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http://dx.doi.org/10.7735/ksmte.2016.25.4.271

Simulating Cutting Forces in Milling Machines Using Multi-layered Neural Networks  

Lee, Sin-Young (School of Mechanical & Automotive Engineering, Kunsan National University)
Publication Information
Journal of the Korean Society of Manufacturing Technology Engineers / v.25, no.4, 2016 , pp. 271-280 More about this Journal
Abstract
Predicting cutting forces in machine tools is essential to productivity improvement and process control in the manufacturing field. Furthermore, milling machining is more complicated than turning machining. Therefore, several studies have been conducted previously to simulate milling forces; this study aims to simulate the cutting forces in milling machines using multi-layered neural networks. In the experiments, the number of layers in these networks was 3 and 4 and the number of neurons in the hidden layers was varied from 20 to 200. The root mean square errors of simulated cutting force components were obtained from taught and untaught data for the various neural networks. Results show that the error trends for untaught data were non-uniform because of the complex nature of the cutting force components, which was caused by different cutting factors and nonlinear characteristics coming into play. However, trends for taught data showed a very good coincidence.
Keywords
Milling; Neural network; Average cutting forces; Simulation; Teach signals;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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