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This review article focuses on the aluminum-based metal matrix composites (Al-based MMCs). Studies or investigations of their mechanical and tribological properties performed by researchers worldwide in the past are presented in detail. The processing techniques and applications for Al-based MMCs are also documented here. A brief background on the composite materials, their constituents, and their classification, as well as the different matrix materials and particulates used in Al-based MMCs, can be found in this review. Then, an overview of dual-particle-size reinforced composites, heat treatment of Al alloys, and temper designations used in heat treatment are also included. In addition, the factors influencing the mechanical and wear properties of Al-based MMCs are discussed. The primary objective is that both present and future researchers and investigators will be assisted by the comprehensive knowledge compiled in this article to further explore and work towards the betterment of society in general. [ABSTRACT FROM AUTHOR]

The study of microstructures for the accurate control of material properties is of industrial relevance. Identification and characterization of microstructural properties by manual measurement are often slow, labour intensive, and have a lack of repeatability. In the present work, the intermetallic phase ratio and grain size in the microstructure of known Mg-Sn-Al alloys were measured by computer vision (CV) technology. New Mg (Magnesium) alloys with different alloying element contents were selected as the work materials. Mg alloys (Mg-Al-Sn) were produced using the hot-pressing powder metallurgy technique. The alloys were sintered at 620 °C under 50 MPa pressure in an argon gas atmosphere. Scanning electron microscopy (SEM) images were taken for all the fabricated alloys (three alloys: Mg-7Al-5Sn, Mg-8Al-5Sn, Mg-9Al-5Sn). From the SEM images, the grain size was counted manually and automatically with the application of CV technology. The obtained results were evaluated by correcting automated grain counting procedures with manual measurements. The accuracy of the automated counting technique for determining the grain count exceeded 92% compared to the manual counting procedure. In addition, ASTM (American Society for Testing and Materials) grain sizes were accurately calculated (approximately 99% accuracy) according to the determined grain counts in the SEM images. Hence, a successful approach was proposed by calculating the ASTM grain sizes of each alloy with respect to manual and automated counting methods. The intermetallic phases (Mg17Al12 and Mg2Sn) were also detected by theoretical calculations and automated measurements. The accuracy of automated measurements for Mg17Al12 and Mg2Sn intermetallic phases were over 95% and 97%, respectively. The proposed automatic image processing technique can be used as a tool to track and analyse the grain and intermetallic phases of the microstructure of other alloys such as AZ31 and AZ91 magnesium alloys, aluminium, titanium, and Co alloys. [ABSTRACT FROM AUTHOR]

The stringent demand to develop lightweight materials with enhanced properties suitable for various engineering applications is the focus of this research work. Industrial wastes such as fly ash (FA) and S-glass-fibres (GF) were used as reinforcement materials for high-strength alloy, i.e., Al 7005. Stir casting routes were employed for fabricating the four samples, Al 7005, Al 7005 + 5% GF, Al 7005 + 6% FA and Al 7005 + 5% GF + 6% FA. The extrusion process with different extrusion ratios (ER: 5.32:1, and 2.66:1) was used to examine the properties of all four samples. Extruded samples with ER: 5.32: 1 resulted in equiaxed grains with refined structure compared to stir casting parts. The effect of the extrusion process and the addition of reinforcements (GF and FA) on the gravimetric, electrochemical, and electrochemical impedance corrosion behaviour of Al 7005 composites in 1M HCl (Hydrochloric acid) solution were investigated. The results of all three corrosion methods showed that Al 7005 + 6% FA exhibited higher corrosion resistance. Corrosion rate of Al 7005, Al 7005 + 5% GF, Al 7005 + 6% FA and Al 7005 + 5% GF + 6% FA is found equal to 3.25, 2.41, 0.34, and 0.76 mpy, respectively. The FA particles remain inert and act as a physical barrier with corrosive media during the corrosion test. GF undergoes fibre degradation or disrupts the continuity of the glass network as a result of fibre leaching, which increases the corrosion rate in the sample. The gravimetric study showed that the corrosion rates decreased with an increase in extrusion ratio, which might be due to corrosion passivation increases and improved properties. The scanning electron microscopy reveals that corrosion fits, flakes and micro-cracks were observed more in the as-cast composites than that of extrusion composites, promoting the corrosion rate. [ABSTRACT FROM AUTHOR]

In this study, the burr and slot widths formed after the micro-milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user-defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open-sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user-friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation. [ABSTRACT FROM AUTHOR]