Weld and base metals require hot or cold working during the steel equipment manufacturing process. As a result, the components should be subjected to a normalizing heat treatment in order to recover their mechanical properties. In this study, the submerged-arc welding of the high strength low alloy (HSLA) thick steel plate(13MnNiMoR) is adapted for the vessel head under the normalizing and tempering heat treatment. The findings showed that the material toughness decreases after heating to simulate a vessel head forming process. The stamping process is carried out under the conditions of 980 °C for one hour, normalizing at 920 °C for 1 h and tempering between 600–660 °C for 2 h, respectively. The martensite-austenite (M-A) constituent is distributed in granular bainite and the boundary of austenite in island constituent. Therefore, it was deemed to be the most detrimental to Charpy-V impact toughness. Between normalizing and tempering, intercritical normalizing at 740 °C was added. As a result of the ferrite with fine particles M-A constituent, the toughness increases significantly.

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.

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