Data-driven and physics-based methods to optimize structures against delamination

被引:0
|
作者
Kumar, Tota Rakesh [1 ,2 ]
Paggi, Marco [1 ]
机构
[1] IMT Sch Adv Studies Lucca, Res Unit MUSAM Multiscale Anal Mat, Computat Mech Grp, Piazza San Francesco 19, I-55100 Lucca, Italy
[2] Indian Maritime Univ, Sch Marine Engn & Technol, Chennai, Tamil Nadu, India
关键词
Cohesive fracture; joining technologies; graded interfaces; particle swarm optimization; SIMP topology optimization; TOPOLOGY OPTIMIZATION; LEVEL SET; IDENTIFICATION; ALGORITHM; FRAMEWORK; ADHESION; DESIGN; FORMULATION; PARAMETERS; CURVES;
D O I
10.1080/15376494.2024.2372696
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Composite materials and multi-material components often fail at their internal interfaces/adhesive joints, and hence special attention should be given to such catastrophic delamination events to guarantee the system's functional requirements. So far, however, the majority of structural topology optimization problems have focused on optimal distribution of the bulk materials by considering interfaces as perfectly bonded. This motivates the introduction of optimization methods that explicitly take into account the role of the material interfaces to optimize structures against delamination. In this work, we propose a data-driven heuristic optimization approach for the identification of optimal cohesive interfaces with linearly graded fracture properties to increase the ability of the composite structure to withstand peeling. Moreover, for given cohesive interface properties, we investigate the applicability of the physics-based Solid Isotropic Material with Penalty (SIMP) topology optimization approach to optimize the internal structure of a substrate in problems where the stress field is affected by interface delamination.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Combining physics-based and data-driven methods in metal stamping
    Abanda, Amaia
    Arroyo, Amaia
    Boto, Fernando
    Esteras, Miguel
    JOURNAL OF INTELLIGENT MANUFACTURING, 2024,
  • [2] Autonomous Golf Putting with Data-Driven and Physics-Based Methods
    Junker, Annika
    Fittkau, Niklas
    Timmermann, Julia
    Traechtler, Ansgar
    2022 SIXTH IEEE INTERNATIONAL CONFERENCE ON ROBOTIC COMPUTING, IRC, 2022, : 134 - 141
  • [3] Physics-based Or Data-driven Models?
    Mason, Richard
    Hart's E and P, 2019, (April):
  • [4] Hybrid physics-based modeling and data-driven method for diagnostics of masonry structures
    Napolitano, Rebecca
    Glisic, Branko
    COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2020, 35 (05) : 483 - 494
  • [5] The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety
    Finegan, Donal P.
    Zhu, Juner
    Feng, Xuning
    Keyser, Matt
    Ulmefors, Marcus
    Li, Wei
    Bazant, Martin Z.
    Cooper, Samuel J.
    JOULE, 2021, 5 (02) : 316 - 329
  • [6] Physics-Based and Data-Driven Polymer Rheology Model
    Abdullah, M. B.
    Delshad, M.
    Sepehrnoori, K.
    Balhoff, M. T.
    Foster, J. T.
    Al-Murayri, M. T.
    SPE JOURNAL, 2023, 28 (04): : 1857 - 1879
  • [7] Physics-based and data-driven modeling for biomanufacturing 4.0
    Ogunsanya, Michael
    Desai, Salil
    MANUFACTURING LETTERS, 2023, 36 : 91 - 95
  • [8] Data-driven physics-based modeling of pedestrian dynamics
    Pouw, Caspar A. S.
    Van Der Vleuten, Geert G. M.
    Corbetta, Alessandro
    Toschi, Federico
    Physical Review E, 2024, 110 (06)
  • [9] Physics-based and data-driven modeling for stability evaluation of buried structures in natural clays
    Lai, Fengwen
    Shiau, Jim
    Keawsawasvong, Suraparb
    Chen, Fuquan
    Banyong, Rungkhun
    Seehavong, Sorawit
    JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING, 2023, 15 (05) : 1248 - 1262
  • [10] Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods
    Fan, Xingyu
    Li, Jun
    Hao, Hong
    ADVANCES IN STRUCTURAL ENGINEERING, 2021, 24 (16) : 3609 - 3626