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

Abstract

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.

Advancements in Laser Powder Bed Fusion of Carbon Nanotubes-Reinforced AlSi10Mg Alloy: A Comprehensive Analysis of Microstructure Evolution, Properties, and Future Prospects

“…Consequently, a new generation of manufacturing technologies has emerged, known as additive manufacturing. This innovative approach enables the high-quality fabrication of intricate parts within a short timeframe, while also providing the flexibility to customize processing variables [74,75]. By utilizing additive manufacturing techniques, manufacturers can overcome the limitations of traditional methods and achieve efficient production of complex components.…”

“…Subsequently, the molten material solidifies upon cooling to ambient temperature, forming a solid layer. This layer-by-layer approach is repeated until the desired component is fully formed [74,75,82].…”

“…Figure 12 provides a visual representation of crack fronts in relation to the melt pool structure. Notably, the melt pool exhibits a stretched configuration, displaying a high aspect ratio near the crack front [74]. Additionally, it allows for the observation of the presence of the CNT phase within the nanocomposites.…”

“…The tensile test specimen reveals distinct features in its crack front melt pool morphology, including (a) deviations in the crack propagation path caused by the presence of melt pool boundaries and (b) the presence of an elongated melt pool structure. Reproduced from[74] with permission of the publisher, (De Gruyter).…”

Development of an accurate “composition-process-properties” dataset for SLMed Al-Si-(Mg) alloys and its application in alloy design

“…It can be utilized to establish the quantitative relation of "composition-processes-properties", and even accelerate the design of high-performance alloys over a high dimensional parameter space. Mondal et al [37] employed a physical information-based machine learning method to systematically investigate the cracking mechanism of SLMed 6061Al, 2024Al, and AlSi10Mg alloys. The decision trees, support vector machines, and logistic regression techniques were used to predict crack formation conditions, and the cracking susceptibility maps were established for optimizing the process parameters.…”