Marwin Gihr
Papers
1
Total Citations
26
H-Index
1
About
Marwin Gihr is a rising researcher in advanced manufacturing, specializing in directed energy deposition (DED) and the application of machine learning to additive manufacturing process optimization. His most-cited work, "Bead geometry prediction and optimization for corner structures in directed energy deposition using machine learning" (2024, 26 citations), addresses a critical challenge in DED: achieving precise, defect-free geometries at complex corner features. By developing predictive models that optimize bead geometry, Gihr’s research enables more reliable fabrication of intricate metal parts, reducing trial-and-error in production. This contribution is particularly valuable for industries like aerospace and automotive, where structural integrity and material efficiency are paramount. Though early in his career, Gihr’s work has already garnered attention for its practical impact, bridging the gap between computational modeling and real-world manufacturing. His focus on data-driven optimization positions him at the forefront of smart manufacturing, where AI enhances control over additive processes. For students and researchers, Gihr exemplifies how machine learning can transform traditional fabrication methods, offering a pathway to more intelligent, adaptive production systems.
Research Focus
Key Achievements
Top Papers
- 1