With the increasing demand for ultra-precision machining parts in the industrial field, surface
roughness, as a key quality indicator, faces dual challenges of precision and speed in …

Real-time surface roughness measurement is crucial for modern manufacturing. Machine
vision-based roughness measurement algorithms have the advantages of non-contact, high …

Color image indices used to characterize surface roughness are more sensitive than
grayscale images or spectral indices owing to the rich information contained in color images …

Most existing vision-based roughness measurements primarily rely on statistical information
from grayscale images or intensity information from color images. However, the structural …

Microscopic images of surfaces can be used for non-contact roughness measurement by
visual methods. However, the images are usually acquired manually and need to be as …

Considering that titanium alloy is a critical aviation resource, ensuring its machining quality
is of significant importance. Surface roughness remains a key parameter for surface quality …

Current machine vision methods for surface measurement rely excessively on feature
design to quantify surface morphology and build predictive models, but metric design suffers …

Many roughness measurement methods rely on designed feature indexes that cannot
accurately characterize the roughness and are demanding on the workpiece imaging …

Surface roughness evaluation is crucial for enhancing surface properties. Traditional offline
human-assisted inspection techniques are labor-intensive and time-consuming, hindering …

Surface roughness is an important quality indicator of machining processing, which is often
needed as a feedback. To meet the requirement of on-line roughness measurement, the …