Predicting the remaining useful life of a cutting tool during turning titanium metal matrix composites

Abstract

A cutting tool's remaining useful life is what is left for a tool, at a particular working age, in order to reach a pre-specified level of acceptable performance. The prediction of remaining useful life is crucial in order to decrease the scrapped products or the unnecessary interruption of the machining process in order to replace the tool. Consequently, the accuracy of its estimation affects the cost of machining, particularly when the product's material is very expensive. In this article, the remaining useful lifes of 25 identical tools are estimated during turning titanium metal matrix composites. These composites are extensively used in aerospace and aviation industries. Accurate estimation of the remaining useful life has positive impact on product quality in terms of producing the required specifications. In this article, experimental data are gathered, and the proportional hazard model are used in order to model the tool's reliability and hazard functions with EXAKT software and then the remaining useful life curves are developed for different machining conditions, namely, the cutting speed and the feed rate. The use of the proportional hazard model is validated using a normalization process and Kolmogorov-Smirnov test. The proportionality assumption is verified using log minus log plot. The final result is the development of the curves that represent the tools' reliability and the remaining useful life for different machining conditions of the titanium metal matrix composites.

Pipeline reliability assessment and predictive maintenance considering multi-crack dependent degradation

“…Conversely, if the two reliability thresholds are too low, the inspection cycle will be too long, increasing the probability of pipeline failure and related maintenance costs. 46 In conclusion, optimal decision modeling uses the minimum maintenance cost rate as its optimization goal, and the inspection reliability threshold R I and PM reliability threshold R p as decision variables. This optimal decision problem can then be transformed into the following optimization problem:…”

“…Conversely, if the two reliability thresholds are too low, the inspection cycle will be too long, increasing the probability of pipeline failure and related maintenance costs. 46…”

Dynamic artificial neural network-based reliability considering operational context of assets.

“…It is also proposed for the wind energy sector, to model failure rate in wind turbines [28] and to assess the turbines stress condition [29]. The research in the cutting tool industry also provides interesting applications of the model [30,31]. And among others, the model have also been proposed for bearings reliability prediction [32], for the machinery in the processing industry [33], for electronic components [34,35] and for piping infrastructure [36].…”

A statistical data-based approach to instability detection and wear prediction in radial turning processes

“…The resulting information is used to predict whether the tool can still machine the piece to an acceptable finish. [15] developed a proportional hazard model for the remaining useful life of 25 identical tools during the turning of titanium metal matrix composites. The remaining useful life curves were developed for two different machining conditions: cutting speed and feed rate.…”