A signal segmentation method for CFRP/CFRP stacks drilling-countersinking monitoring. (1st August 2023)

• Record Type: Journal Article
• Authors: Li, Changgen; Guo, Liang; Lei, Yuncong; Gao, Hongli; Zio, Enrico
• Is Part Of: Mechanical systems and signal processing. Volume 196(2023)
• Publication Date: 2023-08-01
• Subjects: CFRP/CFRP stacks -- Condition monitoring -- Drilling-countersinking -- Signal segmentation; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2023.110332
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: 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 superiority in tackling this engineering challenge.