Simulation and measurement of surface roughness via grey scale image of tool in finish turning

Study on the surface roughness of specimen is a significant field of research because this parameter affects the performance of the machined parts. Meanwhile, the evaluation of surface roughness of specimens using a vision system via the images captured from the specimen is an interesting method which is widely used. Although the effect of flank and crater wear has been investigated extensively in the past researches on surface profiles, some reports indicated that, in finish turning, the nose radius wear has a greater effect on the surface profile of specimen. Although, vibration can affect the surface profile of a specimen in rough turning, the final surface profile in the product used is usually shaped by finish turning that may not be affected by vibration using the robust machine tool. In this work, a machine vision was used to capture the images of the tool tip in-cycle. The 2-D images of the nose area of tool tips were used to simulate the surface profile of specimens in finish turning. The simulated images of specimens in a range of machining condition were detected using the algorithm of this work. The results showed that this method can be used successfully to simulate and evaluate the surface profile of a specimen in finish lathe machining as a fingerprint of the tool tip. This method can be used for forecasting the final surface profile in order to control the performance of products. (C) 2015 Elsevier Inc. All rights reserved.