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

被引：0
|
作者：
Donnini R. [1 ]
Montanari R. [1 ]
Santo L. [1 ]
Tagliaferri V. [1 ]
Ucciardello N. [1 ]
机构：
[1] Department of Mechanical Engineering, University of Rome Tor Vergata, Rome 00133
来源：
International Journal of Computational Materials Science and Surface Engineering | 2010年 / 3卷 / 2-3期
关键词：
Aluminium alloy; ANN; Artificial neural network; Hot drilling; Thrust force; Torque;
D O I：
10.1504/IJCMSSE.2010.033152
中图分类号：
学科分类号：
摘要：
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. Copyright © 2010 Inderscience Enterprises Ltd.
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页码：175 / 187
页数：12
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