{
  "abstract": "Control of mold-cavity surface temperature strongly affects filling, premature frozen-layer formation, and cycle time in thin-wall injection molding. This study evaluates an induction self-heating insert with a thin heating zone directly coupled to a cooling layer using experiments, a sequentially coupled electromagnetic-thermal model, and an Artificial Neural Network (ANN). The design space contains 25 Taguchi L25 geometries defined by T, W, and L; X and Y coordinates are added so that the ANN predicts the surface temperature field. Three cooling states are considered: No Water (NW), Stagnant Water (SW), and Flowing Water (FW). Experimental ANN targets are the mean of five repeated tests. The simulation reproduced the location of the hot zone; among four cases with reported quantitative differences, three differed by no more than about 10 °C and one showed a peak difference of about 20 °C. To prevent data leakage, all 846 points from each geometry were kept together during geometry-wise validation. The resulting R² values were 0.746, 0.653, and 0.493 for NW, SW, and FW, with Root Mean Square Error (RMSE) values of 17.03, 12.97, and 12.62 °C, respectively. The effects of T, W, and L depended on the cooling state and were not consistently monotonic. A proof-of-concept molding test showed complete filling of a Chip 2 part using Low-Density Polyethylene (LDPE); the mean spacing was 28.6 µm for a nominal 30 µm, and the mean height was 106.6 µm for a nominal 100 µm. These results show that combining insert geometry, cooling-water state, and a surrogate model can support local thermal design, delay frozen-layer formation, and improve filling in injection molding.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Mechanical Engineering, HCM City University of Technology and Engineering, Ho Chi Minh City, Vietnam"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Thanh Trung Do"
    },
    {
      "affs": [
        0
      ],
      "name": "Tran Minh The Uyen"
    },
    {
      "affs": [
        0
      ],
      "name": "Pham Son Minh",
      "orcid": "0000-0002-8236-2202"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7sb40vyr1dkhf.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359849",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13712609/",
  "id": "7sb40vyr1dkhf",
  "issns": [
    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0359849",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
    [
      "heading",
      "Artificial Intelligence"
    ],
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      "heading",
      "Artificial Neural Networks"
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      "heading",
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      "heading",
      "Convection"
    ],
    [
      "heading",
      "Electricity"
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    ],
    [
      "heading",
      "Interpolation"
    ],
    [
      "heading",
      "Macromolecules"
    ],
    [
      "heading",
      "Material Properties"
    ],
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      "heading",
      "Materials"
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      "Polymers"
    ],
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      "heading",
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    ],
    [
      "heading",
      "Surface Properties"
    ],
    [
      "heading",
      "Surface Temperature"
    ],
    [
      "heading",
      "Temperature Gradients"
    ],
    [
      "heading",
      "Thermal Conductivity"
    ],
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      "heading",
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    ],
    [
      "openalex",
      "2210|Injection Molding Process and Properties"
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      "/Biology and life sciences/Computational biology/Computational neuroscience/Artificial neural networks"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359849&type=printable",
  "pmcid": "PMC13712609",
  "pmid": "42945195",
  "published": "2026-10-27",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/7sb40vyr1dkhf.ris",
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    {
      "first_seen": "2026-10-30T12:17:17Z",
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  "subjects": [
    {
      "field": "biology",
      "origin": "heading:Computational Biology",
      "subfield": "bioinformatics"
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    {
      "field": "biology",
      "origin": "heading:Neuroscience",
      "subfield": "neuroscience"
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    {
      "field": "chemistry",
      "origin": "plos:polymer chemistry",
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      "origin": "heading:Numerical Analysis",
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      "field": "physics",
      "origin": "heading:Thermodynamics",
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  "title": "Geometry and cooling effects on an induction self-heating insert for rapid mold temperature control: Experimental, Multiphysics, and artificial neural network-based analysis",
  "title_text": "Geometry and cooling effects on an induction self-heating insert for rapid mold temperature control: Experimental, Multiphysics, and artificial neural network-based analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:23:09Z",
  "url": "https://research.draconitic.com/paper/7sb40vyr1dkhf",
  "venue": "PLOS One",
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}