Optimization of machining feature sequencing based on bi-level directed acyclic graphs

The major challenge of machining feature (MF) sequencing and setup partitioning is how to minimize processing cost while satisfying various kinds of constraints including precedence constraints and joint constraints. In this paper, a bi-level directed acyclic graph is developed to deal with different kinds of constraints, which can be applied to large-scale optimization problems with huge constraints. Moreover, the classic heuristic algorithm genetic algorithm and forward topological sorting algorithm are combined together to optimize the machining feature sequencing and setup partitioning. Finally, large-scale data experiments are used to test the feasibility of the algorithm.