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http://dx.doi.org/10.14775/ksmpe.2017.16.6.125

Prediction of Machining Performance using ANN and Training using ACO  

Oh, Soo-Cheol (Department of Systems Management & Engineering, Pukyong National University)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.16, no.6, 2017 , pp. 125-132 More about this Journal
Abstract
Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.
Keywords
ACO; Neural Network; Surface Roughness; Electric Current Consumption;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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