Implementation of neural network for the thrust force prediction in hot drilling of 6082 aluminium alloy
by R. Donnini, R. Montanari, L. Santo, V. Tagliaferri, N. Ucciardello
International Journal of Computational Materials Science and Surface Engineering (IJCMSSE), Vol. 3, No. 2/3, 2010

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.

Online publication date: Mon, 10-May-2010

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