Papers
15
Total Citations
143
H-Index
6
About
Gisela Lanza is a leading researcher in remanufacturing, production system planning, and the integration of machine learning into industrial automation. Her work focuses on transforming remanufacturing—the process of restoring used products to like-new condition—through agile, data-driven production systems. She has pioneered the use of reinforcement learning for flexible disassembly, enabling autonomous and manual workstations to adapt to uncertain product conditions. Her highly cited paper on modelling condition-based control for hybrid disassembly systems (51 citations) is a cornerstone in this field. Lanza also introduced "Fluid Automation," a concept for adaptive production systems that respond dynamically to changing requirements. Her contributions extend to robotic assembly line balancing, spatial alignment of components, and the development of explainable AI interfaces for human-robot interaction. With over 100 citations across her top papers, Lanza’s research is instrumental in advancing circular economy strategies, reducing waste, and making remanufacturing economically viable. Her work on automated inspection and dataset generation for small electric motors further underscores her commitment to practical, scalable solutions for sustainable manufacturing.
Research Focus
Key Achievements
Top Papers
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