In this study, artificial neural network (ANN) is applied to predict the erosion rate of nylon-12 polymer to ensure the accuracy of soft computing. The ANN model was developed with 4 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer. A backpropagation algorithm was utilized in a multilayered perception. The inputs include impact velocity (m/s), impingement angle (°), erodent size (μm), and stand-off distance (mm). Experimental data were used to predict the erosion rate in relation to the input parameters. The size of the erosive element of randomly shaped sand particles (silicon dioxide) is set between 300 and 600 μm, the impact velocity between 30 and 50 m/s, the impingement angle between 30° and 90°, and the stand-off distance between 15 and 25 mm. The consistency between the experimental and ANN model values, with an accuracy of 94.428 % and root mean square error of 9.729, signifies that the proposed ANN model is suitable for predicting the erosion rate of nylon-12 polymer. The prediction made using the ANN model was in good agreement with the experimental results. The ANN model can be used to estimate the maximum and total erosion rate of nylon-12 with high reliability. Therefore, this model can be applied for practical purposes.

The corrosiveness of biodiesel affects the fuel processing infrastructure and different parts of an internal combustion (IC) engine. The present study investigates the corrosion behaviour of automotive materials such as stainless steel, aluminium, cast iron, and copper in 20% (B20) and 30% (B30) by volume second-generation Jatropha biodiesel using an immersion test. The results were compared with petro-diesel (B0). Various fuel properties such as the viscosity, density, water content, total acid number (TAN), and oxidation stability were investigated after the immersion test using ASTM D341, ASTM D975, ASTM D445, and ASTM D6751 standards. The morphology of the corroded materials was investigated using optical microscopy and scanning electron microscopy SEM), whereas the elemental analysis was carried out using energy-dispersive X-ray spectroscopy (EDS). The highest corrosion using biodiesel was detected in copper, while the lowest was detected in stainless steel. Using B20, the rate of corrosion in copper and stainless steel was 17% and 14% higher than when using diesel, which further increased to 206% and 86% using B30. After the immersion test, the viscosity, water content, and TAN of biodiesel were increased markedly compared to petro-diesel.

While titanium alloy (Ti-6Al-4V) made by laser powder bed fusion (L-PBF) exhibits complex deformation behaviors, its important micromechanical properties in relation to loading directions are not fully understood. This research aims to investigate the micromechanical behaviors of printed L-PBF Ti-6Al-4V alloys under vertical (i.e., the loading direction perpendicular to printed layers) and horizontal (i.e., the loading direction parallel to printed layers) compressions using in-situ scanning electron microscopy (SEM) micropillar techniques. Ti-6Al-4V alloys were L-PBF-printed using a 45° rotate scanning strategy with vertical and horizontal build directions. The microstructures of the two alloys were analyzed using the SEM with energy-dispersive X-ray spectroscopy (EDS). The titanium alloy micropillars were produced using focused ion beam (FIB) milling in the SEM. In-situ SEM micropillar compressions were conducted using a flat diamond indenter. Vertical alloy had smaller cross-patterned finer α' martensite than horizontal one. While both vertical and horizontal micropillars showed elastic-plastic behaviors, the former had significantly higher yield, fracture, and compression strength values, as well as resilience and toughness, than the latter, leading to the formation of favorable shear bands. Both micropillars exhibited ductile fractures but had distinct failure mechanisms. The ductile fracture in the vertical micropillars was due to strain hardening, large plastic deformation, and shear band formation, while the ductile fracture in the horizontal ones was attributed to compression-induced bending and plastic buckling. The micromechanical characteristics of L-PBF Ti-6Al-4V materials provides an important insight into the small-scale deformation and failure mechanisms of the alloys influenced by loading directions.

Natural fibers such as jute, cotton, and bamboo composites are becoming alternative materials to synthetic fiber composites, as their use raises awareness of environmental protection. Among natural fibers, jute and cotton fibers were used in this research to fabricate six-layered composites reinforced by spent tea leaves. Varying amounts (0, 5, 10, and 15 g) of spent tea leaf powder were incorporated as reinforcement with resin to improve and observe properties and determine usability. The prepared composites were investigated comparatively in terms of mechanical, microstructural, morphological, and thermal properties. As regards mechanical characterization, tensile, compression, and bending properties were tested in this research to compare the obtained data with the data available in the literature to show its practical application. The results indicated that significant improvements in mechanical properties were obtained from the composites up to a certain proportion of reinforcement. The addition of 10 g reinforcement of spent tea leaves improved tensile strength by 33.46% and compressive strength by 38.86%. In terms of microstructural, morphological, and thermal characterization, in-depth SEM, EDS, XRD, UV, FTIR, TGA, and DSC analyses were performed. The results revealed that advanced microstructural, morphological, and thermal properties were improved with a certain proportion of spent tea leaf reinforcement.