The effect of different parameters on mechanical properties of PA-6/clay nanocomposite through genetic algorithm and response surface methods

Abstract

Abstract This paper presents a methodology for determination of the optimal material and processing parameters (i.e., nanoclay content, melt temperature, feeding rate, and screw speed) to maximize simultaneously tensile modulus and tensile strength of injection-molded PA-6/clay nanocomposites through coupling response surface method and genetic algorithm. The tensile tests on PA-6/clay nanocomposites are conducted to obtain tensile modulus and tensile strength values, and then analysis of variance is performed. The predicted models for tensile modulus and tensile strength are created by response surface method, and then the functions are optimized by a genetic algorithm code implemented in MATLAB. Acceptable agreement has been observed between the values of the process parameters predicted by the response surface method and genetic algorithm and those of the process parameters obtained through experimental measurements. This study shows that the response surface method coupled with the GA can be utilized effectively to find the optimum process variables in tensile test of PA-6/NC nanocomposites.