Data mining and knowledge discovery in materials science and engineering: A polymer nanocomposites case study

  • Authors:

  • O. Abuomar;S. Nouranian;R. King;J. L. Bouvard;H. Toghiani;T. E. Lacy;C. U. Pittman, Jr.

  • Affiliations:

  • Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA and Center for Advanced Vehicular Systems (CAVS), Mississippi State, MS 39762, USA;Center for Advanced Vehicular Systems (CAVS), Mississippi State, MS 39762, USA;Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA and Center for Advanced Vehicular Systems (CAVS), Mississippi State, MS 39762, USA;Center for Material Forming (CEMEF), Mines ParisTech, 06904 Sophia Antipolis Cedex, France;The Dave C. Swalm School of Chemical Engineering, Mississippi State University, Mississippi State, MS 39762, USA;Department of Aerospace Engineering, Mississippi State University, Mississippi State, MS 39762, USA;Department of Chemistry, Mississippi State University, Mississippi State, MS 39762, USA

  • Venue:
  • Advanced Engineering Informatics
  • Year:
  • 2013

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Abstract

In this study, data mining and knowledge discovery techniques were employed to validate their efficacy in acquiring information about the viscoelastic properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites solely from data derived from a designed experimental study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. SOMs also showed how to prepare different VGCNF/VE nanocomposites with the same storage and loss modulus responses. A clustering technique, i.e., fuzzy C-means algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens (i.e., specimens containing no VGCNFs) in separate clusters. Most importantly, the results from data mining are consistent with previous response surface characterizations of this nanocomposite system. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics.