Initial results from processing of acoustic emission signals from small diameter drilling

Tool condition monitoring (TCM) measures the condition of a cutting tool used in machining, and provides the operator with information relating to the state of the process. A TCM system gathers data about a process using a number of different sensor types, including force, motor power, strain, vibration and acoustic emission. With pressure on the manufacturing industry to reduce material waste and increase productivity, the dependence on TCM systems is ever increasing. Unreliable TCM can allow a process to continue with a broken or worn tool which may damage the workpiece. This is problematic as workpieces can have a high embedded value, or the machine can be making scrap for significant periods which can be costly. On the other hand, time is wasted changing a tool too often or resetting after false alarms which is also costly. Therefore increasing the reliability of TCM systems is of critical importance to a manufacturing system. A further challenge to TCM is presented in micro machining processes, in particular with small diameter drilling. In small diameter drilling it can be very difficult to detect variations in the measurable phenomena due to the low levels recorded by sensors. One sensor that has been shown to detect valuable information during micro machining and micro drilling is an Acoustic Emission (AE) sensor. This paper outlines the an approach to processing an AE signal from micro drilling in order to provide information on the process parameters. The challenges and limitations of the analysis are discussed.