Applications of Artificial Neural Network Simulation for Prediction of Wear Rate and Coefficient of Friction Titanium Matrix Composites

Applications of Artificial Neural Network Simulation for Prediction of Wear Rate and Coefficient of Friction Titanium Matrix Composites

The Artificial Neural Network (ANN) techniques were utilized to predict wear rate and CoF of the Ti-5Al-2.5Sn matrix reinforced with B4C particle manufactured by the powder metallurgy. TMCs and wear test samples were characterized by the Scanning Electron Microscope (SEM). Dry sliding wear narrative...

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Bibliographic Details
Main Authors: Arun, K.K (Author), Jasmin, N.M (Author), Kamesh, V.V (Author), Krishnaraj, S. (Author), Pramod, V.R (Author), Subbiah, R. (Author), Suresh, V. (Author)
Format: Article
Language:English
Published: Universidade Federal de Sao Carlos 2023
Subjects:
ANN
Artificial neural network simulation
Boron carbide
Coefficient of frictions
Forecasting
Friction
Mean square error
Neural networks
Powder metallurgy
Regression analysis
Scanning electron microscope
Scanning electron microscopy
Scanning electrons
SEM
Sliding distances
Sliding velocities
Titanium matrix composites
TMC
TMCs
Wear
Wear coefficient
Wear of materials
Wear resistance
Wear-rate
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