Acoustic Emission and Machine Learning Approaches for Assessing Mechanical Degradation in Aged Unidirectional Glass Fiber-Reinforced Thermoplastics

Jorge Palacios Moreno, Pierre Mertiny

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DOI: 10.3390/metrology6010011
Published: 2026-02-13
Container: Metrology
Publisher: MDPI AG
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3390/metrology6010011,
  title = {Acoustic Emission and Machine Learning Approaches for Assessing Mechanical Degradation in Aged Unidirectional Glass Fiber-Reinforced Thermoplastics},
  author = {Jorge Palacios Moreno and Pierre Mertiny},
  year = {2026},
  journal = {Metrology},
  doi = {10.3390/metrology6010011},
  url = {https://doi.org/10.3390/metrology6010011}
}

RIS

TY  - JOUR
TI  - Acoustic Emission and Machine Learning Approaches for Assessing Mechanical Degradation in Aged Unidirectional Glass Fiber-Reinforced Thermoplastics
AU  - Jorge Palacios Moreno
AU  - Pierre Mertiny
PY  - 2026
JO  - Metrology
DO  - 10.3390/metrology6010011
UR  - https://doi.org/10.3390/metrology6010011
ER  -

APA

Moreno, J. P., & Mertiny, P. (2026). Acoustic Emission and Machine Learning Approaches for Assessing Mechanical Degradation in Aged Unidirectional Glass Fiber-Reinforced Thermoplastics. Metrology. https://doi.org/10.3390/metrology6010011

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crossref · retrieved 2026-09-25T05:06:56.551Z