Global yield surface construction of polymethacrylimide foam by an integrated approach combining nanoindentation, machine learning and microstructure-informed modeling

材料科学 产量(工程) 屈服面 有限元法 纳米压痕 极限抗拉强度 曲面(拓扑) 复合材料 结构工程 沃罗诺图 响应面法 反问题 反向 主成分分析 模数 椭球体 拉伸试验 多物理 抗压强度 弹性模量 微观力学 不对称 机械工程 杨氏模量 压力(语言学) 应力空间 逆方法
作者
Qianying Cen,Xiaodong Wang,Xiaowei Jiang,Ling Liu,Zhanjun Wu
出处
期刊:Materials & Design [Elsevier BV]
卷期号:257: 114412-114412 被引量:38
标识
DOI:10.1016/j.matdes.2025.114412
摘要

• The yield surface for PMI foam exhibits tension-compression asymmetry in stress/strain plane, and ellipsoidal in principal strain space. • Machine learning-enhanced inverse method reveals foam cell walls are 10.49% stiffer and 8.00% stronger than bulk matrix material. • Microstructure-based uniaxial simulations reveal distinct gas effects: a 6.33% enhancement in compressive strength. Polymethacrylimide (PMI) foam is extensively utilized in lightweight sandwich structures yet lacks a reliable global yield surface for design guidance. This study presents an integrated experimental-computational framework for PMI foam yield surface construction. Cell-wall properties were precisely determined via nanoindentation combined with an inverse identification approach employing finite element modeling, machine learning, and traversal algorithms. A microstructure-informed Voronoi model, generated from micro-CT data and incorporating gas-filled cells with cell-wall properties, was developed for PMI foam simulation. Following validation against experimental uniaxial stress–strain curves, the model was subjected to multiaxial simulations under varying loading ratios to acquire comprehensive yield points for global yield surface construction, which was verified by equivalent biaxial tensile/compressive tests. The results show the cell-wall possesses 10.49 % higher Young’s modulus and 8.00 % greater initial yield stress than PMI matrix. Uniaxial microstructural analysis reveals intracellular gas enhances compressive strength by 6.33 % while minimally affecting tensile behavior. Notably, the yield surface shows distinct tension–compression asymmetry in stress/strain plane, near-perfect ellipsoidal fitting ( R 2 = 0.998) in principal strain space, and excellent predictive accuracy with equivalent biaxial test errors of −4.06 % (tension) and + 9.85 % (compression). These results provide theoretical foundations for safety optimization and structural design of PMI foam in engineering applications.
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