Abstract: For determining the distribution model of wire electrical discharge machining (WEDM) reliability data, the radial basis function (RBF) neural network is applied to simulating the original reliability data, and more reliability data are achieved that have the same distribution rules with the original reliability data. The cluster learning algorithm is chosen as the learning method of the neural networks. The data centers of hidden nodes are determined by unsupervised learning, and the extended constants of the hidden nodes are determined by the distances of each data center, then the output weights of the hidden nodes are achieved by the supervised learning method. After simulating and calculating, the extended reliability data is achieved by the trained RBF neural networks, and the reliability distribution model of WEDM reliability data is confirmed as log-normal distribution model by the graphical estimation method and Kolmogorov-smirnov (K-S) test method. And it is more accurate for estimating characteristic parameters of the reliability distribution model.

Key words: Cluster learning algorithm, Kolmogorov-smirnov test method, Radial basis function neural network, Reliability, Wire electrical discharge machining