ARTIFICIAL NEURAL NETWORKS MODELING OF THE VISCOELASTIC PROPERTIES OF VAPOR-GROWN CARBON NANOFIBER/VINYL ESTER NANOCOMPOSITES

An artificial neural network (ANN) is a modeling approach to non-linear function approximation based on “artificial intelligence”. This technique is very useful in modeling phenomena that are not easily modeled analytically [1]. ANNs consist of a host of simple processors (neurons) that are interconnected in an organized architecture and are associated with a learning algorithm that resembles a biological process [1]. There are numeric values associated with the interconnections of the simple processors that are adjusted over time to emulate learning. The weights associated with the interconnections (a measure of the strength of the connections) encode knowledge about the problem domain. The architectures (neurons and their interconnections) provide a computational structure for simulating a biological neural network [2]. Learning in an ANN can occur in either a supervised or an unsupervised fashion [1]. A supervised approach uses a learning algorithm that creates an input/output mapping based on a labeled training set; thus, creating a mapping between an n-dimensional input space and m-dimensional output space. In this case, the network will learn a functional approximation from the input/output pairings and will have the ability to recognize or classify a new input vector into a correct output vector. On the other hand, an unsupervised learning architecture presents the network with only a set of unlabeled input vectors from which it must learn. In other words, the unsupervised ANN is expected to discover new knowledge as it characterizes the input vectors and produces outputs corresponding to learned patterns. The use of the ANN techniques in the context of materials science and engineering is considered an important extension of materials informatics [3-6]. This interdisciplinary study integrates computer science, information science, and other domain areas to provide new understanding and to facilitate knowledge discovery. Materials informatics is a tool for material scientists to interpret their acquired experimental data through the use of ANNs and machine learning approaches, integrated with new visualization schemes, more human-like interactions with the data, and guided by domain experts. It can also accelerate the research process and guide the development of new materials with desired engineering properties. Materials informatics is being fueled by the new and dynamic growth in information technology and is driving the interest in ANNs, data mining, machine learning, information retrieval, and other knowledge representation or discovery schemes in the engineering disciplines [7]. There are several recent published applications utilizing materials informatics and ANNs. Yassar, et al. [8] developed a novel computational model based on dislocation structures to predict the flow stress response of 6022 aluminum alloy. An ANN model was used to back-calculate the in-situ non-linear material parameters and flow stress for different dislocation microstructures [8]. Guessasma and Coddet [9] characterized the microstructural features of alumina-13 wt. % titania coating obtained under various atmospheric plasma spray (APS) conditions by implementing ANNs, which were detailed in the case of APS process parameters. These parameters were related to alumina, titania, porosity and unmolten particle contents. Yoshitake, et al. [10] used an ANN to model the changes in lattice phases as a function of their chemical composition and temperature. Reasonable phase predictions were made for several alloys, which agreed with X-ray measurements. The variation of the lattice constant with the concentration of individual alloying elements and temperature could be embodied into other computer programs, which deal with the partitioning of solutes between the different phases. Hu, et al. [11] used materials informatics to resolve the problem of materials science image data sharing. They presented an ontology-based approach that can ARTIFICIAL NEURAL NETWORKS MODELING OF THE VISCOELASTIC PROPERTIES OF VAPOR-GROWN CARBON NANOFIBER/VINYL ESTER NANOCOMPOSITES