Joshua, R. J. N. et al. Powder bed fusion 3D printing in precision manufacturing for biomedical applications. Compr. Rev. Materials. 17 (3), 769 (2024).
Zhou, L. et al. Additive manufacturing: A comprehensive review. Sensors 24 (9), 2668 (2024).
Onuike, B. & Bandyopadhyay, A. Additive manufacturing of inconel 718–Ti6Al4V bimetallic structures. Additive Manuf. 22, 844–851 (2018).
Ji, S. et al. Microstructural evolution and high-temperature resistance of functionally graded material Ti-6Al-4V/Inconel 718 coated by directed energy deposition-laser. J. Alloys Compd. 848, 156255 (2020).
Park, C. W., Hajra, R. N., Kim, S. H., Lee, S. H. & Kim, J. H. Optimizing multi-interlayered additive manufacturing for high-strength robust joints in inconel 718 and Ti–6Al–4V alloys. J. Mater. Res. Technol. 25, 855–872 (2023).
Hashemi, S. M. et al. Computational modeling of process–structure–property–performance relationships in metal additive manufacturing: a review. Int. Mater. Rev. 67 (1), 1–46 (2022).
Sharma, A. S., Yadav, S., Biswas, K. & Basu, B. High-entropy alloys and metallic nanocomposites: processing challenges, microstructure development and property enhancement. Mater. Sci. Engineering: R: Rep. 131, 1–42 (2018).
Kouraytem, N., Li, X., Tan, W., Kappes, B. & Spear, A. D. Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches. J. Physics: Mater. 4 (3), 032002 (2021).
Yigezu, B. S., Mahapatra, M. M. & Jha, P. K. Influence of reinforcement type on microstructure, hardness, and tensile properties of an aluminum alloy metal matrix composite. J. Minerals Mater. Charact. Eng. 1 (4), 124–130 (2013).
Shahzad, K., Mardare, A. I. & Hassel, A. W. Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances. Sci. Technol. Adv. Materials: Methods. 4 (1), 2292486 (2024).
Shen, S. C. et al. Computational design and manufacturing of sustainable materials through first principles and materiomics. Chem. Rev. 123 (5), 2242–2275 (2023).
Herriott, C. & Spear, A. D. Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine and deep-learning methods. Comput. Mater. Sci. 175, 109599 (2020).
Lee, K. H., Lim, H. J. & Yun, G. J. A data-driven framework for designing microstructure of multifunctional composites with deep-learned diffusion-based generative models. Eng. Appl. Artif. Intell. 129, 107590 (2024).
Guo, S. et al. Machine learning for metal additive manufacturing: towards a physics-informed data-driven paradigm. J. Manuf. Syst. 62, 145–163 (2022).
Nasiri, S. & Khosravani, M. R. Machine learning in predicting mechanical behavior of additively manufactured parts. J. Mater. Res. Technol. 14, 1137–1153 (2021).
Min, Y. I. et al. Machine learning for predicting fatigue properties of additively manufactured materials. Chin. J. Aeronaut. 37 (4), 1–22 (2024).
Valizadeh, M. & Wolff, S. J. Convolutional neural network applications in additive manufacturing: A review. Adv. Industrial Manuf. Eng. 4, 100072 (2022).
Battula, S., Alla, S. N., Ramana, E. V., Kumar, N. K. & Murthy, S. B. Uncertainty quantification for digital twins in smart manufacturing and robotics: A review. In Journal of Physics: Conference Series (Vol. 2837, No. 1, p. 012059). IOP Publishing. (2024), October.
Akbari, P., Zamani, M. & Mostafaei, A. Machine learning prediction of mechanical properties in metal additive manufacturing. Additive Manuf. 91, 104320 (2024).
Pouchard, L., Reyes, K. G., Alexander, F. J. & Yoon, B. J. A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows. Digit. Discovery. 2 (5), 1251–1258 (2023).
Sanyal, S. et al. Mt-cgcnn: integrating crystal graph convolutional neural network with multitask learning for material property prediction. arXiv Preprint arXiv:1811.05660. (2018).
Kong, S., Guevarra, D., Gomes, C. P. & Gregoire, J. M. Materials representation and transfer learning for multi-property prediction. Applied Phys. Reviews, 8(2). (2021).
Massa, D., Chmielna, I. N., Kaszuba, G., Papanikolaou, S. & Sankowski, P. Transfer Learning in Materials Informatics: structure-property relationships through minimal but highly informative multimodal input.
Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120 (14), 145301 (2018).
Karamad, M. et al. Orbital graph convolutional neural network for material property prediction. Phys. Rev. Mater. 4 (9), 093801 (2020).
Shin, S. J. et al. Material-Adaptive anomaly detection using Property-Concatenated transfer learning in wire Arc additive manufacturing. Int. J. Precis Eng. Manuf. 25, 383–408 (2024).
Yang, K. et al. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59 (8), 3370–3388 (2019).
Luo, Q., Shimanek, J. D., Simpson, T. W. & Beese, A. M. An image-based transfer learning approach for using in situ processing data to predict laser powder bed fusion additively manufactured Ti-6Al-4V mechanical properties: 3D printing and additive manufacturing. 12(1), 48–60 (2024).
Li, L. et al. Uncertainty quantification in multivariable regression for material property prediction with bayesian neural networks. Sci. Rep. 14 (1), 10543 (2024).
Tang, W. et al. Modeling and prediction of fatigue properties of additively manufactured metals. Acta Mech. Solida Sin. 36, 181–213 (2023).
Battula, S., Alla, S. N., Ramana, E. V., Kumar, N. K. & Murthy, S. B. Uncertainty quantification for digital twins in smart manufacturing and robotics: A review. Journal of Physics: Conference Series, 2837(1), 012059. (2024), October.
Toprak, C. B. & Dogruer, C. U. Optimal process parameter determination in selective laser melting via machine learning-guided sequential quadratic programing. Proc. Institution Mech. Eng. Part. C: J. Mech. Eng. Sci. 239 (3), 807–820 (2025).
ASTM F2924-14. Standard Specification for Additive Manufacturing Titanium-6 Aluminium-4 Vanadium with Powder Bed Fusion (ASTM International, 2014).
ASTM B213-13. Standard Test Methods for Flow Rate of Metal Powders Using the Hall Flowmeter Funnel (ASTM International, 2013).
Dadbakhsh, S., Speirs, M., Kruth, J. P. & Van Humbeeck, J. Influence of SLM parameters on mechanical properties of austenitic stainless steel 316L. Rapid Prototyp. J. 22 (4), 654–664 (2016).
ASTM E11-20. Standard Specification for Woven Wire Test Sieve Cloth and Test Sieves (ASTM International, 2020).
ASTM E8/E8M-21. Standard Test Methods for Tension Testing of Metallic Materials (ASTM International, 2021).
Vrancken, B., Thijs, L., Kruth, J. P. & Van Humbeeck, J. Heat treatment of Ti6Al4V produced by selective laser melting: microstructure and mechanical properties. J. Alloys Compd. 541, 177–185 (2012).
ASTM E112-13. Standard Test Methods for Determining Average Grain Size (ASTM International, 2013).
Savitzky, A. & Golay, M. J. E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36 (8), 1627–1639 (1964).
Li, R. et al. Application of unsupervised learning methods based on video data for real-time anomaly detection in wire Arc additive manufacturing. J. Manuf. Process. 143, 37–55 (2025).
Zheng, Y. et al. A thermal history-based approach to predict mechanical properties of plasma Arc additively manufactured IN625 thin-wall. J. Manuf. Process. 140, 91–107 (2025).
Zuo, X. et al. 3D printed All-Natural hydrogels: Flame-Retardant materials toward attaining green sustainability. Adv. Sci. 11 (3), 2306360 (2024).
Wang, L. et al. Multi-unit global-local registration for 3D bent tube based on implicit structural feature compatibility. Adv. Eng. Inform. 65, 103120. https://doi.org/10.1016/j.aei.2025.103120 (2025).
Yao, S., Chen, F., Wang, Y., Zhou, H. & Liu, K. Manufacturing defect-induced multiscale weakening mechanisms in carbon fiber reinforced polymers captured by 3D CT-based machine learning and high-fidelity modeling. Compos. Part A: Appl. Sci. Manufac. 197, 109052 (2025).
Zhang, Z. et al. Robotic wire-based friction stir additive manufacturing. Additive Manuf. 88, 104261 (2024).
Niu, S. et al. Breaking the Trade-Off between complexity and absorbing performance in metamaterials through intelligent design. Small 21 (24), 2502828 (2025).
Ren, D. et al. Harmonizing physical and deep learning modeling: A computationally efficient and interpretable approach for property prediction. Scripta Mater. 255, 116350 (2025).
Jain, A. et al. Commentary: the materials project: A materials genome approach to accelerating materials innovation. APL Mater. 1 (1), 011002. https://doi.org/10.1063/1.4812323 (2013).
Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials design and discovery with High-Throughput density functional theory: the open quantum materials database (OQMD). JOM 65 (11), 1501–1509 (2013).
Shen, C. et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel. Acta Mater. 179, 201–214 (2019).
Sun, X. et al. Wire-based friction stir additive manufacturing of AZ31B magnesium alloy: precipitate behavior and mechanical properties. J. Magnesium Alloys. https://doi.org/10.1016/j.jma.2025.04.025 (2025).
Chen, S., Khan, S. B., Li, N. & Xiao, C. In-depth analysis of sintering, exposure time, and layer height (µm) in LRS 3D printed devices with DLP. J. Manuf. Process. 133, 576–591 (2025).
Ding, C., Li, C., Xiong, Z. & Li, Z. Computer-based big data technology in additive manufacturing and product design in sustainable manufacturing. Int. J. Adv. Manuf. Technol. 135 (9), 4855–4863 (2024).
Long, J. et al. Printing dense and low-resistance copper microstructures via highly directional laser-induced forward transfer. Additive Manuf. 103, 104755 (2025).
Xu, K. et al. Data-Driven materials research and development for functional coatings. Adv. Sci. 11 (42), 2405262 (2024).
Shi, S. et al. Significance of α-Al cellular matrix in tensile behavior and work-hardening of additive manufactured AlSi10Mg alloy. Virtual Phys. Prototyp., 20(1), e2449189. (2025).
Zheng, Y. et al. A thermal history-based approach to predict mechanical properties of plasma Arc additively manufactured IN625 thin-wall. J. Manuf. Process. 140, 91–107. https://doi.org/10.1016/j.jmapro.2025.02.043 (2025).
Wang, Z. et al. Digital-twin-enabled online wrinkling monitoring of metal tube bending manufacturing: A multi-fidelity approach using forward-convolution-GAN. Appl. Soft Comput. 171, 112684. https://doi.org/10.1016/j.asoc.2024.112684 (2025).
Sun, S. et al. Wire-based friction stir additive manufacturing enables enhanced interlayer bonding in aluminum-matrix composites. J. Manuf. Process. 153, 1–15. https://doi.org/10.1016/j.jmapro.2025.08.078 (2025).
Luo, J., Cheng, Z., Yu, N., Tian, Y. & Meng, J. A flexible skin material with switchable wettability for trans-medium vehicles. Int. J. Smart Nano Mater. 16 (2), 419–442. https://doi.org/10.1080/19475411.2025.2504442 (2025).
Sun, F. et al. A versatile microporous design toward toughened yet softened Self-Healing materials. Adv. Mater. 36 (50), 2410650. https://doi.org/10.1002/adma.202410650 (2024).
Zhang, Z. et al. Robotic wire-based friction stir additive manufacturing. Additive Manuf. 88, 104261. https://doi.org/10.1016/j.addma.2024.104261 (2024).
Yang, G. et al. Prediction of restrained stress for UHPC: considering relationship between long-term and in-situ creep. Constr. Build. Mater. 484, 141722. https://doi.org/10.1016/j.conbuildmat.2025.141722 (2025).
Zhang, J. et al. Modeling sediment flux in river confluences: A comprehensive momentum-based study. Water Resour. Res. 61, e2024WR039154. https://doi.org/10.1029/2024WR039154 (2025).
Ren, D. et al. Harmonizing physical and deep learning modeling: A computationally efficient and interpretable approach for property prediction. Scripta Mater. 255, 116350. https://doi.org/10.1016/j.scriptamat.2024.116350 (2025).
Shen, C. et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel. Acta Mater. 179, 201–214. https://doi.org/10.1016/j.actamat.2019.08.033 (2019).
Zhang, Z. et al. Optimization of low-power femtosecond laser Trepan drilling by machine learning and a high-throughput multi-objective genetic algorithm. Opt. Laser Technol. 148, 107688. https://doi.org/10.1016/j.optlastec.2021.107688 (2022).
Wang, H., Li, Y., Men, T. & Li, L. Physically interpretable Wavelet-Guided networks with dynamic frequency decomposition for machine intelligence fault prediction. IEEE Trans. Syst. Man. Cybernetics: Syst. 54 (8), 4863–4875. https://doi.org/10.1109/TSMC.2024.3389068 (2024).
Liu, H., Zhang, D. & Geng, D. Design of a self-excited vibration tool bar for cutting difficult-to-machine alloys. Int. J. Mech. Sci. 300, 110456. https://doi.org/10.1016/j.ijmecsci.2025.110456 (2025).
Xu, H. et al. An innovative normal self-positioning method with gravity and friction compensation for wall-climbing drilling robot in aircraft assembly. Int. J. Adv. Manuf. Technol. 138 (7), 3687–3704. https://doi.org/10.1007/s00170-025-15677-7 (2025).
Cao, Y. et al. Structural-Functional-Integrated Ultra-Wideband Microwave-Absorbing composites based on in Situ-Grown graphene Meta-nanointerface. Adv. Funct. Mater. 34 (52), 2411271. https://doi.org/10.1002/adfm.202411271 (2024).
Peng, J. et al. Numerical simulation and process optimization of laser welding in 6056 aluminum alloy T-Joints. Crystals 15 (1), 35. https://doi.org/10.3390/cryst15010035 (2025).
Cao, Y. et al. Multi-Functional Self-Sensing electronic gasket for structural health monitoring of transportation pipelines. Adv. Funct. Mater. 35 (20), 2412634. https://doi.org/10.1002/adfm.202412634 (2025).
Shi, S. et al. Significance of α-Al cellular matrix in tensile behavior and work-hardening of additive-manufactured AlSi10Mg alloy. Virtual Phys. Prototyp. 20 (1), e2449189. https://doi.org/10.1080/17452759.2024.2449189 (2025).