Implementation of neural network for the thrust force prediction in hot drilling of 6082 aluminium alloy. (11th May 2010)

• Record Type: Journal Article
• Authors: Donnini, R.; Montanari, R.; Santo, L.; Tagliaferri, V.; Ucciardello, N.
• Is Part Of: International journal of computational materials science and surface engineering. Volume 3:Number 2/3(2010)
• Page Start: 175
• Page End: 187
• Publication Date: 2010-05-11
• Subjects: artificial neural networks -- ANNs -- hot drilling -- aluminium alloys -- thrust force -- torque -- dry drilling -- dry machining -- spindle speed -- temperature; Surfaces (Technology) -- Periodicals; Materials -- Computer simulation -- Periodicals; Materials -- Mathematical models -- Periodicals; 620.110151
• Languages: English
• ISSNs: 1753-3465

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.