Microstructural Evolution And Mechanical Properties of FCAW Joints In 9% Ni Steel Prepared With Two Types of Ni-Based Weld Metals

The microstructural and mechanical evaluation of 9% Ni steel with Flux-Cored Arc Welding was performed with two different Ni-based weld metals: Inconel 625 and Hastelloy 609. Weld metals showed the microstructural change depending on the temperature gradient and crystal growth rate for each region during the cooling after welding. At the bottom of the weld metal, which is rapidly cooled in contact with the cold base metal, a cellular/planar growth was exhibited due to a large temperature gradient and low crystal growth rate. While, columnar dendrites were exhibited in the central region cooled relatively slowly and precipitates were observed in the interdendritic region. In the low-temperature toughness test, the absorbed impact energies were 89 and 55 J for Inconel 625 and Hastelloy 609, respectively. When Inconel 625 is used as the weld metal compared to Hastelloy 609, the high content of the γ stabilizer and martensite start temperature decreasing elements leads to the formation of a thicker γ-phase layer and thinner martensite layer in the transition region. In addition, high content of these elements suppresses the martensite transformation and maintains the stability of the weld joint interface even at low temperatures, resulting in the higher absorbed impact energy.

Influence of the formulation of a flux-cored wire on the microstructure and hardness of welded metal

For this paper, the microstructure and hardness of the weld metal were investigated by conducting experiments with the flux cored arc welding process in underwater and air conditions. A rutile/oxidizing tubular wire was used, manufactured by the Robotics, Welding and Simulation Laboratory at Minas Gerais Federal University, especially for underwater wet welding. Underwater welds had a lower volumetric fraction of acicular ferrite in the weld metal compared to air welds. In the thermally affected zone, for both welds, there was a predominant formation of martensite. However, the grain size and width of the thermally affected zone of underwater welds are smaller. The hardness values shown correspond to the microstructure formed in the weld metal. On the other hand, in the region of the thermally affected zone, the hardness values were higher underwater welds, due to the smaller martensite grains presented.

Long-term Metal Fume Exposure Assessment of Workers in a Shipbuilding Factory

This study aims to assess the metal fume exposure of welders and to determine exposure rates for similar exposure groups in a shipyard through the use of Near-field/Far-field (NF/FF) mathematical models and Bayesian decision analysis (BDA) technique. Emission rates of various metal fumes (i.e., total chromium (Cr), iron (Fe), lead (Pb), manganese (Mn), and nickel (Ni)) were experimentally determined for the gas metal arc welding and flux cored arc welding processes, which are commonly used in shipyards. Then the NF/FF field model which used the emission rates were further validated by welding simulation experiment, and together with long-term operation condition data obtained from the investigated shipyard, the predicted long-term exposure concentrations of workers was established and used as the prior distribution in the BDA. Along with the field monitoring metal fume concentrations which served as the likelihood distribution, the posterior decision distributions in the BDA were determined and used to assess workers’ long-term metal exposures. Results show that welders’ Fe, Mn and Pb exposures were found to exceed their corresponding action levels with a high probability, indicating preventive measures should be taken immediately. The proposed approach provides a universal solution for conducting exposure assessment with usual limited number of personal exposure data.

A Study on the Weldment Hardening Discrimination Procedure and Improvement of Flux Cored Arc Welding Process of ASTM A553-1 (9% Nickel Steel) Material Using Bead Geometry Distribution

As a result of strengthened sulfur content standards for ship fuel oil in IMO regulations, major domestic and foreign carriers have a high and growing demand for liquefied natural gas (LNG) powered ships and related equipment. For LNG operation in a cryogenic environment, a storage tank and fuel supply system that uses steel with excellent brittleness and fatigue strength is required. Ships that use LNG have a high vulnerability to explosion and fire. For this reason, 9% Ni is typically used, since a ship requires high quality products with special materials and structural technologies that guarantee operability at cryogenic temperatures. However, there is an urgent need for research to derive a uniform welding quality, since high process difficulty and differences in welding quality related to a welder’s skills can cause a deterioration of the weld quality in the 9% Ni steel welding process. For 9% Ni steel, the higher the dilution ratio of the base metal, the lower the strength. In order to secure the required strength, excessive dilution of the base metal should be avoided, and the relationship between dilution ratio and strength should be investigated. According to previous research, if it exceeds 25% it may be lower than the API standard of 363 MPa for hardening welds. Therefore, in this study, the flux cored arc welding process is performed by establishing criteria that can be evaluated based on the SVM method in order to determine the structure of the weld to be cured according to the dilution rate of the base metal. We would like to propose a multipurpose optimization algorithm to ensure uniform quality of 9% Ni steel.

Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning

In the flux-cored arc welding process, which is most widely used in shipbuilding, a constant external weld bead shape is an important factor in determining proper weld quality; however, the size of the weld gap is generally not constant, owing to errors generated during the shell forming process; moreover, a constant external bead shape for the welding joint is difficult to obtain when the weld gap changes. Therefore, this paper presents a method for monitoring the weld gap and controlling the weld deposition rate based on a deep neural network (DNN) for the automation of the hull block welding process. Welding experiments were performed with a welding robot synchronized with the welding machine, and the welding quality was classified according to the experimental results. Welding current and voltage signals, as the robot passed through the weld seam, were measured using a trigger device and analyzed in the time domain and frequency domain, respectively. From the analyzed data, 24 feature variables were extracted and used as input for the proposed DNN model. Consequently, the offline and online performance verification results for new experimental data using the proposed DNN model were 93% and 85%, respectively.