Tool sequence optimization using synchronous and asynchronous parallel multi-objective evolutionary algorithms with heterogeneous evaluations

Selecting the sequence of tools to use for the rough machining of components is an important task in manufacturing, which greatly affects the overall machining time and cost of the process. In this paper a multi-objective approach is presented, which supports the use of tools with different geometrical properties and offers the process planner a set of Pareto optimal solutions. An industrial simulator is employed, which allows important information to be captured in the model but has the disadvantage of being computationally expensive. A master/slave approach to parallelization is implemented, which can be used on existing grid or cloud computing infrastructures. Synchronous generational and asynchronous steady-state multi-objective algorithms are compared on their search performance and runtimes on two components. Particular attention is paid to potential problems faced by asynchronous search caused by heterogeneous evaluation times due to characteristics present in individual tool sequences. Results show that the algorithms achieve a similar search performance, with the synchronous algorithm occasionally finding a slightly more diverse spread of solutions. However, the asynchronous algorithm is considerably faster, and provides good solutions in a short runtime that means this approach could be easily and inexpensively implemented in an industrial setting.