ABSTRACT
The aim of this article is to describe an application of acoustic emission to characterise a process of laser droplet formation from a metal wire. Laser droplet formation is a crucial process in new laser droplet welding technology, where parts are joined by means of the heat content of a liquid metal droplet deposited onto the parts to be joined. A laser beam is used for heating and melting the wire tip, and for detaching the molten pendant droplet. Depending on the process parameters, three different outcomes of the process can be observed: (1) no droplet formed; (2) a droplet formed but not detached; (3) a droplet formed and detached from the wire. It is shown that AE can be used to monitor the process and to indicate the different process outcomes.
ABSTRACT
Monitoring of a machining process on the basis of sensor signals requires a selection of informative inputs in order to reliably characterize and model the process. In this article, a system for selection of informative characteristics from signals of multiple sensors is presented. For signal analysis, methods of spectral analysis and methods of nonlinear time series analysis are used. With the aim of modeling relationships between signal characteristics and the corresponding process state, an adaptive empirical modeler is applied. The application of the system is demonstrated by characterization of different parameters defining the states of a turning machining process, such as: chip form, tool wear, and onset of chatter vibration. The results show that, in spite of the complexity of the turning process, the state of the process can be well characterized by just a few proper characteristics extracted from a representative sensor signal. The process characterization can be further improved by joining characteristics from multiple sensors and by application of chaotic characteristics.
