Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine-and deep-learning methods

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Deep learning

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

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Deep learning in neural networks: an overview

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

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,475

Ridge regression: Biased estimation for nonorthogonal problems

Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648

Untitled work

(2012) · Cited by 8,009

Ridge Regression: Biased Estimation for Nonorthogonal Problems

Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.2307/1271436 · Cited by 7,001

Improving neural networks by preventing co-adaptation of feature detectors

Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196

Deep Learning using Rectified Linear Units (ReLU)Preprint

Agarap, A. F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08375 · Cited by 3,792

On the Convergence of Adam and Beyond

Reddi, S. J., Kale, S., & Kumar, S. (2019) · arXiv · doi:10.48550/arxiv.1904.09237 · Cited by 1,771

DREAM.3D: A Digital Representation Environment for the Analysis of Microstructure in 3D

Groeber, M. A., & Jackson, M. (2014) · Integrating materials and manufacturing innovation · doi:10.1186/2193-9772-3-5 · Cited by 1,147

Compatibility of deformation in two-phase Ti-Al alloys: Dependence on microstructure and orientation relationships

Luster, J., & Morris, M. A. (1995) · Metallurgical and Materials Transactions A · doi:10.1007/bf02670762 · Cited by 665

An elasto-viscoplastic formulation based on fast Fourier transforms for the prediction of micromechanical fields in polycrystalline materials

Lebensohn, R. A., Kanjarla, A. K., & Eisenlohr, P. (2011) · International Journal of Plasticity · doi:10.1016/j.ijplas.2011.12.005 · Cited by 586

Micromechanical and macromechanical effects in grain scale polycrystal plasticity experimentation and simulation

Raabe, D., Sachtleber, M., Zhao, Z., Roters, F., & Zaefferer, S. (2001) · Acta Materialia · doi:10.1016/s1359-6454(01)00242-7 · Cited by 432

A three-dimensional cellular automation-finite element model for the prediction of solidification grain structures

Gandin, C., Desbiolles, J.-L., Rappaz, M., & Thévoz, P. (1999) · Metallurgical and Materials Transactions A · doi:10.1007/s11661-999-0226-2 · Cited by 360

Material structure-property linkages using three-dimensional convolutional neural networks

Cecen, A., Dai, H., Yabansu, Y. C., Kalidindi, S. R., & Song, L. (2017) · Acta Materialia · doi:10.1016/j.actamat.2017.11.053 · Cited by 341

Descriptive tools for the analysis of texture projects with large datasets usingMTEX: strength, symmetry and components

Mainprice, D., Bachmann, F., Hielscher, R., & Schaeben, H. (2014) · Geological Society London Special Publications · doi:10.1144/sp409.8 · Cited by 320

Predicting tensile properties of Ti-6Al-4V produced via directed energy deposition

Hayes, B. J., Martin, B. W., Welk, B. A., Kuhr, S. J., Ales, T. K., Brice, D. A., Ghamarian, I., Baker, A. H., Haden, C. V., Harlow, D. G., et al. (2017) · Acta Materialia · doi:10.1016/j.actamat.2017.05.025 · Cited by 257

Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition?

Nielsen, D. (2016) · BIBSYS Brage (BIBSYS (Norway)) · Cited by 230

Effect of manufacturing parameters on mechanical properties of 316L stainless steel parts fabricated by selective laser melting: A computational framework

Ahmadi, A., Mirzaeifar, R., Moghaddam, N. S., Turabi, A. S., Karaca, H. E., & Elahinia, M. (2016) · Materials & Design · doi:10.1016/j.matdes.2016.09.043 · Cited by 199

An integrated process–structure–property modeling framework for additive manufacturing

Yan, W., Lian, Y., Yu, C., Kafka, O. L., Liu, Z., Liu, W. K., & Wagner, G. J. (2018) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2018.05.004 · Cited by 154

Numerical investigation of effects of nucleation mechanisms on grain structure in metal additive manufacturing

Li, X., & Tan, W. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.06.019 · Cited by 153

Predicting the mechanical response of oligocrystals with deep learning

Frankel, A., Jones, R. E., Alleman, C. N., & Templeton, J. A. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2019.109099 · Cited by 133

Multi-scale modeling of solidification and microstructure development in laser keyhole welding process for austenitic stainless steel

Tan, W., & Shin, Y. C. (2014) · Computational Materials Science · doi:10.1016/j.commatsci.2014.10.063 · Cited by 132

An efficient machine learning approach to establish structure-property linkages

Jung, J., Yoon, J. I., Park, H. K., Kim, J. Y., & Kim, H. S. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.09.034 · Cited by 88

A multi-scale, multi-physics modeling framework to predict spatial variation of properties in additive-manufactured metals

Herriott, C., Li, X., Kouraytem, N., Tari, V., Tan, W., Anglin, B. S., Rollett, A. D., & Spear, A. D. (2018) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/1361-651x/aaf753 · Cited by 73

Micromechanics modeling of metallic alloys 3D printed by selective laser melting

Andani, M. T., Karamooz-Ravari, M. R., Mirzaeifar, R., & Ni, J. (2017) · Materials & Design · doi:10.1016/j.matdes.2017.10.026 · Cited by 68

Validation of micro-mechanical FFT-based simulations using High Energy Diffraction Microscopy on Ti-7Al

Tari, V., Lebensohn, R. A., Pokharel, R., Turner, T. J., Shade, P. A., Bernier, J. V., & Rollett, A. D. (2018) · Acta Materialia · doi:10.1016/j.actamat.2018.05.036 · Cited by 68

Effect of microstructure on the elasto-viscoplastic deformation of dual phase titanium structures

Öztürk, T., & Rollett, A. D. (2017) · Computational Mechanics · doi:10.1007/s00466-017-1467-3 · Cited by 40

Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

Spear, A. D., Kalidindi, S. R., Meredig, B., Kontsos, A., & le Graverend, J. (2018) · JOM · doi:10.1007/s11837-018-2894-0 · Cited by 36

Artificial intelligence and machine learning in design of mechanical materials

Guo, K., Yang, Z., Yu, C., & Buehler, M. J. (2020) · Materials Horizons · doi:10.1039/d0mh01451f · Cited by 732

Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption

Wu, Y., Fang, J., Wu, C., Li, C., Sun, G., & Li, Q. (2023) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2023.108102 · Cited by 455

Research and application of machine learning for additive manufacturing

Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C. L., Rosen, D. W., Simpson, T. W., Lu, Y., & Tang, Q. (2022) · Additive Manufacturing · doi:10.1016/j.addma.2022.102691 · Cited by 391

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing

Ng, W. L., Goh, G. L., Goh, G. D., Ten, J. S., & Yeong, W. Y. (2024) · Advanced Materials · doi:10.1002/adma.202310006 · Cited by 348

Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm

Guo, S., Agarwal, M., Cooper, C., Tian, Q., Gao, R. X., Guo, W., & Guo, Y. B. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.11.003 · Cited by 336

Data-driven models for structure-property prediction in additively manufactured steels

Sadeghpour, E., & Nonn, A. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111782 · Cited by 26

Prediction of Mechanical Properties of Additively Manufactured 316L Steel Based on Microstructural Features Using Data-Driven Approaches

Parmar, A., Kumar, R. M., Atwater, M. A., & Shin, Y. C. (2025) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-025-11953-5 · Cited by 4

Data-Driven Modeling of Mechanical Properties for 17-4 PH Stainless Steel Built by Additive Manufacturing

Porro, M., Zhang, B., Parmar, A., & Shin, Y. (2022) · Integrating Materials and Manufacturing Innovation · doi:10.1007/s40192-022-00261-8 · Cited by 15

Beyond Micrographs: Multimodal Learning for Predicting Mechanical Properties of Additively Manufactured TI-6AI-4v

Alnemer, L., & Rodan, A. (2026) · Studies in big data · doi:10.1007/978-3-032-19498-5_53

Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

Xie, X., Bennett, J. L., Saha, S., Lu, Y., Cao, J., Liu, W. K., & Gan, Z. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00555-z · Cited by 154