This paper presents an automatic programming system on PC, it has also solved the technic problem in the combination of different curves or surfaces. The NURBS is applied to modeling and fitting complicated curves a...This paper presents an automatic programming system on PC, it has also solved the technic problem in the combination of different curves or surfaces. The NURBS is applied to modeling and fitting complicated curves and surfaces. The circular spline is combined with the NURBS to determine the cutter path in accordance with the features of the interpolation movement of NC machine tool. Three methods have been developed to solve the overcutting problems.展开更多
In a reliability-based design optimization (RBDO), computation of the failure probability (Pf) at all design points through the process may suitably be avoided at the early stages. Thus, to reduce extensive computatio...In a reliability-based design optimization (RBDO), computation of the failure probability (Pf) at all design points through the process may suitably be avoided at the early stages. Thus, to reduce extensive computations of RBDO, one could decouple the optimization and reliability analysis. The present work proposes a new methodology for such a decoupled approach that separates optimization and reliability analysis into two procedures which significantly improve the computational efficiency of the RBDO. This technique is based on the probabilistic sensitivity approach (PSA) on the shifted probability density function. Stochastic variables are separated into two groups of desired and non-desired variables. The three-phase procedure may be summarized as: Phase 1, apply deterministic design optimization based on mean values of random variables;Phase 2, move designs toward a reliable space using PSA and finding a primary reliable optimum point;Phase 3, applying an intelligent self-adaptive procedure based on cubic B-spline interpolation functions until the targeted failure probability is reached. An improved response surface method is used for computation of failure probability. The proposed RBDO approach could significantly reduce the number of analyses required to less than 10% of conventional methods. The computational efficacy of this approach is demonstrated by solving four benchmark truss design problems published in the structural optimization literature.展开更多
文摘This paper presents an automatic programming system on PC, it has also solved the technic problem in the combination of different curves or surfaces. The NURBS is applied to modeling and fitting complicated curves and surfaces. The circular spline is combined with the NURBS to determine the cutter path in accordance with the features of the interpolation movement of NC machine tool. Three methods have been developed to solve the overcutting problems.
文摘In a reliability-based design optimization (RBDO), computation of the failure probability (Pf) at all design points through the process may suitably be avoided at the early stages. Thus, to reduce extensive computations of RBDO, one could decouple the optimization and reliability analysis. The present work proposes a new methodology for such a decoupled approach that separates optimization and reliability analysis into two procedures which significantly improve the computational efficiency of the RBDO. This technique is based on the probabilistic sensitivity approach (PSA) on the shifted probability density function. Stochastic variables are separated into two groups of desired and non-desired variables. The three-phase procedure may be summarized as: Phase 1, apply deterministic design optimization based on mean values of random variables;Phase 2, move designs toward a reliable space using PSA and finding a primary reliable optimum point;Phase 3, applying an intelligent self-adaptive procedure based on cubic B-spline interpolation functions until the targeted failure probability is reached. An improved response surface method is used for computation of failure probability. The proposed RBDO approach could significantly reduce the number of analyses required to less than 10% of conventional methods. The computational efficacy of this approach is demonstrated by solving four benchmark truss design problems published in the structural optimization literature.
