Search Results (3)

The main purpose of this study is to estimate the welding current using the arc light signal emitted during the welding process. Traditionally, welding operators determine this current from the arc light based on their visual perception. This study shows that, using artificial intelligence techniques, welding current can be automatically estimated through arc light and can also be useful for monitoring of the process and detecting its disturbances. For this purpose, initially, a data acquisition system is designed to synchronize the movement of the light sensor with the electrode holder. The electrode welding machine is set to different maximum current levels, and two electrodes with different diameters are used at each level. During the welding process, the arc light and current signals are acquired simultaneously. The obtained data are filtered and aligned by cross-correlation. For the ANFIS (adaptive neuro-fuzzy inference system) model, the arc light is defined as the input and the current as the output. The estimation results of ANFIS are further improved through filtering, shifting, and current-limiting processes. The maximum cross-correlation values for training and testing data are 0.9587, 0.9598, 0.9565, and 0.9323, respectively, while the R-squared values are 0.7033, 0.7640, 0.6449, and 0.5853. Compared with the artificial neural network (ANN) model, it is observed that the ANFIS model provides better prediction results. The results confirm that arc light signals can be effectively used for welding current prediction. Therefore, the proposed approach can contribute to the development of intelligent welding systems and quality welding processes by reducing operator dependency. Full article

The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis. Full article

Tube walls are an essential part of the thermal power plant boiler. During the operation of the boiler, the heating surface of the tube walls are exposed to furnace particles, intense heat, and chemical components resulting from the combustion reaction. These cause corrosion and wear, which permanently collapse the tubes, and affect the reliability and performance of the boiler. Therefore, a protection layer of heat and corrosion-resistant material is typically welded on the surface of the tube walls. In this study, a dedicated weld overlay automatic system is proposed. The downward welding technique with the pulse gas metal arc welding (GMAW) process is used to accomplish the proposed approach. The system generates and plans beads sequence based on the analysis of the tube walls geometry. The inverse kinematic theory was used to calculate the coordinates and transformations of the welding torch. Then, a mathematical model for the welding torch trajectory was established. A SIMOTION controller was adapted for motion control. A weld-tracking system based on the adaptive neuro-fuzzy inference system (ANFIS) was used to solve the welding distortion and the assembly error. The experiment results show that the proposed design is efficient and reliable compared to previous methods. The degree of automation and the weld overlay quality of the boiler tube walls have been notably improved. Full article