Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant
This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds’ mechanical and thermal properties, making the...
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| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/10/587 |
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| author | Francis T. Omigbodun Norman Osa-Uwagboe Amadi Gabriel Udu Bankole I. Oladapo |
| author_facet | Francis T. Omigbodun Norman Osa-Uwagboe Amadi Gabriel Udu Bankole I. Oladapo |
| author_sort | Francis T. Omigbodun |
| collection | DOAJ |
| description | This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds’ mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R<sup>2</sup> scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics. |
| format | Article |
| id | doaj-art-1f3453ebfa604e41b7b99cf1009358fa |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-1f3453ebfa604e41b7b99cf1009358fa2025-08-20T02:10:58ZengMDPI AGBiomimetics2313-76732024-09-0191058710.3390/biomimetics9100587Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone ImplantFrancis T. Omigbodun0Norman Osa-Uwagboe1Amadi Gabriel Udu2Bankole I. Oladapo3Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UKWolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UKAir Force Research and Development Centre, Nigerian Air Force Base, Kaduna PMB 2104, NigeriaSchool of Science and Engineering, University of Dundee, Dundee DD1 4HN, UKThis study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds’ mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R<sup>2</sup> scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics.https://www.mdpi.com/2313-7673/9/10/587machine learningpredictive modellingdata-driven optimizationregression algorithmsartificial intelligence in biomedical engineeringadditive manufacturing |
| spellingShingle | Francis T. Omigbodun Norman Osa-Uwagboe Amadi Gabriel Udu Bankole I. Oladapo Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant Biomimetics machine learning predictive modelling data-driven optimization regression algorithms artificial intelligence in biomedical engineering additive manufacturing |
| title | Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant |
| title_full | Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant |
| title_fullStr | Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant |
| title_full_unstemmed | Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant |
| title_short | Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant |
| title_sort | leveraging machine learning for optimized mechanical properties and 3d printing of pla chap for bone implant |
| topic | machine learning predictive modelling data-driven optimization regression algorithms artificial intelligence in biomedical engineering additive manufacturing |
| url | https://www.mdpi.com/2313-7673/9/10/587 |
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