Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations
Published 2011 View Full Article
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Title
Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations
Authors
Keywords
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Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 57, Issue 5-8, Pages 521-532
Publisher
Springer Nature
Online
2011-05-06
DOI
10.1007/s00170-011-3300-z
References
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Related references
Note: Only part of the references are listed.- Surface Roughness Generation and Material Removal Rate in Ball End Milling Operations
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- (2008) M. Correa et al. EXPERT SYSTEMS WITH APPLICATIONS
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- Artificial neural network models for the prediction of surface roughness in electrical discharge machining
- (2008) Angelos P. Markopoulos et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Estimation of cutting forces and surface roughness for hard turning using neural networks
- (2008) Vishal S. Sharma et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion
- (2008) Sultan Binsaeid et al. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
- Prediction of workpiece surface roughness using soft computing
- (2008) B Samanta et al. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
- Classifier ensembles: Select real-world applications
- (2007) Nikunj C. Oza et al. Information Fusion
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