In the development of a digital multi-process welding machine, we aimed to analyze the heat dissipation effects resulting from changes in the transformer's shape. Two installation configurations for the transformer, vertical and horizontal, were proposed. Thermal-flow analysis was conducted for the welding machine, taking into account variations in spacing between each proposed configuration. The results indicated that the shape and spacing of the components did not significantly alter the airflow around the reactor coil, which is the main heat-generating component of the machine. When comparing the heat dissipation effects across models with different transformer spacings, it was observed that models with narrower spacing exhibited improved heat dissipation, while the vertical configuration demonstrated a slightly higher heat dissipation effect overall. Transient analysis revealed the irregularities in internal flow and the resulting scattered temperature distribution over time within the welding machine.

In this study, we proposed a simulator for the development of a digital multi-process welding machine and a welding process monitoring system. The simulator, which mimics the data generation process of the welding machine, is composed of process control circuit, peripheral device circuit, and wireless communication circuit. Utilizing this simulator, we aimed to develop a welding process monitoring system that can monitor the welding situations of four multi-process welding machines and three processes each, with data transmission through wireless communication. Through the operation of the proposed simulator, sequential digital processing of multi-process welding data and wireless communication were achieved. The welding process monitoring system enabled real-time monitoring and accumulation of the process data. The selection of upper and lower limits for process variables was carried out using a deep neural network based on allowable changes in bead shape, enabling the management of welding quality by applying a process control technique based on the trend of received data.