Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor

Abstract

The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear predictor. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. A neural network is used in tool condition monitoring system (TCM) as a decision making system to discriminate different malfunction states from measured signal. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modeling. By developed tool condition monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit.