Environmental conditions fundamentally shape the reliability and sustainability of additive manufacturing. As additive manufacturing moves into real-world environments, these factors need to be considered in modelling and design.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to the full article PDF.
USD 39.95
Prices may be subject to local taxes which are calculated during checkout

References
Graziosi, S. et al. A vision for sustainable additive manufacturing. Nat. Sustain. 7, 698–705 (2024).
Nipu, S. M. A. et al. Advances and perspectives in multi-material additive manufacturing of heterogenous metal-polymer components. npj Adv. Manuf. 2, 31 (2025).
Reitz, B. et al. Additive manufacturing under lunar gravity and microgravity. Microgravity Sci. Technol. 33, 25 (2021).
Surjadi, J. U. & Portela, C. M. Enabling three-dimensional architected materials across length scales and timescales. Nat. Mater. 24, 493–505 (2025).
De Rosa, C., Park, C., Thomas, E. L. & Lotz, B. Microdomain patterns from directional eutectic solidification and epitaxy. Nature 405, 433–437 (2000).
Lau, D., Broderick, K., Buehler, M. J. & Büyüköztürk, O. A robust nanoscale experimental quantification of fracture energy in a bilayer material system. Proc. Natl Acad. Sci. USA 111, 11990–11995 (2014).
Zhang, Y. et al. 3D printing technology in concrete construction. Nat. Rev. Clean Technol. 1, 288–303 (2025).
Wu, P., Qian, C. & Okwudire, C. E. Design, modeling and feedforward control of a hybrid extruder for material extrusion additive manufacturing. Addit. Manuf. 92, 104378 (2024).
Tiwari, K. & Kumar, S. Analysis of the factors affecting the dimensional accuracy of 3D printed products. Mater. Today Proc. 5, 18674–18680 (2018).
Jin, Z., Zhang, Z., Demir, K. & Gu, G. X. Machine learning for advanced additive manufacturing. Matter 3, 1541–1556 (2020).
Gunasegaram, D. R. et al. Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing. Addit. Manuf. 46, 102089 (2021).
Wang, X. Q., Jin, Z., Zheng, B. & Gu, G. X. Transformer-based approach for printing quality recognition in fused filament fabrication. npj Adv. Manuf. 2, 15 (2025).
Zhu, Z., Ng, D. W. H., Park, H. S. & McAlpine, M. C. 3D-printed multifunctional materials enabled by artificial-intelligence-assisted fabrication technologies. Nat. Rev. Mater. 6, 27–47 (2021).
Ren, Z. et al. Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion. Science 379, 89–94 (2023).
Wang, X. Q., Jin, Z., Ravichandran, D. & Gu, G. X. Artificial intelligence and multiscale modeling for sustainable biopolymers and bioinspired materials. Adv. Mater. 37, 2416901 (2025).
Acknowledgements
This work was supported by the Barbara and Gerson Bakar Foundation and Air Force Office of Scientific Research.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Wang, X.Q., Gu, G.X. Environment–process–structure–property linkages in additive manufacturing. Nat. Rev. Clean Technol. 1, 751–752 (2025). https://doi.org/10.1038/s44359-025-00109-2
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1038/s44359-025-00109-2