On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion

This paper examines the feasibility for an intelligent sensor fusion technique to estimate on-line surface finish (Ra) and dimensional deviations (DD) during machining. It first presents a systematic method for sensor selection and fusion using neural networks. Specifically, the turning of free-machining and low carbon steel is considered. The relationships of the readily sensed variables in machining to Ra and DD, and their sensitivity to process conditions are established. Based on this experimental data and using statistical tools, the sensor selection and fusion method assists the experimenter in determining the average effect of each candidate sensor on the performance of the measuring system, In the case studied, it appeared that the cutting feed, depth of cut and two components of the cutting force (the feed and radial force components) provided the best combination to build a fusion model for on-line estimation of Ra and DD in turning. Surface finish was assessed with an error varying from 2 to 25% under different process conditions, while errors ranging between 2 and 20 mu m were observed for the prediction of dimensional deviations. (C) 1997 Published by Elsevier Science Ltd. All rights reserved.