Prediction Analysis of Surface Roughness of Aluminum Al6061 in End Milling CNC Machine Using Soft Computing Techniques

Serge Balonji, Lagouge K. Tartibu, Imhade P. Okokpujie

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Computer numerically controlled (CNC) milling has been one of the most commonly used manufacturing processes for the performance of multiple operations, from tiny integrated circuits to heavy-duty mining machine gearboxes. It is a well-known machining process that offers close tolerances and repeated operations. However, the choice of machining parameters to achieve a desired part’s surface roughness (SR) remains a challenge. In the present study, artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) approaches have been used to predict and monitor the surface roughness of aluminum Al6061 machined blocks. Furthermore, both models have been hybridized with genetic algorithm (GA) and particle swarm optimization (PSO) to investigate the potential enhancement in the prediction performance of the hybrid approach. The results show that factors such as the population size, the acceleration values, the choice of membership functions, and the number of neurons and layers significantly influence the prediction performance of the proposed models. Through a parametric analysis, this study demonstrates how the configuration of the models could affect the prediction performance. While exhibiting the impact of models’ hyperparameter combination on the prediction ability, this study provides insight into the development of suitable prediction models and the potential of soft computing techniques to predict the surface roughness of aluminum Al6061 blocks on CNC machines.

Original languageEnglish
Article number4147
JournalApplied Sciences (Switzerland)
Volume13
Issue number7
DOIs
Publication statusPublished - Apr 2023

Keywords

  • ANFIS-GA
  • ANFIS-PSO
  • ANN-GA
  • ANN-PSO
  • CNC machine
  • adaptive network-based fuzzy inference system (ANFIS)
  • aluminum Al6061
  • artificial neural network (ANN)
  • genetic algorithm (GA)
  • particle swarm optimization (PSO)

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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