An Extended Kalman Filter (EKF) is employed in this work for tracking tool flank wear area in wet-turning of Inconel 718 (INC718) Nickel-based alloy in variable feed condition. The tool wear area evolution is modeled with a 3rd order polynomial empirical function and an analytical solution for discrete state space system is derived. The state uncertainty was found to decrease up to 200-250μm of average flank wear length and then increase abruptly with an increase in tool wear. Therefore, the tool wear uncertainty was modeled with failure probability density, i.e. the bathtub function. While a constant uncertainty was considered for the measurement signal (spindle power). The root mean square error (RMSE) and the mean absolute error (MAE) were calculated in estimation of the tool wear area with experimental results and it was shown that the EKF was able to estimate the tool wear area with less than 0.05mm 2 RMSE but did not perform well in estimating the rate of the tool wear area.
Niaki, Farbod Akhavan; Michel, Martin; and Mears, Laine, "Extended Kalman Filter for Stochastic Tool Wear Assessment in Turning of INC718 Hard-to-Machine Alloy" (2016). Publications. 85.