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 · see citing works

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 · see citing works

Untitled work

(1996) · Cited by 20,179 · 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

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 · see citing works

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 2,655 · see citing works

The status, challenges, and future of additive manufacturing in engineering

Gao, W., Zhang, Y., Ramanujan, D., Karthik Ramani, A., 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 2,638 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Selective laser melting of AlSi10Mg alloy: Process optimisation and mechanical properties development

Read, N., Wang, W., Essa, K., & Attallah, M. M. (2014) · Materials & Design (1980-2015) · doi:10.1016/j.matdes.2014.09.044 · Cited by 1,224 · see citing works

Additive manufactured AlSi10Mg samples using Selective Laser Melting (SLM): Microstructure, high cycle fatigue, and fracture behavior

Brandl, E., Heckenberger, U., Holzinger, V., & Buchbinder, D. (2011) · Materials & Design (1980-2015) · doi:10.1016/j.matdes.2011.07.067 · Cited by 1,154 · see citing works

Mechanical Properties of AlSi10Mg Produced by Selective Laser Melting

Kempen, K., Thijs, L., Humbeeck, J. V., & Kruth, J. (2012) · Physics Procedia · doi:10.1016/j.phpro.2012.10.059 · Cited by 853 · see citing works

Effect of heat treatment on AlSi10Mg alloy fabricated by selective laser melting: Microstructure evolution, mechanical properties and fracture mechanism

Li, W., Li, S., Liu, J., Zhang, A., Zhou, Y., Wei, Q., Yan, C., & Shi, Y. (2016) · Materials Science and Engineering A · doi:10.1016/j.msea.2016.03.088 · Cited by 853 · see citing works

Process defects and <i>in situ</i> monitoring methods in metal powder bed fusion: a review

Grasso, M., & Colosimo, B. M. (2017) · Measurement Science and Technology · doi:10.1088/1361-6501/aa5c4f · Cited by 833 · see citing works

A strategy to apply machine learning to small datasets in materials science

Zhang, Y., & Ling, C. (2018) · npj Computational Materials · doi:10.1038/s41524-018-0081-z · Cited by 791 · see citing works

Machine Learning: The art and science of algorithms that make sense of data

Flach, P. A. (2012) · Cited by 694 · see citing works

Processing AlSi10Mg by selective laser melting: Parameter optimisation and material characterisation

Kempen, K., Thijs, L., Humbeeck, J. V., & Kruth, J. (2014) · Materials Science and Technology · doi:10.1179/1743284714y.0000000702 · Cited by 411 · see citing works

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Bock, F. E., Aydin, R. C., Cyron, C. J., Huber, N. W., Kalidindi, S. R., & Klusemann, B. (2019) · Frontiers in Materials · doi:10.3389/fmats.2019.00110 · Cited by 410 · see citing works

Metallurgy of high-silicon steel parts produced using Selective Laser Melting

Garibaldi, M., Ashcroft, I., Simonelli, M., & Hague, R. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.03.037 · Cited by 358 · see citing works

Gradient in microstructure and mechanical property of selective laser melted AlSi10Mg

Liu, Y., Liu, Z., Jiang, Y., Wang, G. W., Yang, Y., & Zhang, L. (2017) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2017.11.020 · Cited by 352 · see citing works

A new approach for ductile fracture prediction on Al 2024-T351 alloy

Khan, A. S., & Liu, H. (2012) · International Journal of Plasticity · doi:10.1016/j.ijplas.2012.01.003 · Cited by 319 · see citing works

The AlSi10Mg samples produced by selective laser melting: single track, densification, microstructure and mechanical behavior

Wei, P., Wei, Z., Chen, Z. X., Du, J., He, Y., Li, J., & Zhou, Y. (2017) · Applied Surface Science · doi:10.1016/j.apsusc.2017.02.215 · Cited by 309 · see citing works

Large deformation response of additively-manufactured FCC metamaterials: From octet truss lattices towards continuous shell mesostructures

Bonatti, C., & Mohr, D. (2017) · International Journal of Plasticity · doi:10.1016/j.ijplas.2017.02.003 · Cited by 276 · see citing works

Tensile properties, strain rate sensitivity, and activation volume of additively manufactured 316L stainless steels

Li, Z., Voisin, T., McKeown, J. T., Ye, J., Braun, T., Kamath, C., King, W. E., & Wang, Y. (2019) · International Journal of Plasticity · doi:10.1016/j.ijplas.2019.05.009 · Cited by 257 · see citing works

Heat treatment effect on the mechanical properties and fracture mechanism in AlSi10Mg fabricated by additive manufacturing selective laser melting process

Rosenthal, I., Shneck, R. Z., & Stern, A. (2018) · Materials Science and Engineering A · doi:10.1016/j.msea.2018.05.074 · Cited by 240 · see citing works

Machine-learning based temperature- and rate-dependent plasticity model: Application to analysis of fracture experiments on DP steel

Li, X., Roth, C. C., & Mohr, D. (2019) · International Journal of Plasticity · doi:10.1016/j.ijplas.2019.02.012 · Cited by 232 · see citing works

Experimental characterization and crystal plasticity modeling of anisotropy, tension-compression asymmetry, and texture evolution of additively manufactured Inconel 718 at room and elevated temperatures

Ghorbanpour, S., Alam, M. E., Ferreri, N. C., Kumar, A., McWilliams, B., Vogel, S. C., Bicknell, J., Beyerlein, I. J., & Knežević, M. (2019) · International Journal of Plasticity · doi:10.1016/j.ijplas.2019.09.002 · Cited by 224 · see citing works

Strain rate sensitivity and fracture mechanism of AlSi10Mg parts produced by Selective Laser Melting

Rosenthal, I., Stern, A., & Frage, N. (2016) · Materials Science and Engineering A · doi:10.1016/j.msea.2016.11.070 · Cited by 220 · see citing works

Direction and location dependency of selective laser melted AlSi10Mg specimens

Hitzler, L., Janousch, C., Schanz, J., Merkel, M., Heine, B., Mack, F., Hall, W., & Öchsner, A. Ö. (2016) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2016.11.029 · Cited by 210 · see citing works

A crystal plasticity model incorporating the effects of precipitates in superalloys: Application to tensile, compressive, and cyclic deformation of Inconel 718

Ghorbanpour, S., Zečević, M., Kumar, A., Jahedi, M., Bicknell, J., Jorgensen, L., Beyerlein, I. J., & Knežević, M. (2017) · International Journal of Plasticity · doi:10.1016/j.ijplas.2017.09.006 · Cited by 210 · see citing works

On microstructure and mechanical properties of additively manufactured AlSi10Mg_200C using recycled powder

Asgari, H., Baxter, C., Hosseinkhani, K., & Mohammadi, M. (2017) · Materials Science and Engineering A · doi:10.1016/j.msea.2017.09.041 · Cited by 201 · see citing works

Application of artificial neural networks in micromechanics for polycrystalline metals

Ali, U., Muhammad, W., Brahme, A. P., Skiba, O., & Inal, K. (2019) · International Journal of Plasticity · doi:10.1016/j.ijplas.2019.05.001 · Cited by 195 · see citing works

Stress relaxation and the cellular structure-dependence of plastic deformation in additively manufactured AlSi10Mg alloys

Li, Z., Li, Z., Tan, Z., Xiong, D., & Guo, Q. (2019) · International Journal of Plasticity · doi:10.1016/j.ijplas.2019.12.003 · Cited by 192 · see citing works

Using neural networks to represent von Mises plasticity with isotropic hardening

Zhang, A., & Mohr, D. (2020) · International Journal of Plasticity · doi:10.1016/j.ijplas.2020.102732 · Cited by 190 · see citing works

Columnar to equiaxed transition during direct metal laser sintering of AlSi10Mg alloy: Effect of building direction

Hadadzadeh, A., Amirkhiz, B. S., Li, J., & Mohammadi, M. (2018) · Additive manufacturing · doi:10.1016/j.addma.2018.08.001 · Cited by 181 · see citing works

Investigation of the accuracy and roughness in the laser powder bed fusion process

Calignano, F. (2018) · Virtual and Physical Prototyping · doi:10.1080/17452759.2018.1426368 · Cited by 167 · see citing works

Structural representation of additively manufactured 316L austenitic stainless steel

Bronkhorst, C. A., Mayeur, J. R., Livescu, V., Pokharel, R., Brown, D. W., & Gray, G. T. (2019) · International Journal of Plasticity · doi:10.1016/j.ijplas.2019.01.012 · Cited by 145 · see citing works

Quantitative texture prediction of epitaxial columnar grains in additive manufacturing using selective laser melting

Liu, J., & To, A. C. (2017) · Additive manufacturing · doi:10.1016/j.addma.2017.05.005 · Cited by 143 · see citing works

Identification and characterization of spatter particles and their effect on surface roughness, density and mechanical response of 17-4 PH stainless steel laser powder-bed fusion parts

Ali, U., Esmaeilizadeh, R., Ahmed, F., Sarker, D., Muhammad, W., Keshavarzkermani, A., Mahmoodkhani, Y., Marzbanrad, E., & Toyserkani, E. (2019) · Materials Science and Engineering A · doi:10.1016/j.msea.2019.04.026 · Cited by 140 · see citing works

Integration of phase-field model and crystal plasticity for the prediction of process-structure-property relation of additively manufactured metallic materials

Liu, P. W., Wang, Z., Xiao, Y., Lebensohn, R. A., Liu, Y., Horstemeyer, M. F., Cui, X., & Chen, L. (2020) · International Journal of Plasticity · doi:10.1016/j.ijplas.2020.102670 · Cited by 136 · see citing works

Anisotropic Mechanical Behavior of AlSi10Mg Parts Produced by Selective Laser Melting

Ming, T., & Pistorius, P. C. (2017) · JOM · doi:10.1007/s11837-016-2230-5 · Cited by 135 · see citing works

Role of hierarchical microstructure of additively manufactured AlSi10Mg on dynamic loading behavior

Hadadzadeh, A., Amirkhiz, B. S., Odeshi, A. G., Li, J., & Mohammadi, M. (2019) · Additive manufacturing · doi:10.1016/j.addma.2019.04.012 · Cited by 133 · see citing works

The Use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-O sheets

Jenab, A., Sarraf, I. S., Green, D. E., Rahmaan, T., & Worswick, M. J. (2016) · Materials & Design · doi:10.1016/j.matdes.2016.01.038 · Cited by 124 · see citing works

Nanoindentation based properties of Inconel 718 at elevated temperatures: A comparison of conventional versus additively manufactured samples

Wang, H., Dhiman, A., Ostergaard, H. E., Zhang, Y., Siegmund, T. H., Kruzic, J. J., & Tomar, V. (2019) · International Journal of Plasticity · doi:10.1016/j.ijplas.2019.04.018 · Cited by 116 · see citing works

Size dependence of microstructure of AlSi10Mg alloy fabricated by selective laser melting

Takata, N., Kodaira, H., Suzuki, A., & Kobashi, M. (2017) · Materials Characterization · doi:10.1016/j.matchar.2017.11.052 · Cited by 112 · see citing works

Applied machine learning to predict stress hotspots I: Face centered cubic materials

Mangal, A., & Holm, E. A. (2018) · International Journal of Plasticity · doi:10.1016/j.ijplas.2018.07.013 · Cited by 112 · see citing works

Dynamic response of AlSi10Mg alloy fabricated by selective laser melting

Zaretsky, E. B., Stern, A., & Frage, N. (2017) · Materials Science and Engineering A · doi:10.1016/j.msea.2017.02.004 · Cited by 110 · see citing works

Effects of build orientation and heat treatment on the evolution of microstructure and mechanical properties of alloy Mar-M-509 fabricated via laser powder bed fusion

Ferreri, N. C., Ghorbanpour, S., Bhowmik, S., Lussier, R., Bicknell, J., Patterson, B. M., & Knežević, M. (2019) · International Journal of Plasticity · doi:10.1016/j.ijplas.2019.06.002 · Cited by 92 · see citing works

Relationship between ductility and the porosity of additively manufactured AlSi10Mg

Laursen, C., DeJong, S. A., Dickens, S., Exil, A. N., Susan, D. F., & Carroll, J. D. (2020) · Materials Science and Engineering A · doi:10.1016/j.msea.2020.139922 · Cited by 89 · see citing works

An analysis of phase stresses in additively manufactured 304L stainless steel using neutron diffraction measurements and crystal plasticity finite element simulations

Pokharel, R., Patra, A., Brown, D. W., Clausen, B., Vogel, S. C., & Gray, G. T., III (2019) · International Journal of Plasticity · doi:10.1016/j.ijplas.2019.06.005 · Cited by 88 · see citing works

Quasi-static and dynamic response, and texture evolution of two overaged Al 7056 alloy plates in T761 and T721 tempers: Experiments and modeling

Ku, A. Y., Khan, A. S., & Gnaupel-Herold, T. H. (2020) · International Journal of Plasticity · doi:10.1016/j.ijplas.2020.102679 · Cited by 84 · see citing works

Three-dimensional maps of geometrically necessary dislocation densities in additively manufactured Ni-based superalloy IN718

Witzen, W. A., Polonsky, A. T., Pollock, T. M., & Beyerlein, I. J. (2020) · International Journal of Plasticity · doi:10.1016/j.ijplas.2020.102709 · Cited by 81 · see citing works

Transversely isotropic hyperelastic-viscoplastic model for glassy polymers with application to additive manufactured photopolymers

Zhang, P., & To, A. C. (2016) · International Journal of Plasticity · doi:10.1016/j.ijplas.2015.12.012 · Cited by 80 · see citing works

Probabilistic fracture of Ti–6Al–4V made through additive layer manufacturing

Tancogne‐Dejean, T., Roth, C. C., Woy, U., & Mohr, D. (2015) · International Journal of Plasticity · doi:10.1016/j.ijplas.2015.09.007 · Cited by 60 · see citing works

A unified model for coupling constitutive behavior and micro-defects evolution of aluminum alloys under high-strain-rate deformation

Yan, S.-L., Yang, H., Li, H., & Yao, X. (2016) · International Journal of Plasticity · doi:10.1016/j.ijplas.2016.07.011 · Cited by 54 · see citing works

Modelling of forming limit diagram of perforated commercial pure aluminium sheets using artificial neural network

Elangovan, K., Narayanan, C. S., & Narayanasamy, R. (2010) · Computational Materials Science · doi:10.1016/j.commatsci.2009.12.016 · Cited by 53 · see citing works

Bendability enhancement of an age-hardenable aluminum alloy: Part II — multiscale numerical modeling of shear banding and fracture

Muhammad, W., Brahme, A. P., Ali, U., Hirsch, J., Engler, O., Aretz, H., Kang, J., Mishra, R. K., & Inal, K. (2019) · Materials Science and Engineering A · doi:10.1016/j.msea.2019.03.050 · Cited by 51 · see citing works

Artificial Neural Network Modeling to Evaluate and Predict the Deformation Behavior of ZK60 Magnesium Alloy During Hot Compression

Qin, Y. J., Pan, Q., He, Y., Li, W. B., Liu, X. Y., & Fan, X. (2010) · Materials and Manufacturing Processes · doi:10.1080/10426910903124894 · Cited by 49 · see citing works

Prediction of Forming Limit Diagram for Ti-6Al-4V Alloy Using Artificial Neural Network

Kotkunde, N., Deole, A. D., & Gupta, A. K. (2014) · Procedia Materials Science · doi:10.1016/j.mspro.2014.07.043 · Cited by 39 · see citing works

Mean-field polycrystal plasticity modeling with grain size and shape effects for laser additive manufactured FCC metals

Liu, J., Xiong, W., Behera, A., Thompson, S., & To, A. C. (2017) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2017.02.024 · Cited by 36 · see citing works

Artificial Neural Network Modeling of Forming Limit Diagram

Derogar, A., & Djavanroodi, F. (2011) · Materials and Manufacturing Processes · doi:10.1080/10426914.2010.544818 · Cited by 34 · see citing works

Deformation banding in a precipitation hardened aluminum alloy during simple shear deformation

Muhammad, W., Sabat, R. K., Brahme, A. P., Kang, J., Mishra, R. K., Amirkhiz, B. S., Hirsch, J., & Inal, K. (2018) · Scripta Materialia · doi:10.1016/j.scriptamat.2018.11.032 · Cited by 34 · see citing works

Characterization of stress–strain relationships in Al over a wide range of testing temperatures

Csanádi, T., Chinh, N. Q., Gubicza, J., Vörös, G., & Langdon, T. G. (2013) · International Journal of Plasticity · doi:10.1016/j.ijplas.2013.08.014 · Cited by 31 · see citing works

Experimental investigation and through process crystal plasticity-static recrystallization modeling of temperature and strain rate effects during hot compression of AA6063

Ali, U., Odoh, D., Muhammad, W., Brahme, A. P., Mishra, R. K., Wells, M. A., & Inal, K. (2017) · Materials Science and Engineering A · doi:10.1016/j.msea.2017.06.030 · Cited by 26 · see citing works

Computational materials design using artificial intelligence methods

Kiselyova, N. N., Gladun, V. P., & Vashchenko, N. D. (1998) · Journal of Alloys and Compounds · doi:10.1016/s0925-8388(98)00606-9 · Cited by 22 · see citing works

Application of artificial neural networks in modelling of normalised structural steels mechanical properties

Dobrzański, L. A., & Honysz, R. (2009) · Journal of Achievements of Materials and Manufacturing Engineering · Cited by 19 · see citing works

Large Strain Cyclic Simple Shear Behavior of Aluminum Extrusions: An Experimental and Numerical Study

Muhammad, W., Brahme, A. P., Kang, J., Mishra, R. K., & Inal, K. (2016) · Springer eBooks · doi:10.1007/978-3-319-48251-4_29 · Cited by 10 · see citing works

Measurement of Plastic Strain Ratio Using Digital Image Correlation

Kang, J., & Muhammad, W. (2016) · Journal of Testing and Evaluation · doi:10.1520/jte20160192 · Cited by 6 · see citing works

Making sustainable aluminum by recycling scrap: The science of “dirty” alloys

Raabe, D., Ponge, D., Uggowitzer, P. J., Roscher, M., Paolantonio, M., Liu, C., Antrekowitsch, H., Kozeschnik, E., Seidman, D. N., Gault, B., et al. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.100947 · Cited by 658 · 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

Review on the correlation between microstructure and mechanical performance for laser powder bed fusion AlSi10Mg

Zhao, L., Song, L., Macías, J. G. S., Zhu, Y., Huang, M., Simar, A., & Li, Z. (2022) · Additive manufacturing · doi:10.1016/j.addma.2022.102914 · Cited by 295 · see citing works

A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions

Zhang, X., Gong, J., & Xuan, F. (2021) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2021.106236 · Cited by 248 · see citing works

Review of conventional and advanced non-destructive testing techniques for detection and characterization of small-scale defects

Silva, M. I., Malitckii, E., Santos, T. G., & Vilaça, P. (2023) · Progress in Materials Science · doi:10.1016/j.pmatsci.2023.101155 · Cited by 244 · see citing works

Multifunctional high-entropy materials

Han, L., Zhu, S., Rao, Z., Scheu, C., Ponge, D., Ludwig, A., Zhang, H., Gutfleisch, O., Hahn, H., Li, Z., et al. (2024) · Nature Reviews Materials · doi:10.1038/s41578-024-00720-y · Cited by 255 · see citing works

The potency of defects on fatigue of additively manufactured metals

Peng, X., Wu, S., Qian, W., Bao, J., Hu, Y., Zhan, Z., Guangping, G., & Withers, P. J. (2022) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2022.107185 · Cited by 198 · see citing works

Physics-guided machine learning frameworks for fatigue life prediction of AM materials

Wang, L., Zhu, S., Luo, C., Liao, D., & Wang, Q. (2023) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2023.107658 · Cited by 148 · see citing works

Machine learning framework for predicting the low cycle fatigue life of lead-free solders

Long, X., Lu, C., Su, Y., & Dai, Y. (2023) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2023.107228 · Cited by 138 · see citing works

Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives

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

Selective laser melting of dense and crack-free AlCoCrFeNi2.1 eutectic high entropy alloy: Synergizing strength and ductility

He, L. J., Wu, S., Dong, A., Tang, H., Du, D., Zhu, G., Sun, B., & Yan, W. (2022) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2021.11.049 · Cited by 135 · see citing works

Optimization with artificial intelligence in additive manufacturing: a systematic review

Ciccone, F., Bacciaglia, A., & Ceruti, A. (2023) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-023-04200-2 · Cited by 139 · see citing works

Process modeling in laser powder bed fusion towards defect detection and quality control via machine learning: The state-of-the-art and research challenges

Wang, P. E., Yang, Y., & Moghaddam, N. S. (2021) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2021.11.037 · Cited by 130 · see citing works

Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions

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

A new ANN based crystal plasticity model for FCC materials and its application to non-monotonic strain paths

Ibragimova, O., Brahme, A. P., Muhammad, W., Lévesque, J., & Inal, K. (2021) · International Journal of Plasticity · doi:10.1016/j.ijplas.2021.103059 · Cited by 115 · see citing works

Accelerating the design of compositionally complex materials via physics-informed artificial intelligence

Raabe, D., Mianroodi, J. R., & Neugebauer, J. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00412-7 · Cited by 114 · see citing works

A deep learning energy-based method for classical elastoplasticity

He, J., Abueidda, D., Al‐Rub, R. K. A., Korić, S., & Jasiuk, I. (2023) · International Journal of Plasticity · doi:10.1016/j.ijplas.2023.103531 · Cited by 99 · see citing works

A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction

Sun, X., Zhou, K., Shi, S., Song, K., & Chen, X. (2022) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2022.106996 · Cited by 97 · see citing works

Concrete Strength Prediction Using Machine Learning Methods CatBoost, k-Nearest Neighbors, Support Vector Regression

Beskopylny, A. N., Stel’makh, S. A., Shcherban’, E. M., Маилян, Л. Р., Meskhi, B. C., Razveeva, I., Chernil'nik, A. A., & Beskopylny, N. (2022) · Applied Sciences · doi:10.3390/app122110864 · Cited by 97 · see citing works

Modeling structure-property relationships with convolutional neural networks: Yield surface prediction based on microstructure images

Heidenreich, J. N., Gorji, M. B., & Mohr, D. (2022) · International Journal of Plasticity · doi:10.1016/j.ijplas.2022.103506 · Cited by 88 · see citing works

Machine learning-based modeling of the coupling effect of strain rate and temperature on strain hardening for 5182-O aluminum alloy

Shang, H., Wu, P., Lou, Y., Wang, J., & Chen, Q. (2022) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2022.117501 · Cited by 83 · see citing works

A review of laser additive manufacturing (LAM) aluminum alloys: Methods, microstructures and mechanical properties

Fan, H., Hu, J., Wang, Y., Zhang, H., Guo, W., Li, J., Xu, S., Liu, P., & Liu, P. (2024) · Optics & Laser Technology · doi:10.1016/j.optlastec.2024.110722 · Cited by 80 · see citing works

Fatigue crack growth behavior of an additively manufactured titanium alloy: Effects of spatial and crystallographic orientations of α lamellae

Liu, Z., Dash, S. S., Zhang, J., Lyu, T., Lang, L., Chen, D. L., & Zou, Y. (2023) · International Journal of Plasticity · doi:10.1016/j.ijplas.2023.103819 · Cited by 78 · see citing works

Laser Additive Manufacturing of High-Strength Aluminum Alloys: Challenges and Strategies

Dixit, S., & Liu, S. (2022) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp6060156 · Cited by 76 · see citing works

Predicting grain boundary damage by machine learning

Zhang, S., Wang, L., Zhu, G., Diehl, M., Maldar, A., Shang, X., & Zeng, X. (2021) · International Journal of Plasticity · doi:10.1016/j.ijplas.2021.103186 · Cited by 75 · see citing works

Machine learning for predicting fatigue properties of additively manufactured materials

Yi, M., Xue, M., Peihong, C. O. N. G., Song, Y., ZHANG, H., Lingfeng, W. A. N. G., Liucheng, Z. H. O. U., Li, Y., & Guo, W. (2023) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2023.11.001 · Cited by 70 · see citing works

Time lapse in situ X-ray imaging of failure in structural materials under cyclic loads and extreme environments

Qian, W., Wu, S., Lei, L., Hu, Q., & Liu, C. (2023) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2023.07.041 · Cited by 63 · see citing works

A machine learning approach for accelerated design of magnesium alloys. Part A: Alloy data and property space

Ghorbani, M., Boley, M., Nakashima, P. N. H., & Birbilis, N. (2023) · Journal of Magnesium and Alloys · doi:10.1016/j.jma.2023.09.035 · Cited by 57 · 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

Research progress in laser additive manufacturing of aluminum alloys: Microstructure, defect, and properties

Mao, Y., Chen, H., & Xiong, J. (2024) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2024.03.099 · Cited by 55 · see citing works

Effect of printing direction and thickness on the mechanical behavior of SLM fabricated Hastelloy-X

Pilgar, C. M., Fernandez, A. M., Lucarini, S., & Segurado, J. (2022) · International Journal of Plasticity · doi:10.1016/j.ijplas.2022.103250 · Cited by 55 · see citing works

Determination of ductile fracture properties of 16MND5 steels under varying constraint levels using machine learning methods

Sun, X., Liu, Z., Wang, X., & Chen, X. (2022) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2022.107331 · Cited by 53 · see citing works

The effect of vanadium on microstrain partitioning and localized damage during deformation of unnotched and notched DP1300 steels

Pelligra, C., Samei, J., Kang, J., & Wilkinson, D. S. (2022) · International Journal of Plasticity · doi:10.1016/j.ijplas.2022.103435 · Cited by 50 · see citing works

A microstructure-based porous crystal plasticity FE model for additively manufactured Ti-6Al-4V alloys

Pinz, M., Benzing, J. T., Pilchak, A. L., & Ghosh, S. (2022) · International Journal of Plasticity · doi:10.1016/j.ijplas.2022.103254 · Cited by 48 · 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

Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization

Kusampudi, N., & Diehl, M. (2023) · International Journal of Plasticity · doi:10.1016/j.ijplas.2023.103776 · Cited by 46 · see citing works

An additively manufactured and direct-aged AlSi3.5Mg2.5 alloy with superior strength and ductility: micromechanical mechanisms

Zhang, X. X., Lutz, A., Andrä, H., Lahres, M., Sittig, D., Maawad, E., Gan, W., & Knoop, D. (2021) · International Journal of Plasticity · doi:10.1016/j.ijplas.2021.103083 · Cited by 45 · see citing works

Development of microstructure-sensitive damage models for zirconium polycrystals

Abdolvand, H. (2021) · International Journal of Plasticity · doi:10.1016/j.ijplas.2021.103156 · Cited by 45 · see citing works

A comprehensive investigation on application of machine learning for optimization of process parameters of laser powder bed fusion-processed 316L stainless steel

Eshkabilov, S. L., Ara, I., & Azarmi, F. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10331-y · Cited by 45 · see citing works

State-of-the-Art Review of Machine Learning Applications in Additive Manufacturing; from Design to Manufacturing and Property Control

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

Shrinkage behaviour of material extrusion steel 316L: influence of primary 3D printing parameters

Obadimu, S. O., & Kourousis, K. I. (2022) · Rapid Prototyping Journal · doi:10.1108/rpj-07-2022-0224 · Cited by 41 · see citing works

Deep learning-based indentation plastometry in anisotropic materials

Jeong, K., Lee, K., Lee, S., Kang, S.-G., Jung, J., Lee, H., Kwak, N., Kwon, D., & Han, H. N. (2022) · International Journal of Plasticity · doi:10.1016/j.ijplas.2022.103403 · Cited by 39 · see citing works

Artificial neural network enhanced plasticity modeling and ductile fracture characterization of grade-1 commercial pure titanium

Ebrahim, A. S., Zhang, Q., & Ha, J. (2024) · International Journal of Plasticity · doi:10.1016/j.ijplas.2024.104044 · Cited by 39 · see citing works

Simulation of metal forming – Visualization of invisible phenomena in the digital era

Yanagimoto, J., Banabic, D., Banu, M., & Madej, Ł. (2022) · CIRP Annals · doi:10.1016/j.cirp.2022.05.007 · Cited by 39 · see citing works

Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys

Muhammad, W., Kang, J., Ibragimova, O., & Inal, K. (2022) · Welding in the World · doi:10.1007/s40194-022-01445-8 · Cited by 37 · see citing works

Machine Learning Algorithms for Predicting Mechanical Stiffness of Lattice Structure-Based Polymer Foam

Hooshmand, M. J., Sakib-Uz-Zaman, C., & Khondoker, M. A. H. (2023) · Materials · doi:10.3390/ma16227173 · Cited by 34 · see citing works

Microstructural feature-driven machine learning for predicting mechanical tensile strength of laser powder bed fusion (L-PBF) additively manufactured Ti6Al4V alloy

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

A preliminary discussion about the application of machine learning in the field of constitutive modeling focusing on alloys

Li, D., Liu, J., Fan, Y., Yang, X., & Huang, W. (2023) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2023.173210 · Cited by 31 · see citing works

Coupling of the phase field approach to the Armstrong-Frederick model for the simulation of ductile damage under cyclic load

Aygün, S., Wiegold, T., & Klinge, S. (2021) · International Journal of Plasticity · doi:10.1016/j.ijplas.2021.103021 · Cited by 30 · see citing works

Very high cycle fatigue life prediction of Ti60 alloy based on machine learning with data enhancement

Qian, H., Huang, Z., Xu, Y., Zhou, Q., Wang, J., Shen, J., & Shen, Z. (2023) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2023.109431 · Cited by 30 · see citing works

Simultaneously enhancing strength and plasticity via direct ageing in additive manufactured Al–Ni–Sc–Zr alloys

Luo, G., Chen, H., Hu, L., Yang, C., Zong, S., Chen, Y.-C., Lian, Q., Wang, H.-Z., Chen, Z., Wu, Y., et al. (2025) · International journal of plasticity · doi:10.1016/j.ijplas.2025.104243 · Cited by 24 · see citing works

A Machine Learning-based surrogate modeling framework for predicting the history-dependent deformation of dual phase microstructures

Khandelwal, S., Basu, S., & Patra, A. (2021) · Materials Today Communications · doi:10.1016/j.mtcomm.2021.102914 · Cited by 26 · see citing works

Predicting plastic anisotropy using crystal plasticity and Bayesian neural network surrogate models

Zapiain, D. M. D. O., Lim, H., Park, T., & Pourboghrat, F. (2021) · Materials Science and Engineering A · doi:10.1016/j.msea.2021.142472 · Cited by 26 · see citing works

A review on the enhancement of failure mechanisms modeling in additively manufactured structures by machine learning

Awd, M., Saeed, L., & Walther, F. (2023) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2023.107403 · Cited by 25 · see citing works

A systematic review on high cycle and very high cycle fatigue behavior of laser powder bed fused (L-PBF) Al-Si alloys

Tusher, M. M. H., & Ince, A. (2023) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2023.107667 · Cited by 28 · see citing works

Advancing material simulations: Physics-Informed Neural Networks and Object-Oriented Crystal Plasticity Finite Element Methods

Keshavarz, S., Mao, Y., Reid, A. C. E., & Agrawal, A. (2024) · International Journal of Plasticity · doi:10.1016/j.ijplas.2024.104221 · Cited by 24 · see citing works

Crystal plasticity modeling and data-driven approach for fatigue life estimation of additively manufactured Ti-6Al-4V alloy

Tiwari, K., Trivedi, A., Reddy, G. B., Kumawat, B. K., Bhardwaj, A., Raman, R. K. S., Jones, R., & Alankar, A. (2025) · International Journal of Plasticity · doi:10.1016/j.ijplas.2025.104319 · Cited by 24 · see citing works

Advances in machine learning for predicting fatigue behavior: from material properties to fatigue life and fatigue crack growth

Zhan, Z., Zhang, M., He, X., Li, X., Wang, Z., Chen, X., Han, B., Hu, W., Meng, Q., & Li, H. L. (2025) · International Journal of Structural Integrity · doi:10.1108/ijsi-12-2024-0209 · Cited by 23 · see citing works

Thermomechanical fatigue behavior and lifetime modeling of powder metallurgy superalloy considering phase angle effect

Shen, J., Hu, X., Mi, D., Qian, Z., Teng, X., Song, X., Jiang, Y., & Nie, X. (2022) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2022.107164 · Cited by 22 · see citing works

Prediction of ductile damage evolution based on experimental data using artificial neural networks

Schowtjak, A., Gerlach, J., Muhammad, W., Brahme, A. P., Clausmeyer, T., Inal, K., & Tekkaya, A. E. (2022) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2022.111950 · Cited by 22 · see citing works

Machine learning assisted approach to design lattice materials with prescribed band gap characteristics

Shendy, M., Alkhader, M., Abu-Nabah, B. A., Jaradat, M. A., & Venkatesh, T. A. (2023) · European Journal of Mechanics - A/Solids · doi:10.1016/j.euromechsol.2023.105125 · Cited by 21 · see citing works

Design of fabric rubber composite seals with multilevel structure using machine learning method

Yan, H., Xu, X., Yao, X., Qu, T., & Yang, Y. (2024) · Composites Part A Applied Science and Manufacturing · doi:10.1016/j.compositesa.2024.108053 · Cited by 21 · see citing works

Prediction of Rheological Parameters of Polymers by Machine Learning Methods

Kondratieva, T. N., & Сергеевич, Ч. А. (2024) · Advanced Engineering Research · doi:10.23947/2687-1653-2024-24-1-36-47 · Cited by 20 · see citing works

Integration of thermal imaging and neural networks for mechanical strength analysis and fracture prediction in 3D-printed plastic parts

Boiko, D. A., Korabelnikova, V. A., Gordeev, E. G., & Ananikov, V. P. (2022) · Scientific Reports · doi:10.1038/s41598-022-12503-y · Cited by 20 · see citing works

Anisotropic and high-temperature deformation behavior of additively manufactured AlSi10Mg: Experiments and microscale modeling

Dai, S., Hu, D., Grilli, N., Zou, S., Deng, Z., & Yan, W. (2024) · Additive manufacturing · doi:10.1016/j.addma.2024.104285 · Cited by 20 · see citing works

An efficient machine learning-based model for predicting the stress-strain relationships of thermoplastic polymers with limited testing data

Ling, S., Wu, Z., Mei, J., & Lv, S. (2024) · Composites Part B Engineering · doi:10.1016/j.compositesb.2024.111600 · Cited by 18 · see citing works

A genetic evolved machine learning approach for 3D DEM modelling of anisotropic materials with full consideration of particulate interactions

Gu, Z., Ding, X., Hou, X., & Ye, J. (2022) · Composites Part B Engineering · doi:10.1016/j.compositesb.2022.110432 · Cited by 18 · see citing works

The anisotropic oxidation behavior of Hastelloy X alloy fabricated by laser powder-bed fusion (LPBF) during the cyclic oxidation process

He, L., Dong, A., Du, D., Gao, Y., Huo, X., Xiong, L., Zhang, X., Lü, Y., & Sun, B. (2023) · Corrosion Science · doi:10.1016/j.corsci.2023.111156 · Cited by 18 · see citing works

Estimation of surface roughness of direct metal laser sintered AlSi10Mg using artificial neural networks and response surface methodology

Paturi, U. M. R., Vanga, D. G., Duggem, R. B., Kotkunde, N., Reddy, N. S., & Dutta, S. (2023) · Materials and Manufacturing Processes · doi:10.1080/10426914.2023.2217890 · Cited by 18 · see citing works

Plastically heterogeneity-driven fracture in additive manufactured steels

Li, W., Li, X., Fang, Q., Liu, B., & Jiang, C. (2023) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2023.109203 · Cited by 16 · see citing works

Optical measurement, characterization and quantification for plastic deformations and stress in three-dimensional structures: Recent developments, challenges, and prospects

Ju, Y., Xing, D., Mao, L., Ren, Z., & Chiang, F. P. (2021) · Measurement · doi:10.1016/j.measurement.2021.110212 · Cited by 15 · see citing works

Framework for electrochemical-mechanical behavior of all-solid-state batteries: From the reconstruction method to multi-physics and multi-scale modeling

Huang, P., & Guo, Z. (2024) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2024.113078 · Cited by 14 · see citing works

Convolutional neural networks for expediting the determination of minimum volume requirements for studies of microstructurally small cracks, Part I: Model implementation and predictions

DeMille, K. J., & Spear, A. D. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111290 · Cited by 14 · see citing works

A crystal plasticity-informed data-driven model for magnesium alloys

Tang, D., Qi, S., Zhou, K., Haggag, M., Sun, X., Li, D., Wang, H., & Wu, P. (2025) · International Journal of Plasticity · doi:10.1016/j.ijplas.2025.104480 · Cited by 13 · see citing works

Correlating the evolution of spatial-resolved microscale residual stress and the associated dislocation behavior in additively manufactured 316L stainless steel upon short-term annealing

An, D., Liu, X., Xiao, Y., Li, X., & Chen, J. (2025) · Additive manufacturing · doi:10.1016/j.addma.2025.104807 · Cited by 13 · see citing works

Gaussian process autoregression models for the evolution of polycrystalline microstructures subjected to arbitrary stretching tensors

Hashemi, S., & Kalidindi, S. R. (2023) · International Journal of Plasticity · doi:10.1016/j.ijplas.2023.103532 · Cited by 12 · see citing works

Virtual rapid prototyping of materials with deep learning: spatiotemporal stress fields prediction in ceramics employing convolutional neural networks and transfer learning

Rezasefat, M., & Hogan, J. D. (2024) · Virtual and Physical Prototyping · doi:10.1080/17452759.2024.2399186 · Cited by 12 · see citing works

A framework for developing a machine learning-based finite element model for structural analysis

Li, G., Luo, R., & Yu, D. (2024) · Computers & Structures · doi:10.1016/j.compstruc.2024.107617 · Cited by 12 · see citing works

Melt-based additive manufacturing of refractory metals and alloys: Experiments and modeling

araghi, M. Y., Dashti, A., Fani, M., Ghamarian, I., Ruiz, C., & Xu, S. (2025) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2025.05.207 · Cited by 12 · see citing works

Dislocation descriptors of low and high angle grain boundaries with convolutional neural networks

Noh, W., & Chew, H. B. (2024) · Extreme Mechanics Letters · doi:10.1016/j.eml.2024.102138 · Cited by 11 · see citing works

Artificial neural network based stamping process design for three-point bending

Alhalaybeh, T. S., Muhamaiti, Y., Shang, H., Zhou, L., Liang, X., & Lou, Y. (2025) · Acta Mechanica Sinica · doi:10.1007/s10409-025-24895-x · Cited by 11 · see citing works

Simultaneously enhancing strength and ductility of laser directed energy deposited TiB2/AlSi10Mg by inducing dispersed Mg2Si precipitation and reducing strain concentration

Wang, A., Wei, Q., Tang, Z., Ren, P., Zhang, X., Wu, Y., Zhu, H., Li, Q., Li, Y., Wang, H., et al. (2025) · International Journal of Plasticity · doi:10.1016/j.ijplas.2025.104455 · 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

Machine learning-assisted process-structure-property correlation in laser metal additive manufacturing: a critical review

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

Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression

Yuan, Z., Peng, X., Ma, C., Zhang, A., Chen, Z., Jiang, Z., & Zhang, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad4383 · Cited by 11 · see citing works

Machine learning powered predictive modelling of complex residual stress for nuclear fusion reactor design

Zhu, B., Leung, N., Steel, B., England, D., Yinglong, H., London, A. J., Zhang, H., Gorley, M. J., Wang, Y., Whiting, M. J., et al. (2024) · Materials & Design · doi:10.1016/j.matdes.2024.113449 · Cited by 10 · see citing works

A machine learning based prediction model for the impact mechanical response of composite laminates considering microstructure sensitive transverse properties

Yiben, Z., Feng, G., & Liu, B. (2024) · Polymer Composites · doi:10.1002/pc.29203 · Cited by 10 · see citing works

Soft computing-based process optimization in laser metal deposition of Ti-6Al-4 V

Ngwoke, C. C., Mahamood, R. M., Aigbodion, V. S., Jen, T., Adedeji, P. A., & Akinlabi, E. T. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-08781-5 · Cited by 9 · see citing works

Design optimization of high-performance, cost-efficient concrete enhanced with nano-CNTs: A hybrid approach using machine learning and NSGA-II

Salih, A. F. M. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.112251 · Cited by 9 · see citing works

Machine learning for mechanical design of composite materials and composite structures

Wu, W., Xu, K., Zhao, J., Yang, H., Yang, X., Yu, H., Jiang, D., Song, Z., & Xiong, J. (2026) · MechanoEngineering · doi:10.1063/5.0285502 · Cited by 9 · see citing works

Predict the evolution of mechanical property of Al-Li alloys in a marine environment

Li, W., Xiang, L., Wu, G., Si, H., Chen, J., Jin, Y., Su, Y., Tao, J., & Huang, C. (2022) · Defence Technology · doi:10.1016/j.dt.2022.07.007 · Cited by 9 · see citing works

Rapid Prediction of Grain Boundary Network Evolution in Nanomaterials Utilizing a Generative Machine Learning Approach

Wang, Y., Kazemi, A., Jing, T., Ding, Z., Li, L., & Yang, S. (2024) · Extreme Mechanics Letters · doi:10.1016/j.eml.2024.102172 · Cited by 9 · see citing works

A Systematic Literature Review of Machine Learning Approaches for Optimization in Additive Manufacturing

Breitenbach, J., Seidenspinner, F., Vural, F., Beisswanger, P., & Buettner, R. (2022) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac54236.2022.00180 · Cited by 9 · see citing works

Structure-to-process modeling drives experimentally validated unified dual-phase steel

Ma, X., Zhang, Y., Wang, C., Wang, M., Huang, M., & Xu, W. (2025) · Acta Materialia · doi:10.1016/j.actamat.2025.121167 · Cited by 8 · see citing works

An anisotropic constitutive model for LPBF AlSi10Mg alloy considering tension-compression asymmetry and build-orientation effect

Zhao, J., Hu, Y., Yu, C., Xiong, X., Xu, X., Ding, L., Kan, Q., & Kang, G. (2026) · International Journal of Plasticity · doi:10.1016/j.ijplas.2026.104677 · Cited by 8 · see citing works

Thermodynamics-informed attention networks for concurrent multiscale modeling of nonlinear composites

Zhang, X., Chen, Q., & Chen, X. (2026) · International Journal of Plasticity · doi:10.1016/j.ijplas.2026.104681 · Cited by 8 · see citing works

Effect of Microstructural Evolution on Mechanical Properties and Fracture Modes of AlSi10Mg Blocks Fabricated by Selective Laser Melting after Stress Relief Annealing

Li, J., Li, Y., Wang, Z., Li, C., Yuan, H., & Hao, Y. (2022) · Advanced Engineering Materials · doi:10.1002/adem.202200585 · Cited by 8 · see citing works

Effects of defects on the high-temperature performance of selective laser melting K418 superalloys: An in-situ 3D X-ray analysis

Zhang, X., Liu, X., Li, R., Wu, H., Ma, Y., Miao, K., Wu, H., Li, X., & Fan, G. (2024) · Materials Characterization · doi:10.1016/j.matchar.2024.114533 · Cited by 7 · see citing works

Investigation of full-field strain evolution behavior of Cu/Ni clad foils by interpretable machine learning

Hu, Y., Wang, C., Wang, H., Chen, G., Chu, X., Chu, G., Wang, H., & Wu, S. (2024) · International Journal of Plasticity · doi:10.1016/j.ijplas.2024.104181 · Cited by 6 · see citing works

Artificial Intelligence Technologies and Applications in Additive Manufacturing

Shah, S. A., Lee, I. H., & Kim, H.-C. (2025) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-025-01288-5 · Cited by 6 · see citing works

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 · see citing works

Mathematical modelling to predict the tensile strength of additively manufactured AlSi10Mg alloy using artificial neural networks

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

Crystal plasticity modeling on the selective laser melted AlSi10Mg alloy

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 · see citing works

Predictive Modeling of Fracture Behavior in Ti6Al4V Alloys Manufactured by SLM Process

Sarparast, M., Shafaie, M., Babakan, A. M., Davoodi, M., & Zhang, H. (2024) · Frattura ed Integrità Strutturale · doi:10.3221/igf-esis.68.23 · Cited by 11 · see citing works

Fracture prediction of additively manufactured AlSi10Mg materials

Irmak, E. F. A., & Tröster, T. (2019) · Procedia Structural Integrity · doi:10.1016/j.prostr.2019.12.101 · Cited by 4 · see citing works