基于文本挖掘与神经网络的复杂产品装配工时估算方法

Aiming at the problems such as low accuracy, slow formulation speed and nonstandard management caused by artificial experience, an assembly man hour estimation method for complex products based on text mining and neural network model is proposed. Taking satellite as an example, the characteristics of assembly process data are analyzed, and the influencing factors of assembly man hour are summarized, and the process categories are classified according to the process characteristics. Text mining technology is used to extract and classify the process text features; on this basis, the neural network model of man hour prediction is constructed to realize the accurate estimation of quota man hour for complex product assembly. Finally, an assembly man hour quota and management system for complex products is designed and developed, and the system is put into trial operation in an Aerospace Institute. The application effect is good, and the feasibility and practicability of the proposed method are verified.