今日更新：Journal of the Mechanics and Physics of Solids 1 篇，Mechanics of Materials 1 篇，Thin-Walled Structures 2 篇
Journal of the Mechanics and Physics of Solids
Physics-infused deep neural network for solution of non-associative Drucker–Prager elastoplastic constitutive model
Arunabha M. Roy, Suman Guha, Veera Sundararaghavan, Raymundo Arróyave
doi:10.1016/j.jmps.2024.105570
用于求解非关联德鲁克-普拉格弹塑性结构模型的物理注入型深度神经网络
In the present work, a physics-informed deep learning-based constitutive modeling approach has been introduced, for the first time, to solve non-associative Drucker–Prager elastoplastic solid governed by a linear isotropic hardening rule. A purely data-driven surrogate modeling approach for representing complex and highly non-linear elastoplastic constitutive response prevents accurate predictions due to the absence of prior physical information. To mitigate this, we design an efficient physics-constrained training approach leveraging prior physics-driven optimization procedures. It has been achieved by formulating a highly physics-augmented multi-objective loss function that includes elastoplastic constitutive relations, Drucker–Prager yield criterion, non-associative flow rule, Kuhn–Tucker consistency conditions, and various boundary conditions. Utilizing multiple densely connected independent feed-forward deep neural networks fed with high-fidelity numerical solutions in a data-driven loss function, the model obtains the accurate elastoplastic solution by minimizing the proposed loss function. The strength and robustness of the approach have been demonstrated by accurately solving the benchmark problem where a plastically deformed isotropic shallow stratum has been subjected to compressive pressure under plane strain Drucker–Prager yield condition. To optimize the performance and trainability of the model, extensive experiments on network architecture and various degrees of data-driven estimate shed light on significant improvement in terms of the accuracy of the elastoplastic solution, particularly, that exhibits sharp, or very localized features. Moreover, we propose a transfer learning-based PINNs modeling approach that elucidates the possibility of predicting solutions for different sets of applied stress and material parameters. Requiring significantly less training data, the framework can simultaneously enhance the accuracy of the solution and adaptability of training by demonstrating rapid convergence in critical loss components. The current study highlights a systematic development of a novel physics-informed deep learning approach which is quite generic in nature, yet robust and highly physics-augmented for transferability of known knowledge for vastly accelerated convergence with improved accuracy of predicting an accurate description of non-associative elastoplastic solution in the regime of continuum mechanics.
The coupled effect of aspect ratio and strut micro-deformation mode on the mechanical properties of lattice structures
Stylianos Kechagias, Kabelan J. Karunaseelan, Reece N. Oosterbeek, Jonathan R.T. Jeffers
doi:10.1016/j.mechmat.2024.104944
高宽比和支柱微变形模式对晶格结构力学特性的耦合效应
Lattice structures have been integrated into various industrial applications owing to their unique compressive properties. Mechanical characterisation is usually done by testing a small specimen which is assumed representative of the utilised lattice. A specimen's aspect ratio (height to diameter/width ratio) is known to affect compressive properties in various engineering materials, yet its influence in lattices has not been investigated thoroughly. In this study, titanium lattice specimens designed with different aspect ratios (ranging from 0.5 to 3.0) and four different topologies (displaying bend and stretch-dominated micro-deformation modes) were fabricated using powder bed fusion and tested in quasi-static compression. Their compressive properties and failure modes were evaluated using acquired stress-strain curves and digital image correlation (DIC) analysis. Reducing the aspect ratio in the bend-dominated lattices increased the measured stiffness of the specimens by up to 40%. Conversely, increasing the aspect ratio of the stretch dominated lattices increased the measured stiffness of the specimens by up to 30%. For both topology types, decreasing the aspect ratio increased the measured strength of the specimens, but the effect was less than that observed for stiffness. Different responses were attributed to gradient strain accumulation and different failure patterns (densification versus shear banding) that were observed depending on the combination of aspect ratio and topology. These findings are particularly important for better predicting the mechanical behaviour of lattice-based components that have aspect ratios outside the range of conventional test specimens.
Simulating the Hysteretic Behaviour of Thin-Walled H-Section Steel Members Using the Geometrically Exact Beam Theory
Jian Fan, Junjie Yan, Zhifeng Wu
doi:10.1016/j.tws.2024.111688
利用几何精确梁理论模拟薄壁 H 型钢构件的滞回行为
Based on Gonçalves's geometrically exact beam theory considering cross-section deformation, the warping and distortional deformation of thin-walled members are described in this paper by means of a combination of section deformation modes. By integrating with the J2 elastic‒plasticity theory for the steel, a numerical model is established for the hysteretic behaviour of thin-walled steel members. Four classes of H-section members are selected on account of the design codes, and the influence of section warping and distortion deformation modes on the calculation of the hysteretic behaviour of components with different section types is analysed. The crucial aspect lies in the bending hysteresis behaviour around the strong and weak axes of the cross-section for H-beam steel members subjected to typical thin-walled members with relatively large widths and thicknesses. Test results on the hysteretic behaviour of H-section steel members are compared with those calculated from the proposed model in terms of the hysteric behaviour of components, strength degradation, energy-dissipating capacity, etc. These findings validate the correctness and feasibility of the established non-linear analysis model for thin-walled members.
本文以 Gonçalves 考虑截面变形的几何精确梁理论为基础，通过截面变形模式的组合来描述薄壁构件的翘曲和扭曲变形。通过与钢的 J2 弹塑性理论相结合，建立了薄壁钢构件滞后行为的数值模型。根据设计规范选择了四类 H 型截面构件，分析了截面翘曲和扭曲变形模式对不同截面类型构件滞回行为计算的影响。关键在于 H 型钢构件在横截面强轴和弱轴周围的弯曲滞后行为，这些构件都是典型的薄壁构件，宽度和厚度相对较大。从构件的滞后行为、强度退化、能量耗散能力等方面，将 H 型钢构件的滞后行为测试结果与拟议模型的计算结果进行了比较。这些结果验证了所建立的薄壁构件非线性分析模型的正确性和可行性。
Quasi-static indentation responses of FMLs/2.5D woven carbon-fiber honeycomb sandwich structures under different structural parameters
Ya-nan Zhang, Yu Tian, Xin-yang Liu, Hao Zhou, Jin Zhou, Yu-bing Hu
doi:10.1016/j.tws.2024.111702
不同结构参数下 FMLs/2.5D 编织碳纤维蜂窝夹层结构的准静态压痕响应
Here, a new type of sandwich structure made of fiber metal laminates (FMLs) as facesheet and 2.5D woven carbon-fiber (CFRP) honeycomb as core was developed. The FMLs/CFRP honeycomb sandwich structures were subjected to quasi-static indentation tests to investigate the mechanical response with the variation of panel structure, honeycomb cell element size, honeycomb core wall thickness, and honeycomb core height. In addition to experiments, a finite element method (FEM) model was also created in ABAQUS/Explicit to analyze the damage mechanisms and failure modes of honeycomb sandwich structures under quasi-static indentation. Both the experimental and FEM simulation results revealed that the honeycomb sandwich structure underwent two different failure modes under quasi-static loading: localized damage dominated by indentation and global damage due to overall bending deformation. The combination of panel strength and honeycomb core strength determined the failure mode of the sandwich structure. Damage analysis of the honeycomb core showed that it mainly underwent fiber compression damage and matrix compression damage under quasi-static indentation. This study provides guidance to optimize the design of honeycomb sandwich structures.
