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A Multilevel Isolation Forrest and Convolutional Neural Network Algorithm for Impact Characterization on Composite

Amin Ebrahim Salehzadeh Nobari1, M H Ferri Aliabadi1

  • 1Department of Aeronautics, Imperial College London, Exhibition Road, South Kensington, London SW7 2AZ, UK.

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Summary

This study uses deep learning and Convolutional Neural Networks (CNN) to accurately classify hard and soft impacts on composite panels using piezoelectric sensor data. The method identifies impact type, location, and energy efficiently.

Keywords:
Convolutional Neural NetworksIsolation ForestsPiezo-Electric sensorsfeature extractionminimalistic and automated

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computer Science

Background:

  • Composite panels are susceptible to impacts, necessitating accurate detection methods.
  • Distinguishing between hard and soft impacts is crucial for damage assessment and structural integrity.
  • Traditional methods for impact classification can be complex and time-consuming.

Purpose of the Study:

  • To propose a Deep Learning approach for classifying impact data from composite panels.
  • To automatically extract and select relevant features from piezoelectric sensor voltage signals.
  • To accurately identify impact type (Hard or Soft), location, and energy levels.

Main Methods:

  • Utilized piezoelectric sensors to capture voltage signals from impacts on a composite panel.
  • Employed Convolutional Neural Networks (CNN) for minimalistic and automated feature extraction and selection.
  • Applied Isolation Forests (IF) to identify anomalous data for efficient training set creation.
  • Classified impacts based on extracted features, differentiating between hard (steel impactors) and soft (silicon impactors) events.

Main Results:

  • Achieved high accuracy in identifying Hard and Soft Impacts.
  • Successfully classified the corresponding locations and energy levels of the impacts.
  • Demonstrated the effectiveness of deep learning for automated feature extraction in impact analysis.

Conclusions:

  • The proposed Deep Learning approach, utilizing CNNs, provides an accurate and efficient method for classifying impacts on composite materials.
  • This technique enables precise identification of impact type, location, and energy, crucial for structural health monitoring.
  • Automated feature extraction and anomaly detection significantly speed up the impact classification process.