An approach for condition monitoring of a turning tool

Abstract Manufacturing has changed markedly in recent years. The trend is for saving on the cost of production because of market pressure. In order to achieve this goal, greater consideration has been given to automation in manufacturing. In this regard, a fundamental step is to know the condition of the cutting tool, which requires a reliable system to monitor the condition of the tool. Experimental investigation of cutting tool wear and a model for tool wear estimation is reported in the current paper. The changes in the values of cutting forces, vibrations, and acoustic emissions with cutting tool wear are recorded and analysed. On the basis of experimental results a model is developed for tool wear estimation in turning operations using an adaptive neuro fuzzy inference system (ANFIS). Acoustic emission (ring down count), vibrations (acceleration), and cutting forces, along with time, have been used to formulate the model. This model is capable of estimating the wear rate of the cutting tool. The wear estimation results obtained by the model are compared with the practical results and are presented. The model performed quite satisfactorily and gave good results with the actual and predicted tool wear values.