Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network

VAROL T., ÇANAKÇI A., ÖZŞAHİN Ş.

SCIENCE AND ENGINEERING OF COMPOSITE MATERIALS, cilt.21, sa.3, ss.411-420, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 21 Sayı: 3
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1515/secm-2013-0148
  • Dergi Adı: SCIENCE AND ENGINEERING OF COMPOSITE MATERIALS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.411-420
  • Anahtar Kelimeler: artificial neural networks, mechanical alloying, metal-matrix composites (MMCs), powder processing, MECHANICAL ALLOYING PROCESS, METAL-MATRIX COMPOSITES, NANOCOMPOSITE POWDERS, ANN, REINFORCEMENT, BEHAVIOR, POROSITY, DENSITY, SCIENCE
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

In this study, an artificial neural network approach was employed to predict the effect of B4C size, B4C content, and milling time on the particle size and particle hardness of Al2024-B4C composite powders. Al2024B(4)C powder mixtures with various reinforcement weight percentages (5%, 10%, and 20% B4C), reinforcement size (49 and 5 mu m), and milling times (0-10 h) were prepared by mechanical alloying process. The properties of the composite powders were analyzed using a laser particle size analyzer for the particle size and a microhardness tester for the powder microhardness. The three input parameters in the proposed artificial neural network (ANN) were the reinforcement size, reinforcement ratio, and milling time. Particle size and particle hardness of the composite powders were the outputs obtained from the proposed ANN. The mean absolute percentage error for the predicted values did not exceed 4.289% for the best prediction model. This model can be used for predicting properties of Al2024-B4C composite powders produced with different reinforcement size, reinforcement ratio, and milling times.