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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.

E-spring is a recent innovation in vehicle suspension springs. Its behavior and characteristics are investigated experimentally and verified numerically. The mechanical and frequency-response-based properties of E-springs are investigated experimentally at both of the structural and constitutional levels. Thermoplastic-based and thermoset-based fibrous composite structures of the E-springs are modified at micro-scale with various additives and consequently they are compared. The experimental results reveal that additives of micrometer-sized particles of mineral clay to an ISO-phthalic polyester resin of the composite E-spring can demonstrate distinguished characteristics. A hybrid approach of the inter-laminar shear stress and Tsai-Wu criteria is implemented in order to identify failure indices numerically at the utmost level of loading and verify the experimental results.