Solidification Processing of Cast Metal Matrix Composites Over the Last 50 Years and Opportunities for the Future

Rohatgi, Pradeep K.; Kumar, Praveen; Chelliah, Nagaraj M.; Rajan, T.P.D.

doi:10.1007/s11837-020-04253-x

Cited by 44 publications

(21 citation statements)

References 57 publications

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“…Unfortunately, the main approaches to the production of powder systems for use in additive manufacturing are classical metallurgical processes that are related to melting the metal and introducing ceramic inclusions into the melt. This approach has numerous problems associated with insufficient wetting of ceramic particles by liquid metal, non-uniform distribution of particles inside the metal matrix and the flotation of these particles [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

High-Temperature Synthesis of Metal–Matrix Composites (Ni-Ti)-TiB2

et al. 2021

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Currently, metal–matrix composite materials are some of the most promising types of materials, and they combine the advantages of a metal matrix and reinforcing particles/fibres. Within the framework of this article, the high-temperature synthesis of metal–matrix composite materials based on the (Ni-Ti)-TiB2 system was studied. The selected approaches make it possible to obtain composite materials of various compositions without contamination and with a high degree of energy efficiency during production processes. Combustion processes in the samples of a 63.5 wt.% NiB + 36.5 wt.% Ti mixture and the phase composition and structure of the synthesis products were researched. It has been established that the synthesis process in the samples proceeds via the spin combustion mechanism. It has been shown that self-propagating high-temperature synthesis (SHS) powder particles have a composite structure and consist of a Ni-Ti matrix and TiB2 reinforcement inclusions that are uniformly distributed inside it. The inclusion size lies in the range between 0.1 and 4 µm, and the average particle size is 0.57 µm. The obtained metal-matrix composite materials can be used in additive manufacturing technologies as ligatures for heat-resistant alloys, as well as for the synthesis of composites using traditional methods of powder metallurgy.

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Section: Introductionmentioning

confidence: 99%

High-Temperature Synthesis of Metal–Matrix Composites (Ni-Ti)-TiB2

et al. 2021

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“…Stir casting technology belongs to liquid phase preparation technology and materials with high strength and high plasticity can be obtained under good interfacial layer control. At the same time, stir casting processes represent the earliest commercial production process of metal matrix composite materials, which can be used for continuous and semi-continuous large-scale production with traditional equipment [13][14][15]. Al-Zn-Mg-Cu alloys are common precipitation-strengthened alloys.…”

Section: Methodsmentioning

confidence: 99%

Effects of Graphene Nanoplates on the Mechanical Behavior and Strengthening Mechanism of 7075Al Alloy

et al. 2020

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7075Al alloy is the preferred material for lightweight automotive applications, but the existing problem is that it is difficult to combine high strength and high toughness. This paper presents our research aimed at obtaining high strength and high toughness materials by adding a nano-phase to realize microstructure refinement. Graphene nanoplates (GNP)/7075Al composites and 7075Al alloy were prepared by a stirring casting method in the present study. In comparison to 7075Al, the tensile strength of GNP/7075Al composites was increased from 572 MPa to 632 MPa while maintaining good uniform elongation of 8% to 10%. The increased strength behavior of GNP/7075Al composites while maintaining the plasticity is discussed in terms of grain refinement and dislocation evolution by analyzing the composite microstructure and quantitatively analyzing the contributions of grain boundary strengthening, solid solution strengthening, precipitation strengthening and dislocation strengthening. GNP’s strengthening of GNP/7075Al composites is mainly attributed to the refinement of grain size and the increase of dislocation density.

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“…To compensate for the lower strength and stiffness of these alternative alloys, use of reinforcing particles in the matrix of alloys have been considered a viable solution. Many reinforcing particles such as SiC, Al 2 O 3 , B 4 C, TiB 2 , and TiC 14 have been incorporated in the matrix of metals for this purpose. Once a reinforcing particle is added to the base metal, it increases the level of complexity of the system, which makes it hard to explore the complex correlation between reinforcing particle size and volume percentage, chemical composition of the alloy, interfacial bonding, shape and distribution of reinforcements, and processing and post-processing parameters with mechanical properties of the final products via the conventional two-parameters approach.…”

Section: Property Prediction Of MMC Using Machine Learning Mechanical Propertiesmentioning

confidence: 99%

“…Object detection found each individual pedestrian and car, and instance segmentation assigned pixels to the pedestrian/car. The same approach can be used for the microstructure of the silicon carbide particle-reinforced aluminum composite 14 shown in Fig. 4e.…”

Section: Microstructure Analysis Of MMC Using Machine Learningmentioning

confidence: 99%

A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications

Kordijazi

Zhao

Zhang

et al. 2021

JOM

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In this article we provide an overview on the current and emerging applications of machine learning (ML) in the design, synthesis, and characterization of metal matrix composites (MMC). We have demonstrated that ML methods can be applied in three distinct categories, namely property prediction, microstructure analysis, and process optimization, which are associated with three major classes of ML techniques, i.e., regression, classification, and optimal control, respectively. ML algorithms have been successfully applied for prediction of mechanical, tribological, corrosion, and wetting properties of different MMCs. However, ML methods (e.g., computer vision, which is suitable for microstructural characterization and defect detections) and optimization algorithms (e.g., reinforcement learning) have not been fully utilized for design, processing, and characterization of metal matrix composites despite their enormous capacities. We conclude that ML methods are promising not only to predict various properties but also to automate microstructural analysis and optimization of manufacturing MMCs.

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Solidification Processing of Cast Metal Matrix Composites Over the Last 50 Years and Opportunities for the Future

Cited by 44 publications

References 57 publications

High-Temperature Synthesis of Metal–Matrix Composites (Ni-Ti)-TiB2

High-Temperature Synthesis of Metal–Matrix Composites (Ni-Ti)-TiB2

Effects of Graphene Nanoplates on the Mechanical Behavior and Strengthening Mechanism of 7075Al Alloy

A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications

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