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Welding is an excellent attachment or repair method. The advanced industries such as oil, automotive industries, and other important industries need to rely on reliable welding operations; collapse because of this welding may lead to an excessive cost in money and risk in human life. In the present research, an automatic system has been described to detect, recognize and classify welding defects in radiographic images. Such system uses a texture feature and neural network techniques. Image processing techniques were implemented to help in the image array of weld images and the detection of weld defects. Therefore, a proposed program was build in-house to automatically classify and recognize eleven types of welding defects met in practice.

Extensive studies of off-road non-pneumatic tires (NPTs) were conducted for light and heavy equipment due to their advantages over conventional pneumatic tires in terms of low rolling resistance, thus no need for air pressure maintenance. Finite element (FE) simulations of NPT contact pressure, contact shear stress, vertical stiffness, von mises stress, and rolling resistance were performed using ABAQUS software in a series of vertical loads to simulate tire models of three different spokes geometries on unpaved soil to verify NPT performance under different conditions. The spokes geometries were hexagonal (honeycomb) spoke, hexagonal re-entrant (Lattice) spoke and spoke with curvature called spoke pairs. It was found that the rolling resistance of the honeycomb structure has the lowest value, while the contact shear stress and contact pressure were the highest.