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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.

Proportional integral derivative (PID) control method is an effective, easy in implementation and famous control technique applied in several engineering systems. Also, Genetic Algorithm (GA) is a suitable approach for optimum searching problems in science, industrial and engineering applications. This paper presents the usage of GA for determining the optimal PID controller gains and their implementation in the active quarter-vehicle suspension system to achieve good ride comfort and vehicle stability levels. The GA is applied to solve a combined multi-objective (CMO) problem to tune PID controller gains of vehicle active suspension system for the first time. The active vehicle suspension system is modeled mathematically as a two degree-of-freedom mechanical system and simulated using Matlab/Simulink software.