Parametric Investigation of Injection Moulding for LDPE using Capuchin Search Algorithm and Honey Badger Algorithm

Abstract

AbstractIn our rapidly expanding world, there is a rising demand for low-cost, long-lasting, and less dangerous medicinal products. Medical items ranging from intravenous fluid containers to medical syringes are created to satisfy their demands using a range of thermoplastics and Plastic Injection Moulding (PIM). However, even advanced profile mouldings can suffer from dimensional inaccuracies. The current study advances our understanding of thermoplastics, specifically Low-Density Polyethylene (LDPE) material moulding for medical syringe plungers using injection moulding equipment. Eight injection moulding input factors were investigated in order to decrease the depth sink marks and weight created during the injection moulding of thermoplastic LDPE material. The 27 trials were piloted in accord with Taguchi's Design of Experiment, and the variables were optimised using the newly developed Honey Badger and Capuchin Search Algorithms, as well as analysis of variance, for determining the most dominating parameter. The cooling time and melt temperature of the plastic injection moulded part are the most significant factors influencing the sink-mark depth and weight of the part, respectively, according to the Analysis of Variance (ANOVA) test.KeywordsLow-density polyethylene (LDPE)Plastic injection mouldingMedical syringeTaguchi methodHoney badger algorithmCapuchin search algorithm