Diagnosing of Risk State in Subsystems of CNC Turning Center using Interval Type-2 Fuzzy Logic System with Semi Elliptic Membership Functions

Precise monitoring, diagnosis, and control of subtractive machines like turning centers are challenging tasks in automated manufacturing industries. To identify the failures in a subsystem of a turning center, all the required parameters need to be recorded, stored, and retrieved systematically for effective monitoring and control. In this work, the experimental data of a CNC turning center available in a repository are classified with interval type 2 fuzzy logic system (IT2FLS) using semi-elliptic membership function (SEMF) to predict the risk state of each subsystem. The geomantic property of SEMF has shown faster convergence and less computation load. The simulated results of each subsystem governed by the SEMF are compared with Gaussian membership function (GMF) to evaluate its potential. The research out comes confirms that the computation load of IT2FLS is considerably reduced based on the implementation of Wu–Mendel uncertainty bounds.