Assessment of delta ferrite in multipass TIG welds of 40 mm thick SS 316L: A comparative study of ferrite number (FN) prediction and measurements

10th Asia Plasma and Fusion Association Conference

Kumar Buddu, Ramesh^1 ; Raole, P.M.^1 ; Sarkar, B.^1

FRMDC Division, Institute for Plasma Research, Near Indira Bridge, Bhat, Gandhinagar

382 428, India^1

关键词: Chemical compositions;    Comparative studies;    Multi-pass welding;    Prediction methods;    Structural component;    TIG welding process;    Weld thermal cycles;    WRC-1992 diagrams;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/823/1/012032/pdf
DOI  :  10.1088/1742-6596/823/1/012032

来源: IOP

【 摘 要 】

Austenitic stainless steels are widely used in the fabrication of fusion reactor major systems like vacuum vessel, divertor, cryostat and other structural components development. Multipass welding is used for the development of thick plates for the structural components fabrication. Due to the repeated weld thermal cycles, the microstructure adversely alters owing to the presence of complex phases like austenite, ferrite and delta ferrite and subsequently influences the mechanical properties like tensile and impact toughness of joints. The present paper reports the detail analysis of delta ferrite phase in welded region of 40 mm thick SS316L plates welded by special design multipass narrow groove TIG welding process under three different heat input conditions. The correlation of delta ferrite microstructure of different type structures acicular and vermicular is observed. The chemical composition of weld samples was used to predict the Ferrite Number (FN), which is representative form of delta ferrite in welds, with Schaeffler's, WRC-1992 diagram and DeLong techniques by calculating the Creq and Nieq ratios and compared with experimental data of FN from Feritescope measurements. The low heat input conditions (1.67 kJ/mm) have produced higher FN (7.28), medium heat input (1.72 kJ/mm) shown FN (7.04) where as high heat input (1.87 kJ/mm) conditions has shown FN (6.68) decreasing trend and FN data is compared with the prediction methods.