Journal
SOFT COMPUTING
Volume 16, Issue 8, Pages 1427-1437Publisher
SPRINGER
DOI: 10.1007/s00500-012-0846-0
Keywords
High-torque milling; Surface roughness; Ensemble methods; Linear projections
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Funding
- Spanish Ministry of Economy and Competitiveness [CENIT-2008-1028, TIN2011-24046, IPT-2011-1265-020000]
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Industrial solutions for surface roughness prediction are in great demand, especially in high-torque milling operations, owing to the exponential expansion of wind power energy generation over the past decade. In this paper, we use Boosting Projections to predict surface roughness in high-torque, high-power face milling operations. A data set is generated from experiments performed under industrial conditions, using a milling machine with a high working volume, to train and validate the new algorithm. The experimental data comprise a very extensive set of parameters that influence surface roughness: cutting tool properties, machining parameters and cutting phenomena. The proposed method is based on non-linear boosting projections (although it uses linear projections to speed up the training process). To the best of our knowledge this is the first time it has been used in an industrial context. It demonstrates a higher prediction accuracy when compared with single multilayer perceptrons, decision trees and classical ensemble methods.
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