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Generation of human–robot collaboration disassembly sequences for end-of-life lithium–ion batteries based on knowledge graph

Published online by Cambridge University Press:  12 November 2024

Jie Li ,
Weibin Qu Open the ORCID record for Weibin Qu [Opens in a new window] ,
Hangbin Zheng ,
Rong Zhang  and
Shimin Liu
Jie Li
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, China
Weibin Qu*
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, China
Hangbin Zheng
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, China
Rong Zhang
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, China
Shimin Liu
Affiliation:
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
*
Corresponding author: Weibin Qu; Email: [email protected]
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Abstract

The disassembly of end-of-life lithium–ion batteries (EOL-LIBs) is inherently complex, owing to their multi-state and multi-type characteristics. To mitigate these challenges, a human–robot collaboration disassembly (HRCD) model is developed. This model capitalizes on the cognitive abilities of humans combined with the advanced automation capabilities of robots, thereby substantially improving the disassembly process’s flexibility and efficiency. Consequently, this method has become the benchmark for disassembling EOL-LIBs, given its enhanced ability to manage intricate and adaptable disassembly tasks. Furthermore, effective disassembly sequence planning (DSP) for components is crucial for guiding the entire disassembly process. Therefore, this research proposes an approach for the generation of HRCD sequences for EOL-LIBs based on knowledge graph, providing assistance to individuals lacking relevant knowledge to complete disassembly tasks. Firstly, a well-defined disassembly process knowledge graph integrates structural information from CAD models and disassembly operating procedure. Based on the acquired information, DSP is conducted to generate a disassembly sequence knowledge graph (DSKG), which serves as a repository in graphical form. Subsequently, knowledge graph matching is employed to align nodes in the existing DSKG, thereby reusing node sequence knowledge and completing the sequence information for the target disassembly task. Finally, the proposed method is validated using retired power LIBs as a case study product.

Keywords

human–robot collaborationknowledge graphend-of-life lithium–ion batteriesdisassembly sequences planning
Type
Research Article
Information
AI EDAM , Volume 38 , 2024 , e13
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Copyright
© The Author(s), 2024. Published by Cambridge University Press

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