An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations

In this research, a new intelligent neural-fuzzy in-process surface roughness monitoring (INF-SRM) system for an end milling operation was developed. The success of the INF-SRM system depends on an accurate decision-making algorithm, which can analyze the input factors and then generate an accurate output. A new neural-fuzzy model was proposed and implemented as decision-making algorithm for the INF-SRM system. The objective of the new model is to achieve higher accuracy for surface roughness prediction and solve the disadvantages of both neural networks and fuzzy logic. The neural-assisted method was implemented to generate the fuzzy IF-THEN rules for the model. To evaluate the performance of the new neural-fuzzy model, a neural networks model was applied to develop another surface roughness monitoring system for comparison. A statistical method was finally employed to analyze the accuracy between these systems.