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 of the composites was estimated on a pin-on-disc machine at various loads of 20-60N, sliding velocity of 2-6m/s and sliding distance from 1000m-3000m. The wear rate of the composite was reduced by augmentation in weight fraction of boron carbide from 3-9%. The benefits of interfacial TMCs with B4C are: increase in strength, wear-resistance, and volume fraction. ANN was planned and utilizes a Levenburg-Marquardt program algorithm to reduce the mean squared error using a back-propagation technique. The input parameters are considered to include load, sliding velocity, and sliding distance. The experimental results of an ANN model and regression model are compared. ANN replicas have been urbanized to foreshow experimental rate of wear and CoF of TMCs and examined that ANN predictions have exceptional concord with deliberated values. Accordingly, the prediction of wear rate and CoF of TMCs using ANN in earlier actual manufacture will significantly save the manufacturing time, exertion, and expenditure.