Machine learning for impact detection on composite structures

Stefano Cuomo, Mario Emanuele De Simone, Christos Andreades, Francesco Ciampa, Michele Meo

Research output: Contribution to journalConference articlepeer-review

8 Citations (SciVal)


In order to overcome the current limitations of the impact localisation process in composite materials, such as the a-priori knowledge of the mechanical properties and the direction dependency of the wave speed, a novel method is here proposed based on the machine learning approach. The algorithm is formed by two steps: the first is the training process, in which a baseline consisting of the structural responses due to impact tests is acquired; the second one evaluates the impact location exploiting the highest cross-correlation coefficient, obtained after the interpolation of the impact response baseline using the Radial Basis Function (RBF) method. Numerous experimental tests are performed on a simple carbon fibre reinforced polymer (CFRP) plate fitted with three piezo-sensors at three different drop heights to validate the training process. The results showed high accuracy in both the reconstruction and the impact localisation, with an error less than 10 mm.
Original languageEnglish
Pages (from-to)93-98
Number of pages6
JournalMaterials Today: Proceedings
Issue numberPart 1
Early online date5 Feb 2020
Publication statusPublished - 31 Dec 2021


  • BVID
  • Cross correlation
  • Impact localization
  • Low velocity impact
  • Machine learning

ASJC Scopus subject areas

  • Materials Science(all)


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