The hybrid approach of genetic algorithm and particle swarm optimization on reduced weld line defect in plastic injection molding

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dc.authorscopusid8952893400en_US
dc.authorscopusid6603118192en_US
dc.authorscopusid59251095400en_US
dc.authorscopusid56587590700en_US
dc.contributor.authorOktem, Hasan
dc.contributor.authorUygur, Ilyas
dc.contributor.authorSari, Ece Simooglu
dc.contributor.authorShinde, Dinesh
dc.date.accessioned2024-08-23T16:04:06Z
dc.date.available2024-08-23T16:04:06Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractWeld lines are a serious defect observed in plastic injection molded parts, impacting both their cosmetic appearance and mechanical properties. Controlling the conditions of plastic injection is crucial to mitigate these weld lines. This study introduces a novel approach to identify polypropylene injection molding (PIM) conditions aimed at reducing weld lines in polypropylene parts. The PIM conditions considered in this study include melt temperature, injection pressure, packing pressure, packing time, and cooling time. An orthogonal array Taguchi L27 design was employed for the experimental setup, producing 27 polypropylene parts with varying combinations of process conditions. The width of weld lines generated on the parts' surfaces was measured using an optimum microscope for all trials. Parametric analysis was conducted using response surface plots and contour plots to estimate the process conditions yielding minimum weld lines. Analysis of variance and regression analysis were employed to interpret the experimental data, with the resulting regression equation used to predict weld lines for a set of PIM process conditions. Finally, two efficient optimization algorithms, genetic algorithm (GA), and particle swarm optimization (PSO), were implemented using MATLAB programming to estimate the optimum process conditions for minimizing weld lines. The GA and PSO predicted weld line widths of 6.12302 mu m and 6.123 mu m, respectively, representing an 18.51% improvement in results. These findings demonstrate that the novel approach presented in this study can be effectively and reliably applied to address plastic product defects in the industry.en_US
dc.description.sponsorshipKocaeli University [KOU-BAP-2011/77]en_US
dc.description.sponsorshipThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Kocaeli University (KOU-BAP-2011/77).en_US
dc.identifier.doi10.1177/14777606241270516
dc.identifier.issn1477-7606
dc.identifier.issn1478-2413
dc.identifier.scopus2-s2.0-85200691938en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1177/14777606241270516
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14072
dc.identifier.wosWOS:001285217900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofProgress in Rubber Plastics And Recycling Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPlastic injection moldingen_US
dc.subjectprocess conditionsen_US
dc.subjectanalysis of varianceen_US
dc.subjectparametric analysisen_US
dc.subjectgenetic algorithmen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectProcess Parameter Optimizationen_US
dc.subjectMechanical-Propertiesen_US
dc.subjectMicrostructureen_US
dc.subjectWeldlinesen_US
dc.titleThe hybrid approach of genetic algorithm and particle swarm optimization on reduced weld line defect in plastic injection moldingen_US
dc.typeArticleen_US

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