Prediction of the Mechanical Properties of LDPE-Thermoplastic Corn Starch Nanocomposites Using the Adaptive Neuro-Fuzzy Inference System

In this work, LDPE-Thermoplastic Corn Starch (TPCS) blends containing different amounts (0.5-3phr) of Cloisite®15A nanoparticles was prepared using the extrusion process. In practice, it is difficult to carry out several experiments for identification the relationship between the extrusion process parameters and the mechanical properties. To address this issue, the relationship between the processing parameters and the mechanical properties of the LDPE-TPCS nanocomposites have been mapped using non-linear system identification approach namely, adaptive-neuro fuzzy inference system (ANFIS). ANFIS model combines the merits of both fuzzy systems and neural networks technology. So, in this way, multi input-single output (MISO) models were developed to predict mechanical properties such as ultimate tensile strength, elongation at break, Young’s modulus and relative impact strength of all the samples. The proposed ANFIS model utilize temperature, torque and Cloisite®15A content as input parameters to predict the desired mechanical property. The results obtained in this work indicated that ANFIS is an effective and intelligent method for prediction of the mechanical properties of the LDPE-TPCS nanocomposites with a good accuracy. The statistical quality of the ANFIS model was significant due to its good correlation coefficient values > 0.8 between experimental and simulated outputs.

Prediction, Cloisite®15A, Temperature, Torque, Nanocomposite, Mechanical Properties, ANFIS.