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Journal of Materials Science

, Volume 52, Issue 1, pp 569–580 | Cite as

Methodology to classify the shape of reinforcement fillers: optimization, evaluation, comparison, and selection of models

  • Roberto Fernandez Martinez
  • Maider Iturrondobeitia
  • Julen Ibarretxe
  • Teresa Guraya
Original Paper

Abstract

This work applies statistical analysis, and classical and advanced machine learning algorithms to classify 7714 aggregates into four categories according to their shape. The aggregates under study are obtained from several grades of carbon black: Vulcan XC 605, Vulcan XC 72, CSX 691, Printex 25, N990, and N762. The classification of the shape is of great significance in order to explain and predict the end-use properties of the composite materials, like mechanical properties. The proposed approach combines transmission electron microscopy and automated image analysis to obtain the dataset of the morphological features that defines the shape of the aggregate, and statistical analysis and machine learning techniques to create the classification models using feature transformation and reduction, parameter tuning, and validation methods in order to achieve robust classification models. The best result is obtained from a classification tree based on evolutionary algorithms with a principal component analysis-based feature reduction that reports an acceptable accuracy, thereby validating both the final chosen model and the methodology.

Keywords

Carbon Black Classification Tree Validation Dataset Feature Reduction Testing Stage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank the Basque Government for financial support through the UEGV09/C19 and S-PE11UN047 projects and the Biscay Regional Government for financial support through the DFV 6-12-TK-2010-25 and DFV 6-12-TK-2012-12 Projects. In addition, we would also like to acknowledge Ana Martinez-Amesti from SGIker (UPV/EHU) for TEM measurements and the help of Dr. Fernando Tusell and his group for their encouragement and scientific assistance in statistics.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.eMERG, College of Engineering of BilbaoUniversity of the Basque Country UPV/EHUBilbaoSpain

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