Aluminium metal matrix composites(AMCs) is widely used in the industrial applications right now. Aluminum metal matrix composites have properties that no other monolithic material can match. Due to their superior strength to conventional materials, aluminium matrix composites (AMCs) have a broad variety of industrial applications. The nature of reinforcing, that can take the form of constant or undefined fibres, has a big influence on the properties of aluminum metal matrix composites. Thus it depends on the fabrication methods for aluminium matrix composites, which are influenced by a number of factors including the type of reinforcement and matrix used, its required degree with surface morphology integrity, as well as physical, mechanical, electro-chemical, and thermal properties. This article provides an overview of the manufacturing processes and different reinforcing elements used during the synthesis of Al-MMCs. Generally, the reinforced particles like carbides, nitrides, and compounds of oxides are used. This paper gives a brief overview on various methods that are beung used to manufacture aluminium metal matrix composites. The present study offers a description of the synthesis, mechanical behaviour, and utilisation of aluminium metal matrix composites. The main processing methods for making or production of aluminium metal matrix composites(AMCs) are thoroughly discussed. Finally, questions of commercialization as well as business issues are also discussed.

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Drowsy driving is one of the leading causes of traffic accidents all over the world. Driving in a monotonous manner for an extended amount of time without stopping causes tiredness and catastrophic accidents. Drowsiness has the potential to ruin many people’s lives. As a result, a real-time system that is simple to create and configure for early and accurate sleepiness detection is required. In this study, a real-time vision-based system called Driver Drowsiness Detection System has been developed utilizing machine learning. In this study, the Haar Cascade classifier was used to recognize the driver’s face characteristics and functions present in OpenCV library to detect the region of the face. The following step is to examine the open/close state of the eyes, followed by sluggishness depending on the sequence of ocular conditions. The non-intrusive and cost-effective nature of this vision-based driver tiredness detection is its distinguishing attribute.

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