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.
New materials widely used for automobile related industry, aircraft, space development area are mostly high hardness materials. The hardness value of some hardened materials is over HRC45 and machining of this hardened materials is called as hard turning. Hard turning has its advantage on processing flexibility, cycle time and tool cost reduction.Also this process obtains high efficiency in processing and precise surface roughness through application of the CBN tools. In hard turning process with CBN tool, surface integrity is the important factor for considering the design of machine part and component under high stress and load conditions. A purpose of this study is to analyze optimal condition in hard turning process of AISI 52100 steel (HRC62) with high CBN and low CBN on turning characteristics, tool wear mechanism comparison and surface integrity.
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