No 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.

An experimental investigation was conducted to determine the effects of tool cutting edge geometry on the cutting forces in finish turning, where the applied feed and depth of cut are small and often comparable with the tool edge radius. If a tool with large tool edge radius is used in finish turning, the ploughing effect begins to determine the machined surface. This paper presents the results of analytical considerations concerning the unit forces on a cutting edge. The aim of this paper is to indicate possibilities of modelling the unit forces and stress distribution based on cutting resistance. The forces calculated in the feed and cutting speed directions were projected onto the tangential and normal directions of the rounded cutting edge surface. An important assumption in all the considerations was that the thermo-mechanical properties of the materials used remained constant. The minimum thickness of cut was defined, and some characteristic points were identified dividing the cutting zone into three subregions: where a chip is formed, where the machined surface is formed and an unstable region.

Industrial applications require functional surfaces with a strictly defined micro-texture. Therefore engineered surfaces need to undergo a wide range of finishing processes. One of them is the belt grinding process, which changes the surface topography on a range of roughness and micro-roughness scales. The article describes the use of machined surface images in the monitoring process of micro-smoothing. Machined surface images were applied in the estimation of machined surface quality. The images were decomposed using two-dimensional Discrete Wavelet Transform. The approximation component was analyzed and described by the features representing the geometric parameters of image objects. Determined values of image features were used to create the model of the process and estimation of appropriate time of micro-smoothing.

The turning of hardened steels has been applied in many cases in production. Currently, the most important problem is concerned with the properties of the surface finish. This paper investigates the effect on surface finish in a continuous dry turning of hardened steel when using polycrystalline cubic boron nitride tools. The surface profiles (2D arrangement) and surface topography (3D arrangement), generated during the hard turning operation on an EN 41Cr4 low chromium alloy steel, heat treated to the hardness of 58 HRC, were evaluated. This paper introduces the multiparameter characterization of the surface when cutting with different tool materials.