Predicting the hardness of carbon nanotube reinforced copper matrix nanocomposites using two adaptive fuzzy inference system identifiers

The paper deals with devising two different fuzzy inference systems to predict the hardness of copper/carbon nanotube nanocomposite. These composites are outstanding candidates for thermal management applications in electronic packaging due to the high conductivity of copper. Knowing the extraordinary properties of carbon nanotubes, it seems that copper-based composites reinforced with small amount of carbon nanotubes, resulted in improved mechanical properties. Hence, carbon nanotube reinforced copper matrix nanocomposites are fabricated by hot-press sintering of high energy ball milled copper/carbon nanotube powders. Different milling factors are investigated. Finally the Vickers hardness of sintered nanocomposites is reported. To simulate a predictive framework for current case study, two different machine learning algorithms are engaged. The first learning algorithm is the classic least square optimization method, which provides the requirements for fast adaption of the consequent parts of fuzzy inference system. The second method learning algorithm uses the rudiments of neural computing through layering the fuzzy inference system and using back-propagation optimization algorithm. Based on the experiments, the authors realize that the adopted fuzzy systems can effectively extract the knowledge required for predicting the hardness of copper/carbon nanotube nanocomposite.