Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants
Abstract The purpose of this research is to develop data‐driven machine learning (ML) models capable of estimating the specific wear rate of ultra‐high molecular weight polyethylene (UHMWPE) used in hip replacement implants. The results of the data‐driven models are demonstrating a high level of con...
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | Biosurface and Biotribology |
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| Online Access: | https://doi.org/10.1049/bsb2.12085 |
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| author | Vipin Kumar Ravi Prakash Tewari Anubhav Rawat |
| author_facet | Vipin Kumar Ravi Prakash Tewari Anubhav Rawat |
| author_sort | Vipin Kumar |
| collection | DOAJ |
| description | Abstract The purpose of this research is to develop data‐driven machine learning (ML) models capable of estimating the specific wear rate of ultra‐high molecular weight polyethylene (UHMWPE) used in hip replacement implants. The results of the data‐driven models are demonstrating a high level of consistency with the experimental findings acquired from the pin‐on‐disk (POD) trials. With a performance evaluation of 0.06 mean absolute error (MAE), 0.17 Root Mean Square Error (RMSE), and 0.96 R2, the Random Forest Regression is found to be the best model. Another machine learning model, called Gradient Boosting Regression, is also found to possess satisfactory predictive performance by having an MAE of 0.09, RMSE of 0.24, and R2 of 0.96. According to the findings of a parametric analysis that made use of an ML model, the surface texture geometry has a substantial dependence on the wear behaviour of UHMWPE bearings that are used in hip replacement implants. This strategy has the potential to enhance experiment design and lessen the necessity for time‐consuming POD trials for the purpose of assessing the wear of hip replacement implants. |
| format | Article |
| id | doaj-art-0a7cad5e8e6a420bae9d2376bbf931a8 |
| institution | Kabale University |
| issn | 2405-4518 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Biosurface and Biotribology |
| spelling | doaj-art-0a7cad5e8e6a420bae9d2376bbf931a82024-12-21T15:14:43ZengWileyBiosurface and Biotribology2405-45182024-11-0110414315810.1049/bsb2.12085Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implantsVipin Kumar0Ravi Prakash Tewari1Anubhav Rawat2Department of Applied Mechanics Motilal Nehru National Institute of Technology Allahabad Prayagraj Uttar Pradesh IndiaDepartment of Applied Mechanics Motilal Nehru National Institute of Technology Allahabad Prayagraj Uttar Pradesh IndiaDepartment of Applied Mechanics Motilal Nehru National Institute of Technology Allahabad Prayagraj Uttar Pradesh IndiaAbstract The purpose of this research is to develop data‐driven machine learning (ML) models capable of estimating the specific wear rate of ultra‐high molecular weight polyethylene (UHMWPE) used in hip replacement implants. The results of the data‐driven models are demonstrating a high level of consistency with the experimental findings acquired from the pin‐on‐disk (POD) trials. With a performance evaluation of 0.06 mean absolute error (MAE), 0.17 Root Mean Square Error (RMSE), and 0.96 R2, the Random Forest Regression is found to be the best model. Another machine learning model, called Gradient Boosting Regression, is also found to possess satisfactory predictive performance by having an MAE of 0.09, RMSE of 0.24, and R2 of 0.96. According to the findings of a parametric analysis that made use of an ML model, the surface texture geometry has a substantial dependence on the wear behaviour of UHMWPE bearings that are used in hip replacement implants. This strategy has the potential to enhance experiment design and lessen the necessity for time‐consuming POD trials for the purpose of assessing the wear of hip replacement implants.https://doi.org/10.1049/bsb2.12085artificial jointbiomaterialsbiomechanicsbiomedical applicationbiomedical devicesbionic surface |
| spellingShingle | Vipin Kumar Ravi Prakash Tewari Anubhav Rawat Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants Biosurface and Biotribology artificial joint biomaterials biomechanics biomedical application biomedical devices bionic surface |
| title | Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants |
| title_full | Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants |
| title_fullStr | Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants |
| title_full_unstemmed | Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants |
| title_short | Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants |
| title_sort | machine learning based investigations of the effect of surface texture geometry on the wear behaviour of uhmwpe bearings in hip joint implants |
| topic | artificial joint biomaterials biomechanics biomedical application biomedical devices bionic surface |
| url | https://doi.org/10.1049/bsb2.12085 |
| work_keys_str_mv | AT vipinkumar machinelearningbasedinvestigationsoftheeffectofsurfacetexturegeometryonthewearbehaviourofuhmwpebearingsinhipjointimplants AT raviprakashtewari machinelearningbasedinvestigationsoftheeffectofsurfacetexturegeometryonthewearbehaviourofuhmwpebearingsinhipjointimplants AT anubhavrawat machinelearningbasedinvestigationsoftheeffectofsurfacetexturegeometryonthewearbehaviourofuhmwpebearingsinhipjointimplants |