Papers
3
Total Citations
29
H-Index
3
About
Johannes Buhl is a leading researcher at the intersection of robotic manufacturing and advanced forming processes. His primary research areas include wire-arc additive manufacturing (WAAM), single point incremental forming (SPIF), and the integration of industrial robots for high-precision metal fabrication. Buhl’s most influential work, “Decomposition Algorithm for Tool Path Planning for Wire-Arc Additive Manufacturing” (2018, 17 citations), addresses a critical limitation of three-axis machines by enabling robot-based WAAM to produce complex overhangs without support material—a breakthrough that reduces waste and expands design freedom. He further advanced the field with “Deformation Error Compensation of Industrial Robots in Single Point Incremental Forming by Means of Data-Driven Stiffness Model” (2021, 9 citations), where he developed a model to correct robot compliance errors, making robotic hole flanging viable for small-batch production. Most recently, his 2024 paper on “Online Force Prediction by Neural Networks in Single Point Incremental Hole Flanging Operations” (3 citations) introduces machine learning to predict process forces in real time, enhancing safety and defect prevention. Buhl’s work is pivotal for industries seeking cost-effective, flexible automation, and his data-driven approaches continue to shape the future of robotic metal forming.
Research Focus
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