Title of article
NEURAL NETWORK FOR PREDICTING TOOL WEAR AND CURRENT CONSUMPTION IN TURNING AL10% SiCp
Author/Authors
ERFAN, O. M. University of Garyounis - Department of Industrial Engineering and Manufacturing Systems, Libya , ELMISERY, F. A. Ohio University - Department of Electrical and Computer Engineering, USA , EL-METWALLY, H. T. Bani Suef University - Faculty of Industrial Education - Department of Production Engineering, Egypt
From page
243
To page
257
Abstract
This study considers the performance of multilayered perceptions neural network MLPNN for predicting the flank wear and spindle motor current during a turning process. A cemented carbide cutting tool has been used to machine Al/10%SiCp composite material. The input parameters of the MLP model were the cutting speed, feed rate and depth of cut. The output parameters were, flank wear and spindle motor current. The model consists of a three layered feed forward back propagation neural network BPNN. A very good performance of MLP, in terms of agreement with experimental data was achieved.
Keywords
Composite materials , neural network , turning , flank wear.
Journal title
Journal of Engineering and Applied Science
Journal title
Journal of Engineering and Applied Science
Record number
2588002
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