Yiyuan Li¹, Yongjing Li^1,2, Shilin Yan^1,2,*, Pin Wen^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3105-3119, 2024, DOI:10.32604/cmes.2024.051375 - 08 July 2024

Abstract Based on the traditional re-entrant honeycomb, a novel re-entrant octagon honeycomb (ROH) is proposed. The deformation mode of the honeycomb under quasi-static compression is analyzed by numerical simulation, and the results are in good agreement with the experimental ones. The deformation modes, mechanical properties, and energy absorption characteristics of ROH along the impact and perpendicular directions gradient design are investigated under different velocities. The results indicated that the deformation mode of ROH is affected by gradient design along the direction of impact and impact speed. In addition, gradient design along the direction of impact can… More >

Weida Wu, Yiqiang Wang, Zhonghao Gao, Pai Liu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2001-2026, 2024, DOI:10.32604/cmes.2023.046670 - 29 January 2024

Abstract Negative Poisson’s ratio (NPR) metamaterials are attractive for their unique mechanical behaviors and potential applications in deformation control and energy absorption. However, when subjected to significant stretching, NPR metamaterials designed under small strain assumption may experience a rapid degradation in NPR performance. To address this issue, this study aims to design metamaterials maintaining a targeted NPR under large deformation by taking advantage of the geometry nonlinearity mechanism. A representative periodic unit cell is modeled considering geometry nonlinearity, and its topology is designed using a gradient-free method. The unit cell microstructural topologies are described with the… More >

Yingjun Wang¹, Zhongyuan Liao¹, Shengyu Shi^{1, *}, Zhenpei Wang^{2, *}, Leong Hien Poh³

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 433-458, 2020, DOI:10.32604/cmes.2020.08680 - 01 February 2020

Abstract Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity More >