Novel Method for Classification of Material Type and Its Surface Properties

This paper presents a novel method of material type and its surface roughness classification by means of a light weighted plunger probe with optical encoder. An experimental prototype was developed which involves bouncing or hopping of the plunger based impact probe freely on the plain surface of an object under test. The time and features of bouncing signal are related to the material type and its surface properties, and each material has a unique set of such properties. During the bouncing of the probe, a time varying signal is generated from optical encoder that is recorded in a data file on PC. Some dominant unique features are then extracted using digital signal processing tools to optimize a multilayer perceptron neural network (MLP NN) model designed as classifier in the existing system. The classification accuracy of MLP NN is found very good even after repeating the simulation experiments a number of times on different data partitions