Learning-Based Damage Recovery for Healable Soft Electronic Skins

Seppe Terryn, David Hardman, Thomas George Thuruthel, Ellen Roels, Fatemeh Sahraeeazartamar, Fumiya Iida

Research output: Contribution to journalArticlepeer-review

Abstract

Natural agents display various adaptation strategies to damages, including damage assessment, localization, healing, and recalibration. This work investigates strategies by which a soft electronic skin can similarly preserve its sensitivity after multiple damages, combining material-level healing with software-level adaptation. Being manufactured entirely from self-healing Diels–Alder matrix and composite fibers, the skin is capable of physically recovering from macroscopic damages. However, the simultaneous shifts in sensor fiber signals cannot be modeled using analytical approaches because the materials viscoelasticity and healing processes introduce significant nonlinearities and time-variance into the skin's response. It is shown that machine learning of five-layer networks after 5000 probes leads to highly sensitive models for touch localization with 2.3 mm position and 95% depth accuracy. Through health monitoring via probing, damage and partial recovery are localized. Although healing is often successful, insufficient recontact leads to limited recovery or complete loss of a fiber. In these cases, complete resampling and retraining recovers the networks’ full performance, regaining sensitivity, and further increasing the system's robustness. Transfer learning with a single frozen layer provides the ability to rapidly adapt with fewer than 200 probes.
Original languageEnglish
Article number2200115
Pages (from-to)187-205
Number of pages14
Journaladvanced Intelligent Systems
Volume4
Issue number2200115
Early online date31 Oct 2022
DOIs
Publication statusPublished - 31 Oct 2022

Keywords

  • Self-Healing
  • Diels-Alder
  • Machine Learning
  • Transfer Learning
  • Flexible Electronics
  • Soft Sensors

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