Abstract
Dry turning of Ti6Al4V alloy is a hard process due to the low thermal conductivity and flexibility of this difficult-to-machine material, generating very high temperatures in both workpiece and tool cutting edge, rapid tool wear and high vibrations during machining. The use of coolants offers the advantage to reduce the high process temperatures, but is not suitable in a green technology perspective due to its high environmental impact. With the aim to allow for dry turning of Ti6Al4V alloy, monitoring of tool wear during the process is required. To achieve this goal, a cognitive sensor monitoring procedure based on the acquisition and processing of force, acoustic emission and vibration sensor signals is implemented, allowing for an accurate tool wear curve reconstruction.
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