Stiffness estimation of planar spiral spring based on Gaussian process regression
Planar spiral spring is important for the dimensional miniaturisation of motor-based elastic actuators. However, when the stiffness calculation of the spring arm is based on simple beam bending theory, the results possess substantial errors compared with the stiffness obtained from finite-element an...
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Nature Research
2023
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my.uniten.dspace-266552023-05-29T17:36:04Z Stiffness estimation of planar spiral spring based on Gaussian process regression Liu J. Abu Osman N.A. Al Kouzbary M. Al Kouzbary H. Abd Razak N.A. Shasmin H.N. Arifin N. 57223432161 8511221500 57202956887 57216612501 42261165400 35778974400 18133590700 Planar spiral spring is important for the dimensional miniaturisation of motor-based elastic actuators. However, when the stiffness calculation of the spring arm is based on simple beam bending theory, the results possess substantial errors compared with the stiffness obtained from finite-element analysis (FEA). It deems that the errors arise from the spiral length term in the calculation formula. Two Gaussian process regression models are trained to amend this term in the stiffness calculation of spring arm and complete spring. For the former, 216 spring arms� data sets, including different spiral radiuses, pitches, wrap angles and the stiffness from FEA, are employed for training. The latter engages 180 double-arm springs� data sets, including widths instead of wrap angles. The simulation of five spring arms and five planar spiral springs with arbitrary dimensional parameters verifies that the absolute values of errors between the predicted stiffness and the stiffness from FEA are reduced to be less than 0.5% and 2.8%, respectively. A planar spiral spring for a powered ankle�foot prosthesis is designed and manufactured to verify further, of which the predicted value possesses a 3.25% error compared with the measured stiffness. Therefore, the amendment based on the prediction of trained models is available. � 2022, The Author(s). Final 2023-05-29T09:36:04Z 2023-05-29T09:36:04Z 2022 Article 10.1038/s41598-022-15421-1 2-s2.0-85133326702 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133326702&doi=10.1038%2fs41598-022-15421-1&partnerID=40&md5=d4c567411312c35bfd3c6def1750495f https://irepository.uniten.edu.my/handle/123456789/26655 12 1 11217 All Open Access, Gold Nature Research Scopus |
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Planar spiral spring is important for the dimensional miniaturisation of motor-based elastic actuators. However, when the stiffness calculation of the spring arm is based on simple beam bending theory, the results possess substantial errors compared with the stiffness obtained from finite-element analysis (FEA). It deems that the errors arise from the spiral length term in the calculation formula. Two Gaussian process regression models are trained to amend this term in the stiffness calculation of spring arm and complete spring. For the former, 216 spring arms� data sets, including different spiral radiuses, pitches, wrap angles and the stiffness from FEA, are employed for training. The latter engages 180 double-arm springs� data sets, including widths instead of wrap angles. The simulation of five spring arms and five planar spiral springs with arbitrary dimensional parameters verifies that the absolute values of errors between the predicted stiffness and the stiffness from FEA are reduced to be less than 0.5% and 2.8%, respectively. A planar spiral spring for a powered ankle�foot prosthesis is designed and manufactured to verify further, of which the predicted value possesses a 3.25% error compared with the measured stiffness. Therefore, the amendment based on the prediction of trained models is available. � 2022, The Author(s). |
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57223432161 |
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57223432161 Liu J. Abu Osman N.A. Al Kouzbary M. Al Kouzbary H. Abd Razak N.A. Shasmin H.N. Arifin N. |
| format |
Article |
| author |
Liu J. Abu Osman N.A. Al Kouzbary M. Al Kouzbary H. Abd Razak N.A. Shasmin H.N. Arifin N. |
| spellingShingle |
Liu J. Abu Osman N.A. Al Kouzbary M. Al Kouzbary H. Abd Razak N.A. Shasmin H.N. Arifin N. Stiffness estimation of planar spiral spring based on Gaussian process regression |
| author_sort |
Liu J. |
| title |
Stiffness estimation of planar spiral spring based on Gaussian process regression |
| title_short |
Stiffness estimation of planar spiral spring based on Gaussian process regression |
| title_full |
Stiffness estimation of planar spiral spring based on Gaussian process regression |
| title_fullStr |
Stiffness estimation of planar spiral spring based on Gaussian process regression |
| title_full_unstemmed |
Stiffness estimation of planar spiral spring based on Gaussian process regression |
| title_sort |
stiffness estimation of planar spiral spring based on gaussian process regression |
| publisher |
Nature Research |
| publishDate |
2023 |
| _version_ |
1806427590233161728 |
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13.211869 |