Monitoring the Cold Spray Process: Real-Time Particle Velocity Monitoring Through Airborne Acoustic Emission Analysis

Stratos Koufis*, Nathan Eskue, Dimitrios Zarouchas, John Alan Pascoe

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Continuous monitoring of spray velocity during the cold spray process would be desirable to support quality control, as spray velocity is the key process parameter determining the deposit quality. This study explores the feasibility of utilising Airborne Acoustic Emission (AAE) for real-time monitoring of spray velocity. Six spray tests were conducted, varying pressure and temperature to achieve different velocities. Optical means were used to measure velocity; while, the signal from the AAE was captured during deposition via a microphone. Features demonstrating a strong correlation with velocity were extracted from the acoustic signals. Both rule-based and machine learning models were employed to identify the moments where the nozzle was engaged with the substrate and diagnose the velocity. The results indicate that monitoring the spray velocity of the cold spray process using AAE is feasible.

Original languageEnglish
Pages (from-to)2657-2671
Number of pages15
JournalJournal of Thermal Spray Technology
Volume33
Issue number8
DOIs
Publication statusPublished - 2024

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • acoustic emission
  • cold spray
  • machine learning
  • particle velocimetry
  • process monitoring

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