I-Kaz methodology for predicting tool life of AlCrNCoated WCCo inserts in the machining of AISI 304 steel
Year: 2025
Type: article
Abstract: 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 Z 2 α =a(VB) -n , achieving R 2 >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.
Source: MethodsX
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,260
OpenAlex ID: W4415898017