Yingjie Guo

Dr. Yingjie Guo

orcid

Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 October 2015 in Robotics and Computer-Integrated Manufacturing
Verified
Journal Article
Closed access
YGYingjie Guo
Yingjie Guo
HDHuiyue Dong
Huiyue Dong
YKYinglin Ke
Yinglin Ke

1 February 2016 in International Journal of Machine Tools and Manufacture
Verified
Journal Article
Closed access
YGYingjie Guo
Yingjie Guo
HDHuiyue Dong
Huiyue Dong
GWGuifeng Wang
Guifeng Wang
YKYinglin Ke
Yinglin Ke

1 March 2022 in Precision Engineering
Verified
Journal Article
Closed access
SCSiming Cao
Siming Cao
QCQunlin Cheng
Qunlin Cheng
YGYingjie Guo
Yingjie Guo
WZWeidong Zhu
Weidong Zhu
HWHaijin Wang
Haijin Wang
YKYinglin Ke
Yinglin Ke

6 March 2023 in Robotics and Computer-Integrated Manufacturing
Verified
Journal Article
Closed access
LMLei Miao
Lei Miao
YZYongliang Zhang
Yongliang Zhang
ZSZhichao Song
Zhichao Song
YGYingjie Guo
Yingjie Guo
WZWeidong Zhu
Weidong Zhu
YKYinglin Ke
Yinglin Ke

29 October 2022 in Precision Engineering
Verified
Journal Article
Closed access
MDMingming Du
Mingming Du
YGYingjie Guo
Yingjie Guo
HWHaijin Wang
Haijin Wang
HDHuiyue Dong
Huiyue Dong
WLWei Liang
Wei Liang
YKYinglin Ke
Yinglin Ke

18 January 2023 in Journal of Mechanisms and Robotics
Verified
Journal Article
Closed access
PDF file
XXXiaokang Xu
Xiaokang Xu
LCLiang Cheng
Liang Cheng
YGYingjie Guo
Yingjie Guo
JLJiangxiong Li
Jiangxiong Li
YKYinglin Ke
Yinglin Ke
The accuracy of industrial robots is critical in many manufacturing applications. In this case, a self-developed automated fiber placement heavy-duty robot is used for the lay-up of carbon fiber composite prepregs, which requires high positioning accuracy. The mass of the robot body and end-effector exceeds 3 tons and 1 ton, respectively, resulting in considerable deformation and other errors due to the huge size and mass. Paradoxically, the work that our robot engaged in requires high precision, and the positioning accuracy needs to be less than 0.5 mm. This paper conducts a comprehensive analysis of the robot joint error, so as to improve the accuracy of the robot. A torsional and capsize deformation model for joints is established. In addition, the capsize torque and capsize axis of joint deformation are derived to determine the transformation relationship between joint frames. Chebyshev polynomials are used to describe joint-dependent errors. The Levenberg–Marquarelt (L–M) algorithm was used to identify the error model parameters. The effects of different error factors on the positioning accuracy were compared and analyzed. The validation result shows that the proposed model describes 88.11% of the positioning error, and the average residual error of the calibration can reach 0.132 mm. The identified joint-dependent error and the compliance error are 0.296 mm and 0.240 mm, accounting for 26.67% and 21.62% of the positioning error, respectively.