Vision-based surface roughness evaluation system for end milling
Author:
Affiliation:
1. Industrial and Information Systems Engineering, Chonbuk National University, Jeonju, South Korea
2. Korea Marine Equipment Research Institute, Gunsan-si, Jeollabuk-do, South Korea
Funder
Ministry of Education
Publisher
Informa UK Limited
Subject
Electrical and Electronic Engineering,Computer Science Applications,Mechanical Engineering,Aerospace Engineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/0951192X.2017.1407451
Reference25 articles.
1. Feasibility assessment of vision-based surface roughness parameters acquisition for different types of machined specimens
2. Optimization of feedrate in a face milling operation using a surface roughness model
3. Investigation of cutting condition monitoring by visual measurement of surface texture parameters
4. Image Data Processing via Neural Networks for Tool Wear Prediction
5. Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method
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