Development of a Theoretical Model for Prediction of Surface Roughness of Metallic Surfaces using Acoustic Signals

Objectives: To develop a theoretical model to predict the surface Roughness (R_a) of different metallic surfaces using acoustic signals. Methods: Acoustic signals are generated with the help of dry friction contact between two metallic surfaces. In this work, Cast iron and Mild Steel Samples from different machining processes are collected and the dry friction contact is made with HSS and Tungsten Carbide Tools to generate the acoustic signals. The acoustic signals obtained through a microphone are processed using MATLAB. Number of Samples versus Amplitude is plotted and then the resulting output is plotted as Time versus Amplitude. Subsequently the external noises from the acoustic signals are removed to get a reliable roughness value. Findings: The maximum amplitudes of the samples are tabulated and used for the deriving model for the surface roughness prediction. Theoretical model of HSS with various machining processes samples is y = 1.6865 ln(x) + 8.9978. Theoretical model of Tungsten carbide with various machining processes samples is y = 6.302 ln(x) + 27.337. Both the models are correlating with a trend line of 0.9. Application: This theoretical model can be used to predict the surface roughness (R_a) of metallic surfaces. This approach can be implemented in surface finish measuring devices.