An automated monitoring solution for avoiding an increased number of surface anomalies during milling of aerospace alloys

The demand for automated machining systems to enable the increase of process productivity and quality in milling of aerospace safety critical components requires advanced on-line monitoring and supervision systems to identify and reduce the number of workpiece surface anomalies caused by faulty tooling. It is well known that chipping or breakage of only one tooth of a milling cutter can lead to extensive damage to the machined surface. Therefore, implementing a process monitoring solution that can detect workpiece surface anomalies associated with the cutting tooth that generated them could be beneficial for proposing more efficient process supervision systems. The system presented in this paper is directed towards real-time control of the contact length of each tooth of a milling cutter with the scope of reducing the amount of workpiece surface anomalies. The proposed control solution consists of: (i) an automated process monitoring part, that, through signal analysis, automatically improves the reliability of fault detection; (ii) a signal based decision and supervision system for the avoidance of surface anomalies or tool malfunctions. The novelty of this supervision system comes from the fact that it is automatically acquiring Acoustic Emission (AE) and cutting forces, adjusts the monitoring parameters accordingly and transmits decision commands to the machine. The supervision system makes use of the sensor fusion and an original methodology for the detection of process malfunctions, to control the movement of the tool. This is directed towards the adjustment of the individual feed per tooth for each cutting edge, and if necessary reduce it to zero for a cutting tooth that is creating the surface damage. The efficiency of the proposed process supervision system is proven to avoid the generation of surface anomalies in milling of a Ni-based alloy used for the manufacture of parts for gas turbine engines. (c) 2010 Elsevier Ltd. All rights reserved.