A signal segmentation method for CFRP/CFRP stacks drilling-countersinking monitoring

This research project tackles the problem of monitoring drilling and countersinking in carbon‑fiber‑reinforced plastic (CFRP) stacks, which are essential parts of aerospace structures. When these mixed‑material parts are machined, they often develop defects such as delamination, so real‑time sensor monitoring is needed. Raw sensor data usually contain long stretches of irrelevant information, which slows down processing and reduces monitoring accuracy. To solve this, we introduce a systematic signal‑segmentation method. First, a Butterworth filter removes high‑frequency noise and reveals the signal’s baseline. Next, a dynamic‑threshold algorithm detects key landmark points that mark transition phases in the machining cycle. By linking these landmark points with the geometry of the cutting tool and

the CFRP stack, we can clearly separate the actual material‑removal intervals from idle periods. This approach offers a reliable way to streamline data handling in tool‑condition monitoring, improve defect‑detection sensitivity, and lower computational load during high‑precision manufacturing quality control.

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