Name
Title | ||
|---|---|---|
An Automatic And Accurate Method For Tool Wear Inspection Using Grayscale Image Probability Algorithm Based On Bayesian Inference |
Abstract | ||
|---|---|---|
Accurate, rapid and automated tool wear inspection is critical to manufacturing quality, cost and efficiency in smart manufacturing systems. However, manual inspection of tool wear is still a common industrial practice which is inefficient, prone to human errors and not suitable for digitized manufacturing. Previously reported automatic tool wear inspection methods were inaccurate because they only used the remaining worn boundary (i.e., the partial-absence original tool boundary) to approximate tool wear. The authors discovered the association principle between the change law of the cutting edge grayscale and the relative position of the original and worn boundary, which was used to establish the probability functions to accurately reconstruct the curved original tool boundary via Bayesian Inference. The experiment results reported in this paper proved higher efficiency and accuracy than previous automatic tool wear inspection methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.rcim.2020.102079 | ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING |
Keywords | DocType | Volume |
Digital manufacturing, Tool wear, Automatic inspection, Bayesian inference, Grayscale image | Journal | 68 |
ISSN | Citations | PageRank |
0736-5845 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Guang Li | 1 | 137 | 21.67 |
| Wenping Mou | 2 | 0 | 0.34 |
| Jingjing Li | 3 | 0 | 0.34 |
| Changqing Liu | 4 | 13 | 4.43 |
| James Xiaoyu Gao | 5 | 27 | 5.33 |