Condition Monitoring and Remaining Useful Life Prediction for Tool Wear in CNC Machines

In this study, a cutting tool condition monitoring (TCM) platform for CNC machines to be used in metal part manufacturing is proposed to estimate the remaining useful life (RUL) of machine cutting tools. For this purpose, operational and situational data obtained from CNC machine and sensors will be analyzed with artificial intelligence algorithms, anomalies will be detected, and total equipment performance will be supported by using remaining life estimates.The innovative side of the system is the development of an artificial intelligence application that includes classification and regression methods with artificial neural networks. The use of RUL concept is relatively limited in the literature, but general interest by the industry is high. It will be among the first applications that machinery and machine cutting tools will be monitored and remaining useful life estimation will be made as an important contribution in the field.In the literature, examples that predict RUL of system are not included in the state monitoring of machine and machine cutting tools and in TCM applications. In our research, classification, and regression models and three different artificial neural network algorithms will be compared by using RUL estimation results that can be widely used and have a high impact potential, and corresponding studies will be carried out for the use of industry and increasing efficiency in the manufacturing sector.