In-line cutting operation control in finishing by measuring surface roughness

In this thesis, a milling system based on real-time surface roughness measurement during machining process is developed using Artificial Neural Network technique. In the developed system, desired surface roughness is provided with determining the optimum feed rate and cutting speed. Surface roughness control has an important role to achieve high surface quality for the metal cutting. Dynamic behavior of the system is investigated in the study. Natural frequency modal analysis and experimental studies of the system are mentioned. Then, hardware and software studies of the Surface Roughness Control System (SRCS) are elucidated. An algorithm determining the operating principle of the system is developed. According to this algorithm, the optimum cutting parameters are predicted for end milling (finishing) operation by measuring semi finish machining surface roughness via an optic sensor and then end milling operation is performed with the cutting parameters determined by the system. In the last part of the study, surface qualities are observed for the milling process before and after the intervention of the system and the results are compared. The experimental results show that the developed milling system can be integrated with modern machining systems in order to obtain the desired surface quality levels.