{
  "abstract": "Injection molding technology involves fully melting plastic material through screw agitation at a specific temperature, injecting it into the mold cavity under high pressure, and obtaining the molded part after cooling and solidification. Injection molding offers high production efficiency while meeting the molding demands of complex plastic components. However, enhancing the dimensional accuracy of injection-molded parts has become a critical aspect of this process. The injection-molded components of the XG-99Q2 light guide column were selected as the subject of this study. Initially, three runner system designs were created: “Wang” shape, ‘Gong’ shape, and “Yi” shape configurations. Each design underwent process analysis for its respective injection molding process. Subsequently, six fundamental injection parameters were identified, and an L32 orthogonal experimental design was employed to systematically investigate their effects on two critical objectives: plastic volume shrinkage and warpage deformation. The experimental design incorporated signal-to-noise ratio processing, with weights for total warpage and volume shrinkage derived using the entropy weight method. This approach facilitated qualitative analysis of gray correlation coefficients and correlations among identified factors, revealing significant relationships between injection time, holding pressure, and melt temperature. Prediction models for warpage and shrinkage were developed using the Box-Behnken experimental design from response surface methodology, providing a foundation for comprehensive quantitative analysis. Additionally, the NSGA-II algorithm was applied to multi-objective optimization, yielding a set of Pareto optimal solutions. Finally, the reliability of the method was verified through simulation based validation.",
  "abstract_shown": true,
  "affiliations": [
    "Anhui Technical College of Water Resources and Hydroelectric Power, Hefei, China."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Huajun Xu",
      "orcid": "0009-0000-4034-3962"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d1pxytmrw2n90.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0361371",
  "fulltext_url": "https://europepmc.org/articles/PMC13722155",
  "id": "d1pxytmrw2n90",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0361371",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Algorithms"
    ],
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      "heading",
      "Applied Mathematics"
    ],
    [
      "heading",
      "Classical Mechanics"
    ],
    [
      "heading",
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    ],
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      "heading",
      "Deformation"
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    ],
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      "heading",
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      "heading",
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    ],
    [
      "heading",
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    [
      "heading",
      "Optimization"
    ],
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      "heading",
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    ],
    [
      "heading",
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    ],
    [
      "heading",
      "Plastics"
    ],
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      "heading",
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    ],
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      "heading",
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    ],
    [
      "heading",
      "Research Design"
    ],
    [
      "heading",
      "Research and Analysis Methods"
    ],
    [
      "heading",
      "Signal Processing"
    ],
    [
      "heading",
      "Signal to Noise Ratio"
    ],
    [
      "heading",
      "Simulation and Modeling"
    ],
    [
      "heading",
      "Thermodynamics"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Signal to noise ratio"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Plastics"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Optimization"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Damage mechanics/Deformation"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Deformation"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Thermodynamics/Entropy"
    ],
    [
      "plos",
      "/Research and analysis methods/Research design/Experimental design"
    ],
    [
      "plos",
      "/Research and analysis methods/Research design/Quantitative analysis"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://europepmc.org/articles/PMC13722155?pdf=render",
  "pmcid": "PMC13722155",
  "pmid": "42961894",
  "published": "2026-10-30",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/d1pxytmrw2n90.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:20:55Z",
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      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0361371"
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    {
      "first_seen": "2026-11-02T03:21:21Z",
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      "source": "europepmc",
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    {
      "first_seen": "2026-11-03T12:22:43Z",
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  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "plos:materials science",
      "subfield": "materials-polymers"
    },
    {
      "field": "mathematics",
      "origin": "heading:Mathematics",
      "subfield": "general"
    },
    {
      "field": "physics",
      "origin": "plos:thermodynamics",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Multi-objective optimization of light guide column injection molding process using RSM and NSGA‑II with numerical verification",
  "title_text": "Multi-objective optimization of light guide column injection molding process using RSM and NSGA‑II with numerical verification",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:43Z",
  "url": "https://research.draconitic.com/paper/d1pxytmrw2n90",
  "venue": "PLOS One",
  "versions": []
}