An investigation on sample size distribution in electric discharge machining (EDM)

In this study, 150 experiments are conducted at different machining conditions to determine the sample size for workpiece removal rate and tool wear rate with a certain acceptable error in Electric Discharge Machining (EDM). Experimental workpiece removal rate and tool wear rate data are found to be distributed normally with a good fit. Statistically, it is shown that 150 experiments are large enough random sample to reflect the characteristics of the population. Two methods are proposed to find the sample size by using the experimental data. Sample sizes obtained from the proposed methods are compared with those obtained from the classical sample size determination formula. The results from the formulation and one of the two proposed methods are found to be quite consistent. Also, the behavior of maximum error terms with increasing sample size is investigated for different machining conditions. (A

Statistical Characterization of Particulate Properties of Some Industrial Powders

In this investigation, the statistical optic characterization approach is covered for four industrial powders, namely; gelatin, iron, tin and sodium chloride. For all of these powders few size fractions from sieve analysis are optically analyzed and the particles shape and size distributions are obtained. A discussion of the effect of these primary properties statistical distributions on some of the physical parameters (i.e. the secondary properties) including friction, flow and density is presented. A standard sample size determination technique for proper number of experimental data (i.e. particles) to obtain reliable and accurate results is utilized. Several standard statistical probability density function are attempted in order to obtain the best fit of the experimental statistical diameter histograms of particles. The Gamma probability function is found to be a suitable statistical model to fit the experimental particle diameter histograms of the four powders. Finally, a non-linear correlation is displayed between the fitted distribution function parameters and the measured secondary powder parameters