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 powder metallurgy. TMCs test samples characterized Scanning Electron Microscope (SEM). Dry sliding narrative composites was estimated on a pin-on-disc machine at various loads 20-60N, velocity 2-6m/s distance from 1000m-3000m. composite reduced augmentation in weight fraction boron carbide 3-9%. benefits interfacial are: increase strength, wear-resistance, volume fraction. ANN planned utilizes Levenburg-Marquardt program algorithm reduce mean squared error using back-propagation technique. input parameters are considered include load, velocity, distance. experimental results an model regression compared. replicas have been urbanized foreshow examined that predictions exceptional concord deliberated values. Accordingly, prediction earlier actual manufacture will significantly save manufacturing time, exertion, expenditure.