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Machine Learning and Statistical Approach to Predict and Analyze Wear Rates in Copper Surface Composites

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Abstract

This research demonstrates the application of machine learning models and statistics methods in predicting and analyzing dry sliding wear rates on novel copper-based surface composites. Boron nitride particles of varying fractions was deposited experimentally over the copper surface through friction stir processing. Experimental and statistical analysis proved that the presence of BN particles can reduce wear rate considerably. Analysis of worn-out surface revealed a mild adhesive wear during low load condition and an abrasive mode of wear during higher load conditions. Artificial neural network based feed forward back propagation model with topology 4-7-1 was modeled and prediction profiles displayed good agreement with experimental outcomes.

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Authors and Affiliations

  1. Mechanical Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641 032, India

    Titus Thankachan

  2. Mechanical Engineering, Anna University Regional Campus, Coimbatore, Tamil Nadu, 641 046, India

    K. Soorya Prakash & S. R. Silambarasan

  3. Mechanical Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, 641 021, India

    V. Kavimani

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  1. Titus Thankachan
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Thankachan, T., Soorya Prakash, K., Kavimani, V. et al. Machine Learning and Statistical Approach to Predict and Analyze Wear Rates in Copper Surface Composites. Met. Mater. Int. 27, 220–234 (2021). https://doi.org/10.1007/s12540-020-00809-3

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