Title: Implementation of neural network for the thrust force prediction in hot drilling of 6082 aluminium alloy

Authors: R. Donnini, R. Montanari, L. Santo, V. Tagliaferri, N. Ucciardello

Addresses: Department of Mechanical Engineering, University of Rome 'Tor Vergata', Via del Politecnico 1, 00133 Rome, Italy. ' Department of Mechanical Engineering, University of Rome 'Tor Vergata', Via del Politecnico 1, 00133 Rome, Italy. ' Department of Mechanical Engineering, University of Rome 'Tor Vergata', Via del Politecnico 1, 00133 Rome, Italy. ' Department of Mechanical Engineering, University of Rome 'Tor Vergata', Via del Politecnico 1, 00133 Rome, Italy. ' Department of Mechanical Engineering, University of Rome 'Tor Vergata', Via del Politecnico 1, 00133 Rome, Italy

Abstract: A multilayered neural network have been implemented for predicting force in hot drilling of the 6082 aluminium alloy. Experimental tests were performed in dry drilling operation, using a conventional milling machine and HSS-Co 8% (DIN338) twist drills, 2.5, 5 and 7 mm in diameter. The spindle speed has been changed in the range 5,000-15,000 rev/min, the feed in the range 0.0076-0.042 mm/rev, the temperature in the range 25-140°C. As test temperature increases, a remarkable reduction in thrust forces was observed, low wear and no significant damage of the hole surface was also found. The influence of each parameter was investigated and a neural network was implemented for the force prediction obtaining a good agreement between experimental and numerical results.

Keywords: artificial neural networks; ANNs; hot drilling; aluminium alloys; thrust force; torque; dry drilling; dry machining; spindle speed; temperature.

DOI: 10.1504/IJCMSSE.2010.033152

International Journal of Computational Materials Science and Surface Engineering, 2010 Vol.3 No.2/3, pp.175 - 187

Published online: 10 May 2010 *

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