Machine learning approach based on fractal analysis for optimal tool life exploitation in CFRP composite drilling for aeronautical assembly. Issue 1 (2018)

• Record Type: Journal Article
• Authors: Caggiano, Alessandra; Rimpault, Xavier; Teti, Roberto; Balazinski, Marek; Chatelain, Jean-François; Nele, Luigi
• Is Part Of: CIRP annals. Volume 67:Issue 1(2018)
• Page Start: 483
• Page End: 486
• Publication Date: 2018
• Subjects: Machine learning -- Condition monitoring -- Fractal analysis; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2018.04.035
• Languages: English
• ISSNs: 0007-8506

Abstract

Abstract: A machine learning approach based on fractal analysis is developed for optimal tool life exploitation in intensive drilling operations for aeronautical assembly. Fractal analysis of sensor signals detected during the drilling process allows for the extraction of key features to build cognitive paradigms in view of condition monitoring for tool life diagnosis. The effectiveness of the proposed data analytics methodology is validated through an experimental campaign of CFRP composite drilling, using a setup that reproduces as faithfully as possible the real industrial operations, in order to acquire a suitable dataset of sensor signals.