今日更新:International Journal of Solids and Structures 2 篇,Journal of the Mechanics and Physics of Solids 1 篇,Mechanics of Materials 1 篇,Thin-Walled Structures 1 篇
Machine learning enabled identification of sheet metal localization
Muhammed Adil Yatkın, Mihkel Kõrgesaar
doi:10.1016/j.ijsolstr.2023.112592
通过机器学习识别板材定位
The Forming Limit Curve (FLC), which describes the maximum applicable strain before localization, depends on the particular material, but also on the applied load and history of the load. Recent investigations have shown that the non-proportional loading effect on the FLC can be predicted with data-driven or machine-learning-based methods. Here we compare different ML methods to their applicability in predicting localization points under multi-segmented non-proportional loading. Therefore, an FE-based metamodel is developed that allows imposing an arbitrary loading history on sheet metal to predict the point of localization. A series of virtual experiments are conducted with this metamodel to generate a database of bi-linear loading paths that are used for training. Different ML-based methods were used to predict the localization point based on the strain history data. The 1D-Convolutional Neural Network (1D-CNN), with the ability to learn dependency between input features, has the best accuracy in predicting the localization point.
成型极限曲线(FLC)描述了局部化之前的最大适用应变,它不仅取决于特定的材料,还取决于施加的载荷和载荷的历史。最近的研究表明,基于数据驱动或机器学习的方法可以预测非比例加载对 FLC 的影响。在此,我们比较了不同的 ML 方法在预测多分段非比例加载下的定位点时的适用性。因此,我们开发了一种基于有限元分析的元模型,可以在金属板上施加任意加载历史记录来预测定位点。利用该元模型进行了一系列虚拟实验,生成了一个用于训练的双线性加载路径数据库。根据应变历史数据,使用不同的基于 ML 的方法来预测定位点。一维卷积神经网络(1D-CNN)具有学习输入特征之间依赖关系的能力,在预测定位点方面具有最佳准确性。
Predicting Moisture Penetration Dynamics in Paper with Machine Learning Approach
Mossab Alzweighi, Rami Mansour, Alexander Maass, Ulrich Hirn, Artem Kulachenko
doi:10.1016/j.ijsolstr.2023.112602
用机器学习方法预测纸张的水分渗透动态
In this work, we predicted the gradient of the deformational moisture dynamics in a sized commercial paper by observing the curl deformation in response to the one-sided water application. The deformational moisture is a part of the applied liquid which ends up in the fibers causing swelling and subsequent mechanical response of the entire fiber network structure. The adapted approach combines traditional experimental procedures, advanced machine learning techniques and continuum modeling to provide insights into the complex phenomenon relevant to ink-jet digital printing in which the sized and coated paper is often used, meaning that not all the applied moisture will reach the fibers. Key material properties including elasticity, plastic parameters, viscoelasticity, creep, moisture dependent behavior, along with hygroexpansion coefficients are identified through extensive testing, providing vital data for subsequent simulation using a continuum model. Two machine learning models, a Feedforward Neural Network (FNN) and a Recurrent Neural Network (RNN), are probed in this study. Both models are trained using exclusively numerically generated moisture profile histories, showcasing the value of such data in contexts where experimental data acquisition is challenging. These two models are subsequently utilized to predict moisture profile history based on curl experimental measurements, with the RNN demonstrating superior accuracy due to its ability to account for temporal dependencies. The predicted moisture profiles are used as inputs for the continuum model to simulate the associated curl response comparing it to the experiment representing “never seen” data. The result of comparison shows highly predictive capability of the RNN. This study melds traditional experimental methods and innovative machine learning techniques, providing a robust technique for predicting moisture gradient dynamics that can be used for both optimizing the ink solution and paper structure to achieve desirable printing quality with lowest curl propensities during printing.
在这项工作中,我们通过观察单面施水时的卷曲变形,预测了规格商业用纸中的变形水分动态梯度。变形水分是施用液体的一部分,它最终会进入纤维,导致纤维膨胀,进而引起整个纤维网络结构的机械响应。这种方法结合了传统的实验程序、先进的机器学习技术和连续体建模,有助于深入了解喷墨数字印刷的复杂现象,因为在喷墨数字印刷中,通常会使用施胶纸和涂布纸,这意味着并非所有的水分都会到达纤维。通过大量测试,确定了关键材料特性,包括弹性、塑性参数、粘弹性、蠕变、湿度依赖行为以及湿膨胀系数,为随后使用连续模型进行模拟提供了重要数据。本研究中使用了两种机器学习模型,即前馈神经网络(FNN)和循环神经网络(RNN)。这两个模型都是通过数字生成的湿度曲线历史记录进行训练的,在实验数据获取具有挑战性的情况下展示了这些数据的价值。这两个模型随后被用于根据卷曲实验测量结果预测水分曲线历史,其中 RNN 由于能够考虑时间依赖性而显示出更高的准确性。预测的水分曲线被用作连续模型的输入,以模拟相关的卷曲响应,并与代表 "从未见过 "数据的实验进行比较。比较结果表明,RNN 具有很强的预测能力。这项研究融合了传统的实验方法和创新的机器学习技术,提供了一种预测湿度梯度动态的可靠技术,可用于优化油墨溶液和纸张结构,从而在印刷过程中以最低的卷曲倾向获得理想的印刷质量。
Coupling diffusion and finite deformation in phase transformation materials
Tao Zhang, Delin Zhang, Ananya Renuka Balakrishna
doi:10.1016/j.jmps.2023.105501
相变材料中的耦合扩散和有限变形
We present a multiscale theoretical framework to investigate the interplay between diffusion and finite lattice deformation in phase transformation materials. In this framework, we use the Cauchy-Born Rule and the Principle of Virtual Power to derive a thermodynamically consistent theory coupling the diffusion of a guest species (Cahn-Hilliard type) with the finite deformation of host lattices (nonlinear gradient elasticity). We adapt this theory to intercalation materials—specifically Li1−2Mn2O4— to investigate the delicate interplay between Li-diffusion and the cubic-to-tetragonal deformation of lattices. Our computations reveal fundamental insights into the microstructural evolution pathways under dynamic discharge conditions, and provide quantitative insights into the nucleation and growth of twinned microstructures during intercalation. Additionally, our results identify regions of stress concentrations (e.g., at phase boundaries, particle surfaces) that arise from lattice misfit and accumulate in the electrode with repeated cycling. These findings suggest a potential mechanism for structural decay in Li2Mn2O4. More generally, we establish a theoretical framework that can be used to investigate microstructural evolution pathways, across multiple length scales, in first-order phase transformation materials.
我们提出了一个多尺度理论框架,用于研究相变材料中扩散与有限晶格变形之间的相互作用。在这一框架中,我们利用考奇-伯恩法则和虚拟力量原理,推导出一种热力学上一致的理论,将客体物种的扩散(卡恩-希利亚德型)与主晶格的有限变形(非线性梯度弹性)耦合在一起。我们将这一理论应用于插层材料--特别是 Li1-2Mn2O4--研究锂扩散与晶格立方到四方变形之间微妙的相互作用。我们的计算揭示了动态放电条件下微结构演化路径的基本观点,并对插层过程中孪生微结构的成核和生长提供了定量见解。此外,我们的研究结果还确定了应力集中区域(如相边界、颗粒表面),这些应力集中区域由晶格错配引起,并随着反复循环在电极中累积。这些发现表明了 Li2Mn2O4 结构衰变的潜在机制。更广泛地说,我们建立了一个理论框架,可用于研究一阶相变材料中跨越多个长度尺度的微结构演化路径。
Microstructural effects in rate-dependent responses of smooth and notched magnesium bars
Shahmeer Baweja, Shailendra P. Joshi
doi:10.1016/j.mechmat.2023.104877
光滑镁条和缺口镁条随速率变化的微观结构效应
We perform three-dimensional crystal plasticity simulations of smooth and notched bar geometries made of polycrystalline hexagonal close-packed material representing a magnesium alloy. The polycrystalline microstructure is explicitly resolved to investigate the combined effect of initial texture and grain size on the rate-dependent macroscopic responses and their micromechanical underpinnings under uniaxial and multiaxial stress states. The simulations reveal that in addition to the textural effect recently investigated by Ravaji et al. (2021), grain size plays an important role in the anisotropy of macroscopic responses. For a given texture, the lateral deformation anisotropy increases with grain size refinement for all strain rates considered here. The load-deformation responses exhibit a synergistic strengthening effect in microstructures with stronger initial textures and finer grain sizes, which is enhanced with increasing notch acuity. A transition from a conventional power-law type load-deformation response to a sigmoidal load-deformation response may occur, which depends on the imposed strain rate. It is a result of the interaction between textural weakening and grain size refinement that influence extension twinning together with an equitable landscape of the different slip mechanisms. We discuss possible implications of the net material plastic anisotropy due to texture and grain size on macroscopic failure using a micromechanical basis.
我们对以镁合金为代表的多晶六方紧密堆积材料制成的光滑和缺口棒材几何形状进行了三维晶体塑性模拟。我们明确解析了多晶微观结构,以研究在单轴和多轴应力状态下,初始纹理和晶粒大小对随速率变化的宏观响应及其微观力学基础的综合影响。模拟结果表明,除了 Ravaji 等人(2021 年)最近研究的纹理效应外,晶粒尺寸在宏观响应的各向异性中也起着重要作用。对于给定的纹理,在本文考虑的所有应变速率下,横向变形各向异性随着晶粒尺寸的细化而增加。在具有较强初始纹理和较细晶粒尺寸的微结构中,载荷-变形响应表现出协同强化效应,这种效应随着凹口敏锐度的增加而增强。从传统的幂律型载荷变形响应到西格玛载荷变形响应的转变可能会发生,这取决于施加的应变率。这是纹理弱化和晶粒细化相互作用的结果,它们与不同滑移机制的公平分布一起影响着延伸孪晶。我们以微观力学为基础,讨论了由纹理和晶粒尺寸引起的净材料塑性各向异性对宏观破坏的可能影响。
Experimental study on the impact resistance and damage tolerance of thermoplastic FMLs
Lei Yang, Zhenhao Liao, Cheng Qiu, Zijing Hong, Jinglei Yang
doi:10.1016/j.tws.2023.111435
热塑性 FML 的抗冲击性和损伤耐受性实验研究
This study aimed to enhance the impact resistance of fiber metal laminates (FMLs) and achieve lightweight optimization by incorporating a new thermoplastic resin, a titanium alloy and ultra-high-molecular-weight polyethylene (UHMWPE) fiber to produce a novel type of FMLs (PEFMLs). The impact resistance of PEFMLs was analyzed through low-velocity impact tests conducted at different energy levels. Subsequently, the residual compression-after-impact (CAI) strength of the PEFMLs was evaluated through compression tests on the impacted specimens. The experimental findings revealed that PEFMLs exhibited subcritical failure when subjected to impact energies less than 35 J, with a penetration energy threshold of 55 J. Higher impact energies resulted in larger damage areas and increased plate buckling of PEFMLs, consequently leading to reduced CAI strength. The presence of metal, thermoplastic resin and UHMWPE in the PEFMLs effectively dissipated a substantial amount of impact energy while maintaining their structural integrity during both the impact and compression processes.
本研究旨在通过加入新型热塑性树脂、钛合金和超高分子量聚乙烯(UHMWPE)纤维,生产出一种新型金属纤维层压板(PEFMLs),从而提高金属纤维层压板(FMLs)的抗冲击性,并实现轻量化优化。通过在不同能量水平下进行的低速冲击试验,分析了 PEFMLs 的抗冲击性。随后,通过对冲击试样进行压缩试验,评估了 PEFMLs 的冲击后残余压缩强度(CAI)。实验结果表明,PEFML 在受到小于 35 J 的冲击能量时表现出亚临界破坏,穿透能量阈值为 55 J。PEFML 中金属、热塑性树脂和超高分子量聚乙烯的存在有效地消散了大量冲击能量,同时在冲击和压缩过程中保持了结构完整性。