Application of Surfactant Material as Anti-hygroscopicity agent for Ammonium Nitrate (NH4NO3)

Ammonium nitrate is a promising rocket propellant oxidizer. It is present as the major component in most industrial explosives. Due to the surface polarity of ammonium nitrate, the particles can easy absorb moisture. In this study, ammonium nitrate particles were coated by cetylalcohol surfactant in order to reduce the hygroscopicity. The optimized physical coating process using cetylalcohol was achieved by (L9 (34 )) Taguchi orthogonal array (TOA).The analysis of TOA revealed that the highest decline of absorption rate was 35.45% with the mass ratio of coating layer was 0.95%. Scanning electron microscopy (SEM) was used to characterize the surface of coated and uncoated ammonium nitrate. The idea and approach presented in this study can help the researchers to improving anti-hygroscopicity of ammonium nitrate.

Influence of the Kinematic System on the Geometrical and Dimensional Accuracy of Holes in Drilling

This article attempts to show how the kinematic system affects the geometrical and dimensional accuracy of through-holes in drilling. The hole cutting tests were performed using a universal turning center. The tool was a TiAlN-coated Ø 6 mm drill bit, while the workpiece was a C45 steel cylinder with a diameter of 30 mm and a length of 30 mm. Three kinematic systems were studied. The first consisted of a fixed workpiece and a rotating and linearly moving tool. In the second, the workpiece rotated, while the tool moved linearly. The third system comprised a rotating workpiece and a rotating and linearly moving tool, but they rotated in opposite directions. The geometrical and dimensional accuracy of the hole was assessed by analyzing the cylindricity, straightness, roundness, and diameter errors. The experiment was designed using the Taguchi orthogonal array method to determine the significance of the effects of the input parameters (cutting speed, feed per revolution, and type of kinematic system) on the accuracy errors. A multifactorial statistical analysis (ANOVA) was employed for this purpose. The study revealed that all the input parameters considered had a substantial influence on the hole quality in drilling.

Noncontact Surface Roughness Evaluation of Milling Surface Using CNN-Deep Learning Models

This paper presents the analysis of end milled machined surfaces backed with experimental and deep learning model investigations. The effect of process parameters like spindle speed, feed rate, depth of cut, cutting speed, and machining duration were investigated to find machined surface roughness using Taguchi orthogonal array. The experiments were conducted on Aluminum A3003, a common material widely used in industries. Following standard DOE using Taguchi orthogonal array, surface roughness was recorded for each machining experiment. Surface roughnesses for the current study were categorized into four classes viz., fine, smooth, rough, and coarse based on the roughness value Ra. Images of the machined surface were used to develop CNN models for surface roughness class prediction. The prediction accuracies of the CNN models were compared for five types of optimizers. It was found that RAdam optimizer performed better among others with the training and test accuracy of 96.30% and 92.91% respectively. The accuracy of the prediction is higher than 90% thus has the potential to substitute human quality control procedures, saving time, energy, and cost. Conversely, the developed CNN model can assist in acquiring preferred machining conditions in advance. Finally, it can eliminate the dependency on expensive surface roughness measuring devices and have enormous practical applications in quality control processes.