Discriminating different materials by means of vibrations

Abstract

Material characterization and discrimination is of interest for multiple applications, ranging from mechanical engineering to medical and industrial sectors. Despite the need for automated systems, the majority of the existing approaches necessitate expensive and bulky hardware that cannot be used outside ad-hoc laboratories. In this work, we propose a novel technique for discriminating between different materials and detecting intra-material variations using active stimulation through vibration and machine learning techniques. A voice-coil actuator and a tri-axial accelerometer are used for generating and sampling mechanical vibration propagated through the materials. Results of the present analysis confirm the effectiveness of the proposed approach. Processing a mechanical vibration signal that propagates through a material by means of a neural network is a viable means for material classification. This holds not only for distinguishing materials having gross differences, but also for detecting whether a material underwent some slight changes in its structure. In addition, mechanical vibrations at 500 Hz demonstrated an ability to provide a compact and meaningful representation of the data, sufficient to categorize 8 different materials, and to distinguish reference materials from other defective materials, with an average accuracy greater than 90%.