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This study aims to optimize the process parameters of wire arc additive manufacturing for ER70S6 steel and to build a machine‐learning (ML) model to predict the properties of deposited specimens. Process parameters such as current, voltage, and travel speed are optimized considering other process parameters constant (gas flow rate, contact tip to the work distance, and preheat). The optimization is made using the response surface method and validated the properties by experimentation, including tensile testing and metallography. A support vector regression (SVR) ML model is implemented to predict the material's properties to substantiate the outcomes' values in every possible combination for the given parameters. In the study's findings, a significant enhancement is revealed in specimen quality, marked by reduced irregularities and porosity, and a remarkable increase in ultimate tensile strength up to 40%, validated through the SVR model. In this study, a valuable path that can be extended to predict properties of other material systems is sketched. [ABSTRACT FROM AUTHOR]

Recently, there is a significant demand of lightweight materials for advanced engineering applications namely automotive, sports, aerospace, electrical applications, etc. Aluminium or its alloy-based metal matrix nano-composite materials reinforced with various carbon based nanomaterials are considered to be one of the futuristic lightweight materials. Carbon nanomaterials have a few excellent properties which make them ideal nano reinforcing materials for various matrix phases. An attempt has been made in this article to review various carbon nanomaterials reinforced aluminium or its alloy matrix-based nano-composite materials, their fabrication, properties and prospective applications in various fields. This paper has identified a few challenges of the carbon nanomaterials reinforced aluminium matrix nanocomposites which can be undertaken by researchers in future. [ABSTRACT FROM AUTHOR]

A novel hybrid aluminium-based nano-B4C and carbon nanofibers (CNF) reinforced nanocomposite has been developed using powder metallurgy. The microstructure and mechanical properties have been evaluated using field emission scanning electron microscopy, transmission electron microscopy, hardness, and compression testing. The microstructural analysis reveals that the nano-B4C and CNF are dispersed in the aluminium matrix, resulting fine-grained structure. The 2 vol% hybrid nanocomposite shows a 182.9% increase in compressive strength, while the 4 vol% nano-B4C composite achieves the highest hardness, 62.18% greater than pure aluminium. Tribological tests using a dry sliding pin-on-disc indicate a 53.2% reduction in wear rate for the 2 vol% hybrid nanocomposites. These findings highlight the potential application of this material in the automotive and aerospace industries, where enhanced strength and wear resistance are crucial. [ABSTRACT FROM AUTHOR]