Tribology and Materials | Volume 3 | Issue 1 | 2024 | 44-50
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https://doi.org/10.46793/tribomat.2024.004
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Swarm intelligence algorithms for optimising sliding wear of nanocomposites
Nikolaos A. Fountas
1,
John D. Kechagias2,
Nikolaos M. Vaxevanidis
1,
1 School of Pedagogical and Technological Education, Athens, Greece
2 School of Technology, University of Thessaly, Karditsa, Greece
Abstract: This paper presents simulation results obtained by a
set of modern algorithms adhering to swarm intelligence for minimising
wear rate in the case of A356/Al2O3 nanocomposites produced using a
compocasting process. Grey wolf optimisation (GWO) algorithm, moth-flame
optimisation (MFO) algorithm, dragonfly algorithm (DA) and whale
optimisation algorithm (WOA) were the algorithms under examination. A
full quadratic regression equation that predicts wear rate, as the
optimisation objective by considering reinforcement content, sliding
speed, normal load and reinforcement size as the independent process
parameters, was utilised as the objective function. Simulation results
obtained by the selected algorithms were quite promising in terms of
fast convergence and global optimum result arrival, thus prompting to
further investigation of applying swarm intelligence to general
problem-solving aspects related to tribology.
Keywords:
swarm intelligence, sliding wear, nanocomposites, optimisation.
Received: 14-01-2024, Revised: 24-03-2024, Accepted: 29-03-2024
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, which allows users to distribute, remix, adapt,
and build upon the material in any medium or format for non-commercial purposes only, and only so long as attribution is given to the creator.
2 School of Technology, University of Thessaly, Karditsa, Greece
Abstract: This paper presents simulation results obtained by a set of modern algorithms adhering to swarm intelligence for minimising wear rate in the case of A356/Al2O3 nanocomposites produced using a compocasting process. Grey wolf optimisation (GWO) algorithm, moth-flame optimisation (MFO) algorithm, dragonfly algorithm (DA) and whale optimisation algorithm (WOA) were the algorithms under examination. A full quadratic regression equation that predicts wear rate, as the optimisation objective by considering reinforcement content, sliding speed, normal load and reinforcement size as the independent process parameters, was utilised as the objective function. Simulation results obtained by the selected algorithms were quite promising in terms of fast convergence and global optimum result arrival, thus prompting to further investigation of applying swarm intelligence to general problem-solving aspects related to tribology.
Keywords: swarm intelligence, sliding wear, nanocomposites, optimisation.
Received: 14-01-2024, Revised: 24-03-2024, Accepted: 29-03-2024
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, which allows users to distribute, remix, adapt, and build upon the material in any medium or format for non-commercial purposes only, and only so long as attribution is given to the creator.