Condition Based Maintenance (CBM) systems are crucial for today’s high accuracy machining of exotic materials. For reliable results, CBM systems need early and reliable warning based on prediction models that use multiple types of sensors. In this study, tool flank wear during end milling difficult-to-machine alloys was measured using an optical microscope. Then, vibration data collected with an accelerometer was investigated for its relationship to tool flank wear. The developed relationship between accelerometer output and tool flank wear was validated with further experiments. It was observed from frequency domain responses of these outputs that specific harmonics of the tool pass frequency were dominant, and tool flank wear can be related to the amplitude of these harmonics during machining. This way, it was shown that through accurate online prediction of tool wear, premature interruption of the process as well as machining with a worn tool can both be avoided, improving end-product quality as well as reducing machining costs.

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