A genetic fuzzy based modeling of effective thermal conductivity for polymer composites

  • Authors:

  • Arup Kumar Nandi;Kalyanmoy Deb;Shubhabrata Datta;Juhani Orkas

  • Affiliations:

  • Department of Advanced Design and Optimization, Central Mechanical Engineering Research Institute CSIR-CMERI, MG Avenue, Durgapur, West Bengal, India;Kanpur Genetic Algorithms Laboratory KanGAL, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, Uttar Pradesh, India;School of Materials Science and Engineering, Bengal Engineering and Science University, Shibpur, Howrah, West Bengal, India;Deparment of Engineering Design and Production, Foundry Engineering, Aalto University, Espoo, Finland

  • Venue:
  • Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
  • Year:
  • 2013

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Abstract

Evaluation of equivalent thermal conductivity ETC of particle reinforced polymer composites PRPCs is a complex process since some of the influencing parameters are associated with uncertainties and ambiguities e.g., dispersion state of filler in the matrix, uniformity of filler particle size and shape, etc. By realizing it, an attempt has been made to model the ETC of 2-phase PRPCs based on a genetic fuzzy approach. The model performance is rigorously tested in three stages to establish its practical applicability: based on experimental data not used in model development cited in literature, new measured thermal conductivities of flexible mould composites and finally by assessing the feasibility of values of missing data in the reported in-complete data set based on the developed model. Estimations of ETC by the proposed model are shown reasonable, even better compare to existing models and suggesting a generic model applicable to a wide range of 2-phase PRPCs.