Papers
4
Total Citations
148
H-Index
4
About
Nabil Anwer is a leading researcher at the intersection of smart manufacturing, digital twin technology, and sustainable recycling systems. His work focuses on developing intelligent, AI-driven solutions for complex disassembly processes, particularly in the critical domain of electric vehicle (EV) battery recycling. Anwer’s major contributions lie in integrating multi-agent reinforcement learning and dynamic task allocation to optimize human–robot collaborative disassembly. His most cited work, "Multi-Agent Reinforcement Learning Method for Disassembly Sequential Task Optimization" (2023, 54 citations), provides a foundational framework for systematic battery recycling, addressing both resource waste and environmental impact. He further advanced the field with "Digital Twin towards Smart Manufacturing and Industry 4.0" (2020, 48 citations), a pivotal piece linking virtual modeling to real-world production efficiency. More recently, his 2025 papers—including "Dynamic Task Allocations with Q-learning Based Particle Swarm Optimization"—push the boundaries of adaptive, real-time decision-making in recycling systems. Anwer’s research is not only technically innovative but also deeply relevant to global sustainability challenges, making him a key voice in the transition toward circular, automated manufacturing economies.
Research Focus
Key Achievements
Top Papers
- 1
- 2Digital twin towards smart manufacturing and industry 4.048 citations · 2020
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- 4