Multi-objective runner optimization of injection mold based on variable-grain strategy

Present approaches for the runner optimization of multi-cavity injection molds usually simplify the complex problem and aim at achieving either balanced melt flow or low production cost. The optimal schemes in the point of view of injection molding practice can hardly be obtained due to the lack of comprehensive and exact criteria for evaluating different schemes. A variable-grain strategy is proposed to tackle the complex problem with multiple objectives and restrictions involved. Mathematical models of various grains are established, which include cost, efficiency and the uniformity of product quality as criteria for evaluating different runner schemes. The coarse-grain model is utilized in the early design to efficiently determine the direction to the globally optimal solution. Then more accurate models can be utilized during the optimization procedure and finally the optimal schemes evaluated from comprehensive point of view can be acquired on the basis of the fine-grain model. An algorithm is put forward for fast locating the Pareto optimal sets of multi-objective optimization problem based on flow simulation and superiority-of-feasible-points criterion, which is used to obtain the Pareto optimal sets of the runner optimization models in various grains. The feasibility of proposed strategy and algorithm is demonstrated through the runner optimization of a compound-cavity mold for producing mice.