Search Results (4)

Accurate prediction of weld bead geometry is critical for optimizing laser overlap welding of low-carbon galvanized steel, as it directly affects joint quality and mechanical performance. Traditional finite element method (FEM)-based models provide reliable predictions but are computationally expensive and impractical for real-time applications. This study presents an artificial neural network (ANN)-based predictive model trained on a combination of experimental data and validated FEM simulations to estimate key weld characteristics, including depth of penetration (DOP), weld bead width at the surface (WS), and weld bead width at the interface (WI). The ANN model was evaluated using various improved statistical metrics. Results demonstrated a strong correlation between ANN predictions and experimental measurements, with R² values exceeding 95% for WS and DOP and 92% for WI, and mean errors below 7%. A comparative analysis between ANN, FEM, and experimental data confirmed the model’s reliability across different welding conditions. Additionally, ANN significantly reduced computational time compared to FEM while maintaining high accuracy, making it a practical tool for real-time process optimization. These findings highlight the potential of ANN models as efficient alternatives to conventional simulation techniques in laser overlap welding applications. Future improvements may involve integrating real-time sensor data and deep learning techniques to further enhance predictive performance. Full article

In gas metal arc welding (GMAW), the weld bead shape is an important factor that is directly related to the weld quality of welded joints. This study investigates the effects of process parameters, including welding speed (WS) and leading and trailing wire feed rates (WFR), on the weld bead shape, including the leg length and penetration depth, in the tandem GMAW of aluminum 5083-O alloy. An asynchronous direct current–direct current pulse tandem GMAW system and a tandem GMAW torch were designed and applied to improve welding productivity and welding quality. Response surface methodology was used to analyze the effects of the process parameters on the weld bead shape and to estimate regression models for predicting the weld bead shape. As a result of observing arc behavior using a high-speed camera, it was confirmed that the leading WFR affects the penetration depth and the trailing WFR affects the leg length. The coefficient of determination (R²) of the regression models was 0.9414 for the leg length and 0.9924 for the penetration depth. It was also validated that the estimated models were effective in predicting the weld bead shape (leg length and penetration depth) representative of weld quality in the tandem GMAW process. Full article

Directed energy deposition (DED) provides a promising additive manufacturing method to fabricate and repair large metallic parts. However, it may suffer from excessive heat accumulation due to a high build rate, particularly during a wire feeding-type DED process. The implementation of interpass time in between two depositions of beads plays an important process role to passively control the interpass temperature. In this study, a method to estimate the proper interpass time using regression analysis from heat transfer finite element analysis is proposed for maintaining the interpass temperature during a wire feeding-type DED deposition of a planar layer. The overlapping beads of a planar layer are estimated using a polygonal-shaped bead profile in the finite element model. From the estimated proper interpass time, a selected proper interpass time scheme (PITS) is suggested for practical implementation. The selected PITS is applied in a thermo-mechanical finite element model to evaluate the temperature distribution and its effects on the depth of the melt pool, the depth of the heat-affected zone (HAZ), displacement, and residual stresses. By comparing the predicted results with those using a constant interpass time scheme (CITS), the selected PITS shows better control in reducing the depths of the melt pool and HAZ without severely inducing large displacement and residual stresses. Full article

The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments. Full article