Multi-objective tool sequence and parameter optimization for rough milling applications

In this paper a new, evolutionary multi-objective approach is introduced to tool sequence optimization in rough milling. Previous research has focused on the optimization of either the tool sequence or associated cutting parameters. Here, the tool sequence and a machining parameter, the cutting speeds of the individual tools, are simultaneously optimized, producing a Pareto front with both discrete and continuous properties. This is the first time that a multiple-tool multi-objective approach has been taken to tool selection, offering a set of solutions to the process planner. Three objectives are considered, thickness of excess stock, machining time and tooling costs. Unconstrained NSGA-II is used as the base algorithm but several preferential search strategies are tested to attempt to deal with constraints and guide search towards the Pareto optimal front. These include the established reference point (R-NSGA-ii) and weighted objective (WO) methods, as well as two novel techniques - “Guided Elitism” (GE) and “Precedential Objective Order Ranking” (PR). While WO performs best on average when assessed using the hypervolume indicator, the algorithms behave differently in terms of the quality and diversity of solutions found. A hybrid method using GE for exploration and PR for exploitation is shown to outperform the other techniques across all performance measures.