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Stiffness estimation of planar spiral spring based on Gaussian process regression

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dc.citedby1
dc.contributor.authorLiu J.en_US
dc.contributor.authorAbu Osman N.A.en_US
dc.contributor.authorAl Kouzbary M.en_US
dc.contributor.authorAl Kouzbary H.en_US
dc.contributor.authorAbd Razak N.A.en_US
dc.contributor.authorShasmin H.N.en_US
dc.contributor.authorArifin N.en_US
dc.contributor.authorid57223432161en_US
dc.contributor.authorid8511221500en_US
dc.contributor.authorid57202956887en_US
dc.contributor.authorid57216612501en_US
dc.contributor.authorid42261165400en_US
dc.contributor.authorid35778974400en_US
dc.contributor.authorid18133590700en_US
dc.date.accessioned2023-05-29T09:36:04Z
dc.date.available2023-05-29T09:36:04Z
dc.date.issued2022
dc.description.abstractPlanar 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).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo11217
dc.identifier.doi10.1038/s41598-022-15421-1
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85133326702
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85133326702&doi=10.1038%2fs41598-022-15421-1&partnerID=40&md5=d4c567411312c35bfd3c6def1750495f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26655
dc.identifier.volume12
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleStiffness estimation of planar spiral spring based on Gaussian process regressionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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