4.7 Article

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

Journal

MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 196, Issue -, Pages -

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2023.110332

Keywords

CFRP/CFRP stacks; Condition monitoring; Drilling-countersinking; Signal segmentation

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Carbon-fiber-reinforced plastics (CFRP) stacks are commonly used in the aerospace industry. However, drilling and countersinking processes on CFRP/CFRP stacks can cause defects that weaken the structure. To monitor these processes, a method for segmenting monitoring signals is proposed. This method effectively recognizes drilling and countersink signals from raw data, outperforming other methods in addressing this engineering challenge.
Carbon-fiber-reinforced plastics (CFRP) stacks are widely used in the aerospace industry owing to their superior mechanical properties and lightweight nature. In this industry, CFRP/CFRP stacks are often drilled and countersunk to create holes for assembly fasteners. However, this process often induces defects such as CFRP delamination, which can weaken the reliability of the structure. To ensure proper functioning, it is necessary to monitor the drilling and countersinking process with sensors. However, the signals recorded by these sensors may contain numerous non -informative data segments, which can increase the computational time of data processing and decrease the accuracy of condition monitoring. To solve these problems, we propose a method for segmenting CFRP/CFRP stacks drilling-countersinking monitoring signals. This method identifies the data segments collected during material removal. It uses a Butterworth filter to remove noise and extract the signal baseline. A dynamic threshold is then employed to calculate several key data points for signal segmentation. Finally, the actual cutting signals are recognized based on these landmark points and shape information about the cutting tool and CFRP/CFRP stacks. Experimental results indicate that the proposed method can adaptively recognize the upper-stack drilling signals, lower-stack drilling signals, and countersink signals from the raw monitoring signals. Furthermore, comparisons with several state-of-the-art methods demonstrate its superi-ority in tackling this engineering challenge.

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