摘要
柔性电子在与皮肤佩戴的过程中会对皮肤产生约束,为了提高穿戴舒适性,将分析通孔尺寸、导线焊盘尺寸对皮肤舒适性的影响及蛇形导线宽度对导线弹性应变的影响。并以参数化建模的思想,利用响应面法与多目标遗传算法相结合的实验方法,获取对皮肤界面应力影响最小和最佳弹性应变的通孔、导线焊盘及导线宽度尺寸最佳参数组合。相较于初始尺寸,优化后皮肤界面法向应力最大值及导线弹性应变最大值均有所减小,其中优化后的法向应力相较于初始值31.659 kPa降低到了18.015 kPa,降低了43.1%;导线弹性应变相较于初始值0.258 5降低到了0.169 4,降低了34.5%。并通过Six Sigma方法分析验证了最终优化结果的可靠性。
This article aims to improve wearing comfort by analyzing the effects of through-hole size and wire pad size on skin comfort,as well as the effects of serpentine wire width on elastic strain of the wire.The study utilizes parametric modeling to achieve its objectives.In this study,a novel experimental method that combines response surface method and multi-objective genetic algorithm is proposed to determine the optimal parameter combinations for through holes,wire pads,and wire width dimensions.The objective is to minimize the impact on skin interface stress and achieve the optimal elastic strain.After optimization,both the maximum normal stress at the skin interface and the maximum elastic strain of the wire have decreased.The optimized normal stress is now 18.015 kPa,which is 43.1% lower than the initial value of 31.659 kPa.Additionally,the elastic strain of the conductor has decreased by 34.5% from the initial value of 0.258 5 to 0.169 4.The Six Sigma method was used to verify the reliability of the final optimization results.
作者
唐滔
张烈平
张鑫
卢海钊
彭忠全
Tang Tao;Zhang Lieping;Zhang Xin;Lu Haizhao;Peng Zhongquan(Key Laboratory of Advanced Manufacturing and Automation Technology,Guilin University of Technology,Guilin 541006,China;School of Mechanical and Control Engineering,Guilin University of Technology,Guilin 541006,China;School of Artificial Intelligence,Jiangxi University of Applied Science,Nanchang 330100,China)
出处
《国外电子测量技术》
北大核心
2023年第9期37-48,共12页
Foreign Electronic Measurement Technology
基金
国家自然科学基金(61741303)
广西空间信息与则绘重点实验室基金(19-185-10-08)
江西省教育厅科学技术研究项目一般项目(GJJ213005)资助。
关键词
穿戴舒适性
可延展电路
有限元仿真
响应面法
多目标遗传算法
wearing comfort
scalable circuit finite element simulation
response surface method
multi-objective genet-icalgorithm