概括
这项研究引入了一种新的深度指导方法,用于准确测量复杂的原木结构中的应变. 这种技术克服了传统的数字图像相关性 (DIC) 的局限性,用于精确的机械分析.
科学领域:
- 材料科学 材料科学 材料科学
- 机器人技术 机器人技术 机器人技术
- 智能结构是一个智能结构.
背景情况:
- 原木结构和超材料在材料科学,智能结构和机器人学中越来越突出.
- 精确的机械性能测量对于原木结构分析,设计和应用至关重要.
- 数字图像相关性 (DIC) 是一种非接触式方法,用于3D形状,变形和应变分析,但面临着奥里加米复杂几何学的准确性问题.
研究的目的:
- 开发一种深度引导的应变分析方法,用于准确地测量原木结构上的变形和应变.
- 为了解决由原始设计中的深度不连续性引起的传统DIC的局限性.
主要方法:
- 建议采用一种新的深度引导应变分析方法.
- 数据点从连续的,平滑的子集中选择,由深度图指导.
- 这种方法可以在具有复杂几何特征的3D表面上准确计算应变.
主要成果:
- 拟议的方法准确地测量了复杂的原木结构的变形和应变.
- 在Miura-ori结构上的实验验验证了该方法的有效性.
- 该技术为分析3D结构行为提供了一种可行的实验方法.
结论:
- 深度引导应变分析方法显著提高了DIC对原木结构的准确性.
- 这种方法提供了一种可靠的实验工具,以了解复杂的3D原形的机械行为.
- 它有助于先进的分析,设计和应用的原始材料和系统.
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It is essential to understand how structural members behave under plastic deformation when the bending stress exceeds the material's yield strength. This state of deformation permanently alters the shape of the member, in contrast to the linear elastic behavior observed before yielding. The strain at any point in the member is expressed in terms of maximum strain. Notably, the neutral axis, which coincides with the centroid during elastic bending, shifts away from the centroid under plastic...
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