Publications

Journal paper:

  1. Liu, B., Costa, S., Liu, X., Wilhelmsson, D., and Jia, X., 2025, Convolutional neural network for predicting mechanical behavior of composites with fiber waviness, Composites part A: Applied Science and Manufacturing, 188, 108574. DOI: 10.1016/j.compositesa.2024.108574
  2. Ye, Z., Liu, X., Peng, B., and Kan, C., 2024, Predicting Mechanical Behavior of Additively Manufactured Mechanical Metamaterials using Point Cloud Representation Learning, Journal of Computing and Information Science in Engineering, 24, 060901-1. DOI: 10.1115/1.4064147
  3. Kobir, M., Liu, X., and Yang, Y., 2023, Additive manufacturing of body-centered cubic metamaterials with novel I-shaped beam lattice towards enhanced mechanical properties, Manufacturing Letter, 35, 509-515. DOI: 10.1016/j.mfglet.2023.08.004
  4. Liu, X., Zhou, X.Y., Liu, B. and Gao, C., 2023. Multiscale modeling of woven composites by deep learning neural networks and its application in design optimization. Composite Structures, p.117553. DOI: 10.1016/j.compstruct.2023.117553
  5. Liu, X., Kobir, M.H., Yang, Y., Jiang, F. and Kothari, T., 2023. Improving stiffness and strength of body-centered cubic lattices with an I-shape beam cross-section. Mechanics of Materials, 182, p.104665. DOI: 10.1016/j.mechmat.2023.104665
  6. Yang, Y., Kan, C. and Liu, X., 2022 Point cloud based online detection of geometric defects for the certification of additively manufactured mechanical metamaterials, Journal of Manufacturing Systems, 65, p. 591-604. DOI: 10.1016/j.jmsy.2022.09.011
  7. Tao, F., Liu, X., Du, H., Tian, S. and Yu, W., 2022. Discover failure criteria of composites from experimental data by sparse regression. Composites Part B: Engineering, 239, p.109947. DOI: 10.1016/j.compositesb.2022.109947
  8. Tao, F., Liu, X., Du, H. and Yu, W., 2022. Finite element coupled positive definite deep neural networks mechanics system for constitutive modeling of composites. Computer Methods in Applied Mechanics and Engineering, 391, p.114548. DOI: 10.1016/j.cma.2021.114548
  9. Liu, X., Kan, C. and Ye, Z., 2022. Real-time multiscale prediction of structural performance in material extrusion additive manufacturing. Additive Manufacturing, 49, p.102503. DOI: doi.org/10.1016/j.addma.2021.102503
  10. Liu, X., Tao, F. and Yu, W., 2021. A review of artificial neural networks in the constitutive modeling of composite materials, Composites Part B: Engineering, 224, p.109152. DOI: 10.1016/j.compositesb.2021.109152
  11. Liu, X., Peng, B. and Yu, W., 2021. Multiscale modeling of the effective thermal conductivity of 2D woven composites by mechanics of structure genome and neural networks. International Journal of Heat and Mass Transfer, 179, p.121673. DOI: 10.1016/j.ijheatmasstransfer.2021.121673
  12. Tao, F., Liu, X., Du, H. and Yu, W., 2021. Learning composite constitutive laws via coupling Abaqus and deep neural network. Composite Structures, p.114137. DOI: 10.1016/j.compstruct.2021.114137
  13. Tao F., Lyu, X., Liu, X. and Yu, W., 2021. Multiscale analysis of multilayer printed circuit board using mechanics of structure genome. Mechanics of Advanced Materials and Structures, 28(8), pp.774-783. DOI: 10.1080/15376494.2019.1596335
  14. Liu, X., Tao, F. and Yu, W. 2020. A neural network enhanced system for learning nonlinear constitutive relation and failure criterion of fiber reinforced composites, Composite Structures, 252(15), p.112658. DOI: 10.1016/j.compstruct.2020.112658
  15. Liu, X., Tao, F., Du, H., Yu, W. and Xu, K., 2020. Learning nonlinear constitutive laws using neural network models based on indirectly measurable data, Journal of Applied mechancis, 87(8), p.081003 DOI: 10.2514/1.J058765
  16. Tao, F., Liu, X., Du, H. and Yu, W., 2020. The physics-informed artificial neural network approach for axial compression buckling analysis of thin-walled cylinder, AIAA Journal, 58(6), pp. 2737-2747. DOI: 10.2514/1.J058765
  17. Rique, O., Liu, X., Yu, W. and Pipes, B., 2020. Constitutive modeling for time- and temperature- dependent behavior of composites, Composites Part B: Engineering, 184, p.107726. DOI: 10.1016/j.compositesb.2019.107726
  18. Liu, X., Gasco, F., Goodsell, J. and Yu, W., 2019. Initial failure strength prediction of woven composites using a new yarn failure criterion constructed by deep learning. Composite Structures, 230, p.111505. DOI: 10.1016/j.compstruct.2019.111505
  19. Liu, X., Gasco, F., Yu, W., Goodsell, J. and Rouf, K., 2019. Analyze complex woven composite structures using MSC.Nastran, Advances in Engineering Software, 135, p. 102677. DOI: 10.1016/j.advengsoft.2019.04.008
  20. Liu, X., Yu, W., Gasco, F. and Goodsell, J., 2019. A unified approach for thermoelastic constitutive modeling of composite structures, Composites Part B: Engineering, 172, pp. 649-659. DOI: 10.1016/j.compositesb.2019.05.083
  21. Liu, X., Tang, T., Yu, W. and Pipes, B., 2018. Multiscale modeling of viscoelastic behaviors of textile composites, International Journal of Engineering Science, 130, pp. 175-186. DOI: 10.1016/j.ijengsci.2018.06.003
  22. Rouf, K., Liu, X. and Yu, W., 2018. Multiscale structural analysis of textile composites using mechanics of structure genome, International Journal of Solids and Structures, 136, pp. 89-102. DOI: 10.1016/j.ijsolstr.2017.12.005
  23. Liu, X., Rouf, K., Peng, B. and Yu, W., 2017. Two-step homogenization of textile composites using mechanics of structure genome, Composite Structures, 171, pp. 252-262. DOI: 10.1016/j.compstruct.2017.03.029
  24. Liu, X. and Yu, W., 2016. A novel approach to analyze beam-like composite structures using mechanics of structure genome, Advances in Engineering Software, 100, pp. 238-251. DOI: 10.1016/j.advengsoft.2016.08.003
  25. Liu, X., Liu, W., Wang, S., Xu, F. and Han, J., 2014. Recent developments on theoretical models of sulfate attack on concrete, Materials Review, 28(13), pp. 89-95. (in Chinese)
  26. Liu, X., Liu, W., Miao, Q., Wang, S. and Du, D., 2013. Experimental study on seismic performance of the substructure of isolated masonry with added story after stiffness degeneration, Engineering Mechanics, 30(9), pp. 117-124. (In Chinese)
  27. Liu, X., Liu, W., Wang, S. and Du, D., 2013. Combined application of different elastic-plastic software on performance-based seismic analysis of tall building structures beyond code-specification, Journal of Building Structure, 34(11), pp. 10-17. (In Chinese)
  28. Liu, X., Liu, W., Wang, S. and Du, D., 2013. Construction simulation of high-rise mixed structure based on model analysis, Building Structure, 34(5), pp. 18-22. (in Chinese)
  29. Liu, X., Liu, W., Wang, S. and Du, D., 2012. Shaking table test of damping ratio of strengthening-adding-story isolation structures, Journal of Southeast University, 43(6), pp. 1151-1156. (In Chinese)

Conference paper:

  1. Kothari, T. and Liu, X., 2024. Experiment and Simulation Study of Compression Behaviors of Beam and Honeycomb Lattices. In Proceedings of the American Society for Composites—39th Technical Conference, 10/21-10/24, San Diego, CA.
  2. Liu, B. and Liu, X., 2024. Benchmark Machine Learning Models in Design Optimization of Tow-steered Composite Structures. In Proceedings of the American Society for Composites—39th Technical Conference, 10/21-10/24, San Diego, CA.
  3. Liu, X., Kothari, T., Tao, F., Kobir, M.H. and Yang, Y., 2024. Multiscale modeling of quasi-static crushing behavior of body-centered cubic lattices with I-shape beams and reinforced joints. In AIAA SCITECH 2024 Forum (p. 2290).
  4. Tian, S., Kothari, T., Liu, X. and Yu, W., 2024. A Multiscale Thermomechanical Design Tool for Tailorable Composites and Hybrid Material Systems. In AIAA SCITECH 2024 Forum (p. 1883).
  5. Liu, X., Liu, B., Tian, S. and Yu, W., 2024. Analysis of Tow-steered Laminates of Composites Using a Mixed-fidelity Neural Network Model. In AIAA SCITECH 2024 Forum (p. 0266).
  6. Tian, S., Long, Y., Liu, X., Leone, F.A. and Yu, W., 2023. A New MSG-based Design Framework for Tow-steered Composites. In AIAA SCITECH 2023 Forum (p. 0582).
  7. Liu, X., Liu, B., Kothari, T., Tian, S., Long, Y., Leone, F. and Yu, W., 2023. An Integrated Design Tool for Tow-steering Composites in Abaqus and MSC. Patran/Nastran. In AIAA SCITECH 2023 Forum (p. 2594).
  8. Kobir, M.H., Liu, X., Yang, Y. and Jiang, F., 2022, June. Additive Manufacturing of Novel Beam Lattice Metamaterials With Hollow Cross-Sections Towards High Stiffness/Strength-to-Weight Ratio. In International Manufacturing Science and Engineering Conference (Vol. 85802, p. V001T01A027). American Society of Mechanical Engineers.
  9. Liu, X., Costa, S., Liu, B. and Trehan, S., 2022. Convolutional Neural Network for Predicting Mechanical Behavior of Composites with Fiber Waviness. In 37th Technical Conference of the American Society for Composites, ASC 2022, 19 September 2022 through 21 September 2022. DEStech Publications Inc..
  10. Liu, X., Kan, C., Ye, Z. and Liu, B., 2022. In-process multiscale performance evaluation of FDM-based honeycomb structures with geometric defects. In AIAA SCITECH 2022 Forum (p. 1425).
  11. Liu, X., Peng, B. and Yu, W., 2021. Multiscale modeling of the effective thermal conductivity of 2D woven composites by mechanics of structure genome and neural networks. In Proceedings of the American Society for Composites—36th Technical Conference.
  12. Tao, F., Liu, X., Du, H. and Yu, W., 2021. Discovering Failure Criteria of Composites by Sparse Identification and Compressed Sensing, In Proceedings of the American Society for Composites—36th Technical Conference.
  13. Liu, X., Tian, S., Tao, F., Du, H. and Yu, W., 2021. Machine learning-assisted modeling of composite materials and structures: a review. In AIAA Scitech 2021 Forum (p. 2023).
  14. Tao, F., Liu, X., Du, H. and Yu, W., 2021. Learning damage constitutive law of composites via lamination theory enhanced Abaqus-PDNN mechanics system. In AIAA Scitech 2021 Forum (p. 2022).
  15. Tao, F., Liu, X., Du, H. and Yu, W., 2020. Learning Composite Constitutive Laws Via Coupling Abaqus and Deep Neural Network. In Proceedings of the American Society for Composites—Thirty-fifth Technical Conference.
  16. Liu, X., Tao, F. and Yu, W., 2020. A neural network enhanced system for learning nonlinear constitutive relation of fiber reinforced composites. In AIAA Scitech 2020 Forum (p. 0396).
  17. Tao, F., Liu, X., Du, H. and Yu, W., 2020. Physics-informed artificial neural network approach for axial compression buckling analysis of thin-walled cylinder. In AIAA Scitech 2020 Forum (p. 0398).
  18. Liu, X., Gasco, F. and Yu, W., 2019. Multiscale Initial Failure Analysis of Textile Composite Structures Using Mechanics of Structure Genome in MSC. Nastran. In AIAA Scitech 2019 Forum (p. 0693).
  19. Liu, X., Gasco, F., Goodsell, J. and Yu, W. Powering NASTRAN with SwiftComp for Multiscale Modeling of Composites. In 2018 Proceedings of the American Society for Composites—Thirty-third Technical Conference.
  20. Liu, X. and Yu, W. Multiscale Modeling of Viscoelastic Behaviors of Textile Composites Using Mechanics of Structure Genome. In 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (p. 0899).