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This paper presents an approach to design composite panels via multiple stacking sequence tables (SST) such that the continuity constraints between adjacent regions are maintained. Traditional SST methods determine all the stacking sequences with only one SST, but this simplification limits the design option space. To increase the design freedom, this research utilizes multiple SSTs to blend the stacking sequences of a laminated structure. In the design process of the proposed approach, the monotonicity property of predicted laminate thicknesses is employed to determine the number of SSTs, and an SST rebuilding method is developed to satisfy the blending constraints. In the implementation of the simulated annealing algorithm for solving the optimal design problem, an SST difference code is introduced to represent feasible solutions, and a particular neighborhood structure is proposed to sufficiently explore the solutions in design space. Finally, the 18-region benchmark problem is chosen to validate the efficiency and accuracy of the proposed method. The results reveal that, compared with other existing methods, the proposed method can generate manufacturable solutions with lower weights under the symmetry and balance constraints.

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Computer-aided process planning (CAPP) plays an important role in integrated manufacturing system and it can serve as a bridge between CAD and CAM. As a crucial part of CAPP, setup planning is a multi-constraint problem, in which the precision takes priority over efficiency. However, instead of precision constraints, traditional optimization methods have paid much more attention to efficiency requirements. This leads to the reduction in the precision of the final parts. This paper develops an optimization approach for solving computer-aided setup planning problem, which takes into account various constraints, especially the precision requirements specified by designers. First, objective function of the optimization model is formulated and a series of constraints, including feature precedence, tool approaching direction (TAD), and precision requirements are systematically created. Next, the model is solved by using a hybrid particle swarm optimization algorithm. In order to overcome the local optimum trap, mutation and exchange operations are adopted from the genetic algorithm. Finally, a part is tested in the case study and the validation of this method is proved.

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