A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations

The industrial demands for automated machining systems to increase process productivity and quality in milling of aerospace critical safety components requires advanced investigations of the monitoring techniques. This is focussed on the detection and prediction of the occurrence of process malfunctions at both of tool (e.g. wear/chipping of cutting edges) and workpiece surface integrity (e.g. material drags, laps, pluckings) levels. Acoustic emission (AE) has been employed predominantly for tool condition monitoring of continuous machining operations (e.g. turning, drilling), but relatively little attention has been paid to monitor interrupted processes such as milling and especially to detect the occurrence of possible surface anomalies. This paper reports for the first time on the possibility of using AE sensory measures for monitoring both tool and workpiece surface integrity to enable milling of damage-free surfaces. The research focussed on identifying advanced monitoring techniques to enable the calculation of comprehensive AE sensory measures that can be applied independently and/or in conjunction with other sensory signals (e.g. force) to respond to the following technical requirements: (i) to identify time domain patterns that are independent from the tool path; (ii) ability to calibrate AE sensory measures against the gradual increase of tool wear/force signals; (iii) capability to detect workpiece surface defects (anomalies) as result of high energy transfer to the machined surfaces when abusive milling is applied. Although some drawbacks exist due to the amount of data manipulation, the results show good evidence that the proposed AE sensory measures have a great potential to be used in flexible and easily implementable solutions for monitoring tool and/or workpiece surface anomalies in milling operations. (c) 2008 Elsevier Ltd. All rights reserved.