Artificial neural network analysis of the effect of matrix size and milling time on the properties of flake Al-Cu-Mg alloy particles synthesized by ball milling


VAROL T., ÖZŞAHİN Ş.

PARTICULATE SCIENCE AND TECHNOLOGY, vol.37, no.3, pp.381-390, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 3
  • Publication Date: 2019
  • Doi Number: 10.1080/02726351.2017.1381658
  • Journal Name: PARTICULATE SCIENCE AND TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.381-390
  • Keywords: Artificial neural network, ball milling, flake powder metallurgy, particle shape, particle size, PROCESS-CONTROL AGENT, MECHANICAL-PROPERTIES, MAGNETIC-PROPERTIES, ALUMINUM-ALLOY, POWDER-METALLURGY, COMPOSITE POWDERS, VOLUME FRACTION, MICROSTRUCTURE, NANOCOMPOSITES, PREDICTION
  • Karadeniz Technical University Affiliated: Yes

Abstract

In this study, the effect of matrix size and milling time on the particle size, apparent density, and specific surface area of flake Al-Cu-Mg alloy powders was investigated both by experimental and artificial neural networks model. Four different matrix sizes (28, 60, 100, and 160 mu m) and five different milling times (0.5, 1, 1.5, 2, and 2.5 h) were used in the fabrication of the flake Al-Cu-Mg alloy powders. A feed forward back propagation artificial neural network (ANN) system was used to predict the properties of flake Al-Cu-Mg alloy powders. For training process, the ANN models of the flake size, apparent density, and specific surface area have the mean square error of 0.66, 0.004, and 0.01%. For testing process, it was obtained that the R-2 values were 0.9984, 0.9998, and 0.9932 for the flake size, apparent density, and specific surface area, respectively. The degrees of accuracy of the prediction models were 95.145, 99.705, and 94.25% for the flake size, apparent density, and specific surface area, respectively.