Modern industry requires effective and reliable identification of- and a timely response to breakage tool. In the present study, a fuzzy model is developed to create rule banks to reduce the risk of cutting tool breakage during machining processes. Attention is focused on training the proposed model for early detecting tool breakage and to improve the performance of the process. This paper focuses on developing empirical models using fuzzy logic for predicting tool breakage (tool life). The Fuzzy Inference System (FIS) is used to identify the initial values for cutting parameters (cutting speed, feed rate and depth of cut) and flank wear by using tool life as the output. The model for determining the breakage of tool steel AISI 1060 is trained (design rules) and tested by using experimental data. Results show that fuzzy logic is very effective in detecting cutting tool breakage of the during machining processes.
Keywords: fuzzy logic, tool wear, tool life, risk of breakage cutting tool