Farajpour Ali, Ingman Wendy V
Adelaide Medical School, University of Adelaide, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia.
Robinson Research Institute, University of Adelaide, Adelaide, SA 5006, Australia.
Micromachines (Basel). 2024 Jan 30;15(2):210. doi: 10.3390/mi15020210.
Detecting inclusions in materials at small scales is of high importance to ensure the quality, structural integrity and performance efficiency of microelectromechanical machines and products. Ultrasound waves are commonly used as a non-destructive method to find inclusions or structural flaws in a material. Mathematical continuum models can be used to enable ultrasound techniques to provide quantitative information about the change in the mechanical properties due to the presence of inclusions. In this paper, a nonlocal size-dependent poroelasticity model integrated with machine learning is developed for the description of the mechanical behaviour of spherical inclusions under uniform radial compression. The scale effects on fluid pressure and radial displacement are captured using Eringen's theory of nonlocality. The conservation of mass law is utilised for both the solid matrix and fluid content of the poroelastic material to derive the storage equation. The governing differential equations are derived by decoupling the equilibrium equation and effective stress-strain relations in the spherical coordinate system. An accurate numerical solution is obtained using the Galerkin discretisation technique and a precise integration method. A Dormand-Prince solution is also developed for comparison purposes. A light gradient boosting machine learning model in conjunction with the nonlocal model is used to extract the pattern of changes in the mechanical response of the poroelastic inclusion. The optimised hyperparameters are calculated by a grid search cross validation. The modelling estimation power is enhanced by considering nonlocal effects and applying machine learning processes, facilitating the detection of ultrasmall inclusions within a poroelastic medium at micro/nanoscales.
在小尺度下检测材料中的夹杂物对于确保微机电机器和产品的质量、结构完整性及性能效率至关重要。超声波通常作为一种无损方法用于发现材料中的夹杂物或结构缺陷。数学连续介质模型可用于使超声技术能够提供有关因夹杂物存在而导致的力学性能变化的定量信息。本文中，开发了一种与机器学习相结合的非局部尺寸相关多孔弹性模型，用于描述球形夹杂物在均匀径向压缩下的力学行为。利用埃林根的非局部理论捕捉尺度对流体压力和径向位移的影响。将质量守恒定律应用于多孔弹性材料的固体基质和流体成分，以推导存储方程。通过在球坐标系中解耦平衡方程和有效应力 - 应变关系来推导控制微分方程。使用伽辽金离散化技术和精确积分方法获得精确的数值解。还开发了一种多曼 - 普林斯解用于比较目的。结合非局部模型的轻梯度提升机器学习模型用于提取多孔弹性夹杂物力学响应的变化模式。通过网格搜索交叉验证计算优化的超参数。考虑非局部效应并应用机器学习过程增强了建模估计能力，有助于在微/纳米尺度的多孔弹性介质中检测超小夹杂物。
