Zhenlu Xu
Papers
2
Total Citations
31
H-Index
1
About
Zhenlu Xu is a leading researcher in intelligent manufacturing and sustainable production, with a primary focus on robotic disassembly and digital twin technologies. Their work addresses critical challenges in recycling end-of-life products, particularly the uncertainty of component conditions during disassembly. Xu’s most cited paper, “Digital Twin-Driven Robotic Disassembly Sequence Dynamic Planning Under Uncertain Missing Condition” (2023, 30 citations), introduces a novel framework that integrates digital twins with dynamic planning algorithms to optimize disassembly sequences when components may be missing. This contribution significantly improves disassembly efficiency and adaptability in real-world recycling scenarios. In their recent 2025 study, Xu further advances the field by employing dueling deep Q-networks to handle uncertain irremovable conditions, demonstrating the power of reinforcement learning in robotic disassembly. With a growing citation record, Xu’s work is pivotal for advancing circular economy principles and automation in waste management. Their research not only enhances robotic efficiency but also provides a robust foundation for future studies in adaptive manufacturing and sustainable engineering.
Research Focus
Key Achievements
Top Papers
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- 2