Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

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Uncertainty Quantification and Propagation for Multiscale Materials Systems with Agglomeration and Structural Anomalies

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The convergence of additive manufacturing and artificial intelligence with LLMs for smart product design

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Machine Learning Applications for 3D-Printed Polymers and Their Composites

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Expanding Design Spaces in Digital Composite Materials: A Multi‐Input Deep Learning Approach Enhanced by Transfer Learning and Multi‐kernel Network

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Machine Learning (ML) Based Prediction of Defects in Extrusion-Type Additively Manufactured Parts

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Causal Paths Allowing Simultaneous Control of Multiple Nanoparticle Properties Using Multi‐Target Bayesian Inference

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Deng, H., Zhao, Q., Gao, X., Peng, H., & Zhou, H. (2024) · Extreme Mechanics Letters · doi:10.1016/j.eml.2024.102176 · Cited by 4

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Saldívar, M. C., Tay, E., Pahlavani, H., Doubrovski, E. L., Mirzaali, M. J., & ZADPOOR, A. A. (2025) · Applied Materials Today · doi:10.1016/j.apmt.2025.102722 · Cited by 4

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Recognizing creative visual design

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Mojumder, S., Tao, L., Li, Y., & Liu, W. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.00968 · Cited by 4

ConvFeatNet ensemble: Integrating microstructure and pre-defined features for enhanced prediction of porous material properties

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Integrated machine learning approach for final weight prediction in stimuli-responsive four-dimensional printed single-layer elastomer strip

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Artificial intelligence in additive manufacturing for biomedical engineering

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Tuning local anisotropy for macroscopic auxeticity: Design auxetic meta-laminae via systematic finite element simulations and machine learning approach

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Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery

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Computational analysis of thermally induced stress concentration in structures with geometric constraints

Zhang, Z., Demir, K. G., & Gu, G. X. (2019) · Mechanics of Materials · doi:10.1016/j.mechmat.2019.03.006 · Cited by 3

Investigation of Heat Source Layout Optimization by Using Deep Learning Surrogate Models

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Prediction of Tribological Performance of Ti-3Al-2.5 V-xWC Composite using Machine Learning Models

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A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS

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Automatic Classification of Intramuscular EMG to Recognize Pathologies

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Current Advancement and Future Prospects: Biomedical Nanoengineering

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Artificial Intelligence Applications in Composites: A Survey

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Optimizing Materials for 3D Printing through Artificial Intelligence

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Computational design of structured chemical products

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Effective elastic constants of cortical bone at lamellae and osteon levels considering mineral composition and lamellar arrangement

Asadollahi, S., & Dehestani, M. (2021) · Mechanics of Advanced Materials and Structures · doi:10.1080/15376494.2021.1916135 · Cited by 3

Modeling the biaxial, rate-dependent response of filament-wound FRP tubes

Lanning, A., & Zaghi, A. E. (2023) · Frontiers in Materials · doi:10.3389/fmats.2023.1226624 · Cited by 2

Tailoring the photoluminescence of AIE-type gold nanoclusters via biomineralization-inspired polymorphism

Mahata, S., Sahoo, S. R., Mukhopadhyay, A., Kumari, K., Rakshit, S., & Goswami, N. (2024) · Nanoscale · doi:10.1039/d4nr04022h · Cited by 2

A Novel Polyoxymethylene Fiber‐Based Artificial Muscle Enabled Stable Actuating Behavior

Kong, W., Zhao, X., & Ye, L. (2025) · Small · doi:10.1002/smll.202502065 · Cited by 2

Finite strain self-consistent clustering analysis under multiplicative kinematics

Ferreira, B. P., Coelho, R. P. C., Pires, F. M. A., & Bessa, M. A. (2025) · Computers & Structures · doi:10.1016/j.compstruc.2025.107886 · Cited by 2

Investigating the Surface Characteristics of Complex Geometrical Features on Ti-6Al-4V Alloy Processed through WEDM

Farooq, M. U., Ali, M. A., Anwar, S., Usman, M., & Ali, Z. (2025) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-025-11961-5 · Cited by 2

Prediction of Ultimate Tensile Strength of Heat-Treated Additively Manufactured Ti6Al4V Using Machine Learning Regression

Shirodkar, J., Maheshwari, S., Jatti, V. S., & Gulia, V. R. (2026) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-026-13294-3 · Cited by 2

Cross-Study Machine Learning Analysis of Structure–Property–Performance Relationships in Macroporous PolyHIPE-Based Magnetic Polymer Composites

Recio-Colmenares, C. L., Recio-Colmenares, R., & García-García, C. (2026) · Journal of Composites Science · doi:10.3390/jcs10030128 · Cited by 2

Additive Manufacturing of Ceramics and Ceramic-Based Composites: Processing, Properties, and Engineering Applications

Jose, S. A., Crosby, J., & Menezes, P. L. (2026) · Ceramics · doi:10.3390/ceramics9050043 · Cited by 2

Aluminium alloys process optimization in laser powder bed fusion using machine-learning algorithms

Hung, C., Wang, Y.-H., Wu, S.-H., & Yu, P.-N. (2026) · Materials Technology · doi:10.1080/10667857.2026.2664134 · Cited by 2

Physics-Informed Machine Learning for Property Prediction and Process Optimization in Additively Manufactured Filled Polymer Composites: A Bayesian Optimization Approach

Rooney, K., Mistry, S., Pramanik, A., & Basak, A. K. (2026) · Industries · doi:10.3390/industries1010002 · Cited by 2

Machine learning-driven iterative optimization of TPMS-inspired shell-based mechanical metamaterials for superior energy absorption

Zhou, Z., Zhong, W., Huang, S., & Xi, H. (2026) · Engineering Structures · doi:10.1016/j.engstruct.2026.123002 · Cited by 2

Review: state-of-art for wire arc additive manufacturing of metallic alloys—insights into materials, methods, and future prospects

Goka, S., Manjaiah, M., Davidson, M. J., & Moinuddin, S. Q. (2026) · Journal of Materials Science · doi:10.1007/s10853-026-13199-5 · Cited by 2

Machine learning assisted wrinkling design of hierarchical thin sheets

Qiu, X., Yin, Y., Zhang, J., Wang, H., Tan, H., Liu, Y., & Wang, C. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111638 · Cited by 2

Machine Learning and Regression Analysis Approaches for Investigation of Mechanical Properties of FDM Manufactured Re-Entrant Auxetic Structures Under Flexural Loading

Vyavahare, S. S., Teraiya, S., & Kumar, S. (2023) · Journal of Advanced Manufacturing Systems · doi:10.1142/s0219686723500336 · Cited by 2

Physics‐Informed Multistage Machine Learning Strategy for the Nanomachining‐Induced Plastic Deformation Behavior

Xie, B., Peng, S., Li, J., Fang, Q., & Liaw, P. K. (2023) · Advanced Engineering Materials · doi:10.1002/adem.202300146 · Cited by 2

A comprehensive review in the advancements of bioprinting for tissue engineering using polysaccharide biomaterials and a future strategies

Mozammal, H. M. D., & Lee, H. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.146667 · Cited by 2

Biomimicry for natural and synthetic composites and use of machine learning in hierarchical design

Milazzo, M., Libonati, F., Zhou, S., Guo, K., & Buehler, M. J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821053-6.00002-3 · Cited by 2

Decoupling stiffness and peak moment via hierarchical snapping structures designed with machine learning

Hector, K. W., Dasika, P. S., Rimoli, J. J., & Zavattieri, P. D. (2024) · Materials & Design · doi:10.1016/j.matdes.2024.113189 · Cited by 2

Comparative analysis of direct search methods for material design optimization

Coropeţchi, I. C., Constantinescu, D. M., Vasile, A., Sorohan, Ș., & Apostol, D. A. (2024) · Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · doi:10.1177/14644207241294056 · Cited by 2

Impact of Machine/Deep Learning on Additive Manufacturing: Publication Trends, Bibliometric Analysis, and Literature Review (2013–2022)

Chike, O. G., Chin, Y. J., Ahmad, N. B., & Ali, W. F. F. W. (2025) · Operations Research Forum · doi:10.1007/s43069-025-00440-1 · Cited by 2

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Ebrahimi, M. T., & Fakoor, M. (2025) · Applied Physics A · doi:10.1007/s00339-025-08804-4 · Cited by 2

Applications of Machine Learning in the Field of Polymer Composites

Sharma, A., & Kushvaha, V. (2022) · Composites science and technology · doi:10.1007/978-981-19-6278-3_1 · Cited by 2

Investigation of Heat Source Layout Optimization in Using Deep Learning Surrogate ModelsPreprint

Lang, J., & Wang, Q. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4499133 · Cited by 2

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Safdar, M., Lamouche, G., Paul, P. P., Wood, G., & Zhao, Y. F. (2023) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-031-32154-2_3 · Cited by 1

Moving beyond silver in point-of-use drinking water pathogen control

Huffman, D. P., Pitell, S., Moncure, P. J., Stout, J., Millstone, J. E., Haig, S., & Gilbertson, L. M. (2024) · Environmental Science Water Research & Technology · doi:10.1039/d3ew00564j · Cited by 1

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Dağ, M., Aydoğmuş, E., Yalçın, Z. G., & Arslanoğlu, H. (2025) · Polymer Engineering and Science · doi:10.1002/pen.70230 · Cited by 1

Bio‐inspired nanophotonics: Structural color, chirality, and resonance metasurfaces

Liu, W., Liang, Y., & Tsai, D. P. (2026) · InfoMat · doi:10.1002/inf2.70142 · Cited by 1

Bioinspired cross-aligned multilayered nanocellulose films through shear-induced orientation

Fragal, E. H., Piaget, N., Karrouch, M., Blésès, D., Sztucki, M., Putaux, J., Jean, B., Lancelon‐Pin, C., Bailly, L., Orgéas, L., et al. (2026) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2026.140776 · Cited by 1

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Zmyślony, M., Dradrach, K., & Biggins, J. S. (2024) · Additive manufacturing · doi:10.1016/j.addma.2024.104604 · Cited by 3

Tuning mechanical response of nonuniform triangular lattice material via graph neural network based inverse design algorithm

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Selvido, D. I., Rokaya, D., Jagtap, R., Zafar, M. S., & Tsoi, J. K. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-95464-8.00002-5 · Cited by 1

The green Fibonacci sequence: Natural occurrences, modern innovations, and today’s mathematical applications for clean industrial practices

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Time-Resolved Regression Digital Twin for Continuous Inference of Encoded Mechanical Heterogeneity in Architected Materials

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Effects of Interlocking Profile on the Mechanics of Nacre-Like Materials: Modeling, 3D Printing, and Testing

Shakibanezhad, R., Bennett, B., Yarlagadda, P. K. D. V., Klein, T., Martelli, S., & Mirkhalaf, M. (2026) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2026.114181 · Cited by 1

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Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Xu, W., Bao, X., Chen, G., & Neumann, I. (2020) · Sensors · doi:10.3390/s20226439 · Cited by 1

In silico approaches for polymeric nanocomposites

Cova, T. F. G. G., Nunes, S. C. C., Vitorino, C., Ferreira, M., Rondón-Villarreal, P., & Pais, A. A. C. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90995-2.00010-2 · Cited by 1

Simulation-Driven Design and the Role of Optimization in Design for Additive Manufacturing

Panesar, A. S., Carollo, V., & Jamal, M. (2023) · ASM International eBooks · doi:10.31399/asm.hb.v24a.a0006950 · Cited by 1

Accelerated design and characterization of nonuniformed cellular architected materials with tunable mechanical properties

Xie, B., Zhang, D., Feng, P., & Hu, N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15425-6.00002-x · Cited by 1

Image-Based Predictions

Krishnan, N. M. A., Kodamana, H., & Bhattoo, R. (2024) · Machine intelligence for materials science · doi:10.1007/978-3-031-44622-1_14 · Cited by 1

Optimización de la configuración de materiales compuestos laminados mediante redes neuronales y templado simulado

Chacón, J. C. G., SANCHEZ, A. E. R., & OROZCO, E. R. L. (2024) · Cultura Científica y Tecnológica · doi:10.20983/culcyt.2024.3.2.6 · Cited by 1

Tailoring 3D Buckling and Post Contact in Microlattice Metamaterials

Vangelatos, Z., Komvopoulos, K., & Grigoropoulos, C. P. (2020) · Advanced structured materials · doi:10.1007/978-3-030-50464-9_28 · Cited by 1

Neural networks modeling of strain energy density and Tsai-Wu index in laminated composites

OROZCO, E. R. L., Chacón, J. C. G., & SANCHEZ, A. E. R. (2024) · Journal of Composite Materials · doi:10.1177/00219983241235856 · Cited by 1

Development of Hierarchical Machine Learning for the Modeling and Design of Complex Material Systems

Menon, A. K. (2019)

Bioinspired Hierarchical Composites

Kandemir, A. Ç. (2018)

Bioinspired algorithmic-driven design of additively manufactured composites

Gu, G. X. (2018) · DSpace@MIT (Massachusetts Institute of Technology)

Single-shot forward and inverse hierarchical architected materials design for nonlinear mechanical properties using an Attention-Diffusion model

Lew, A. J., & Buehler, M. J. (2023) · Materials Today · doi:10.1016/j.mattod.2023.03.007 · Cited by 70

Machine Learning-Guided Design of Additively Manufactured Polymer Composites for Next-Generation Wearables

Ma, Q., & Hu, H. (2026) · doi:10.13140/rg.2.2.20369.80486