METHODOLOGY TO COMPARE 3-D AND 2-D PARAMETERS FOR THE OPTIMIZATION OF HARD TURNED SURFACES

Hard Turning provides an alternative to grinding in some finishing applications. Average Surface Roughness R_a has been widely used in industry to establish surface texture needed for a given application. It is known that the single parameter R_a is inadequate to define the functionality of a surface texture. The quality of a surface can be determined by the nature of its interaction with another surface. Thus a surface with significant peaks will not make as good a bearing surface as a surface with deep valleys and low peaks. Two different surfaces with similar values of R_a can behave differently under fatigue loading conditions. 3-D visualization of expected surface texture will facilitate optimization of machining parameters to produce function-specific surfaces. The advantages and shortcomings of some current surface texture prediction models are discussed. A new method based on neuro-fuzzy techniques is proposed. Optimization using some 3-D surface parameters was carried out and compared with the results of those obtained using 2-D parameters.