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International Journal of AI for
Materials and Design ML-driven optimization in additive manufacturing
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Figure 5. Process optimization for metal 3D printing. (A) Outputs of the augmented machine learning framework. Reproduced with
permission from Seifermann et al. Copyright © 2022 Springer Nature. (B) Schematic of high-throughput experimentation and machine
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learning-guided process optimization in laser powder bed fusion. Reproduced from Jain et al. Copyright © 2025 IOP Publishing.
Abbreviations: LPBF: Laser powder bed fusion; ML: Machine learning.
abnormal melt pool fluctuations, effectively identifying and quality control in metal 3D printing. Existing CNN-
critical defects and consequently improving process based analyses have been limited to spatial features, but
stability and quality assurance. In ddition, ML-based combining CNNs with RNNs allows simultaneous analysis
strategies have attracted attention for real-time monitoring of spatial and temporal characteristics. This integrated
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Volume 2 Issue 2 (2025) 41 doi: 10.36922/IJAMD025130010
