Multi-output fuzzy inference system for modeling cutting temperature and tool life in face milling

This paper proposes a method for cutting parameters identification using the multi-inputs-multi-outputs fuzzy inference system (MIMO-FIS). The fuzzy inference system (FIS) was used to identify the initial values for cutting parameters (cutting speed, feed rate and depth of cut) and flank wear using cutting temperature and tool life as outputs. The objective was to determine the influence of cutting parameters on cutting temperature and tool life. The model for determining the cutting temperature and tool life of steel AISI 1060 was trained (design rules) and tested by using the experimental data. The average deviation of the testing data for tool life was 11.6 %, while that of the cutting temperature was 3.28 %. The parameters used in these testing data were different from the data collected for the design rules. The test results showed that the proposed MIMO-FIS model can be used successfully for machinability data selection. The effect of parameters and their interactions in machining is analyzed in detail and presented in this study.

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