ERGOBOSS: onomic ptimization of dy-upporting urfaces

ERGOBOSS: onomic ptimization of dy-upporting urfaces
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ERGOBOSS:dy 支持表面的经济优化

DOI:
10.1109/tvcg.2021.3112127
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发表时间:
2022
影响因子:
5.2
通讯作者:
Barbic, Jernej
Barbic, Jernej
中科院分区:
计算机科学1区
文献类型:
--
作者:
Zhao, Danyong;Li, Yijing;Chaudhuri, Siddhartha;Langlois, Timothy;Barbic, Jernej

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人们经常坐在或靠在支撑表面上,重要的是塑造这些表面是舒适和符合人体工程学的。我们给出了一种设计刚性支撑表面几何形状的方法,以最大限度地提高表面与可变形人体之间的物理接触的工效学效果。我们用一层有限元可变形组织包围刚性核心来模拟柔软的可变形人体,并测量了真实的弹性材料特性,以及大变形的非线性分析。我们定义了一个新的成本函数来衡量人体与支撑表面之间的接触的工效学。我们给出了一个稳定且计算效率高的接触模型,该模型对支撑面形状可微。这使得使用基于梯度的优化器来优化我们的人体工程学成本函数成为可能。我们的优化器生产支持表面优于先前的工作对人体工程学的形状设计。我们的例子包括家具、服装和工具。我们还通过扫描真实的人类受试者的脚来验证我们的结果,并优化鞋底形状,以最大限度地提高足部接触人体工程学。我们3d打印优化的鞋底,使用压力传感器测量接触压力,并证明实际的未优化和优化的压力分布与我们的模拟预测的压力分布定性匹配。
Humans routinely sit or lean against supporting surfaces and it is important to shape these surfaces to be comfortable and ergonomic. We give a method to design the geometric shape of rigid supporting surfaces to maximize the ergonomics of physically based contact between the surface and a deformable human. We model the soft deformable human using a layer of FEM deformable tissue surrounding a rigid core, with measured realistic elastic material properties, and large-deformation nonlinear analysis. We define a novel cost function to measure the ergonomics of contact between the human and the supporting surface. We give a stable and computationally efficient contact model that is differentiable with respect to the supporting surface shape. This makes it possible to optimize our ergonomic cost function using gradient-based optimizers. Our optimizer produces supporting surfaces superior to prior work on ergonomic shape design. Our examples include furniture, apparel and tools. We also validate our results by scanning a real human subject's foot and optimizing a shoe sole shape to maximize foot contact ergonomics. We 3D-print the optimized shoe sole, measure contact pressure using pressure sensors, and demonstrate that the real unoptimized and optimized pressure distributions qualitatively match those predicted by our simulation.
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