An improved material removal model for robot polishing based on neural networks

doi:10.3788/IRLA201948.0317005

CIOMP OpenIR

	An improved material removal model for robot polishing based on neural networks
	Y.Yu; L.Kong; H.Zhang; M.Xu; L.Wang
	2019
发表期刊	Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering
ISSN	10072276
卷号	48 期号:3
摘要	A strategy for improving the precision of material removal model based on deep neural networks was proposed. A deep learning algorithm with ability of feature selecting was proposed. A series of simulation samples composed of a material removal rate and corresponding polishing parameters were generated based on the model of material removal rate for robot polishing. The deep learning algorithm learned both the simulation samples and practical samples and then a deep learning model was established. The error between material removal depth of the test samples and material removal depth estimated by polishing parameters by using proposed deep learning model was calculated and compared. The results show that the improved model can achieve higher accuracy than the traditional models. 2019, Editorial Board of Journal of Infrared and Laser Engineering. All right reserved.
关键词	Learning algorithms,Deep neural networks,Learning systems,Polishing,Robots
DOI	10.3788/IRLA201948.0317005
URL	查看原文
收录类别	EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/62846
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Y.Yu,L.Kong,H.Zhang,et al. An improved material removal model for robot polishing based on neural networks[J]. Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering,2019,48(3).
APA	Y.Yu,L.Kong,H.Zhang,M.Xu,&L.Wang.(2019).An improved material removal model for robot polishing based on neural networks.Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering,48(3).
MLA	Y.Yu,et al."An improved material removal model for robot polishing based on neural networks".Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering 48.3(2019).

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
An improved material（1946KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载