Multi sensor data fusion for high speed machining

Abstract

Surface roughness (Ra) control in High Speed Machining (HSM) demands reliable monitoring systems. A new data fusion model based on a multi-sensor system is developed. The model considers cutting parameters, cutting tool geometry, material properties and process variables. It can be used to predict the Ra pre and in-process. The Response Surface Design methodology was used to minimize the number of experiments. Artificial neural networks were exploited as data fusion techniques. Early results represent the building blocks for a low cost supervisory control system that optimizes the Ra in HSM. © Springer-Verlag Berlin Heidelberg 2007.