Engineering properties of acha (Digitaria exilis) grains in relation to the design of grain processing machines

Engineering properties are very important in the design and manufacturing of processing machines. In this research work, the engineering (physical, aerodynamic and mechanical) properties of acha (Digitaria exilis) grains were determined as design parameters for the development of grain processing machines for the crop. The physical properties determined were length, width, thickness, arithmetic mean diameter, geometric mean diameter, roundness, sphericity, aspect ratio, surface area, projected area, volume, moisture content, one thousand seed weight, bulk density, true density and porosity. The average seed length, width and thickness were found to be 1.84 ± 0.055, 0.85 ± 0.023 and 0.75 ± 0.042 mm respectively. The average arithmetic and geometric mean diameters were 1.15±0.014 and 1.05±0.018 mm respectively. The average roundness, sphericity and aspect ratio were 0.5840±0.011, 0.5732±0.013 and 0.4678±0.012 respectively. The mean surface area, projected area and volume were 3.49±0.25 mm2, 1.23±0.033 mm2 and 0.61±0.029 mm3 respectively. The average moisture content was 14.73±2.14% dry basis and the thousand kernel weight was 0.827±0.053 g. The average bulk and true densities were 1092.86 ± 24.13 and 1626.15 ± 62.91 kgm-3 respectively and the porosity was 32.52 ±1.34%. The aerodynamic properties determined in this study were terminal velocity, drag coefficient and Reynold's number. The average terminal velocity, drag coefficient and Reynold's number of the seeds were found to be 3.97 ± 0.015 m/s, 0.684 ± 0.062 and 311.37 ± 13.47 respectively. The mechanical properties determined in this study were angle of repose, coefficient of static friction, shear strength and compressive strength. The mean angle of repose was 32.50±1.36o while the mean coefficient of static friction on mild steel surface was 0.82±0.043. The mean shear strength and compressive strength of the seeds were 3840±72.32 and 7615±90.28 N/m2 respectively. These parameters would provide important and essential data for the efficient design of acha processing machines