Semantic net knowledge model in auto-reasoning process plan

In process planning, how to use manufacturing features to obtain an optimal process planning is the essential of computer-aided process planning (CAPP) system. The main goal of CAPP system is to translate the manufacturing features into machining operations and sequence the machining operations of the part in a feasible (by some technological constraints) and effective (by some economical standards) order. In this paper, we construct a process-planning model (PP model), which consists of three parts: the features framework, the precedent relation net and the sequencing mathematical model. The features framework makes a mapping from manufacturing features into machining operations. A semantic net named the precedence-relations-net reflects the precedence relationships among the machining operations. And we employ the vectors and the matrixes to construct a mathematical sequencing model. Firstly, a part is decomposed into several basic geometrical units, namely, U1,U2,…UN. For each unit - Ui, two vectors, named Fi and Pi represents the features and machining operations of Ui. Finally, a matrix named PP is used to memorize the process plan, and a matrix; namely, PO (performing objects) represents the object of machining operations.