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Analysing wear behaviour of Al–CaCO3 composites using ANN and Sugeno-type fuzzy inference systems

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Abstract

Design of experiment for the development of stir cast calcium carbonate-reinforced aluminium composite is a search for optimum combination of material and process control parameters for best physical and mechanical properties. A soft-computing model can accurately learn the complex interactions between process parameters to provide great insights in the development of this composite. This paper demonstrates and analyses the potential of artificial neural network (ANN) and Sugeno-type fuzzy inference systems (FIS) for wear behaviour prediction of calcium carbonate-reinforced aluminium composites. The models were trained with data collected from the experiment. The data consist of filler particle size of 150 μm with weights fractions varied from 0 to 25 wt%, in step of 5. Wear test data at different time of contacts (30, 60, 90, 120 and 150 s) and variable loads of 2.27 N, 4.54 N and 6.80 N were collected, resulting to 120 length vectors. Comparing the experimental results of wear test with those predicted using the ANN and Sugeno-type FIS, the integration of calcium carbonate particulate enhanced the wear characteristics of Al matrix up to 200%. On the use of back-propagation neural network with 4–3–1 architecture for wear prediction, the Levenberg–Marquardt training algorithm performs better. For Sugeno-type FIS, the Gaussian membership function resulted to the best prediction of wear rate. When ANN and Sugeno-type FIS performance on the test set were analysed based on some statistical parameters, the later returned an R2 value of 0.9775 as against ANN’s value of 0.3684. The predicted wear rate using ANFIS with Gaussian membership functions was in good agreement with the experimental values.

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Authors and Affiliations

  1. Department of Systems Engineering, University of Lagos, Akoka, Nigeria

    A. A. Sosimi

  2. Department of Metallurgical and Materials Engineering, University of Lagos, Akoka, Nigeria

    O. P. Gbenebor, O. Oyerinde, O. O. Bakare & S. O. Adeosun

  3. Department of Mechanical Engineering, University of Lagos, Akoka, Nigeria

    S. A. Olaleye

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  1. A. A. Sosimi
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  2. O. P. Gbenebor
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  3. O. Oyerinde
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Correspondence to A. A. Sosimi.

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Sosimi, A.A., Gbenebor, O.P., Oyerinde, O. et al. Analysing wear behaviour of Al–CaCO3 composites using ANN and Sugeno-type fuzzy inference systems. Neural Comput & Applic 32, 13453–13464 (2020). https://doi.org/10.1007/s00521-020-04753-6

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  • DOI: https://doi.org/10.1007/s00521-020-04753-6

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