Estimation of Surface Roughness Parameter Based on Machined Surface Image

Abstract

The prediction of machined surface parameters is an important factor in machining centre development. There is a great need to elaborate a method for on-line surface roughness estimation [1-7]. Among various measurement techniques, optical methods are considered suitable fo r in-process measurement of machined surface roughness. These techniques are non-contact, fast, flexible and t ree-dimensional in nature. The optical method suggested in this paper is based on the vision system created to acquire an image of the machined surface during the cutting process. The acqui red image is analyzed to correlate its parameters with surface parameters. In the application of machined sur face image analysis, the wavelet methods were introduced. A digital image of a machined surface was described us ing the one-dimensional Digital Wavelet Transform with the basic wavelet as Coiflet. The statistical descript ion of wavelet components made it possible to develop the quality measure and correlate it with surface roughnes s [8-11]. For an estimation of surface roughness a neural network estimator was applied [12 -16]. The estimator was built to work in a recurrent way. The current value of the R a estimation and the measured change in surface image features were used for forecasting the surface roughne ss Ra parameter. The results of the analysis confirmed the usability of the application of the proposed method in systems for surface roughness monitoring.