### MACHINE LEARNING OF STRUCTURE – PROPERTY RELATIONSHIPS: AN APPLICATION TO HEAT GENERATION DURING PLASTIC DEFORMATION

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Čanađija, M., 2023, Thermomechanics of Solids and Structures: Physical Mechanisms, Continuum Mechanics, and Applications, Elsevier.

Behrens, B.A., Chugreev, A., Bohne, F., Lorenz, R., 2019, Approach for modelling the Taylor-Quinney coefficient of high strength steels, Procedia Manufacturing, 29, pp. 464-471.

Čanađija, M., Mosler, J., 2016, A variational formulation for thermomechanically coupled low cycle fatigue at finite strains, International Journal of Solids and Structures, 100, pp. 388-398.

Čanađija, M., Brnić, J., 2004, Associative coupled thermoplasticity at finite strain with temperature-dependent material parameters, International Journal of Plasticity, 20(10), pp. 1851-1874.

Rusinek, A., Klepaczko, J., 2009, Experiments on heat generated during plastic deformation and stored energy for TRIP steels, Materials & Design, 30(1), pp. 35-48.

Rittel, D., 1999, On the conversion of plastic work to heat during high strain rate deformation of glassy polymers, Mechanics of Materials, 31(2), pp. 131–139.

Kositski, R., Mordehai, D., 2021, Employing molecular dynamics to shed light on the microstructural origins of the Taylor-Quinney coefficient, Acta Materialia, 205, 116511.

Zaera, R., Rodríguez-Martínez, J.A., Rittel, D., 2013, On the Taylor–Quinney coefficient in dynamically phase transforming materials. application to 304 stainless steel, International Journal of Plasticity, 40, pp. 185–201.

Čanađija, M., Munjas, N., Brnić, J., 2019, Thermodynamically consistent homogenization in finite strain thermoplasticity, International Journal for Multiscale Computational Engineering, 17(2), pp. 99-120.

DeCost, B.L., Holm, E.A., 2015, A computer vision approach for automated analysis and classification of microstructural image data, Computational Materials Science, 110, pp. 126–133.

Azimi, S.M., Britz, D., Engstler, M., Fritz, M., Mücklich, F., 2018, Advanced steel microstructural classification by deep learning methods, Scientific Reports, 8(1), 2128.

DeCost, B.L., Lei, B., Francis, T., Holm, E.A., 2019, High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel, Microscopy and Microanalysis, 25(1), 2129.

Ferguson, M.K., Ronay, A., Lee, Y.T.T., Law, K. H., 2018, Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning, Smart and Sustainable Manufacturing Systems, 2(1), pp. 137 - 164.

Nikolić, F., Štajduhar, I., Čanađija, M., 2021, Casting microstructure inspection using computer vision: Dendrite spacing in aluminum alloys, Metals, 11(5), 756.

Nikolić, F., Štajduhar, I., Čanađija, M., 2022, Casting defects detection in aluminum alloys using deep learning: A classification approach, International Journal of Metalcasting, 17, pp. 386–398.

Yucel, B., Yucel, S., Ray, A., Duprez, L., Kalidindi, S.R., 2020, Mining the correlations between optical micrographs and mechanical properties of cold-rolled HSLA steels using machine learning approaches, Integrating Materials and Manufacturing Innovation, 9(3), pp. 240–256.

Tagimalek, H., Maraki, M.R., Mahmoodi, M., Azargoman, M., 2022, A hybrid SVM-RVM algorithm to mechanical properties in the friction stir welding process, Journal of Applied and Computational Mechanics, 8(1), pp. 36–47.

Milićević, I., Popović, M., Dučić, N., Vujičić, V., Stepanić, P., Marinković, D., Ćojbašić, Ž., 2024, Improving the mechanical characteristics of the 3D printing objects using hybrid machine learning approach, Facta Universitatis Series Mechanical Engineering, doi: 10.22190/FUME220429036M.

Zarezadeh, A., Shishesaz, M.R., Ravanavard, M., Ghobadi, M., Zareipour, F., Mahdavian, M., 2023, Electrochemical and mechanical properties of Ni/g-C3N4 nanocomposite coatings with enhanced corrosion protective properties: A case study for modelling the corrosion resistance by ANN and ANFIS models, Journal of Applied and Computational Mechanics, 9(3), pp. 590-606.

Li, X., Zhang, Y., Zhao, H., Burkhart, C., Brinson, L.C., Chen, W., 2018, A transfer learning approach for microstructure reconstruction and structure-property predictions, Scientific Reports, 8, 13461.

Jung, J., Yoon, J.I., Park, H.K., Kim, J.Y., Kim, H.S., 2019, An efficient machine learning approach to establish structure-property linkages, Computational Materials Science, 156, pp. 17–25.

Yang, Z., Yabansu, Y.C., Al-Bahrani, R., Liao, W.K., Choudhary, A.N., Kalidindi, S.R., Agrawal, A., 2018, Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets, Computational Materials Science, 151, pp. 278–287.

Cecen, A., Dai, H., Yabansu, Y.C., Kalidindi, S.R., Song, L., 2018, Material structure-property linkages using three-dimensional convolutional neural networks, Acta Materialia, 146, pp. 76–84.

Latypov, M.I., Kalidindi, S.R., 2017, Data-driven reduced order models for effective yield strength and partitioning of strain in multiphase materials, Journal of Computational Physics, 346, pp. 242–261.

King, E., Li, Y., Hu, S., Machorro, E., 2023, Physics-informed machine-learning model of temperature evolution under solid phase processes. Computational Mechanics, 72(1), pp. 125-136.

Pantalé, O., Mha, P.T., Tongne, A., 2022, Efficient implementation of non-linear flow law using neural network into the Abaqus Explicit FEM code. Finite Elements in Analysis and Design, 198, 103647.

Zlatić, M., Čanađija, M. 2023, Incompressible rubber thermoelasticity: a neural network approach. Computational mechanics, 71(5), pp. 895-916.

Groeber, M.A., Jackson, M.A., 2014, DREAM.3D: a digital representation environment for the analysis of microstructure in 3D, Integrating Materials and Manufacturing Innovation, 3(1), pp. 56–72.

Nikolić, F., Čanađija, M., 2023, Deep learning of temperature – dependent stress – strain hardening curves, Comptes Rendus. Mécanique, 351, pp. 151–170.

Li, X., Liu, Z., Cui, S., Luo, C., Li, C., Zhuang, Z., 2019, Predicting the effective mechanical property of heterogeneous materials by image based modelling and deep learning, Computer Methods in Applied Mechanics and Engineering, 347, pp. 735–753.

Wang, Y., Zhang, M., Lin, A., Iyer, A., Prasad, A. S., Li, X., Zhang, Y., Schadler, L., Chen, W., Brinson, L., 2020, Mining structure–property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks, Molecular Systems Design & Engineering, 5, pp. 962–975.

Liu, Z., Wu, C., Koishi, M., 2019, Transfer learning of deep material network for seamless structure–property predictions, Computational Mechanics, 64(2), pp. 451-465.

Balasivanandha Prabu, S., Karunamoorthy, L., 2008, Microstructure based finite element analysis of failure prediction in particle-reinforced metal-matrix composite, Journal of Materials Processing Technology, 207(1), pp. 53–62.

Phillion, A.B., Cockcroft, S.L., Lee, P.D., 2009, Predicting the constitutive behavior of semi-solids via a direct finite element simulation: application to AA5182, Modelling and Simulation in Materials Science and Engineering, 17(5), 055011.

Kim, K., Forest, B., Geringer, J., 2011, Two-dimensional finite element simulation of fracture and fatigue behaviours of alumina microstructures for hip prosthesis, Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 225(12), pp. 1158–1168.

Patel, S., Vaish, R., Sinha, N., Bowen, C., 2014, Finite element analysis of the microstructure of AlN-TiN composites, Strain, 50 (3), pp. 250–261.

Wei, J., Chu, X., Sun, X.Y., Xu, K., Deng, H. X., Chen, J., Wei, Z., Lei, M., 2019, Machine learning in materials science, InfoMat, 1(3), pp. 338–358.

Herriott, C., Spear, A.D., 2020, Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods, Computational Materials Science, 175, 109599.

Ragone, M., Yurkiv, V., Song, B., Ramsubramanian, A., Shahbazian-Yassar, R., Mashayek, F., 2020, Atomic column heights detection in metallic nanoparticles using deep convolutional learning, Computational Materials Science, 180, 109722.

Barakbayeva, T., Demirci, F.M., 2023, Fully automatic CNN design with inception and RESNET blocks, Neural Computing and Applications, 35, pp. 1569–1580.

Bandyopadhyay, M., 2021, Multi-stack hybrid CNN with non-monotonic activation functions for hyperspectral satellite image classification, Neural Computing and Applications, 33(21), pp. 14809–14822.

Nour, M., Öztürk, Ş., Polat, K., 2021, A novel classification framework using multiple bandwidth method with optimized CNN for brain - computer interfaces with EEG-FNIRS signals, Neural Computing and Applications, 33, pp. 15815–15829.

Leon-Medina, J.X., Anaya, M., Tibaduiza, D.A., Pozo, F., 2021, Manifold learning algorithms applied to structural damage classification, Journal of Applied and Computational Mechanics, 7(Special Issue), pp. 1158-1166.

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