J
Jing Bai
Researcher at Northwestern Polytechnical University
Publications - 16
Citations - 116
Jing Bai is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Artificial muscle & Configuration design. The author has an hindex of 4, co-authored 15 publications receiving 62 citations.
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SME-oriented flexible design approach for robotic manufacturing systems
TL;DR: This paper proposes an SME-oriented design approach based on a configuration design paradigm that can offer decision support to designers to form flexible architecture for robotic manufacturing systems and, at the same time, give more interactive configuration freedom to customers so as to achieve a high productivity and flexibility for customisation but with less risk and cost of product development.
Journal ArticleDOI
Interface model-based configuration design of mechatronic systems for industrial manufacturing applications
TL;DR: For the large and complex mechatronic systems for manufacturing applications, with the support of the interface compatibility rules and the elimination algorithm in the configuration design method, the number of alternative combinations from which designers select the most suitable combination can be significantly reduced.
Journal ArticleDOI
Design and optimization of multi-class series-parallel linear electromagnetic array artificial muscle.
Jing Li,Jing Li,Zhenyu Ji,Xuetao Shi,Fusheng You,Feng Fu,Ruigang Liu,Junying Xia,Wang Nan,Jing Bai,Zhanxi Wang,Xiansheng Qin,Xiuzhen Dong +12 more
TL;DR: Experimental results show that artificial half sarcomere actuator possesses great motion performance such as high response speed, great acceleration, small weight and size, robustness, etc., which presents a promising application prospect of artificial half Sarasota actuator.
Journal ArticleDOI
Knowledge-based program generation approach for robotic manufacturing systems
Chen Zheng,Xing Jiajian,Zhanxi Wang,Xian-Sheng Qin,Benoît Eynard,Jing Li,Jing Bai,Yicha Zhang +7 more
TL;DR: The proposed approach provides effective support for the standardization of the rules and knowledge related to manufacturing programs that have proven successful in previous manufacturing cases and can not only increase the programming efficiency, but can also improve the manufacturing stability and production quality.