Knowledge Bank

The global demand for 3D printing technologies has grown significantly, influencing various industries and extending into the welding sector through additive manufacturing (AM). In welding, AM involves constructing metal parts layer by layer using wire or powder feedstock. This method offers advantages such as material efficiency and design flexibility. However, bead overlapping between layers often results in defects like surface irregularities and weakened structural integrity. This paper presents an automated approach to optimize welding parameters using a multi-layer perceptron (MLP) neural network. Traditional methods for selecting parameters such as current, voltage, and wire feed rate are time-consuming and resource-intensive. To address limited data availability, synthetic data were generated using a generative adversarial network (GAN). By training the MLP on this expanded dataset, the system predicts parameters that improve bead quality and inter-layer bonding. This AI-driven approach reduces the need for extensive physical experimentation, minimizes material waste, and shortens development cycles.