Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

The paper describes a structural stability analysis of fiber reinforced 10kW composite laminate wind turbine blades by using finite element method. The E-glass/epoxy orthotropic materials DB300、DBL850、L900 were employed for construction of a composite laminate shell structure. The composite laminate sheel structures were constructed by two types of lamination method. The rotating effect of wind blade was considered using the linear and the nonlinear static analysis. The results of the nonlinear analysis of displacement and stress show much lower than the linear analysis, because of the geometry nonlinear effect. From the contours of stress and displacement, the maximum stress appeared at the root of the blade, and maximum deformation occurred at the tip of the blade. Finally, the modal properties of the wind blade was investigated, including the natural frequency, modeshaps, and the centrifugal effect.

Industries such as gas, oil, petrochemical, chemical, and electric power have generally employed for the operation and used to enlarge the equipment or structures that require a high capital investment. In order to meet these requirements, the industries are increasingly moved toward saving the experimental verifications and computer simulation. Therefore industries to reduce the maintenance costs without compromising operational safety have been forced on finding better and more efficient methods to inspect their equipment and structures. It was motivated to meet the industrial needs and to secure and maintain the institute's technical initiative and leadership in the development of this new and exciting technology. Also, the system with many sensors could be detected the weld defects, and was useful for real-time monitoring. This paper is focused on the development of the real-time non-contract monitoring system as an efficient tool for the experimental study of weld defects based on the relationship between the measured voltage and input parameters. The monitoring technology involves the use of Ms S (Magnetostrictive Sensors) for the generation and detection of the guided waves. The developed system was employed to the experimental study in order to fine the weld defects for steel object with artificial defects used in the welding field.

Gas Metal Arc (GMA) welding process has widely been employed due to the wide range of applications, cheap consumables and easy handling. A suitable mathematical model to achieve a high level of welding performance and quality should be required to study the characteristics for the effects of process parameters on the bead geometry in the GMA welding process. The objective of this paper is to present development of three empirical models (linear, curvilinear and intelligent model) based on full factorial design with two replications to estimate process parameters on top-bead width in robotic GMA welding process. Regression analysis was employed for optimization of the coefficients of linear and curvilinear models, but Genetic Algorithm (GA) was utilized to estimate the coefficients of intelligent model. ANOVA analysis using experimental data were carried out representation of main and interaction effects between process parameters on top-bead width. Resulting solutions and graphical representation showed that the developed intelligent model can be used for prediction on top-bead width in robotic GMA welding process

Recently, not only robotic welders have replaced human welders in many welding applications, but also reasonable seam tracking systems are commercially available. However, fully adequate process control systems have not been developed due to a lack of reliable sensors and mathematical models that correlate welding parameters to the bead geometry for the automated welding process. Especially, real-time quality control in automated welding process is an important factor contributing to higher productivity, lower costs and greater reliability of the bead geometry. In this paper, on-line empirical models with experimental results are proposed in order to be applicable for the prediction of bead geometry. For development of the proposed predicting model, an attempt has been made to apply for a several methods. For the more accurate prediction, the prediction variables are first used to the surface temperatures measured using infrared thermometers with the welding parameters (welding current, arc voltage, CTWD and gas flow rate) because the surface temperature are strongly related to the formation of the bead geometry. And the developed model has been carried out a learning each time data acquired.