![Morphology of feedstock powder WC-17Co [30]](https://asmedc.silverchair-cdn.com/asmedc/content_public/journal/tribology/147/8/10.1115_1.4067412/1/m_trib_147_8_081403_graphic_abstract.png?Expires=1739489458&Signature=nwZnvVvjdCIxGcTZr2UdAb5k4eqp8x5xjocuO5pukpEqnG5uo6wUR4uOJjz8rR6diieEiKfh5bwOh3biHyWBsthDTDZh6N-Fo7hIQ7xZn6e3rUUjsuz5ucJkrbABxdgo8B0bOjaR8fW6HXpH0ADNpE8wR2jqg2pCenJfzJlk6XnghcPihqxN1W1rLrTZOWR6GjYkUI3Boq46meFuMzeT3Ebmcn77ln5V4Kw9DtApeFauw2ClyDQjMpmRzT81YkCMzZmnHoiXuX5gsQ~DuRRIYdKsmn5ogq~1IlSRDFfwrUH86jg5VJUJNntCsJXP-b9OYSI8XGwcBQ-ktllBrUd1Gg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA)
Abstract
This study evaluates the slurry erosion performance of cold-sprayed WC-17Co ceramic coatings over CA6NM steel substrates that are used in hydro turbine blade applications. Further, an artificial neural network (ANN) model is used to investigate the effects of important factors such as jet velocity, impingement angle, and slurry concentration. A complete experimental setup was developed to assess erosion rates under different situations. The results show that jet velocity is the most important factor influencing erosion rate, followed by impingement angle and slurry concentration. The ANN model predicted erosion behavior with great accuracy, indicating its potential as a trustworthy forecasting tool for real-world applications. This study sheds light on how to optimize coating performance to increase the durability and efficiency of hydro turbine components.
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