Max Robotics — Global Service Robot Directory + US Certification

About

Feiying Lan is a leading researcher in sustainable manufacturing and circular economy, specializing in the automation of disassembly processes for remanufacturing and recycling. Her work addresses the critical challenge of efficiently processing end-of-life products, particularly lithium-ion batteries from electric vehicles. Lan’s major contributions include developing robotic disassembly platforms and intelligent planning algorithms that overcome complex “interlocking problems” in disassembly sequence planning. She has pioneered the use of deep learning, such as the PointNet neural network, for automatic identification of mechanical parts, and the Bees Algorithm for shape recognition in industrial robot manipulation. Her most cited paper, “Interlocking problems in disassembly sequence planning” (2020), has garnered 31 citations, while her case study on robotic disassembly of a plug-in hybrid electric vehicle battery (2024) has 29 citations, reflecting the high relevance of her research. Lan’s work bridges the gap between manual and automated disassembly, enabling more efficient remanufacturing and contributing to a circular economy. Her innovative approaches to automatic subassembly detection and robotic manipulation are shaping the future of sustainable manufacturing.