Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach

Abstract

Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. Also, the information represented by current process plan models for three-axis machining is not sufficient for five-axis machining owing to the two extra degrees of freedom and the difficulty of set-up planning. In this paper, a representation of process plans for five-axis machining is proposed, and the complicated operation sequencing process is modelled as a combinatorial optimization problem. A modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles ‘fly’ intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particle movements to form a modified PSO algorithm. A case study used to verify the performance of the modified PSO algorithm shows that the developed PSO can generate satisfactory results in optimizing the process planning problem.

Pallet operation sequencing based on network part program logic

“…Because of the contemporary application of constraints (2) and (3), each MWS can be executed only once.…”

“…The pallet MWSs (12,13,14,15,29,30,31,32) are locally renumbered (1,2,3,4,5,6,7,8) and the fictitious MWS 0 is added to the sub-group.…”

“…The input MWSs are the extremities of the ordered 6 sub-groups (not considering the fictitious MWSs): 18,1,19,2,20,3,8,23,12,29,34,17. These extremities are respectively renamed 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12. The fictitious MWS 0 is added to the MWSs.…”

“…In such a context, the productivity can be maximised by flexible process planning systems [1,2]. On the one hand, these systems allow the saturation of the machining tool through the mixing of different workpieces on the same pallet (pallet configuration); on the other hand, they cope with unpredictable changes in shop-floor conditions, i.e., an unavailable tool or the breakdown of a machine tool, through a minimum cost re-planning of the previously defined processes.…”

Automated setup planning in CAPP: a modified particle swarm optimisation-based approach

“…During the generation of the SPG, the minimal number of tolerance violations is guaranteed preferentially by the vertex clusters algorithm for serial vertices and the minimal number of setups is achieved using variants of the breadth-first search. Guo et al (2006Guo et al ( , 2009 modelled operation sequencing as a combinatorial optimisation problem, and a modern evolutionary algorithm, i.e. the particle swarm optimisation (PSO) algorithm, was employed and modified to solve it effectively.…”