Abstract
The cutting characteristics of biomaterials (Ti-6Al-4V ELI) by tools are investigated with respect to cutting force, work piece surface roughness and tool flank wear by the vision system. Ti-6Al-4V ELI titanium turning is carried out with various cutting conditions; spindle rotational speed and feed rate. Back propagation neural networks (BPNs) are used for detection of tool wear. The input vectors of neural network comprise of spindle rotational speed, feed rates, vision flank wear, and cutting force signals. The output is the tool wear state which is either usable or failure. The detection of the abnormal states using BPNs achieves 97.5% reliability even when the spindle rotational speed and feed rate are changed.
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