Surface Roughness Prediction via Fuzzy-Neural Networks in Dry Machining

Optimization of machining parameters is very important and the main goal in every machining process. Surface finishing prediction is a pre-requirement to establish a center for automatic machining operations. In this research, a neuro-fuzzy approach is used in order to model and predict the surface roughness in dry turning. This approach has both the learning capability of neural network and linguistic representation of complex and indefinite phenomena in lingual phrases forms. A model which represents the influence of machining parameters and tool properties on surface roughness is established first. Then, this model is edited via the usage of results of training data. Finally, the efficiency of neuro-fuzzy model is evaluated via the comparison between the model's output and the output of surface roughness obtained from the theoretical formula.