Tool wear monitoring using a fast Hough transform of images of machined surfaces

Authors:
M. A. Mannan;Zhu Mian;A. A. Kassim
Affiliations:
Mechanical Engineering Department, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260;Electrical and Computer Engineering Department, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260;Electrical and Computer Engineering Department, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260
Venue:
Machine Vision and Applications
Year:
2004

Citing 7
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
An extension to the randomized Hough transform exploiting connectivity

Pattern Recognition Letters
Hough Transform Modified by Line Connectivity and Line Thickness

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Hough Transform Versus the UpWrite

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and robust techniques for detecting straight line segments using local models

Pattern Recognition Letters
SLIDE: Subspace-Based Line Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast Hough transform for segment detection

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The texture of machined surfaces provides reliable information regarding the extent of tool wear. In this paper, we propose a structure-based approach to analyzing machined surfaces. The original surface images are first preprocessed by a Canny edge detector. A new connectivity-oriented fast Hough transform is then applied to the edge image to detect all the line segments. The distributions of the orientations and lengths of the line segments are used to determine tool wear. Through our experiments, we found a strong correlation between tool wear and features. The computational complexity of the fast Hough transform is also analyzed.