Year: 2025
Type: conference-paper
Abstract: Industry 4.0, in its search for improvements to processes and efficient products, has increasingly invested in the use and development of high-performance materials for its production lines. This is exemplified by the introduction of CFRP in the aeronautical industry, since this composite material has reduced the weight of aircraft and improved their performance. For the construction of large structures, drilling processes are also necessary to fix parts. However, this machining process can cause failures in the structure as a whole. These structural failures occur due to fragmentation, tearing, or detachment of the matrix fiber, significantly reducing the quality and reliability of the final equipment. In this scenario, it is important to preventively detect these intrinsic production failures that lead to the condemnation of the final parts. One indirect detection method is acoustic emission. This work presents a feasibility study focused on the application of data-driven methods for delamination detection and tool wear monitoring in composite machining. A setup for a helical interpolation end-milling drilling process was performed under varying machining conditions, from mild to severe, on CFRP composite plates. Acoustic emission (AE) signals were acquired at each machining pass. The methodology involved selecting an optimal frequency band to obtain information about the wear of the drilling tool through RMS and power spectral density (PSD) analysis, followed by using correlation indices to characterize tool wear progression. The results demonstrate the potential of spectral and statistical techniques to support real-time monitoring and decision-making in advanced composite manufacturing.
Authors: Vinicius Dias, Thiago Lopes, Marcio Silva, Alessandro Rodrigues, Fabio Dotto, Pedro Oliveira Conceição Junior
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4417492011