Target Shape Optimization of Functionally Graded Shape Memory Alloy Compliant Mechanism
Target Shape Optimization of Functionally Graded Shape Memory Alloy Compliant Mechanism
复制标题
功能梯度形状记忆合金柔顺机构的目标形状优化
DOI：
10.1115/smasis2016-9070
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发表时间：
2016
影响因子：
--
通讯作者：
T. Palmer
中科院分区：
文献类型：
--
作者：
J. Jovanova;M. Frecker;R. Hamilton;T. Palmer
镍钛（NiTi）形状记忆合金（SMA）表现出形状记忆和/或超弹性特性，使它们能够通过在选定位置处设计微观结构和成分梯度来展示多功能性。基于自定义目标形状匹配方法，对NiTi柔性机构进行了优化设计，得到了具有功能梯度特性的单件结构。结构内的成分梯度区将表现出按需超弹性效应（SE）响应，利用结构的定制机械行为。通过允许每个区域的几何形状和超弹性性质变化来近似功能分级。超弹性现象已被考虑使用标准的非线性SMA材料模型，只集中在2个区域的利益：较高的杨氏弹性模量的线性区域和具有显着较低的杨氏弹性模量的超弹性区域。由于外部载荷，梯度区基于其组成、位置和几何形状在不同阶段达到临界应力，从而允许结构变形。这个概念已被用来优化结构的几何形状和机械性能，以匹配用户定义的目标形状结构。开发并实施了一种多目标进化算法（NSGA II -非支配排序遗传算法），用于结构的机械性能和几何形状的约束优化。Copyright © 2016 by ASME
Nickel Titanium (NiTi) shape memory alloys (SMAs) exhibit shape memory and/or superelastic properties, enabling them to demonstrate multifunctionality by engineering microstructural and compositional gradients at selected locations. This paper focuses on the design optimization of NiTi compliant mechanisms resulting in single-piece structures with functionally graded properties, based on user-defined target shape matching approach. The compositionally graded zones within the structures will exhibit an on demand superelastic effect (SE) response, exploiting the tailored mechanical behavior of the structure. The functional grading has been approximated by allowing the geometry and the superelastic properties of each zone to vary. The superelastic phenomenon has been taken into consideration using a standard nonlinear SMA material model, focusing only on 2 regions of interest: the linear region of higher Young’s modulus of elasticity and the superelastic region with significantly lower Young’s modulus of elasticity. Due to an outside load, the graded zones reach the critical stress at different stages based on their composition, position and geometry, allowing the structure morphing. This concept has been used to optimize the structures’ geometry and mechanical properties to match a user-defined target shape structure. A multi-objective evolutionary algorithm (NSGA II - Non-dominated Sorting Genetic Algorithm) for constrained optimization of the structure’s mechanical properties and geometry has been developed and implemented.Copyright © 2016 by ASME