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<p>This study investigates the air jet erosion performance of CA6NM turbine steel enhanced with cold-sprayed WC–Co coatings (WC-12Co and WC-17Co). A comprehensive experimental campaign was designed using a full factorial approach to evaluate the influence of particle velocity (20–80&#xa0;m/s), impingement angle (30°-90°), and particle size (100–300&#xa0;μm) on the erosion behavior. Cold spray deposition resulted in dense, oxide-free coatings with low porosity (&lt; 1.2%) and enhanced surface hardness (up to ~ 275 HV). Erosion resistance improved significantly with WC–Co coatings, with WC-12Co outperforming WC-17Co due to its higher ceramic content. Maximum erosion was observed at 60° impact angle across all materials, indicating a mixed-mode erosion mechanism. Additionally, an artificial neural network (ANN) model was developed to predict erosion rates based on input parameters, achieving R² values above 0.97 for all materials, thus validating its predictive capability. The integrated experimental-modeling approach offers valuable insights into optimizing surface coatings for components exposed to dry particle erosion environments.</p>

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Air jet erosion performance and predictive modeling of cold sprayed WC-Co coatings on CA6NM steel

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • Mohd Shukri Ab Yajid,
  • Ripendeep Singh,
  • Binayak Sen,
  • Ankur Kumar,
  • Abhijit Bhowmik,
  • Nitin Kumar,
  • Kaushal Kumar,
  • Abdulaziz Alhazaa

摘要

This study investigates the air jet erosion performance of CA6NM turbine steel enhanced with cold-sprayed WC–Co coatings (WC-12Co and WC-17Co). A comprehensive experimental campaign was designed using a full factorial approach to evaluate the influence of particle velocity (20–80 m/s), impingement angle (30°-90°), and particle size (100–300 μm) on the erosion behavior. Cold spray deposition resulted in dense, oxide-free coatings with low porosity (< 1.2%) and enhanced surface hardness (up to ~ 275 HV). Erosion resistance improved significantly with WC–Co coatings, with WC-12Co outperforming WC-17Co due to its higher ceramic content. Maximum erosion was observed at 60° impact angle across all materials, indicating a mixed-mode erosion mechanism. Additionally, an artificial neural network (ANN) model was developed to predict erosion rates based on input parameters, achieving R² values above 0.97 for all materials, thus validating its predictive capability. The integrated experimental-modeling approach offers valuable insights into optimizing surface coatings for components exposed to dry particle erosion environments.