Modeling and Optimization of Toughness of Hybrid Polyamide 6 Nanocomposites using Artificial Neural Network and Response Surface Methodology

In recent years, one of the most significant technological achievements in plastics and composites industry has been the development and commercialization of polymeric nanocomposites. Clay-based nanocomposites have recently attracted attention in many industries due to stiffness, strength, barrier properties and flame retardancy at relatively low reinforcement loading levels .The polyamde6-based clay nanocomposite has received substantial attention in recent years. PA6 based nanocomposites are typical example of a significant enhancement of all properties by platelet like nanoclay. The only short-coming of these systems is low toughness. The best-balanced mechanical behavior was found for nanocomposite containing finely dispersed non-reactive polar elastomers. In this work Artificial Neural Network (ANN) is used to model the effect of four factors, including SEBS, mSEBS and NC contents as material variables and order of mixing as a processing factor, on toughness of hybrid nanocomposites. The network was trained with pairs of inputs/outputs set generated by the process. The ANN model can be used for the analysis and prediction of the complex relationship between process parameters. Response surface method of experimental design to studied the effect of rubber, compatibilizer and clay contents and order of mixing of components on low temperature Izod impact toughness and strength of these systems.

کلیدواژه ها:

Hybrid Polyamide 6 Nanocomposites ، Artificial Neural Network ، Response surface methodology ، toughness