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

• Published in: Frontiers in materials Vol. 11
• Main Authors: Jatti, Vijaykumar S., Sawant, Dhruv A., Deshpande, Rashmi, Saluankhe, Sachin, Cep, Robert, Nasr, Emad Abouel, Mahmoud, Haitham A.
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 12.04.2024
• Subjects: K-nearest neighboring; support vector machine; titanium metal matrix composite; tribology; wear rate; XGBoost
• ISSN: 2296-8016; 2296-8016
• DOI: 10.3389/fmats.2024.1375200

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 L 27 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.