Roughness Field

Roughness field research focuses on quantifying and characterizing surface irregularities across diverse scales and materials, aiming to improve measurement accuracy and predictive modeling. Current efforts utilize machine learning, particularly neural networks and transformer architectures, alongside traditional statistical methods like log-cumulant analysis and Fourier-based synthesis, to analyze data from various sources including microscopy, LiDAR, and SAR imagery. These advancements have implications for diverse fields, enhancing manufacturing processes (e.g., milling optimization), improving remote sensing capabilities (e.g., terrain classification), and enabling more realistic haptic feedback in teleoperation.