ABSTRACT
Knowledge management is the collection of processes that govern the creation, dissemination, and utilization of knowledge. Data mining is the important approach to realize knowledge discovery. It is the process of extracting patterns or predicting previously unknown and useful trends from large quantities of data by using the knowledge of multidisciplinary fields such as statistics, mode identify, artificial intelligence, machine learning, database, management information system and so on. Assembly is the pivotal component in product life cycle process, The control technology of assembly quality has become a hot topic. Assembly technologic parameters are the important influencing factors of assembly quality, and the loaded stress distribution of the key structure is one of the main evaluation indexes of assembly quality. The description of relations between influencing factors and evaluation index is crucial. The present paper gives an engineering application of data mining based on neural networks. The back propagation neural network is Used as the algorithm of data mining. Then the effects of assembly technologic parameters on loaded stress distribution in the weld region of the shield engine rotor in a submarine are analyzed. The mined data come from the numerical simulations of the finite element method. The effects of different parameters on the stress in the weld region are achieved from the results of the data mining. The discovered knowledge is beneficial to the improvement of assembly quality.
