Alper Yaman
Papers
2
Total Citations
4
H-Index
2
About
Alper Yaman is a rising researcher in advanced robotic manufacturing, specializing in the intersection of artificial intelligence and industrial automation. His work centers on two critical challenges: enhancing the precision of industrial robots and streamlining complex assembly processes. Yaman’s major contributions include developing a hybrid compensation method that integrates artificial neural networks to correct both geometric and non-geometric errors—such as payload variations and tool wear—which traditional calibration techniques often overlook. This approach significantly improves absolute robot accuracy, a key requirement for high-precision tasks. Additionally, his research on skill-based robotic programming for automotive wire harness connector installation demonstrates a practical, human-inspired method to automate intricate assembly operations, reducing programming complexity and increasing efficiency. Though early in his career, with his most-cited papers each garnering 2 citations, Yaman’s work is already recognized for its innovative fusion of machine learning with real-world robotic applications. His contributions hold promise for advancing flexible, adaptive manufacturing systems, particularly in the automotive sector, where precision and adaptability are paramount.
Research Focus
Key Achievements
Top Papers
- 1
- 2