Optimum Design and Application of Nano-Micro-Composite Ceramic Tool and Die Materials with Improved Back Propagation Neural Network

Abstract

At present, the main method of the ceramic tool and die materials research is still the traditional ‘trial-error’ method which needs a large number of experiments to determine the optimum material compositions. This traditional method requires researchers to repeat experiments and to face to the complex preparation processes as well as the high cost of the experiments, and so on. Therefore, the utilization of advanced and even intelligent design technologies for ceramic material design is extremely necessary. The computational intelligence (CI) technique, as an offshoot of artificial intelligence (AI), is a kind of heuristic algorithm including three categories: neural network, fuzzy system and evolutionary computation. Genetic algorithm (GA) and artificial neural network (ANN) are the two important computational intelligence techniques. In recent, the two techniques especially the ANN have got successful application in the material design of ceramics and metal matrix composites, etc. For instance, some researchers used ANN to predict the functional properties of ceramic materials from compositions (Scott et al, 2007) or the bending strength and hardness of particulate reinforced Al-Si-Mg aluminum matrix composites (Altinkok & Korker, 2004) or the mechanical properties of ceramic tool (Huang et al, 2002) or the percentage of alumina in Al2O3/SiC ceramic cakes and the pore volume fraction (Altinkok & Korker, 2005), etc. ANN is a kind of self-learning technology and back propagation (BP) neural network is one of the simply and commonly used network architectures. BP is based on the gradient descent method where connection weights and thresholds are modified in a direction corresponding to the negative gradient of a backward-propagated error measure (Jiang & Adeli, 2004). Although BP neural network has an advantage of high accuracy, it is often plagued by the local minimum point, low convergence or oscillation effects. In order to overcome the disadvantage of BP neural network, GA is usually used to improve the BP neural network. GA has a strong searching capability and high probability in finding the global optimum solution which is suitable for the early stage of data searching. Although these two techniques seem quite different in the number of involved individuals and the process scheme, they can provide more power of problem solving than either alone (Yen &