A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892

Untitled work

(1996) · Cited by 20,179

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones

Khairallah, S. A., Anderson, A. T., Rubenchik, A. M., & King, W. E. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.02.014 · Cited by 2,797

Recent advances and applications of machine learning in solid-state materials science

Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,786

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Gao, W., Zhang, Y., Ramanujan, D., Ramani, K., Chen, Y., Williams, C. B., Wang, C. C. L., Shin, Y. C., Zhang, S., & Zavattieri, P. D. (2015) · Computer-Aided Design · doi:10.1016/j.cad.2015.04.001 · Cited by 2,657

Fine-structured aluminium products with controllable texture by selective laser melting of pre-alloyed AlSi10Mg powder

Thijs, L., Kempen, K., Kruth, J., & Humbeeck, J. V. (2012) · Acta Materialia · doi:10.1016/j.actamat.2012.11.052 · Cited by 1,925

A review on selective laser sintering/melting (SLS/SLM) of aluminium alloy powders: Processing, microstructure, and properties

Olakanmi, E. O., Cochrane, R. F., & Dalgarno, K. W. (2015) · Progress in Materials Science · doi:10.1016/j.pmatsci.2015.03.002 · Cited by 1,772

Materials discovery and design using machine learning

Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276

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Bridgman, W., Zhang, X., Teichert, G. H., Khalil, M., Garikipati, K., & Jones, R. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/2002242 · Cited by 6

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Rahul, M. R. (2025) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-031-78736-2_1 · Cited by 1

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He, L., Dong, A., Du, D., Huo, X., Xiong, L., Zhang, X., Pan, Y., Lu, Y., & Sun, B. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4191174

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Ilinykh, A., & Pankov, A. M. (2023) · AIP conference proceedings · doi:10.1063/5.0117529

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Garapati, D. P., Iniyan, S., Sultanuddin, S. J., Manivannan, R., & Pragadish, N. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7974-5.ch014

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Amirabdollahian, S., & Gerdooei, M. (2025) · Journal of Testing and Evaluation · doi:10.1520/jte20240596

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Yuan, Q., Wang, G., Han, Z., Liu, X., Ji, L., & Li, S. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.113754

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Guo, H., Sun, W., Wang, M., Zhou, X., Lu, W. T., & Xu, L. (2026) · The Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics · doi:10.1080/14786435.2026.2617019

Introduction

Kang, G., Kan, Q., Zhang, X., Hu, Y.-N., & Li, X. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44615-3.00002-8

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Zhu, Z., Zhou, H., Jin, C., Zheng, C., & Kong, X. (2026) · Thin-Walled Structures · doi:10.1016/j.tws.2026.114659

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Wu, W., Yu, H., Xu, K., Yang, H., Wang, H., Xu, F., & Lu, T. (2026) · ENGINEERING Transformative Materials · doi:10.2738/engtm.2026.0007

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Safi, A. R., Campos, P., Bock, F. E., & Klusemann, B. (2026) · Machine learning for computational science and engineering · doi:10.1007/s44379-026-00092-w

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Zhang, P., & Tang, K. (2026) · International Journal of Plasticity · doi:10.1016/j.ijplas.2026.104863

The federspiel technic for removal of abnormal frenum

Rhode, A. C. (1926) · International Journal of Orthodontia Oral Surgery and Radiography · doi:10.1016/s0099-6963(26)90284-6 · Cited by 1

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Srivastava, S. K., & Mathivanan, N. R. (2025) · Frontier materials & technologies · doi:10.18323/2782-4039-2025-1-71-8

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Luo, Z., He, J., Li, D. Y., & Peng, Y. (2022) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1270/1/012024 · Cited by 2

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