Abstract
In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained with data collected from various sources of literature. ETC is predicted using ANFIS with volume fraction and thermal conductivities of fillers and matrixes as input parameters, respectively. The predicted results by ANFIS are in good agreements with experimental values. The predicted results also show the supremacy of ANFIS in comparison with other earlier developed models.
Highlights
The use of computer based modeling techniques is extensive in science and engineering research during these days
Thermal conductivity of boron nitride (BN) reinforced high density polyethylene (HDPE) composites was investigated under a special dispersion state of BN particles in HDPE, and together with the influence on thermal conductivity of particle sizes of filler used by Zhou et al [1]
This enhancement in the effective thermal conductivity (ETC) of linear low-density polyethylene (LLDPE)/aluminum nitride (AlN) composites is mainly because the thermal conductivity of filler (AlN) is significantly higher than that of LLDPE.From the figure, it can be observed that the calculations of the Maxwell and Hamilton and Crosser models [5,6] are mismatched significantly, while the calculated results by Singh et al model [7] of equations are satisfactory in agreement with the experimental [4] and adaptive neuro-fuzzy inference system (ANFIS) results
Summary
The use of computer based modeling techniques is extensive in science and engineering research during these days. A number of experimental studies have been carried out, and various numerical and analytical models have been developed to predict the effective thermal conductivity of particle filled polymer composites. Gu et al [4] investigated the content of AlN influencing the thermal conductivity and ultimate mechanical properties of AlN/ linear low-density polyethylene (LLDPE) composites. Adaptive neuro-fuzzy inference system (ANFIS) has recently been introduced to predict the effective thermal conductivity of metal/non-metal filled polymer composites. Compared with LDPE, LLDPE possesses better strength, toughness, heat-resistance, cold resistance, environmental stress cracking resistance, and tearing resistance properties In this perspective, an attempt has been made to formulate a rule-based model for prediction of the ETC of polymer composites using an adaptive neuro-fuzzy inference system, (ANFIS).
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