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Description
This book is the proceedings of the 8th International Workshop on Autonomous Remanufacturing (IWAR 2024) and contains contributions from innovators in autonomous remanufacturing to strengthen the body of knowledge on design, modelling and control of remanufacturing processes and systems.
Remanufacturing has been identified as having significant financial and environment benefits; however, critical challenges still remain in designing and operating remanufacturing processes and systems. These challenges are complex, spanning all product life cycles and encompassing multiple disciplines within and outside of engineering.
In particular, the book showcases the work of experts on reverse logistics optimization, designing products for disassembly, and advancements in remanufacturing automation. These topics are currently at the forefront of discussions among leading industries and researchers.
1. Optimizing reverse logistics in remanufacturing using the Bees Algorithm.- 2. Remanufacturing in a Circular Value Chain - Information Management Aspects.- 3. Remanufacturing as a service: a new approach to reduce remanufacturing related barriers.- 4. Disassembly Sequence Planning Using an Adaptive Bees Algorithm.- 5. Robotic Disassembly Sequence Planning using the single-parameter Bees Algorithm.- 6. Robotic Autonomous Disassembly System for Automotive Power Battery Remanufacturing.- 7. AI-Driven Value Assessment for Intelligent Remanufacturing.- 8. Integrating Collaborative Robotics, Mixed Reality, and Dynamic Data Driven Application Systems to Achieve a Framework for Perceptive Disassembly.- 9. Time Analysis to Manual versus Robotic Disassembly Modes of An Electric Vehicle Battery.- 10. A Closed-Loop Assessment System for Enhancing Manual Disassembly Efficiency and Operator Health.- 11. Design of a Flexible Disassembly Cell Based on Human-Robot Collaboration for End-of-Life Electric Vehicle Batteries.- 12. Next step towards flexible disassembly cells; continued lessons of a learning factory.- 13. Synthetic data-driven multimodal fusion-based screw pose estimation in robotic disassembly.- 14. An Automated Computer Vision Tool for Recycling of Materials from Decommissioned Wind Turbines.- 15. An Analysis of Rectangular Peg-Hole Disassembly in Three Dimensions.- 16. Vehicle suspension remanufacturing based on Additive Manufacturing and Arc Welding Cladding.
Dr. Jeremy L. Rickli received his B.S. and M.S. Degrees in Mechanical Engineering from Michigan Technological University in 2006 and 2008 and received his Ph.D. in Industrial and Systems Engineering from Virginia Tech prior to joining Wayne State in 2013. At Wayne State, he created the Manufacturing and Remanufacturing Systems Laboratory (MaRSLab). MaRSLab targets fundamental and applied research in manufacturing, remanufacturing, and disassembly processes and systems while encouraging considerations for sustainability and life cycle thinking in design, manufacturing, use, and recovery. Research thrusts include: disassembly automation for enhanced component and material recovery; integrating disassembly and remanufacturing decisions into design phases; the interaction between manufacturing operators and collaborative robots; the potential for point cloud measurement data to transform manufacturing quality monitoring and remanufacturing core condition assessment; and managing core acquisition in remanufacturing and value recovery considering uncertain core quality, quantity.
