Christian A. Bolu

Aluminum Silicon Carbide Particulate Metal Matrix Composite Development Via Stir Casting Processing

In this paper, conventional simple methods of producing MMC with attained properties through the dispersion of silicon carbide in the matrix are investigated. To achieve these objectives a two-step mixing method of stir casting technique was employed. Aluminum (99.66 %C.P) and SiC (320 and 1200 grits) were chosen as matrix and reinforcement materials respectively. Experiments were conducted by varying the weight fraction of SiC for 2.5 %, 5.0 %, 7.5 % and 10 %. The result indicated that the stir casting method was quite successful to obtain uniform dispersion of reinforcement in the matrix. This was evident by the improvement of properties of composites over the base metal. Reinforced Aluminum Silicon Carbide (ASC) showed an increase in Young’s modulus (E) and hardness above the unreinforced case and marginal reduction of electrical conductivity was recorded for the composites. The silicon carbide of 1200 grits (3 μm) showed increased Young’s modulus (E) and hardness of 1517.6 Mpa and 26.1 Hv values at 7.5% volume fraction silicon carbide; when compared with the silicon carbide 320 grit (29 μm). Also; the electrical conductivity properties of the two grit sizes of the silicon carbides were less than the base metal for all the volume fractions of silicon carbide.

Influence of the Grit size of Silicon Carbide Particles on the Mechanical and Electrical Properties of Stir Casting Aluminum Matrix Composite Material

This paper studies the influence of the grit size of silicon carbide particles on the mechanical and electrical properties of stir cast aluminum matrix composites. A two step-mixing method of stir casting technique at (500 rpm) has been adopted. Type 1170 Al with (99.66 % C.P) and silicon carbide (SiC) particulates of 240 grit size (45 µm), 320 grit size (29 µm), 600 grit size (9 µm) and 1200 grit size (3 µm) were used. The incorporation of weight fraction of SiC ranges from 2.5 %, 5 %, 7.5 % and 10 %. The microstructures of the produced composites were examined using a scanning electron microscope. Mechanical properties were determined by using a universal testing machine of 30 KN load. Microhardness was performed on the composite specimens by using a LECO 700 HT tester with a load of 492.3 N and with a dwell time of 10 seconds. The electrical properties were determined using a Keithley instrument Model 2400 point probe machine. The results show that the modulus, yield strength and hardness of the composite increase at lower grit sizes of silicon carbide of 3 micron. The maximum hardness of 26.1 HVN and maximum modulus elasticity of 1517.6 N/mm -2 was obtained at 7.5 % weight fraction of SiC. A boost in the mechanical and electrical properties of the produced alloys was gained by changing the grit size of the silicon carbide.

Dataset showing steel cold rolling process parameters for a 6-high cold rolling mill in Nigeria

The data contained in this article was acquired from the automatic gauge control system for a steel cold rolling mill production line in Nigeria. Accuracy is the one of the most important indices of productivity during a milling process. A total of 486 data points were obtained from selected feedback sensors located on the rolling mill machine via the control panel Human Machine Interface (HMI). The selected rolling parameters were gathered at different time intervals for different sample coils strips during the different milling stages. The data shows parameters such as actual thickness measured, x-ray gauge temperature, mill speed at both entry and exit and the mill power. This dataset could be used to analyze and improve the accuracy of the Automatic gauge control system and reduction in error in thickness variation.

Experimental data-set for prediction of tool wear during turning of Al-1061 alloy by high speed steel cutting tools

In this investigation, the dataset presented will give important information to understand the area of cutting tool wear during turning operations, tool nature is the most difficult tasks in manufacturing process, particularly in the locomotive industry. With the view to optimize the cutting parameters, the tests were carried out to investigate tool wear on high speed steel (HSS) during turning operation of aluminium 1061 alloy and to developed mathematical models using least squares method. The cutting parameters chosen for this investigation are cutting speed, feed rate, and radial depth of cut were used as input parameters in order to predict tool wear. The experiment was designed by using full factorial 33 in which 27 samples were run in a Fanuc 0i TC CNC lathe. After each test, scanning electron microscope (SEM) is used to measure the cutting tool in other to determine the tool wear.