Papers
1
Total Citations
27
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1
About
David Holtz is a leading researcher in sustainable manufacturing and circular economy technologies, with a particular focus on automated disassembly systems for end-of-life products. His most cited work, "Machine learning based screw drive state detection for unfastening screw connections" (2022, 27 citations), addresses a critical bottleneck in battery recycling: the reliable detection of stripped or damaged screw drives during automated disassembly. By applying machine learning to industrial screwdrivers, Holtz enables robots to adapt in real-time to variable screw conditions—a key innovation for handling the unpredictable state of retired electric vehicle batteries. This contribution directly supports the electrification of transport by making battery recycling more efficient and economically viable. Holtz’s research sits at the intersection of robotics, sensor data analysis, and circular economy principles, offering practical solutions for recovering valuable materials from complex assemblies. His work has been recognized for its potential to scale disassembly automation, reducing waste and reliance on virgin raw materials. For students and researchers, Holtz demonstrates how applied machine learning can solve real-world sustainability challenges, bridging the gap between theoretical algorithms and industrial deployment.
Research Focus
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