Laboratory of Precision Engineering, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa
Olufayo, O., Laboratory of Precision Engineering, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa; Abou-El-Hossein, K., Laboratory of Precision Engineering, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa
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. © 2015, Springer-Verlag London.
Acoustic emission testing; Acoustic emissions; Automation; Carbide cutting tools; Carbides; Damage detection; Machine tools; Machining; Machining centers; Milling (machining); Wavelet transforms; Acoustic emission monitoring; Coated carbide insert; High speed end milling; Increased productivity; Manufacturing industries; Process quality control; Reliable monitoring systems; Tool life; Cutting tools