1016/j.compositesa.2024.108657
Cruz J. Prediction of viscoelastic and printability properties
on binder-free TiO2-based ceramic pastes by DIW 164. Zhang Z, Shi J, Yu T, et al. Predicting flexural strength of
through a machine learning approach. Comput Chem Eng. additively manufactured continuous carbon fiber-reinforced
2025;193:108920. polymer composites using machine learning. J Comput Inf
Sci Eng. 2020;20(6):061015.
doi: 10.1016/j.compchemeng.2024.108920
doi: 10.1115/1.4047477
155. Chen W, Zou B, Zheng Q, Huang C, Li L, Liu J. Research
on anti-interference detection of 3D-printed ceramics 165. Raj R, Mahato S, Moharana AP, Dixit AR. Predicting
surface defects based on deep learning. Ceram Int. dimensional accuracy in 3D printed polydimethylsiloxane-
2023;49(13):22479-22491. carbon nanotubes composites via machine learning. Polym
Compos. 2024;45(4):2965-2980.
doi: 10.1016/j.ceramint.2023.04.081
doi: 10.1002/pc.27963
156. Chen W, Zou B, Huang C, et al. The defect detection of
3D-printed ceramic curved surface parts with low contrast 166. Yang H, Fang L, Yuan Z, et al. Machine learning guided
based on deep learning. Ceram Int. 2023;49(2):2881-2893. 3D printing of carbon microlattices with customized
performance for supercapacitive energy storage. Carbon.
doi: 10.1016/j.ceramint.2022.09.272
2023;201:408-414.
157. Zhou J, Li L, Lu L, Cheng Y. Machine learning-based quality
optimisation of ceramic extrusion 3D printing deposition doi: 10.1016/j.carbon.2022.08.083
lines. Mater Today Commun. 2024;41:110841. 167. Poompiew N, Sukmas W, Aumnate C, et al. Strain
sensing characteristics of 3D-printed carbon nanotubes/
doi: 10.1016/j.mtcomm.2024.110841
polypyrrole/UV-curable composites: Experimental
158. Nohut S, Schwentenwein M. Machine learning assisted validation and machine learning predictions. Prog Addit
material development for lithography-based additive Manuf. 2025;10(1):581-591.
manufacturing of porous alumina ceramics. Open Ceram.
2024;18:100573. doi: 10.1007/s40964-024-00642-9
doi: 10.1016/j.oceram.2024.100573 168. Maurya D, Khaleghian S, Sriramdas R, et al. 3D printed
graphene-based self-powered strain sensors for smart tires
159. Tang J, Geng X, Li D, et al. Machine learning-based in autonomous vehicles. Nat Commun. 2020;11(1):5392.
microstructure prediction during laser sintering of alumina.
Sci Rep. 2021;11(1):10724. doi: 10.1038/s41467-020-19088-y
doi: 10.1038/s41598-021-89816-x 169. Zhu J, Cho M, Li Y, et al. Machine learning-enabled textile-
based graphene gas sensing with energy harvesting-assisted
160. Saimon AI, Yangue E, Yue X, Kong Z, Liu C. Advancing IoT application. Nano Energy. 2021;86:106035.
additive manufacturing through deep learning:
A comprehensive review of current progress and future doi: 10.1016/j.nanoen.2021.106035
challenges. IISE Trans. 2025:1-24. 170. Kwon YT, Kim YS, Kwon S, et al. All-printed nanomembrane
doi: 10.1080/24725854.2024.2443592 wireless bioelectronics using a biocompatible solderable
graphene for multimodal human-machine interfaces. Nat
161. Chen Q, Zhang W, Liang X, et al. Machine learning-assisted Commun. 2020;11(1):3450.
multi-property prediction and sintering mechanism
exploration of mullite-corundum ceramics. Materials. doi: 10.1038/s41467-020-17288-0
2025;18(6):1384. 171. Hou Y, Gao M, Gao J, et al. 3D printed conformal strain
doi: 10.3390/ma18061384 and humidity sensors for human motion prediction
and health monitoring via machine learning. Adv Sci.
162. Raj T, Tiwary A, Jain A, et al. Machine learning-assisted 2023;10(36):2304132.
prediction modeling for anisotropic flexural strength
variations in fused filament fabrication of graphene doi: 10.1002/advs.202304132

Volume 2 Issue 2 (2025) 55 doi: 10.36922/IJAMD025130010