Prediction Modeling of Surface Roughness Using Capacitive Sensing Technique in Machining Process

Abstract

The surface roughness measurement of machined specimen is an unproductive time in industries. The contact profile measurement takes more time for its measurements than other measurements. The non-contact optical surface roughness measurement techniques are faster with considerable accuracy. However, it requires the knowledge about surface orientation. In this paper, surfaces obtained from the turning, grinding, and vertical milling were modeled based on the surface data measured using contact type profilometer. The sensor and surface model has been developed using real sensor with 0.5-mm sensing diameter and surface data in ANSYS Maxwell. The roughness values measured using capacitive sensor ( ${\text{R}}_{c}$ ) and stylus profilometer ( ${\text{R}}_{a}$ ) have been compared to find correlation coefficient at the standoff distance of 20, 25, and 50 micron. The capacitive sensor with standoff distance of $50~\mu \text{m}$ gives highest correlation coefficient for surface obtain from turning, grinding, and vertical milling. The proposed method can provide better non-contact faster online measurement.