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A Study on the Flexible Disk Grinding Process Parameter Prediction Using Neural Network  

Yoo, Song-Min (경희대학교 테크노공학대학 기계공학과)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.17, no.5, 2008 , pp. 123-130 More about this Journal
Abstract
In order to clarify detailed mechanism of the flexible disk grinding system, workpiece length was introduced and its performance was evaluated. Flat zone ratio increased as the workpiece length increased. Increasing wheel speed and depth of cut also enhanced process performance by producing larger flat zone ratio. Neural network system was successfully applied to predict minimum depth of engagement and flat zone ratio. An additional input parameter as workpiece length to the neural network system enhanced the prediction performance by reducing error rate. By rearranging the Input combinations to the network, the workpiece length was precisely predicted with the prediction error rate lower than 2.8% depending on the network structure.
Keywords
Flexible disk grinding; Process parameter; Neural network;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
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