Abstract:Hierarchical structures have attracted considerable interest due to their super-oleophobic/super-hydrophobic behavior. However, it is rare to present a novel additive manufacturing (AM) approach to fabricate hierarchical metal structures (HMSs). A micro/nano mixture ink was deposited on a substrate and a laser was used to selectively scan the ink layer. A new layer of ink was deposited on the previous consolidation layer during manufacturing. The surfaces of the as-sintered HMSs exhibit inherently super-hydrop… Show more
Self-lubricating coating has been used in industrial applications with severe conditions, such as high temperatures, vacuum, radiation, etc. In this paper, a selective laser melting based ink-printed metal nanoparticles (SLM-IP metal NPs) rapid manufacturing method was applied to fabricate Cu-MoS2 self-lubricating coating. A tailored ink consisting metal NPs, reductant and dispersant was deposited on a stainless steel substrate, forming the laminated gradient Cu-MoS2 coating. The microstructure and mechanical properties of the composite coating were characterized. The friction and wear behavior were experimentally investigated by dry sliding wear test at room and higher temperature (>200°C). The results indicated that the upper copper sulfur molybdenum compounds layer with homogeneously distributed MoS2 provided a significant friction reduction and wear resistance. The SLM-IP Cu-MoS2 coatings showed reduced friction coefficient by 54% compare to the pure Cu coating. The transitional Cu layer mitigated the abrupt changes in physical properties and enhanced the bonding strength between the coating and substrate. Especially, under the test condition of 200°C, the Cu-40 vol% MoS2 coating also presented an excellent resistance to oxidation and had a lower friction coefficient of 0.24. This research provided a feasibility of fabricating self-lubricating coatings by the SLM-IP metal NPs method for surface engineering technologies.
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.
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