Reliability analysis of PVD-coated carbide tools during high-speed machining of Inconel 800

Abstract

Predicting the cutting tool life is crucial for effectively managing machining costs, ensuring product quality, maintaining equipment availability and minimising waste in machining processes. When machining heat-resistant superalloys such as Inconel, the concern for tool life becomes even more pronounced. Cutting tool failure is a complex phenomenon that depends on several variables, including tool type and material, workpiece material, machine tool type and machining parameters. Traditional run-to-fail tests to predict tool life are costly and time-consuming. To address these challenges, accelerated degradation testing (ADT) offers a promising solution. ADT involves subjecting the component to higher levels of parameters, causing it to fail faster than under normal conditions. This approach saves time and reduces expenses associated with tool life tests for valuable workpieces. In implementing the concept of ADT, the experimental cutting speed $(V_c)$ values are selected much higher than the normal usage levels in the present study. The tool life tests are performed at three levels of $V_c$ , feed rate $(f)$ , depth of cut $(a_p)$ and tool nose radius $(r)$ using the Taguchi L₉ orthogonal array. Parametric statistical approaches, that is, accelerated failure time (AFT) models, are applied with distributions, namely the Weibull, lognormal and log-logistic distributions, to analyse the cutting tool’s reliability based on predictor variables. Various tool wear modes are considered criteria for tool failure. The comparison is made among the mean time to failure (MTTF) of cutting tools as predicted by various fitted models. Additionally, a favourable tool failure pattern is observed when using the middle level of $r$ and operating at relatively higher $V_c$ values while ensuring that $f$ and $a_p$ values fall within the recommended range. The proposed approach has the potential for diverse applications, including assessing the reliability of cutting tools and tool condition monitoring.