@ARTICLE{Zawada-Tomkiewicz_Anna_Estimation_2010,
 author={Zawada-Tomkiewicz, Anna},
 number={No 3},
 journal={Metrology and Measurement Systems},
 pages={493-503},
 howpublished={online},
 year={2010},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 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 for in-process measurement of machined surface roughness. These techniques are non-contact, fast, flexible and tree-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 acquired image is analyzed to correlate its parameters with surface parameters. In the application of machined surface image analysis, the wavelet methods were introduced. A digital image of a machined surface was described using the one-dimensional Digital Wavelet Transform with the basic wavelet as Coiflet. The statistical description of wavelet components made it possible to develop the quality measure and correlate it with surface roughness [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 Ra estimation and the measured change in surface image features were used for forecasting the surface roughness Ra parameter. The results of the analysis confirmed the usability of the application of the proposed method in systems for surface roughness monitoring.},
 type={Artykuły / Articles},
 title={Estimation of Surface Roughness Parameter Based on Machined Surface Image},
 URL={http://ochroma.man.poznan.pl/Content/107065/PDF/Journal10178-VolumeXVII%20Issue3_15%20paper.pdf},
 doi={10.2478/v10178-010-0041-5},
 keywords={machining, surface roughness, wavelet analysis, neural networks},
}