An LSTM-driven thermoelectric coupling response prediction method for shape memory alloy actuators
Abstract Shape memory alloys (SMAs) show exceptional potential in actuator design due to their shape memory effect and superelasticity, yet their thermoelectric hysteresis challenges accurate modeling. This study proposes a hybrid framework integrating long short-term memory (LSTM) networks with phy...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-02306-2 |
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| author | Shaozhe Ding Longbin Liu Shifeng Zhang Mingkun Li |
| author_facet | Shaozhe Ding Longbin Liu Shifeng Zhang Mingkun Li |
| author_sort | Shaozhe Ding |
| collection | DOAJ |
| description | Abstract Shape memory alloys (SMAs) show exceptional potential in actuator design due to their shape memory effect and superelasticity, yet their thermoelectric hysteresis challenges accurate modeling. This study proposes a hybrid framework integrating long short-term memory (LSTM) networks with physical kinematics to predict SMA actuator responses. Unlike conventional approaches, our method decouples material behavior prediction from actuator geometry: A single-layer LSTM network processes voltage-time sequences to predict SMA wire’s temperature and resistance dynamics, while a physics-based model computes angular displacement through phase transformation and constitutive equations. Trained on 10 experimental conditions (1–2 °C/s heating rates) and tested on 3 unseen cases (0.8 °C/s), the model achieves a mean absolute error of < 5% in angular displacement prediction, with root mean square errors of 2.5 × 10−5 for temperature/resistance outputs. The modular architecture eliminates neural network retraining during structural iterations—only ordinary differential equation parameters require adjustment. This approach advances rapid actuator optimization, demonstrating faster computational efficiency compared to full-physics models. Our findings establish a computationally sustainable paradigm for smart material-based actuation systems, extensible to piezoelectric and magnetostrictive materials through constitutive parameter substitution. |
| format | Article |
| id | doaj-art-0eb47b7717fb4b55b0712a3bcda493a2 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0eb47b7717fb4b55b0712a3bcda493a22025-08-20T01:53:12ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-02306-2An LSTM-driven thermoelectric coupling response prediction method for shape memory alloy actuatorsShaozhe Ding0Longbin Liu1Shifeng Zhang2Mingkun Li3College of Aerospace Science and Engineering, National University of Defense TechnologyCollege of Aerospace Science and Engineering, National University of Defense TechnologyCollege of Aerospace Science and Engineering, National University of Defense TechnologyCollege of Aerospace Science and Engineering, National University of Defense TechnologyAbstract Shape memory alloys (SMAs) show exceptional potential in actuator design due to their shape memory effect and superelasticity, yet their thermoelectric hysteresis challenges accurate modeling. This study proposes a hybrid framework integrating long short-term memory (LSTM) networks with physical kinematics to predict SMA actuator responses. Unlike conventional approaches, our method decouples material behavior prediction from actuator geometry: A single-layer LSTM network processes voltage-time sequences to predict SMA wire’s temperature and resistance dynamics, while a physics-based model computes angular displacement through phase transformation and constitutive equations. Trained on 10 experimental conditions (1–2 °C/s heating rates) and tested on 3 unseen cases (0.8 °C/s), the model achieves a mean absolute error of < 5% in angular displacement prediction, with root mean square errors of 2.5 × 10−5 for temperature/resistance outputs. The modular architecture eliminates neural network retraining during structural iterations—only ordinary differential equation parameters require adjustment. This approach advances rapid actuator optimization, demonstrating faster computational efficiency compared to full-physics models. Our findings establish a computationally sustainable paradigm for smart material-based actuation systems, extensible to piezoelectric and magnetostrictive materials through constitutive parameter substitution.https://doi.org/10.1038/s41598-025-02306-2 |
| spellingShingle | Shaozhe Ding Longbin Liu Shifeng Zhang Mingkun Li An LSTM-driven thermoelectric coupling response prediction method for shape memory alloy actuators Scientific Reports |
| title | An LSTM-driven thermoelectric coupling response prediction method for shape memory alloy actuators |
| title_full | An LSTM-driven thermoelectric coupling response prediction method for shape memory alloy actuators |
| title_fullStr | An LSTM-driven thermoelectric coupling response prediction method for shape memory alloy actuators |
| title_full_unstemmed | An LSTM-driven thermoelectric coupling response prediction method for shape memory alloy actuators |
| title_short | An LSTM-driven thermoelectric coupling response prediction method for shape memory alloy actuators |
| title_sort | lstm driven thermoelectric coupling response prediction method for shape memory alloy actuators |
| url | https://doi.org/10.1038/s41598-025-02306-2 |
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