In recent years, the shipbuilding industry has experienced a growing demand for tighter control and higher strength requirements in thick steel plate welding. Electro-gas welding (EGW) is a high heat input welding method, widely used to improve the welding efficiency of thick plates. Modelling the EGW process of thick steel plates has been challenging due to difficulties in accurately depicting the heat source path movement. An EGW experiment on 30 mm thickness E36 steel plates was conducted in this study. A semi-ellipsoid heat source model was implemented, and its movement was mathematically expressed using linear, sinusoidal, or oscillate-stop paths. The geometry of welding joints, process variables, and steel composition are taken from industrial scale experiments. The resulting thermal evolutions across all heat source-path approaches were verified against experimental observations. Practical industrial recommendations are provided and discussed in terms of the fusion quality for E36 steel plates with a heat input of 157 kJ/cm. It was found that the oscillate-stop heat path predicts thermal profile more accurately than the sinusoidal function and linear heat path for EGW welding of 30 mm thickness and above. The linear heat path approach is recommended for E36 steel plate thickness up to 20 mm, whereas maximum thickness up to 30 mm is appropriate for sinusoidal path, and maximum thickness up to 35 mm is appropriate for oscillate-stop path in EGW welding, assuming constant heat input.

Steel production is one of the biggest and most important industries in the world outputting hundreds of tons of steel daily. A steelmaking plant pushes the conventional methods of process monitoring and control to their limits due to the complexity and multidimensionality involved in the physical, mechanical, and chemical metallurgy. The manufacturing process of steel plates involves multiple steps such as blast furnace smelting, converter smelting, and ladle furnace refining, followed by continuous casting, heating, rolling, and cooling. Each physical process generates numerous "key process variables" such as steel composition, additives, environmental control, cooling, and other process parameters, all influencing each other, the subsequent processing steps, and hence the final product. Therefore, modeling and digitally twinning of such processes and predicting the quality of steel through comprehensive finite element approach (FEA) calculations or experimental trials is time-consuming, costly, and impractical. In recent years, this complexity, the increasing global competition, and the drive for more efficient lower waste production created a high demand for new methods to optimize the steel production processes and the mechanical properties of final products. [1] In such a technology-intensive industry, even the smallest variation during the production process causes costly and time-consuming postprocessing or an increase in scrappage. [2] Therefore, smart, agile data-driven prediction models are necessary and urgently needed. To satisfy the demanding requirements for "Industry

No abstract

High heat input welding can improve welding efficiency, but the impact toughness of the heat-affected zone (HAZ) deteriorates significantly. Thermal evolution in HAZ during welding is the key factor affecting welded joints’ microstructures and mechanical properties. In this study, the Leblond–Devaux equation for predicting phase evolution during the welding of marine steels was parameterized. In experiments, E36 and E36Nb samples were cooled down at different rates from 0.5 to 75 °C/s; the obtained thermal and phase evolution data were used to construct continuous cooling transformation diagrams, which were used to derive the temperature-dependent parameters in the Leblond–Devaux equation. The equation was then used to predict phase evolution during the welding of E36 and E36Nb; the quantitative experimental phase fractions of the coarse grain zone were compared with simulated results to verify the prediction results, which are in good agreement. When heat input is 100 kJ/cm, phases in the HAZ of E36Nb are primarily granular bainite, whereas for E36, the phases are mainly bainite with acicular ferrite. When heat input increases to 250 kJ/cm, ferrite and pearlite form in both steels. The predictions agree with experimental observations.