Modeling of Surface Roughness in Honing Processes by Using Fuzzy Artificial Neural Networks

Honing processes are abrasive machining which commonly employed to improve the surface of manufactured parts such as hydraulic or combustion engine cylinders. These can be obtain a cross-hatched pattern on internal surfaces In this present study, fuzzy artificial neural networks for modeling roughness parameters obtained in finishing honing operations. As general trend, main factors influencing grain size and pressure. Mean spacing between profile peaks at mean line parameter, contrary, depends mainly tangential linear velocity. Grain Size 30 pressure 600 N/cm2 lead highest values core (Rk) reduced valley depth (Rvk), were 1.741 µm 0.884 µm, respectively. On other hand, maximum peak-to-valley parameter (Rz) so was 4.44 is close value 4.47 µm. equal 14 density 20, along with both speed 20 m/min 40 m/min, respectively, minimum roughness, peak height (Rpk), within sampling length, were, 0.141 0.065 0.142 0.584