Calibration of polyvinylidene fluoride (PVDF) stress gauges under high-impact dynamic compression by machine learning

doi:10.1063/5.0066090

IMECH-IR > 非线性力学国家重点实验室

	Calibration of polyvinylidene fluoride (PVDF) stress gauges under high-impact dynamic compression by machine learning
	Tan S(覃双); Yu,Zheng; Zhang,Xu; Yang,Shuqi; Peng,Wenyang; Zhao,Feng
通讯作者	Zhang, Xu(caepzx@sohu.com) ; Zhao, Feng(ifpzf@163.com)
发表期刊	JOURNAL OF APPLIED PHYSICS
	2022-01-14
卷号	131 期号:2 页码:8
ISSN	0021-8979
摘要	Calibration of stress gauges is of great importance for understanding the behaviors of materials under high dynamic impacts. However, commonly used calibration models have little transferability due to ignoring the influences of the gauge parameters. In this work, we propose a systematic approach that can generate effective and transferable calibration models including multiple independent variables by machine learning. Specifically, we conduct high-impact dynamic compression experiments using polyvinylidene fluoride (PVDF) stress gauges with two different thicknesses and varying remnant polarizations at shock levels from 0.3 to 10 GPa. To best characterize the comprehensive calibration relationship, we select a set of five features (combined by strain, remnant polarization, and film thickness) by feature engineering and use Lasso with the bagging ensemble as an algorithm to train the machine learning model. For comparison, we also propose semiempirical models that calibrate PVDF gauges effectively, but without including thickness and remnant polarization. Our results show that the machine learning model is more precise and more reasonable in physics. The predicted dependences of the calibration curves on remnant polarization and film thickness by the machine learning model are qualitatively consistent with the physics scenario. This work reveals the potential of machine learning methods to improve gauge calibration for better performance and transferability. The method used in this work is applicable to the calibration of any stress gauges with multiple variables.
DOI	10.1063/5.0066090
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000747278100015
关键词[WOS]	SHOCK ; REGRESSION ; PRESSURE ; POLYMERS
WOS研究方向	Physics
WOS类目	Physics, Applied
资助项目	National Defense Science Foundation of China[JSZL2016212C001] ; Science Challenge Project of China[TZ2018001] ; Science Challenge Project of China[2019-JCJQ-ZD-203]
项目资助者	National Defense Science Foundation of China ; Science Challenge Project of China
论文分区	二类
力学所作者排名	1
RpAuthor	Zhang, Xu ; Zhao, Feng
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/88412
专题	非线性力学国家重点实验室
推荐引用方式 GB/T 7714	Tan S,Yu,Zheng,Zhang,Xu,et al. Calibration of polyvinylidene fluoride (PVDF) stress gauges under high-impact dynamic compression by machine learning[J]. JOURNAL OF APPLIED PHYSICS,2022,131,2,:8.
APA	覃双,Yu,Zheng,Zhang,Xu,Yang,Shuqi,Peng,Wenyang,&Zhao,Feng.(2022).Calibration of polyvinylidene fluoride (PVDF) stress gauges under high-impact dynamic compression by machine learning.JOURNAL OF APPLIED PHYSICS,131(2),8.
MLA	覃双,et al."Calibration of polyvinylidene fluoride (PVDF) stress gauges under high-impact dynamic compression by machine learning".JOURNAL OF APPLIED PHYSICS 131.2(2022):8.

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