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http://dx.doi.org/10.7735/ksmte.2017.26.4.402

Improvement of Cutting Conditions in End-milling Using Deep-layered Neural Networks  

Lee, Sin-Young (Dept. of Mechanical Engineering, Kunsan National University)
Publication Information
Journal of the Korean Society of Manufacturing Technology Engineers / v.26, no.4, 2017 , pp. 402-409 More about this Journal
Abstract
Selection of optimal cutting conditions is important for improving productivity and implementing efficient process control in metal machining. In this study, improvement of cutting conditions in machining using end-mills is studied by using deep-layered neural networks, which comprise an input layer, output layer, and two hidden layers. System networks are designed with inputs as cutting conditions, and they output the cutting force. A pseudo-inverse network is designed that has the adjustable cutting condition as output and cutting force and other cutting conditions as input. The combination of the system network and pseudo-inverse network enables selection or improvement of cutting conditions that results in the expected cutting force.
Keywords
End-milling; Deep-layered neural network; Number of neurons; Average cutting forces; Force simulation; Cutting conditions;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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