{
  "abstract": "Conical picks are critical components in key excavation equipment (including shearers, continuous miners, and roadheaders) employed for rock fragmentation in mining and civil engineering. This study proposes the multi-fidelity transfer learning multi-layer perceptron (MF-TLMLP) method to predict cutting performance. Addressing the scarcity of high-fidelity (HF) data, MF-TLMLP integrates low-fidelity (LF) and HF data through a multi-channel fusion and transfer learning approach. Simulations using Latin hypercube sampling (LHS) generated 2500 LF samples and 500 HF samples. Compared to traditional Multi-Layer Perceptron (MLP) and transfer learning models, MF-TLMLP demonstrated superior performance, achieving a higher coefficient of determination (R2) and a lower mean squared error (MSE). Morris screening based on mean absolute elementary effects revealed output-dependent sensitivities, with cutting thickness dominating the force responses and cutting speed dominating the pulse-number responses within the sampled domain. These results demonstrate the potential of MF-TLMLP for predicting HF numerical cutting responses for the investigated rock properties, parameter ranges, and adjacent double-pick configuration.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China.",
    "Sany Heavy Equipment International Holdings Co., Ltd., Shenyang, China."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Meng Zhou"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaofei Song"
    },
    {
      "affs": [
        0
      ],
      "name": "Lingtao Weng",
      "orcid": "0000-0003-2076-9653"
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    {
      "affs": [
        0
      ],
      "name": "Weiguo Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Ketong Dong"
    },
    {
      "affs": [
        0
      ],
      "name": "Guandong Lu"
    },
    {
      "affs": [
        1
      ],
      "name": "Ruilin Ni"
    },
    {
      "affs": [
        0
      ],
      "name": "Dawei Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5drvra3ksqan4.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359537",
  "fulltext_url": "https://europepmc.org/articles/PMC13693935",
  "id": "5drvra3ksqan4",
  "issns": [
    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0359537",
  "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": [
    [
      "openalex",
      "2205|Tunneling and Rock Mechanics"
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      "plos",
      "/Biology and life sciences/Computational biology/Computational neuroscience/Artificial neural networks"
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      "plos",
      "/Biology and life sciences/Neuroscience/Neuronal tuning"
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Artificial neural networks"
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Machine learning algorithms/Perceptrons/Multilayer perceptrons"
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      "/Physical sciences/Mathematics/Applied mathematics/Finite element analysis"
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      "/Research and analysis methods/Simulation and modeling"
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      "/Research and analysis methods/Simulation and modeling/Algorithms/Machine learning algorithms/Perceptrons/Multilayer perceptrons"
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  "pdf_url": "https://europepmc.org/articles/PMC13693935?pdf=render",
  "pmcid": "PMC13693935",
  "pmid": "42912751",
  "published": "2026-10-23",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/5drvra3ksqan4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.1371/journal.pone.0359537",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359537"
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    {
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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  ],
  "title": "MF-TLMLP: A multi-channel fusion-based multi-fidelity transfer learning method for predicting the cutting performance of conical picks",
  "title_text": "MF-TLMLP: A multi-channel fusion-based multi-fidelity transfer learning method for predicting the cutting performance of conical picks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:03Z",
  "url": "https://research.draconitic.com/paper/5drvra3ksqan4",
  "venue": "PLOS One",
  "versions": []
}