Anwar Al Assadi

Fraunhofer Institute for Manufacturing Engineering and Automation

Papers

Total Citations

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About

Anwar Al Assadi is a leading researcher in sustainable manufacturing and circular economy, specializing in the automated disassembly of end-of-life battery systems for electric vehicles. Their work addresses critical challenges in recycling lithium, cobalt, and other raw materials by developing intelligent robotic solutions for screw unfastening and component separation. Al Assadi’s most influential paper, “Machine learning based screw drive state detection for unfastening screw connections” (2022, 27 citations), pioneers a method using industrial screwdrivers and machine learning to identify stripped or damaged screw drives—a common obstacle in automated disassembly. This work directly supports the circular economy by enabling efficient recovery of battery modules from high-voltage systems. Their subsequent research, “Automated Disassembly of Battery Systems to Battery Modules” (2024, 10 citations), tackles product variance and labor shortages through flexible robotic approaches. Most recently, Al Assadi has advanced the field with deep reinforcement learning for robotic screw unfastening (2025), addressing position errors during the search-and-engagement phase. With a focused portfolio on practical, scalable automation for battery recycling, Al Assadi’s contributions are vital for reducing raw material dependency and advancing sustainable transportation infrastructure.