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What is Predictive Analysis of Wear Rate Using ML about?

The document presents a study on predicting slurry erosion wear in coated stainless steel using machine learning and artificial neural networks. It outlines the objectives, methodology, and key findings, highlighting that decision tree and random forest models yield the most accurate predictions, while wear rate is influenced by speed, material type, and fly ash concentration. The project aims to optimize material selection in slurry systems, ultimately reducing maintenance costs and extending component lif

Author

Sahil Sonawane

Language

EN