Chip segmentation frequency based strategy for tool condition monitoring during turning of Ti-6Al-4V

Tool condition monitoring in machining reduces downtimes, maximizes productivity rate and improves the quality of the end-product. However, it still poses a challenge due to the complex non-stationary character of the tool wear and the several uncertainties coming from the machining processes. Recent studies provide new strategies for indirect tool monitoring. Unfortunately due to the unbalance between big amounts of data, low accuracy and high complexity they are not feasible in an industrial environment. The present research work proposes a strategy for tool condition monitoring during turning of Ti-6Al-4V using acoustic emission signals and the chip segmentation frequency as measurement variable. Three different approaches for wear estimation using different AE-data processing methods are presented. Through their combination, a strategy for qualitative and quantitative tool wear monitoring is proposed