Machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion

In this study, the cellular microstructural features in a subgrain size of carbon nanotube (CNT)-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion (LPBF) (a size range between 0.5–1 μm) were quantitatively extracted and calculated from scanning electron microscopy images...

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Autores principales:	Yu Tianyu, Mo Xuandong, Chen Mingjun, Yao Changfeng
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	laser powder bed fusion alsi10mg machine learning carbon nanotubes additive manufacturing Technology T Chemical technology TP1-1185 Physical and theoretical chemistry QD450-801
Acceso en línea:	https://doaj.org/article/783abfd4c7b14ace9b516bfba39cf84d
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spelling	oai:doaj.org-article:783abfd4c7b14ace9b516bfba39cf84d2021-12-05T14:10:58ZMachine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion2191-909710.1515/ntrev-2021-0093https://doaj.org/article/783abfd4c7b14ace9b516bfba39cf84d2021-10-01T00:00:00Zhttps://doi.org/10.1515/ntrev-2021-0093https://doaj.org/toc/2191-9097In this study, the cellular microstructural features in a subgrain size of carbon nanotube (CNT)-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion (LPBF) (a size range between 0.5–1 μm) were quantitatively extracted and calculated from scanning electron microscopy images by applying a cell segmentation method and various image analysis techniques. Over 80 geometric features for each cellular cell were extracted and statistically analyzed using machine learning techniques to explore the structure–property linkages of carbon nanotube reinforced AlSi10Mg nanocomposites. Predictive models for hardness and relative mass density were established using these subgrain cellular microstructural features. Data dimension reduction using principal component analysis was conducted to reduce the feature number to 3. The results showed that even AlSi10Mg nanocomposite specimens produced using different laser parameters exhibited similar Al–Si eutectic microstructures, displaying a large difference in their mechanical properties including hardness and relative mass density due to cellular structure variance. For hardness prediction, the Extra Tress regression models showed a relative error of 2.47% for prediction accuracies. For the relative mass density prediction, the Decision Tress regression models showed a relative error of 1.42% for prediction accuracies. The results demonstrate that the developed models deliver satisfactory performance for hardness and relative mass density prediction of AlSi10Mg nanocomposites. The framework established in this study can be applied to the LPBF process optimization and mechanical properties manipulation of AlSi10Mg-based alloys and other additive manufacturing newly designed alloys or composites.Yu TianyuMo XuandongChen MingjunYao ChangfengDe Gruyterarticlelaser powder bed fusionalsi10mgmachine learningcarbon nanotubesadditive manufacturingTechnologyTChemical technologyTP1-1185Physical and theoretical chemistryQD450-801ENNanotechnology Reviews, Vol 10, Iss 1, Pp 1410-1424 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	laser powder bed fusion alsi10mg machine learning carbon nanotubes additive manufacturing Technology T Chemical technology TP1-1185 Physical and theoretical chemistry QD450-801
spellingShingle	laser powder bed fusion alsi10mg machine learning carbon nanotubes additive manufacturing Technology T Chemical technology TP1-1185 Physical and theoretical chemistry QD450-801 Yu Tianyu Mo Xuandong Chen Mingjun Yao Changfeng Machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion
description	In this study, the cellular microstructural features in a subgrain size of carbon nanotube (CNT)-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion (LPBF) (a size range between 0.5–1 μm) were quantitatively extracted and calculated from scanning electron microscopy images by applying a cell segmentation method and various image analysis techniques. Over 80 geometric features for each cellular cell were extracted and statistically analyzed using machine learning techniques to explore the structure–property linkages of carbon nanotube reinforced AlSi10Mg nanocomposites. Predictive models for hardness and relative mass density were established using these subgrain cellular microstructural features. Data dimension reduction using principal component analysis was conducted to reduce the feature number to 3. The results showed that even AlSi10Mg nanocomposite specimens produced using different laser parameters exhibited similar Al–Si eutectic microstructures, displaying a large difference in their mechanical properties including hardness and relative mass density due to cellular structure variance. For hardness prediction, the Extra Tress regression models showed a relative error of 2.47% for prediction accuracies. For the relative mass density prediction, the Decision Tress regression models showed a relative error of 1.42% for prediction accuracies. The results demonstrate that the developed models deliver satisfactory performance for hardness and relative mass density prediction of AlSi10Mg nanocomposites. The framework established in this study can be applied to the LPBF process optimization and mechanical properties manipulation of AlSi10Mg-based alloys and other additive manufacturing newly designed alloys or composites.
format	article
author	Yu Tianyu Mo Xuandong Chen Mingjun Yao Changfeng
author_facet	Yu Tianyu Mo Xuandong Chen Mingjun Yao Changfeng
author_sort	Yu Tianyu
title	Machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion
title_short	Machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion
title_full	Machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion
title_fullStr	Machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion
title_full_unstemmed	Machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion
title_sort	machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/783abfd4c7b14ace9b516bfba39cf84d
work_keys_str_mv	AT yutianyu machinelearningassistedmicrostructurepropertylinkagesofcarbonnanotubereinforcedaluminummatrixnanocompositesproducedbylaserpowderbedfusion AT moxuandong machinelearningassistedmicrostructurepropertylinkagesofcarbonnanotubereinforcedaluminummatrixnanocompositesproducedbylaserpowderbedfusion AT chenmingjun machinelearningassistedmicrostructurepropertylinkagesofcarbonnanotubereinforcedaluminummatrixnanocompositesproducedbylaserpowderbedfusion AT yaochangfeng machinelearningassistedmicrostructurepropertylinkagesofcarbonnanotubereinforcedaluminummatrixnanocompositesproducedbylaserpowderbedfusion
_version_	1718371547497889792

Machine-learning-assisted microstructure–property linkages of carbon nanotube-reinforced aluminum matrix nanocomposites produced by laser powder bed fusion

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