Patrick Kaeding
Papers
2
Total Citations
36
H-Index
2
About
Patrick Kaeding is a leading researcher in advanced manufacturing, specializing in the intersection of machine learning, automated composites manufacturing, and digital twin technology. His work focuses on enhancing the quality and reliability of automated fiber placement (AFP), a robotic technique for producing high-performance composite materials. Kaeding’s major contributions include developing machine-learning-based process monitoring systems that analyze thermal histories during AFP, directly addressing the critical challenge of interlaminar strength in composites. His most-cited paper (2023, 31 citations) demonstrates how ML models can predict and optimize manufacturing conditions, while his 2022 work (5 citations) extends this to enable digital twin development for real-time process control. These innovations have significant implications for aerospace, automotive, and renewable energy industries, where lightweight, durable composites are essential. Kaeding’s research is notable for bridging data-driven methods with physical manufacturing processes, offering a pathway to more efficient, defect-free production. His work is increasingly recognized as foundational for the next generation of smart manufacturing systems.
Research Focus
Key Achievements
Top Papers
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