Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites (Record no. 53398)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250625162427.0 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | B-19260 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250602s9999 xx |||||s2 |||| ||und|d |
| 245 10 - TITLE STATEMENT | |
| Title | Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites |
| 490 0# - SERIES STATEMENT | |
| Volume/sequential designation | Smart Materials and structures, 30(8), p.085010, 2021 |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | The mechanical properties of fiber reinforced polymer matrix composites are known to gradually deteriorate as fatigue damage accumulates under cyclic loading conditions. While the steady degradation in elastic stiffness throughout fatigue life is a well-established and studied concept, it remains difficult to continuously monitor such structural changes during the service life of many dynamic engineering systems where composite materials are subjected to random and unexpected loading conditions. Recently, laser induced graphene (LIG)has been demonstrated to be a reliable, in-situ strain sensing and damage detection component in fiberglass composites under both quasi-static and dynamic loading conditions. This work investigates the potential of exploiting the piezoresistive properties of LIG interlayered fiberglass composites in order to formulate cumulative damage parameters and predict both damage progression and fatigue life using artificial neural networks (ANNs)and conventional phenomenological models. The LIG interlayered fiberglass composites are subjected to tension-tension fatigue loading, while changes in their elastic stiffness and electrical resistance are monitored through passive measurements. Damage parameters that are defined according to changes in electrical resistance are found to be capable of accurately describing damage progression in LIG interlayered fiberglass composites throughout fatigue life, as they display similar trends to those based on changes in elastic stiffness. These damage parameters are then exploited for predicting the fatigue life and future damage state of fiberglass composites using both trained ANNs and phenomenological degradation and accumulation models in both specimen-to-specimen and cycle-to-cycle schemes. When used in a specimen-to-specimen scheme, the predictions of a two-layer Bayesian regularized ANN with 40 neurons in each layer are found to be at least 60 percent more accurate than those of phenomenological degradation models, displaying R2 values greater than 0.98 and root mean square error (RMSE)values smaller than 10?3. A two-layer Bayesian regularized ANN with 25 neurons in each layer is also found to yield accurate predictions when used in a cycle-to-cycle scheme, displaying R2 values greater than 0.99 and RMSE values smaller than 2 × 10?4 once more than 30 percent of the initial measurements are used as inputs. The final results confirm that piezoresistive LIG interlayers are a promising tool for achieving accurate and continuous fatigue life predictions in multifunctional composite structures, specifically when coupled with machine learning algorithms such as ANNs. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | LASER INDUCED GRAPHENE |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | FIBERGLASS COMPOSITES |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | FATIGUE LIFE PROGNOSIS |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | ARTIFICIAL NEURAL NETWORKS |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | PHENOMENOLOGICAL MODELS |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Nasser, J. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Groo, L. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sodano, H. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://drive.google.com/file/d/1431kNbF7Yy-F_HACOHaMN4AkyMXDlRbb/view?usp=drivesdk">https://drive.google.com/file/d/1431kNbF7Yy-F_HACOHaMN4AkyMXDlRbb/view?usp=drivesdk</a> |
| Public note | Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Clasificación local |
| Koha item type | Documentos solicitados |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Price effective from | Koha item type |
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| Clasificación local | Ref1 | CICY | CICY | Documento préstamo interbibliotecario | 25.06.2025 | B-19260 | 25.06.2025 | 25.06.2025 | Documentos solicitados |