A knowledge-based auto-reasoning methodology in hole-machining process planning

In process planning, how to obtain an optimal process planning is the essential of computer-aided process planning (CAPP) system. The main goal of CAPP system is to derive manufacturing features and machining operations from a design model 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) for the hole's machining, 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 of hole into its machining operations. A semantic net named the precedence-relations-net reflects the precedence relationships among hole's machining-operations. Some vectors and matrixes are employed to construct a mathematical sequencing model. Usually, a hole should be machined in several operation directions, v1, v2,..., vM. In each operation direction, vi, there are Nl basic geometrical units to be operated, namely, U1l, U2l,..., Unl. For each operation direction, vi, a vector and a matrix are defined to memory the process planning and its operation objects. The mathematical sequencing model will generate an optimal process planning in each operation direction by minimizing the number of tool-changes and decreasing the number of operation steps. Therefore, it can shorten processing times and consume less energy. Finally, two hole-machining examples are employed to illustrate our methodology.

[1]  József Váncza,et al.  Genetic algorithms in process planning , 1991 .

[2]  M. A. Younis,et al.  A CAPP expert system for rotational components , 1997 .

[3]  Yong Se Kim,et al.  Geometric reasoning for mill-turn machining process planning , 1997 .

[4]  Constantin Chassapis,et al.  An IT view on perspectives of computer aided process planning research , 1997 .

[5]  Hao Yong Pattern Knowledge and Artificial Neural Network Based Framework for Intelligent CAD System , 2001 .

[6]  Inyong Ham,et al.  Computer-Aided Process Planning: The Present and the Future , 1988 .

[7]  Nariman Sepehri,et al.  A computer-aided process planning model based on genetic algorithms , 1995, Comput. Oper. Res..

[8]  Richard A. Wysk,et al.  Computer-Aided Manufacturing (3rd Edition) (Prentice Hall International Series on Industrial and Systems Engineering) , 2005 .

[9]  M. J. Pratt Applications of feature recognition in the product life-cycle , 1993 .

[10]  Sang C. Park Knowledge capturing methodology in process planning , 2003, Comput. Aided Des..

[11]  Richard A. Wysk,et al.  Computer-aided manufacturing , 1991 .

[12]  A. Márkus,et al.  Experiments with the integration of reasoning, optimization and generalization in process planning , 1994 .

[13]  Hong-Chao Zhang,et al.  Computer Aided Process Planning: the state-of-the-art survey , 1989 .

[14]  Behrokh Khoshnevis,et al.  Process planning knowledge representation using an object-oriented data model , 1997 .

[15]  Bartholomew O. Nnaji,et al.  Feature representation and classification for automatic process planning systems , 1993 .

[16]  O. W. Salomons,et al.  Review of research in feature-based design , 1993 .

[17]  Andrew Y. C. Nee,et al.  Using genetic algorithms in process planning for job shop machining , 1997, IEEE Trans. Evol. Comput..

[18]  B. K. Choi STOPP: an approach to CADCAM integration☆ , 1985 .