The present requirement of the aerospace industry is seeking light-weight joining material that satisfies the technical and technological requirements with better mechanical characteristics. Aluminum alloy with spot-welding process meet the requirements of modern demands. In this present paper, a modified-interlock friction stir weld lap joint is induced to join AA8011-AA7475 with different wt% of SiC particles. Friction stir machine process parameters were tool rotational speed 1600 rpm, plunge speed rate 0.08 mm/s and traverse speed 40 mm/s maintained constantly. Mechanical and metallurgical characterizations were investigated. EDS analysis and microstructure confirmed the presence of silica particles in the NZ of the weld joints and uniformed homogenous distribution of the particulates throughout the weld. Joints made with SiC particulates showed improved static properties because intensive softening occurred in the stir zone leading to Si-Al-based precipitate particulates. The fracture test showed that the joints with SiC had a ductile fracture. AA8011-AA7475 with 2 wt% SiC showed maximum hardness, tensile strength of 229 HV, 192 MPa and a decrease in elongation was observed from 9.5 to 5%. AA8011-AA7475/2 wt% showed improved hardness, tensile strength and elongation suitable for aircraft wing stringers application.

Influence of SiC Particles on Mechanical and Microstructural Properties of Modified Interlock Friction Stir Weld Lap Joint for Automotive Grade Aluminium Alloy

Currently, numerous studies have shown that carbon nanofibres have mechanical properties that are replaced by other widely used fibres. The high tensile strength of the carbon fibres makes them ideal to use in polymer matrix composites. The high-strength fibres can be used in short form in a composite and mass-produced to meet the high demands of automotive applications. These composites are capable of addressing the strength requirement of nonstructural and structural components of the automotive industry. Due to these composite lightweight and high-strength weight ratios, the applications can be widely varying. The research for these materials is a never-ending process, as researchers and design engineers are yet to tap its full potential. This study fabricated phenolic resin with different wt% of carbon nanofibre (CNF). The percentage of the CNF as a filler material is varied from 1 to 4 wt%. Mechanical properties such as hardness, tensile strength, and XRD were investigated. Phenolic resin with 4 wt% of carbon nanofibre (CNF) exhibits maximum tensile strength and hardness of 43.8 MPa and 37.8 HV.

/pdf/processing-and-characterization-of-carbon-nanofibre-1hfgbeynhc.pdf

Processing and Characterization of Carbon Nanofibre Composites for Automotive Applications

Potential utilization of waste glass powder as a precursor material in synthesizing ecofriendly ternary blended geopolymer matrix

Optimization of darcy-forchheimer squeezing flow in nonlinear stratified fluid under convective conditions with artificial neural network

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.

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

Aluminum matrix composites known for their superior mechanical properties finds its use as liners in engine cylinders, discs, drum brakes, and pistons in automotive applications. This paper investigates the influence of squeeze casting process parameters on AA6061/Al2O3/SiC/Gr hybrid metal matrix composite using the encapsulated feeding technique. Four levels of factors selected for the L16 orthogonal array to optimize the process parameters were squeeze pressure (60, 80, 100, 120 MPa), melt temperature (700,750,800,850 °C), die temperature (100, 150, 200, 250 °C) and pressure holding time (5, 10, 15, 20 s). Hardness and tensile strength were measured for the designed experiments. Scanning electron microscope with energy-dispersive X-ray spectroscopy identified surface morphologies and elemental analysis. Optimized results were predicted using the artificial neural network. The melt temperature and squeeze pressure exhibited a significant contribution in controlling the mechanical behaviour of the hybrid composites. Taguchi analysis suggested that SP3, MT2, DT4 and HT2 casting conditions presented the optimal process parameter level that showed the maximum hardness of 131 HV and the tensile strength of 329 MPa. Scanning electron microscopy and energy-dispersive X-ray spectroscopy showed uniform distribution of reinforcement in the encapsulation process compared with the regular feeding technique. ANN predicted the hardness and tensile strength with 95 % accuracy. Compared with the regression model and experimental data, the ANN prediction was more accurate. The defined hybrid metal matrix composite stands out as the substitute for AA6061 alloy that meets the demands of the modern automotive industry in engine cylinder liner applications.

An integrated artificial neural network and Taguchi approach to optimize the squeeze cast process parameters of AA6061/Al2O3/SiC/Gr hybrid composites prepared by novel encapsulation feeding technique

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An integrated artificial neural network and Taguchi approach to optimize the squeeze cast process parameters of AA6061/Al2O3/SiC/Gr hybrid composites prepared by novel encapsulation feeding technique