Application of Hybrid ANN and PSO for Prediction of Surface Roughness Under Biodegradable Nano-lubricant

Imhade P. Okokpujie, Lagouge K. Tartibu

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Modelling of surface roughness is challenging because machining conditions cannot be well covered by theoretical models. In this section, 50 configurations corresponding to various spindle speed, feed rate, length of cut, depth of cut, helix angle were considered to predict the surface roughness of the end milling machining of an AA8112 alloy. In this study, an ANN-PSO approach has been considered for the development of a suitable prediction model for surface roughness. The sensitivity analysis of numerous Particle Swarm Optimization (PSO) parameters, including the population size of the swarm, the number of neurons in the hidden layer, and the magnitude of the acceleration factors, was carried out. This study reveals that the prediction performance metric is closely related to the model configuration or parameters. The best configurations of the ANN-PSO models were identified. This study shows that the hybrid ANN-PSO enhances the performance of ANN. The proposed approach would address time-consuming and expensive experiment required to identify a configuration that yield the minimum surface roughness.

Original languageEnglish
Title of host publicationStudies in Systems, Decision and Control
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages26
Publication statusPublished - 2023

Publication series

NameStudies in Systems, Decision and Control
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190


  • ANN
  • ANN-PSO models
  • Machining
  • Particle swarm optimization
  • Surface roughness

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Automotive Engineering
  • Social Sciences (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Control and Optimization
  • Decision Sciences (miscellaneous)


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