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This document discusses using an artificial neural network to predict cutting tool wear, surface roughness, and vibration of a workpiece during boring of AISI 316 steel. Experimental data on tool wear, surface roughness, and workpiece vibration was collected during boring with different cutting speeds, feeds, nose radii, and material removal volumes. This data was then used to train a multilayer perceptron neural network model using backpropagation to predict the output parameters based on the input cutting

Author

Samir Bose

Language

EN