Abstract
Solidification cracking (SC) is a defect that occurs in the weld metal at the end of the solidification. It is associated with the presence of mechanical and thermal stresses, besides a susceptible chemical composition. Materials with a high solidification temperature range (STR) are more prone to the occurrence of these defects due to the formation of eutectic liquids wetting along the grain boundaries. The liquid film collapses once the structure shrinks and stresses act during the solidification. Thus, predicting the occurrence of SC before the welding process is important to address the problem and avoid the failure of welded components. The nuclear power industry has several applications with dissimilar welding and SC-susceptible materials, such as austenitic stainless steels, and Ni-based alloys. Compositional optimization stands out as a viable approach to effectively mitigate SC in austenitic alloys. The integration of computational modeling into welding has significantly revolutionized the field of materials science, enabling the rapid and cost-effective development of innovative alloys. In this work, a SC resistance evaluation is used to sort welding materials based on a computational fluid dynamic (CFC) model and the alloy's chemical composition. An index named Flow Resistance Index (FRI) is used to compare different base materials and filler metals as a function of dilution. This calculation provides insights into the susceptibility to SC in dissimilar welding, particularly within a defined dilution range for various alloys. To assess the effectiveness of this approach, the relative susceptibility of the materials was compared to well-established experimental data carried out using weldability tests (Transvarestraint and cast pin tear test). The FRI calculation was programmed in Python language and was able to rank different materials and indicate the most susceptible alloy combination based on the dilution and chemical composition.
