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The main objective of this project is to monitor and control the machining process during ultra-precision machining of titanium alloys. Using advanced signal processing techniques, the hidden information in the acoustic emission signal is extracted to predict the machining parameters with high levels of accuracy. The features from the signal are extracted and a set of predominant features is selected using dimensionality reduction techniques.
These selected features are given as an input to the classification algorithm to decide about the condition of the tool. The control signals are generated to control the process by varying the parameters of machining. During the machining process, the acoustic emission signals, the surface finish, and the tool wear are continuously monitored.
Procurement of Equipment, material and tool, establishment of the experimental setup, signal analysis and classification are complete. Control Strategy and Implementation is the only work left to complete this project.