Tribological analysis of titanium alloy (Ti-6Al-4V) hybrid metal matrix composite through the use of Taguchi’s method and machine learning classifiers

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Jatti V. S., Sawant D. A., Deshpande R., Saluankhe S. S., Cep R., Nasr E. A., ...Daha Fazla

Frontiers in Materials, cilt.11, 2024 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11
  • Basım Tarihi: 2024
  • Doi Numarası: 10.3389/fmats.2024.1375200
  • Dergi Adı: Frontiers in Materials
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: K-nearest neighboring, support vector machine, titanium metal matrix composite, tribology, wear rate, XGBoost
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Gazi Üniversitesi Adresli: Evet

Özet

The preparation and tribological behavior of the titanium metal matrix (Ti-6Al-4V) composite reinforced with tungsten carbide (WCp) and graphite (Grp) particles were investigated in this study. The stir casting procedure was used to fabricate the titanium metal matrix composites (TMMCs), which had 8 weight percent of WCp and Grp. The tribological studies were designed using Taguchi’s L27 orthogonal array technique and were carried out as wear tests using a pin-on-disc device. According to Taguchi’s analysis and ANOVA, the most significant factors that affect wear rate are load and distance, followed by velocity. The wear process was ascertained by scanning electron microscopy investigation of the worn surfaces of the composite specimens. Pearson’s heatmap and Feature importance (F-test) were plotted for data analysis to study the significance of input parameters on wear. Machine learning classification algorithms such as k-nearest neighbors, support vector machine, and XGBoost algorithms accurately classified the wear rate data, giving an accuracy value of 71.25%, 65%, and 56.25%, respectively.