Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine-and deep-learning methods
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Machine learning models can be developed to predict mechanical properties of materials fabricated through additive manufacturing as they can learn and model highly complex relationships.
“Machine learning models can be developed to predict mechanical properties of materials fabricated through additive manufacturing as they can learn and model highly complex relationships [438-445].”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [438]
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
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 · see citing works
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 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
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 14,474 · see citing works
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 · see citing works
(2012) · Cited by 8,009 · see citing works
Ridge Regression: Biased Estimation for Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.2307/1271436 · Cited by 6,553 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Luster, J., & Morris, M. A. (1995) · Metallurgical and Materials Transactions A · doi:10.1007/bf02670762 · Cited by 665 · see citing works
Lebensohn, R. A., Kanjarla, A. K., & Eisenlohr, P. (2011) · International Journal of Plasticity · doi:10.1016/j.ijplas.2011.12.005 · Cited by 586 · see citing works
Raabe, D., Sachtleber, M., Zhao, Z., Roters, F., & Zaefferer, S. (2001) · Acta Materialia · doi:10.1016/s1359-6454(01)00242-7 · Cited by 432 · see citing works
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 · see citing works
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 331 · see citing works
Mainprice, D., Bachmann, F., Hielscher, R., & Schaeben, H. (2014) · Geological Society London Special Publications · doi:10.1144/sp409.8 · Cited by 320 · see citing works
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 · see citing works
Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition?
Nielsen, D. (2016) · BIBSYS Brage (BIBSYS (Norway)) · Cited by 230 · see citing works
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 · see citing works
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 · see citing works
Li, X., & Tan, W. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.06.019 · Cited by 153 · see citing works
Tan, W., & Shin, Y. C. (2014) · Computational Materials Science · doi:10.1016/j.commatsci.2014.10.063 · Cited by 132 · see citing works
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 132 · see citing works
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 · see citing works
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 · see citing works
Self-citationMicromechanics 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citation
Flagged first, then by citations
4 of the 39 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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 702 · see citing works
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 447 · see citing works
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 · see citing works
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 · see citing works
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 323 · see citing works
Machine learning in predicting mechanical behavior of additively manufactured parts
Nasiri, S., & Khosravani, M. R. (2021) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2021.07.004 · Cited by 202 · see citing works
Dai, M., Demirel, M. F., Liang, Y., & Hu, J.-M. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00574-w · Cited by 168 · see citing works
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
Jin, H., Zhang, E., & Espinosa, H. D. (2023) · Applied Mechanics Reviews · doi:10.1115/1.4062966 · Cited by 163 · see citing works
Kouraytem, N., Li, X., Tan, W., Kappes, B. B., & Spear, A. D. (2020) · Journal of Physics Materials · doi:10.1088/2515-7639/abca7b · Cited by 154 · see citing works
Self-citationGunasegaram, D., Murphy, A. B., Barnard, A. S., DebRoy, T., Matthews, M. J., Ladani, L. J., & Gu, D. (2021) · Additive manufacturing · doi:10.1016/j.addma.2021.102089 · Cited by 149 · see citing works
Sepasdar, R., Karpatne, A., & Shakiba, M. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2022.115126 · Cited by 137 · see citing works
Mozaffar, M., Liao, S., Xie, X., Saha, S., Park, C., Cao, J., Liu, W. K., & Gan, Z. (2021) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2021.117485 · Cited by 136 · see citing works
Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds
Conev, A., Litsa, E. E., Perez, M. R., Diba, M., Mikos, A. G., & Kavraki, L. E. (2020) · Tissue Engineering Part A · doi:10.1089/ten.tea.2020.0191 · Cited by 126 · see citing works
Wang, Z., Yang, W., Liu, Q., Zhao, Y., Liu, P., Wu, D., Banu, M., & Chen, L. (2022) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2022.02.053 · Cited by 122 · see citing works
Machine learning prediction of mechanical properties in metal additive manufacturing
Akbari, P., Zamani, M., & Mostafaei, A. (2024) · Additive manufacturing · doi:10.1016/j.addma.2024.104320 · Cited by 128 · see citing works
Xames, M. D., Torsha, F. K., & Sarwar, F. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-01957-6 · Cited by 110 · see citing works
Liu, J. P., Ye, J., Izquierdo, D. S., Vinel, A., Shamsaei, N., & Shao, S. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02012-0 · Cited by 111 · see citing works
Feature selection and feature learning in machine learning applications for gas turbines: A review
Xie, J., Sage, M., & Zhao, Y. F. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105591 · Cited by 99 · see citing works
Tezsezen, E., Yigci, D., Ahmadpour, A., & Taşoğlu, S. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c04486 · Cited by 95 · see citing works
Kalule, R., Abderrahmane, H. A., AlAmeri, W. S., & Sassi, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-36096-2 · Cited by 81 · see citing works
Sharma, S., Joshi, S. S., Pantawane, M. V., Radhakrishnan, M., Mazumder, S., & Dahotre, N. B. (2023) · International Materials Reviews · doi:10.1080/09506608.2023.2169501 · Cited by 73 · see citing works
Zhang, Y., Safdar, M., Xie, J., Li, J., Sage, M., & Zhao, Y. F. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02017-9 · Cited by 78 · see citing works
Perspective: Machine learning in experimental solid mechanics
Brodnik, N. R., Muir, C., Tulshibagwale, N., Rossin, J., Echlin, M. P., Hamel, C. M., Kramer, S., Pollock, T. M., Kiser, J. D., Smith, C. W., et al. (2023) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2023.105231 · Cited by 69 · see citing works
Du, Y., Mukherjee, T., Li, R., Hou, Z., Dutta, S., Arnold, C. B., Elwany, A. H., Kung, S., Tang, J., & DebRoy, T. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101587 · Cited by 73 · see citing works
Pandey, A., & Pokharel, R. (2020) · Scripta Materialia · doi:10.1016/j.scriptamat.2020.10.028 · Cited by 66 · see citing works
Graph neural networks for efficient learning of mechanical properties of polycrystals
Hestroffer, J. M., Charpagne, M., Latypov, M. I., & Beyerlein, I. J. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111894 · Cited by 65 · see citing works
Wu, C., Xu, Y., Fang, J., & Li, Q. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10100-y · Cited by 69 · see citing works
Li, H., Li, X., Li, Y., Xiao, W., Wen, K., Li, Z., Zhang, Y., & Xiong, B. (2022) · Materials & Design · doi:10.1016/j.matdes.2022.111483 · Cited by 58 · see citing works
A comprehensive review of lunar-based manufacturing and construction
Azami, M., Kazemi, Z., Moazen, S., Dubé, M., Potvin, M.-J., & Skonieczny, K. (2024) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2024.101045 · Cited by 57 · see citing works
Fang, L., Cheng, L., Glerum, J. A., Bennett, J. L., Cao, J., & Wagner, G. J. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00808-5 · Cited by 57 · see citing works
Saimon, A. I., Yangue, E., Yue, X., Kong, Z., & Liu, C. (2024) · IISE Transactions · doi:10.1080/24725854.2024.2443592 · Cited by 57 · see citing works
Eghtesad, A., Luo, Q., Shang, S., Lebensohn, R. A., Knežević, M., Liu, Z., & Beese, A. M. (2023) · International Journal of Plasticity · doi:10.1016/j.ijplas.2023.103646 · Cited by 55 · see citing works
Shang, X., Liu, Z., Zhang, J., Lyu, T., & Zou, Y. (2023) · Materials Today · doi:10.1016/j.mattod.2023.09.007 · Cited by 54 · see citing works
Machine learning for advancing laser powder bed fusion of stainless steel
Abd‐Elaziem, W., Elkatatny, S., Sebaey, T. A., Darwish, M. A., El‐baky, M. A. A., & Hamada, A. S. (2024) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2024.04.130 · Cited by 53 · see citing works
Xia, B., Huang, X., Chang, L., Zhang, R., Liao, Z., & Cai, Z. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.105996 · Cited by 52 · see citing works
Casting Defects Detection in Aluminum Alloys Using Deep Learning: a Classification Approach
Nikolić, F., Štajduhar, I., & Čanađija, M. (2022) · International Journal of Metalcasting · doi:10.1007/s40962-022-00777-x · Cited by 51 · see citing works
Chua, C., An, J., Chua, C. K., Kuo, C. N., & Sing, S. L. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101502 · Cited by 51 · see citing works
Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel
Zhu, J.-A., Jia, Y., Lei, J., & Liu, Z. (2021) · Mathematics · doi:10.3390/math9212804 · Cited by 50 · see citing works
Data-driven methods for stress field predictions in random heterogeneous materials
Hoq, E., Aljarrah, O., Li, J., Bi, J., Heryudono, A. R. H., & Huang, W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106267 · Cited by 48 · see citing works
Data-driven modeling of process-structure-property relationships in metal additive manufacturing
Hu, Z., & Yan, W. (2024) · npj Advanced Manufacturing · doi:10.1038/s44334-024-00003-y · Cited by 48 · see citing works
CNN-based prediction of microstructure-derived random property fields of composite materials
Gavallas, P., Stefanou, G., Savvas, D., Mattrand, C., & Bourinet, J. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117207 · Cited by 46 · see citing works
Ackermann, M., & Haase, C. (2022) · Additive manufacturing · doi:10.1016/j.addma.2023.103585 · Cited by 45 · see citing works
Machine learning-supported manufacturing: a review and directions for future research
Ördek, B., Borgianni, Y., & Coatanéa, É. (2024) · Production & Manufacturing Research · doi:10.1080/21693277.2024.2326526 · Cited by 45 · see citing works
Sarkon, G. K., Safaei, B., Kenevisi, M. S., Arman, S., & Zeeshan, Q. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09786-9 · Cited by 45 · see citing works
Deshmankar, A., Challa, J. S., Singh, A. K., & Regalla, S. P. (2024) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4066575 · Cited by 43 · see citing works
Ferdousi, S., Advíncula, R. C., Sokolov, A. P., Choi, W., & Jiang, Y. (2023) · Composites Part B Engineering · doi:10.1016/j.compositesb.2023.110958 · Cited by 36 · see citing works
Dai, M., Demirel, M. F., Liu, X., Liang, Y., & Hu, J. (2023) · Computational Materials Science · doi:10.1016/j.commatsci.2023.112461 · Cited by 36 · see citing works
Machine learning in additive manufacturing: A comprehensive insight
Equbal, M. A., Equbal, A., Khan, Z. A., & Badruddin, I. A. (2024) · International Journal of Lightweight Materials and Manufacture · doi:10.1016/j.ijlmm.2024.10.002 · Cited by 39 · see citing works
Wang, H., Li, B., Zhang, W., & Xuan, F. (2023) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2023.109788 · Cited by 34 · see citing works
AI‐Based Metamaterial Design for Wearables
Yigci, D., Ahmadpour, A., & Taşoğlu, S. (2023) · Advanced Sensor Research · doi:10.1002/adsr.202300109 · Cited by 33 · see citing works
Unsupervised machine learning discovers classes in aluminium alloys
Bhat, N., Barnard, A. S., & Birbilis, N. (2023) · Royal Society Open Science · doi:10.1098/rsos.220360 · Cited by 31 · see citing works
Luo, Q., Huang, N., Fu, T., Wang, J., Bartles, D. L., Simpson, T. W., & Beese, A. M. (2023) · Additive manufacturing · doi:10.1016/j.addma.2023.103804 · Cited by 26 · see citing works
Qiao, L., Zhu, J., & Wang, Y. (2021) · steel research international · doi:10.1002/srin.202100267 · Cited by 26 · see citing works
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 · see citing works
Ahmad, O., Kumar, N. R., Mukherjee, R., & Bhowmick, S. (2023) · Physical Review Materials · doi:10.1103/physrevmaterials.7.083802 · Cited by 26 · see citing works
Discovery of novel materials through machine learning
Akinpelu, A., Bhullar, M., & Yao, Y. (2024) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ad6bdb · Cited by 28 · see citing works
Zaeem, M. A., Thomas, S., Kavousi, S., Zhang, N., Mukhopadhyay, T., & Mahata, A. (2024) · 2D Materials · doi:10.1088/2053-1583/ad63b6 · Cited by 25 · see citing works
Zhao, P., Wang, Y., Jiang, B., Zhang, H., Cheng, X., & Fan, Q. (2023) · Materials Science and Engineering A · doi:10.1016/j.msea.2023.145202 · Cited by 24 · see citing works
Prediction of structural deformation of a deck plate using a GAN-based deep learning method
Oh, S., Jin, H. K., Joe, S. J., & Ki, H. (2021) · Ocean Engineering · doi:10.1016/j.oceaneng.2021.109835 · Cited by 24 · see citing works
Chigilipalli, B. K., & Anandakrishnan, V. (2023) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-023-01617-w · Cited by 23 · see citing works
Predicting tensile-shear strength of nugget using M5P model tree and random forest: An analysis
Dang, S. K., & Singh, K. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103345 · Cited by 23 · see citing works
A deep learning method for predicting microvoid growth in heterogeneous polycrystals
Liu, J., Huang, M., Li, Z., Zhao, L., & Zhu, Y. (2022) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2022.108332 · Cited by 23 · see citing works
Eberle, M., Pinches, S., Osborne, M., Qin, A. K., & Ang, A. S. M. (2024) · Journal of Thermal Spray Technology · doi:10.1007/s11666-024-01760-0 · Cited by 22 · see citing works
Casting Microstructure Inspection Using Computer Vision: Dendrite Spacing in Aluminum Alloys
Nikolić, F., Štajduhar, I., & Čanađija, M. (2021) · Metals · doi:10.3390/met11050756 · Cited by 22 · see citing works
Pribe, J. D., Richter, B., Leser, P. E., Yeratapally, S. R., Weber, G., Kitahara, A. R., & Glaessgen, E. H. (2023) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-023-00303-9 · Cited by 21 · see citing works
Grain Knowledge Graph Representation Learning: A New Paradigm for Microstructure-Property Prediction
Shu, C., He, J., Xue, G., & Xie, C. (2022) · Crystals · doi:10.3390/cryst12020280 · Cited by 21 · see citing works
Small, K., & Taheri, M. L. (2021) · Metallurgical and Materials Transactions A · doi:10.1007/s11661-021-06273-x · Cited by 21 · see citing works
MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE
Frankel, A., Safta, C., Alleman, C. N., & Jones, R. E. (2021) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2021039688 · Cited by 20 · see citing works
Grain and grain boundary segmentation using machine learning with real and generated datasets
Warren, P. A., Raju, N., Prasad, A., Hossain, M. S., Subramanian, R., Kapat, J. S., Manjooran, N. J., & Ghosh, R. R. (2023) · Computational Materials Science · doi:10.1016/j.commatsci.2023.112739 · Cited by 20 · see citing works
Liu, C., Li, X., Ge, J., Liu, X., Li, B., Liu, Z., & Liang, J. (2024) · Composites Part A Applied Science and Manufacturing · doi:10.1016/j.compositesa.2024.108401 · Cited by 20 · see citing works
Azqadan, E., Arami, A., & Jahed, H. (2025) · Journal of Magnesium and Alloys · doi:10.1016/j.jma.2025.07.019 · Cited by 18 · see citing works
Zaman, S., Mahmud, M. S., Mollick, A. A., Lhaden, T., Dantzler, J. Z. R., Arroyo, S., Goona, N. K., Mesbah, M., Ahsan, M. A., & Lin, Y. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18072-y · Cited by 17 · see citing works
Explainable machine learning for predicting the mechanical properties in bainitic steels
Ackermann, M., İren, D., & Yao, Y. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.111946 · Cited by 17 · see citing works
Porro, M., Zhang, B., Parmar, A., & Shin, Y. (2022) · Integrating Materials and Manufacturing Innovation · doi:10.1007/s40192-022-00261-8 · Cited by 15 · see citing works
Chandraker, A., Barik, S., Sai, N. J., & Chauhan, A. (2024) · Metallurgical and Materials Transactions A · doi:10.1007/s11661-024-07661-9 · Cited by 16 · see citing works
Tan, W., & Spear, A. D. (2024) · Accounts of Materials Research · doi:10.1021/accountsmr.3c00108 · Cited by 15 · see citing works
Self-citationMulti-input convolutional network for ultrafast simulation of field evolvement
Wang, Z., Yang, W., Xiang, L., Wang, X., Zhao, Y., Xiao, Y., Liu, P., Liu, Y., Banu, M., Zikanov, O. Y., et al. (2022) · Patterns · doi:10.1016/j.patter.2022.100494 · Cited by 14 · see citing works
Gao, Z., Zhu, C., Wang, C., Shu, Y., Liu, S., Miao, J., & Yang, L. (2025) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2025.117844 · Cited by 14 · see citing works
Failure classification of porous additively manufactured parts using Deep Learning
Johnson, K. L., Maestas, D., Emery, J. M., Grigoriu, M. D., Smith, M. D., & Martinez, C. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2021.111098 · Cited by 14 · see citing works
DeMille, K. J., & Spear, A. D. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111290 · Cited by 14 · see citing works
Self-citationPatel, D., Sharma, R., & Guo, Y. B. (2025) · Annual Reviews of Heat Transfer · doi:10.1615/annualrevheattransfer.2025059621 · Cited by 13 · see citing works
Application of machine learning in the design and optimization of bimodal structural materials
Wang, D., Zhang, Y., Jia, Y.-F., Zhang, X., Yan, J., Shu, W.-X., & Tu, S. (2023) · Computational Materials Science · doi:10.1016/j.commatsci.2023.112040 · Cited by 12 · see citing works
Le, T.-N., & Chou, K. (2023) · Metals · doi:10.3390/met13050976 · Cited by 12 · see citing works
Data-driven phase recognition of steels for use in mechanical property prediction
Zhang, B., & Shin, Y. C. (2021) · Manufacturing Letters · doi:10.1016/j.mfglet.2021.10.005 · Cited by 11 · see citing works
Zhibin, Z., Chenxi, W., Xingwu, 张. Z., Chen, X., & Ying-hong, L. (2023) · Journal of Mechanical Engineering · doi:10.3901/jme.2023.19.253 · Cited by 11 · see citing works
DeMille, K. J., Hall, R., Leigh, J. R., Güven, I., & Spear, A. D. (2024) · Composites Part B Engineering · doi:10.1016/j.compositesb.2024.111751 · Cited by 11 · see citing works
Self-citationWang, P., Tao, Q., Dong, H., & El-Fallah, G. M. A. M. (2025) · Computational Materials Science · doi:10.1016/j.commatsci.2025.113773 · Cited by 11 · see citing works
Gollapalli, A., & Singh, A. K. (2025) · Computational Materials Science · doi:10.1016/j.commatsci.2025.113854 · Cited by 11 · see citing works
Snider-Simon, B., & Frantziskonis, G. N. (2022) · Additive manufacturing · doi:10.1016/j.addma.2022.102629 · Cited by 11 · see citing works
Compositional design of Ni-based superalloys for additive manufacturing: Progress and perspectives
Yin, B., Wei, Y., Zhu, J., Ge, H., Zhu, S., Gui, Y., Zhang, W., Fu, H., & Xie, J. (2026) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2026.101185 · Cited by 10 · see citing works
Yu, M., Zhu, L., Yang, Z., Ning, J., Xue, P., Wang, S., Wang, L., & Tan, X. (2026) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ae5297 · Cited by 10 · see citing works
Wankhade, N. P., Sale, V. P., Yadav, R. S., Jikar, P. C., Gadgekar, S. R., & Dhokey, N. B. (2024) · Materials Today Proceedings · doi:10.1016/j.matpr.2024.05.066 · Cited by 10 · see citing works
Martinitz, L., & Hartmann, C. (2023) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1284/1/012052 · Cited by 10 · see citing works
Sun, L., Chiang, P., Singham, J. J., Tan, W. X., Jangam, J. S. D., & Lai, C. Q. (2024) · Additive manufacturing · doi:10.1016/j.addma.2024.103995 · Cited by 9 · see citing works
Gaikwad, M. U., Gaikwad, P., Ambhore, N., Sharma, A., & Bhosale, S. S. (2024) · Recent Patents on Mechanical Engineering · doi:10.2174/0122127976289578240319102303 · Cited by 9 · see citing works
Stiefel, M., Müller, M., Bachmann, B.-I., Guitar, M. A., Nayak, U. P., & Mücklich, F. (2024) · MRS Communications · doi:10.1557/s43579-024-00549-0 · Cited by 12 · see citing works
Gowtham, N. H., Jegadeesan, J. T., Bhattacharya, C., & Basu, B. (2023) · Computational Materials Science · doi:10.1016/j.commatsci.2023.112512 · Cited by 9 · see citing works
Designing Ti-6Al-4V microstructure for strain delocalization using neural networks
Ahmadikia, B., Beyerlein, A. L., Hestroffer, J. M., Kumar, M. A., & Beyerlein, I. J. (2024) · Journal of Materials Science Materials Theory · doi:10.1186/s41313-024-00055-9 · Cited by 8 · see citing works
Predicting Heat Capacity of Molecular Fluids Using Interpretable Machine Learning Model
Li, S., He, H., & Wang, Y. (2024) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c02495 · Cited by 8 · see citing works
A Data-driven machine learning model for radiation-induced DBTT shifts in RAFM steels
Wang, P., & El-Fallah, G. M. A. M. (2025) · Journal of Nuclear Materials · doi:10.1016/j.jnucmat.2025.155984 · Cited by 8 · see citing works
- Authors
- Herriott, C., & Spear, A. D.
- Affiliations
- University of Utah (US)
- Corresponding author
- Spear, Ashley D.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 175
- Pages
- 109599
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W3007681970
- Cited by
- 187
- References
- 39
- Citations by year
- 2026: 39 · 2025: 26 · 2024: 34 · 2023: 39 · 2022: 28 · 2021: 15 · 2020: 6
- FWCI
- 8.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computational Materials Science (acceptedVersion)
- Funders
- U.S. Department of Defense
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › Microstructure and mechanical properties
- Physical Sciences › Engineering › Mechanical Engineering › Microstructure and Mechanical Properties of Steels
- Keywords
- microstructure-property relationship, additive manufacturing, 316L stainless steel, mechanical property prediction, 3D convolutional neural networks, crystal orientation, XGBoost, ridge regression, feature importance, grain structure, data-driven modeling
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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