Machine Learning Enabled Smart Structural Materials Using Additive Manufacturing

Abstract

This research study describes a machine learning (ML)-driven model for producing smart structural materials via additive manufacturing (AM) by extrusion. A 3D concrete printing system was used to make cementitious composites that were reinforced with carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs). Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models were used to undergo supervised learning on an experimental dataset consisting of 320 specimens to predict compressive strength, electrical conductivity, and print quality as dependent on process parameters and material composition. The highest R² of compressive strength prediction of SVM was 0.946, whereas RF had the highest R² of 0.987, which was used to predict electrical conductivity. Optimization of parameters guided by ML had a 61.8% enhancement of compressive strength and 30.5 times increase in electrical conductivity in comparison to non-optimized baselines. Nanomaterial networks were also found to be conductive, allowing individual networks to detect their strain levels through changes in current at a strain of 0.1%, which facilitates real-time structural health sensing. The artificial system showed a 31% decrease in CO₂ emissions and a 58.8% decrease in material wastage compared with the usual way of building, proving to be a valid route towards intelligent and sustainable infrastructure.

Keywords

Machine learning / Additive manufacturing / Smart structural materials / Sustainable infrastructure / Structural health monitoring

References

[1] Gunasegaram DR, Barnard AS, Matthews MJ, Jared BH, Andreaco AM, Bartsch K, et al. Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing. Addit. Manuf. 2024, 81, 104013. DOI: 10.1016/j.addma.2024.104013

[2] Chen M, Wang J, Wang L, Liu Y, Liu R, Jin Q, et al. AI-enabled end-to-end processing in additive manufacturing: From pre-processing to post-processing. Addit. Manuf. Front. 2026, 200315. S2950-4317(26)00022-5, DOI: 10.1016/j.amf.2026.200315

[3] Zhang J, Yin C, Farbiz F, Jafary-Zadeh M, Sing SL. Advancing machine learning applications for in-situ monitoring and control in laser-based metal additive manufacturing: A state-of-the-art review. Virtual Phys. Prototyp. 2025, 20, e2592732. DOI: 10.1080/17452759.2025.2592732

[4] Yu K, Yao Y, Zhang W, Lu L, Gao Q, Zhang P, et al. Defects, monitoring, and AI-enabled control in soft material additive manufacturing: A review. Virtual Phys. Prototyp. 2025, 20, e2588456. DOI: 10.1080/17452759.2025.2588456

[5] Gurav V, Upadhyay A, Sakhare H. An explainable lightweight framework for process control and fault detection in additive manufacturing. J. Manuf. Mater. Process. 2025, 9, 392. DOI: 10.3390/jmmp9120392

[6] Antony Jose S, Tonner A, Feliciano M, Roy T, Shackleford A, Menezes PL. Smart manufacturing for high-performance materials: Advances, challenges, and future directions. Materials 2025, 18, 2255. DOI: 10.3390/ma18102255

[7] Herzog T, Brandt M, Trinchi A, Sola A, Molotnikov A. Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing. J. Intell. Manuf. 2024, 35, 1407-1437. DOI: 10.1007/s10845-023-02119-y

[8] Grasso M, Colosimo BM. Process defects and in-situ monitoring methods in metal powder bed fusion: A review. Meas. Sci. Technol. 2017, 28, 044005. DOI: 10.1088/1361-6501/aa5c4f

[9] Wang H, Fuh JY. Metal additive manufacturing and its post-processing techniques. J. Manuf. Mater. Process. 2023, 7, 47. DOI: 10.3390/jmmp7010047

[10] Jin Z, Zhang Z, Gu GX. Automated real-time detection and prediction of interlayer imperfections in additive manufacturing using artificial intelligence. Adv. Intell. Syst. 2020, 2, 1900130. DOI: 10.1002/aisy.201900130

[11] Jin Z, Zhang Z, Demir K, Gu GX. Machine learning for advanced additive manufacturing. Matter 2020, 3, 1541-1556. DOI: 10.1016/j.matt.2020.08.023

[12] Lupi F, Pacini A, Lanzetta M. Laser powder bed additive manufacturing: A review on the four drivers for an online control. J. Manuf. Process. 2023, 103, 413-429. DOI: 10.1016/j.jmapro.2023.08.022

[13] Kumar R, Kumar S. Introduction: Additive manufacturing, biomaterials, historical development. In Biomaterials and Additive Manufacturing; Springer: Mahrastra, India, 2025; pp. 1-28. DOI: 10.1007/978-981-96-1274-1_1

[14] Chi X, Xue J, Jia L, Yao J, Miao H, Wu L, et al. Machine learning-based online monitoring and closed-loop controlling for 3D printing of continuous fiber-reinforced composites. Addit. Manuf. Front. 2025, 4, 200196. DOI: 10.1016/j.amf.2025.200196

[15] Ahmed MJ, Chandra L. AI-enhanced additive manufacturing: Intelligent 3D printing for complex designs. J. Primeasia 2024, 5, 1-9. DOI: 10.25163/primeasia.5110247

[16] Ladani LJ. Applications of artificial intelligence and machine learning in metal additive manufacturing. J. Phys. Mater. 2021, 4, 042009. DOI: 10.1088/2515-7639/ac2791

[17] Sun Z, Tang C, Soh V, Lee C, Wu X, Sing SL, et al. Laser powder bed fusion of 316L stainless steel and K220 copper multi-material. Virtual Phys. Prototyp. 2024, 19, e2356078. DOI: 10.1080/17452759.2024.2356078

[18] Vafadar A, Guzzomi F, Rassau A, Hayward K. Advances in metal additive manufacturing: A review of common processes, industrial applications, and current challenges. Appl. Sci. 2021, 11, 1213. DOI: 10.3390/app11031213

[19] Chowdhury S, Yadaiah N, Prakash C, Ramakrishna S, Dixit S, Gupta LR, et al. Laser powder bed fusion: A state-of-the-art review of the technology, materials, properties & defects, and numerical modelling. J. Mater. Res. Technol. 2022, 20, 2109-2172. DOI: 10.1016/j.jmrt.2022.07.121

[20] Scime L, Beuth J. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit. Manuf. 2018, 19, 114-126. DOI: 10.1016/j.addma.2017.11.009

[21] Sousa J, Brandau B, Darabi R, Sousa A, Brueckner F, Reis A, et al. Artificial intelligence for control in laser-based additive manufacturing: A systematic review. IEEE Access 2025, 13, 30845-30860. DOI: 10.1109/ACCESS.2025.3537859

[22] Mo B, Li T, Deng L, Shi F, Liu W, Zhang H. Mechanisms and influencing factors of defect formations during laser-based directed energy deposition with coaxial powder feeding: A review. Virtual Phys. Prototyp. 2024, 19, e2404155. DOI: 10.1080/17452759.2024.2404155

[23] Li J, Cao L, Zhou Q, Liu H, Zhang X. Imbalanced quality monitoring of selective laser melting using acoustic and photodiode signals. J. Manuf. Process. 2023, 105, 14-26. DOI: 10.1016/j.jmapro.2023.09.037

[24] Shevchik SA, Masinelli G, Kenel C, Leinenbach C, Wasmer K. Deep learning for in-situ and real-time quality monitoring in additive manufacturing using acoustic emission. IEEE Trans. Ind. Inf. 2019, 15, 5194-5203. DOI: 10.1109/TII.2019.2910524

[25] Ren W, Wen G, Zhang Z, Mazumder J. Quality monitoring in additive manufacturing using emission spectroscopy and unsupervised deep learning. Mater. Manuf. Process. 2022, 37, 1339-1346. DOI: 10.1080/10426914.2021.1906891

[26] Yu M, Zhu L, Yang Z, Ning J, Xue P, Wang S, et al. Machine learning-assisted process-structure-property correlation in laser metal additive manufacturing: A critical review. Int. J. Extrem. Manuf. 2026, 8, 042005. DOI: 10.1088/2631-7990/ae5297

[27] Prihar A, Gupta S, Esmaeeli HS, Moini R. Tough double-bouligand architected concrete enabled by robotic additive manufacturing. Nat. Commun. 2024, 15, 7498. DOI: 10.1038/s41467-024-51640-y

[28] Caiazzo B, Di Nardo M, Murino T, Petrillo A, Piccirillo G, Santini S. Towards Zero Defect Manufacturing paradigm: A review of the state-of-the-art methods and open challenges. Comput. Ind. 2022, 134, 103548. DOI: 10.1016/j.compind.2021.103548