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Titus Thankachan,K. Soorya Prakash,V. Kavimani,S. R. Silambarasan 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.2
This research demonstrates the application of machine learning models and statistics methods in predicting and analyzingdry sliding wear rates on novel copper-based surface composites. Boron nitride particles of varying fractions was depositedexperimentally over the copper surface through friction stir processing. Experimental and statistical analysis proved thatthe presence of BN particles can reduce wear rate considerably. Analysis of worn-out surface revealed a mild adhesive wearduring low load condition and an abrasive mode of wear during higher load conditions. Artificial neural network based feedforward back propagation model with topology 4-7-1 was modeled and prediction profiles displayed good agreement withexperimental outcomes.