Machine learning-based design of biodegradable Mg alloys for load-bearing implants
For load-bearing applications, biodegradable Mg alloys require high strength and slow degradation rates to support bone regeneration. This study proposes a design guide of Mg-Zn-Mn-Sr-Ca (ZMJX) alloys for load-bearing Mg implants using machine learning. To this end, it quantitatively investigates th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Materials & Design |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127522010656 |
| _version_ | 1811175515414855680 |
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| author | Joung Sik Suh Byeong-Chan Suh Jun Ho Bae Young Min Kim |
| author_facet | Joung Sik Suh Byeong-Chan Suh Jun Ho Bae Young Min Kim |
| author_sort | Joung Sik Suh |
| collection | DOAJ |
| description | For load-bearing applications, biodegradable Mg alloys require high strength and slow degradation rates to support bone regeneration. This study proposes a design guide of Mg-Zn-Mn-Sr-Ca (ZMJX) alloys for load-bearing Mg implants using machine learning. To this end, it quantitatively investigates the correlation between 4 alloying elements with content of 0–3 wt%, ultimate compressive strength (UCS) and in vitro corrosion rate (CR) in ZMJX alloys. Cascade-forward neural networks predict UCS and CR with high accuracy of over 0.95 for a total of 840 data points. Random forest regression identifies Zn as a major determinant of UCS and CR. Based on this, three chemical compositions are recommended with improved compressive strength and in vitro corrosion resistance by well-verified neural network models. The proposed Mg alloys have UCS of 244–305 MPa and CR of 0.31–0.83 mm/y according to the change of the Zn content. These results can not only provide deep insights into ZMJX alloys, but also recommend a compositional window for load-bearing Mg implants. |
| first_indexed | 2024-04-10T19:38:15Z |
| format | Article |
| id | doaj.art-f15dccf5f8e6478e8638c900f9aee1c1 |
| institution | Directory Open Access Journal |
| issn | 0264-1275 |
| language | English |
| last_indexed | 2024-04-10T19:38:15Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj.art-f15dccf5f8e6478e8638c900f9aee1c12023-01-30T04:11:54ZengElsevierMaterials & Design0264-12752023-01-01225111442Machine learning-based design of biodegradable Mg alloys for load-bearing implantsJoung Sik Suh0Byeong-Chan Suh1Jun Ho Bae2Young Min Kim3Corresponding author.; Advanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of KoreaAdvanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of KoreaAdvanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of KoreaAdvanced Metals Division, Korea Institute of Materials Science, Changwon 51508, Republic of KoreaFor load-bearing applications, biodegradable Mg alloys require high strength and slow degradation rates to support bone regeneration. This study proposes a design guide of Mg-Zn-Mn-Sr-Ca (ZMJX) alloys for load-bearing Mg implants using machine learning. To this end, it quantitatively investigates the correlation between 4 alloying elements with content of 0–3 wt%, ultimate compressive strength (UCS) and in vitro corrosion rate (CR) in ZMJX alloys. Cascade-forward neural networks predict UCS and CR with high accuracy of over 0.95 for a total of 840 data points. Random forest regression identifies Zn as a major determinant of UCS and CR. Based on this, three chemical compositions are recommended with improved compressive strength and in vitro corrosion resistance by well-verified neural network models. The proposed Mg alloys have UCS of 244–305 MPa and CR of 0.31–0.83 mm/y according to the change of the Zn content. These results can not only provide deep insights into ZMJX alloys, but also recommend a compositional window for load-bearing Mg implants.http://www.sciencedirect.com/science/article/pii/S0264127522010656Machine learningMagnesium alloyBiodegradableMechanical propertiesCorrosion |
| spellingShingle | Joung Sik Suh Byeong-Chan Suh Jun Ho Bae Young Min Kim Machine learning-based design of biodegradable Mg alloys for load-bearing implants Materials & Design Machine learning Magnesium alloy Biodegradable Mechanical properties Corrosion |
| title | Machine learning-based design of biodegradable Mg alloys for load-bearing implants |
| title_full | Machine learning-based design of biodegradable Mg alloys for load-bearing implants |
| title_fullStr | Machine learning-based design of biodegradable Mg alloys for load-bearing implants |
| title_full_unstemmed | Machine learning-based design of biodegradable Mg alloys for load-bearing implants |
| title_short | Machine learning-based design of biodegradable Mg alloys for load-bearing implants |
| title_sort | machine learning based design of biodegradable mg alloys for load bearing implants |
| topic | Machine learning Magnesium alloy Biodegradable Mechanical properties Corrosion |
| url | http://www.sciencedirect.com/science/article/pii/S0264127522010656 |
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