Tool life estimation based on acoustic emission monitoring in end-milling of H13 mould-steel

摘要

Developing a reliable monitoring system is essential to create an autonomous manufacturing industry for increased productivity. The tool life of a machine tool is a major key parameter in accessing process quality control for developing an automated system. Various machining parameters are known to have different effects on the tool life criterion. Thus, it is essential to estimate the correlation of these parameters on tool life. The aim of this research is geared at estimating the tool life criterion from the effects of machining parameters and monitors the high-speed end-milling process of H13 tool with coated carbide inserts using highly correlated AE features. Furthermore, it proposes a diagnostic scheme using a multi-sensor approach for categorising the state of the tool. This scheme uses feature components extracted via statistical means and wavelet transform to serve as inputs for a neural network. The results found that increased speed decreased tool life and feed rate possesses a negative correlation to wear.