Gate positioning design of injection mould using bi-objective micro genetic algorithm

Abstract

The use of a micro genetic algorithm (mGA)-based approach to solve a bi-objective optimization of an injection mould design problem is presented. The advantage of the mGAbased approach is that it requires fewer computational resources than a conventional GA because it has a smaller population than a conventional GA. The main drawback of the mGAbased approach is that design diversity is not secured when multi-modal and multi-objective designs are investigated. To implement the mGA-based bi-objective optimization procedure, the present study proposes a memory set, a filtering process, weight control, and reproduction from the memory set in order to explore new optimal solutions, and identify more-evenly distributed Pareto surfaces. A number of mathematical functions and a typical structural optimization problem are tested to verify the proposed strategies. The approach is subsequently applied to the bi-objective injection moulding design problem of minimizing both the maximum injection pressure and maximum pressure difference between the gate positions in the runner system.

Optimum Design of Runner System for Router Cover Based on Mold Flow Analysis Technology

“…Section size and length of the runner system affect the pressure loss of melt flow directly [2,3]. Gates location and its number affect the flow behavior of melt in the cavity and eventually affect the quality of product [4]. Using traditional methods which rely on the experience to design gating system can not gain a better runner system, and if we encounter a complex part or not covered before, the gate design can not success in once, we often have to go through a trial mode-modify-try mode-and the modify the phase, that is a great waste for time and cost.…”