{
  "abstract": "The prediction of fatigue life is critical in the design process, and current models offer a viable alternative to costly and time-consuming experimental fatigue testing. The constitutive fatigue model used integrates low-cycle and high-cycle fatigue behavior. This model is grounded on the concept of fatigue damage evolution and incorporates a moving endurance surface within the stress space, eliminating the need for ambiguous cycle-counting methods. An interesting observation is that many polymers exhibit macroscopic fatigue characteristics, specifically, the form of the S-N curve similar to those observed in metals. Consequently, all fatigue model parameters were expressed in terms of the well-established Coffin–Manson–Basquin model parameters. However, the constitutive mathematical modeling itself is computationally time-consuming, particularly when applied to predict high-cycle fatigue across large design spaces. Therefore, the proposed model was utilized exclusively to generate high-quality data for training machine learning models that offer significantly improved computational efficiency. The high-cycle fatigue design of polymers and other ductile materials, traditionally dependent on expensive and time-consuming experimental methods, is now expedited through an advanced modeling framework that combines constitutive mathematical modeling with AI-based approaches.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Thierry Barrière"
    },
    {
      "name": "Stanislas Carbillet"
    },
    {
      "name": "Xavier Gabrion"
    },
    {
      "name": "Christophe Guyeux"
    },
    {
      "name": "Sami Holopainen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/egbejbf1wb2v8.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": null,
  "id": "egbejbf1wb2v8",
  "issns": [
    "2073-4360"
  ],
  "landing_url": "https://hal.science/hal-05605032v1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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  "openalex": null,
  "pdf_url": "https://hal.science/hal-05605032/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": "MDPI",
  "ris": "https://research.draconitic.com/paper/egbejbf1wb2v8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "hal-05605032",
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      "url": "https://hal.science/hal-05605032v1"
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  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2073-4360",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Fast Fatigue Life Prediction of Polymers Through Combined Constitutive Mathematical and AI-Based Modeling",
  "title_text": "Fast Fatigue Life Prediction of Polymers Through Combined Constitutive Mathematical and AI-Based Modeling",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/egbejbf1wb2v8",
  "venue": "Polymers",
  "versions": []
}