Experimental Investigation of Vibration Based Surface Roughness Prediction System

Production Engineering

‘Mass Customization’ is an attempt to provide unique values to the customers in an efficient manner. In the present work, that unique value chosen is required surface quality instead of ‘Best’ quality which has become a choice of today’s customized market. Prediction of surface roughness is an essential requirement for any computer numeric controlled (CNC) machinery. Poor control on the desired surface roughness generates rebellious parts and results in increase in cost and loss of productivity due to rework. Surface roughness value is a result of several process variables among which vibration is of great significant. In this study, full factorial design of experiment (DOE) approach is applied to find the optimum cutting parameters to obtain predicted surface roughness in turning at computer numeric controlled (CNC) cell. Dry turning is performed for aluminum alloy bars using diamond shaped carbide tool insert. The analysis of variance (ANOVA) and correlation technique are applied to study the performance characteristics of machining parameters with surface roughness and cutting tool vibrations. Feed rate and bi-axial cutting tool vibrations are observed to be main parameters affecting surface roughness. Regression equation is formulated for estimating predicted values of surface roughness. Finally, to illustrate the effectiveness of the regression equation, the rate of error is found between the actual and predicted surface roughness values.

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