Abstract
In order for a plastic part manufactured by the injection moulding process to be of the desired quality, it is necessary to maintain control of the process parameters. To increase productivity and reduce defects in parts, various studies have been carried out to propose and evaluate methods for determining injection parameters, which are based on the use of experimental planning techniques as well as the application of optimisation algorithms. This work proposes an experimental design with 6 injection parameters (mould temperature, melt temperature, injection time, percentage of volume filled, packing time and packing pressure) to determine the initial search space for the NSGA-II and MOEA/D optimisation algorithms in relation to the use of aluminium moulds. This procedure was used via a computer code described in the PYTHON programming language to determine the appropriate injection parameters for minimising part warpage and injection pressure. The results showed that an increase in compaction pressure is associated with a reduction in warpage and vice versa. It was observed that the NSGA-II algorithm showed a greater diversity of points on the approximate Pareto frontier compared to the MOEA/D algorithm. In the case of the proposed study, MOEA/D presented a more viable solution, determining a lower packing pressure, less warping of the part and a shorter cycle time. The NSGA-II algorithm required around 4 hours more computational time than MOEA/D.
Published Version
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