Kinematics prediction and experimental validation of machined surface roughness

Abstract

Online monitoring of surface roughness is a desirable capability for machining processes; however, 100 % inspection of all parts is not feasible unless it can be integrated into the machining process itself through real-time monitoring of cutting conditions. One strategy is to feed these conditions into a predictive modeling kernel which would in turn give the properties of the finished part. In the case of roughness, the surface resulting from turning can be largely represented as the trace of the passing tool geometry. The question addressed herein is whether computationally intensive modeling of the surface accounting for tool nose radius is necessary for online monitoring of surface roughness. This paper presents a predictive modeling methodology wherein the tool-workpiece contact position varies under a simple cutting model, and the resulting surface roughness is estimated. It presents the concept of calculating a “pseudo-roughness” value based only on tool tip locations and to compare this value to that determined by full predictive modeling of the tool geometry. Cutting experimental data has been presented and compared to predictions for model validation. It is found that the root mean square roughness calculation is dominated by tool geometry, rather than tool position deviations and surface roughness estimation could be implemented without a computationally intensive modeling component, thereby enabling online monitoring and potentially real-time control of the part finish.