I-Kaz methodology for predicting tool life of AlCrN Coated WC Co inserts in the machining of AISI 304 steel

This study proposes a real-time tool wear monitoring approach for dry milling of AISI 304 stainless steel using a Microflown PU sensor and the I-Kaz™ statistical feature. A Taguchi L18 orthogonal array was adopted to optimize cutting speed, feed rate, depth of cut and tool type (uncoated and AlCrN-coated WC-Co inserts) based on surface roughness and flank wear responses. ANOVA revealed that tool type was the most significant factor, contributing approximately 85 % to flank wear variation, followed by depth of cut and cutting speed. The optimal combination: AlCrN-coated insert, cutting speed of 1250 rpm, depth of cut of 0.50 mm and feed rate of 0.04 mm/rev minimized both responses. The proposed I-Kaz-based monitoring approach established a strong inverse power-law relationship between the I-Kaz coefficient and flank wear Z₂ ^α =<i>a</i>(VB)^-n, achieving R²>0.96, indicating high accuracy and stability across repetitions.•This study introduces the first application of a Microflown PU sensor for near-field acoustic monitoring in milling operations.•The I-Kaz™ feature is demonstrated as a computationally efficient and accurate method for real-time tool wear prediction.•The research integrates cutting parameter optimization and predictive monitoring within a single experimental framework.