Year: 2026
Type: article
Abstract: Product disassembly, critical in remanufacturing, often involves removing screws and bolts, which can be challenging due to degradation, such as rust or thread damage. Here, we develop a digital twin integrated with a Model Predictive Controller to optimise robotic screw unfastening. Using real-time force and torque data from a robot unscrewing an electric vehicle battery pack, the controller predicts and adjusts screwdriver position and spindle speed to minimise applied torque and force. Experimental results demonstrate that this approach improves unscrewing success rates and reduces torque variability compared to manual methods. These findings suggest that combining digital twin technology with MPC can enhance the efficiency and reliability of robotic disassembly processes, supporting sustainable remanufacturing efforts.
Source: Automation
Institution: University of Birmingham
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
Awards: EPSRC Manufacturing Research Hub in Robotics, Automation & Smart Machine Enabled Sustainable Circular Manufacturing & Materials (RESCu-M2), Robotic Triage for Value Retention in a Circular Economy (RoboTriage), Robotic disassembly technology as a key enabler of autonomous remanufacturing, Robotic skill transfer and augmentation for contact-rich tasks in manufacturing (STAMAN)
OpenAlex ID: W7124829959