Flat Inkjet-Printed Copper Induction Coils for Magnetostrictive Structural Health Monitoring: A Comparison with Bulk Air Coils and an anisotropic magnetoresistive sensor (AMR) Sensor

Anirudh Gullapalli, Vimanyu Beedasy, Jamin Daniel Selvakumar Vincent, Zhaoyuan Leong*, Patrick Smith, Nicola Morley

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

Structural health monitoring (SHM) represents the next generation of carbon fiber-reinforced composite nondestructive testing. One challenge facing the application of magnetostrictive SHM is the lightweighting and ease of installation of actuators and sensors. Inkjet printing (IJP) technology is well suited to produce miniaturized electronic induction sensors that can be paired with magnetostrictive actuators to detect strain. These sensors have several advantages: their thicknesses can be minimized, the surface area can be maximized to increase sensitivity, and complex multifilar coil configurations can be fabricated. A parametric study of the efficacy of IJP induction coils with different parameters (number of coils, monofilar/bifilar, size) tested on a number of actuator-functionalized composite coupons (FeSiB ribbon and impregnated epoxy sensors) is conducted. The samples are characterized by measuring their inductance response through induced strains. Increased sensitivity and accuracy of the 10-turn monofilar IJP sensor are shown with respect to 1) 70-turn hand-wound coils, 2) a three-axis AMR sensor, and 3) other IJP actuators with <10 turns. This is attributed to increased contact area to the composite surface and the requirement of minimum sensitivity (i.e., the number of turns and surface area) for strain detection.

Original languageEnglish
Article number2100313
JournalAdvanced engineering materials
Volume23
Issue number9
DOIs
Publication statusPublished - Sep 2021

Keywords

  • inkjet printing
  • magnetostrictive sensors
  • printed electronics
  • strain sensors
  • structural health monitoring

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