MACHINED SURFACE ROUGHNESS PREDICTION USING ADAPTIVE NEUROFUZZY INFERENCE SYSTEM

This work considers the effect of the depth of cut, feed, and number of revolutions on the roughness of the machined surface. The results obtained by experimentally investigating the workpiece diving manifold were used to model the input/output data plan for the adaptive neurofuzzy inference system (ANFIS). Those data were used to generate a fuzzy inference system that made it possible to predict the output (surface roughness) based on the given inputs (feed, number of revolutions, and depth of cut). The surface roughness results obtained by the fuzzy inference system (FIS) were compared with the surface roughness results obtained by neural networks, moving linear least square method and moving linear least absolute deviation method on the same set of experimental data. These methods and systems for prediction of surface roughness are helpful when solving practical technological problems in a manufacturing process, first by determining the cutting parameter values that will add to the demanded quality of a product, and later when optimizing the technological process.