Manufacturing Technology Today

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Determination of Surface Roughness of Milled Surfaces Using Machine Vision


Affiliations
1 Dept. of Mech. Engg., V R Siddhartha Engg. College, Vijayawada, India
2 Dept. of Mech. Engg., National Institute of Technology, Calicut, India
     

   Subscribe/Renew Journal


In this work, a machine vision system has been utilized to determine the surface roughness of the milled surfaces. For checking the effectiveness of the machine vision based results, a wide range of surface roughness were generated on CNC milling centre using DoE technique. Stylus-based parameters Ra and Rsm were acquired and compared with vision-based parameters (cofvar, Ga, R2, contrast etc). Also, experiments are conducted for three different work piece materials to see the effect of work piece material colour change. Model equations have been developed, in terms ofthe machining parameters, image parameters and machining and image parameters using response surface methodology on the basis of experimental results. The experimental result indicates that the surface roughness of milled surfaces could be estimated/predicted with a reasonable accuracy using machine vision.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 239

PDF Views: 0




  • Determination of Surface Roughness of Milled Surfaces Using Machine Vision

Abstract Views: 239  |  PDF Views: 0

Authors

G. Dilli Babu
Dept. of Mech. Engg., V R Siddhartha Engg. College, Vijayawada, India
P. B. Dhanish
Dept. of Mech. Engg., National Institute of Technology, Calicut, India
P. Prabhakara Rao
Dept. of Mech. Engg., V R Siddhartha Engg. College, Vijayawada, India

Abstract


In this work, a machine vision system has been utilized to determine the surface roughness of the milled surfaces. For checking the effectiveness of the machine vision based results, a wide range of surface roughness were generated on CNC milling centre using DoE technique. Stylus-based parameters Ra and Rsm were acquired and compared with vision-based parameters (cofvar, Ga, R2, contrast etc). Also, experiments are conducted for three different work piece materials to see the effect of work piece material colour change. Model equations have been developed, in terms ofthe machining parameters, image parameters and machining and image parameters using response surface methodology on the basis of experimental results. The experimental result indicates that the surface roughness of milled surfaces could be estimated/predicted with a reasonable accuracy using machine vision.